Internal Memorandum on Suitability of Precedent Lease and Amendments Required

June 25, 2017

To: Supervisor Solicitor

From: Legal Advisor

Subject: Draft Lease

Date: 16th June 2017.

Re: Lease Draft Legal Issues and Comments

The primary objective of writing this memorandum to you as the company solicitor is to comment on the draft lease of the company as it plans to enter into a ten years rent lease agreement. The memorandum will highlight areas the need to be changed in the current lease draft so that it can be compliant with Lease Code of 2007 and Landlord and Tenant Act of 1954.

Lease Code of 2007

Dear, solicitor the lease code of 2007 outlines the relationship between the tenant and landlord relating to the leasing of business premises to tenants.  One of the important clauses of the Lease Code 2007 touches on rent.  One of the aspects of the clause is on rent deposits, whereby, it states that the landlord is supposed to outline the amount of rent deposit to be paid by a tenant and the period of payment, plus the accruing interest payable on the basis of fair market rate (Crosby and Hughes, 2009). The clause indicates that tenant is supposed to be safeguarded against landlord insolvency or default. Another aspect of the clause is on rent review; whereby it indicates that rent review should be stated clearly by the landlord in the lease contract.  A close look at the lease draft indicates that it is compliant with the clause on rental deposits, as it clearly shows the amount of rent deposit to be paid by the business renting the premise and the period when the rent deposit term will expire.  However, I think, the clause on rent renewal does not comply with the lease code of 2007 as it does not provide alternative means through which the tenant can continue doing business without interruption if the tenant does not agree with the rent increment.  I believe the clause should be deleted as it is not properly written. Once, it is deleted a new clause on rent review should be written, and the changes introduced should provide detail about alternatives options available to the business concerning use of premise when rent is increased. For instance, where to continue using the premise after disagrees with rent increment or one has to vacate immediately, if he or she refuses to comply with rent increment.

When it comes to the repair clause, it states that tenant repair obligations should only be limited to the lease term and the premises condition at the time of lease. Also, unless stated otherwise, the tenant are supposed to give back the premise at the expiry of the lease within the similar condition the acquired it during the lease grant. The tenant has an obligation of keeping the building in good condition and repair, meaning that the premises should be in the same condition that it was given by the landlord during the lease grant period. The lease draft is not complaint with the lease code when it comes to the issue of repairs, whereby, the landlord give the tenant more repairs than required by the lease code of 2007. The tenant is only supposed to keep the premises in good condition and repair but does not mean to keep replacing different facilities and appliances found in the premise. Therefore, the draft, in this case, violates the lease code law of 2007. I think there is a need to introduce change on the repair clause as provided in the lease draft. The change should be introduced on issues of the tenant being required to make changes to the premise facilities and appliances; this condition is illegal. The tenant should only be required to keep the property in good shape, as long as it is in the same condition that the landlord gave it. However, requiring the tenant to make changes to some appliances after a certain period is not legal. Therefore, as the solicitor I think you need to introduce changes in the lease draft; the change should indicate that the tenant is only obligated to make repairs to the premises appliances and facilities as long as they are not in the same condition as during the period when the premise was granted to the tenant at the beginning of the lease period.

The clause on the future assignment of property by the tenant states that transfer of the premise to another tenant by the current tenant should not be done without the permission of the landlord. The draft lease is in compliance with this clause, as it clearly states that the tenant cannot lease the property to another tenant with making a written application of the landlord for approval. The main objective of the clause is to ensure that the property is leased to another tenant by the current tenant in line with the landlord condition regarding the use of the premise.

Wording of the Tenants Repairing Covenant

The lease agreement draft has a problem when it comes to the wording of the clause related to tenant repairs. The words used require the tenant to make repairs on the appliances on an annual basis are wrong. The wording is illegal as it does not follow the principle of maintaining a premise in good condition and repair and should be deleted (Dupuis, 2007). This principle states that the tenant is only supposed to ensure that the premise is in the same condition as when it was given by the landlord at the start of the lease agreement unless otherwise stated in the Head Terms of the lease covenant.  Therefore, the amendment that I think needs to be introduced on the covenant wording, deleting words that require the tenant to make repairs to the appliances and other things in the facility on annual basis. The new wording should only indicates that the tenant is required to repair the premise annually, if it is not the same condition as it was during the beginning of the lease period.

 

Assignment Clause

There is one major change that needs to be introduced in the lease draft’s assignment clause. I believe change needs to be introduced on the wording of the clause touching on a tenant being responsible for making an application to the landlord concerning renting the premises to another tenant who is going the same business. The change that should be introduced is deleting the statement and rewording it to have a new meaning that is fair to the tenant. The rewording should indicates that the tenant should only make application to the landlord of leasing the premise to another business before the expiry of the lease term, if the new business is involved in a completely new set of transactions from what is stated in the lease agreement. This will ensure that the company will be at liberty to lease the premise to a new tenant, if it feels it no longer need it in the future, even before the expiry of the lease contract.

Summarizing

The landlord and tenant act of 1954 protects the rights of tenants and landlords. I think that the Epharesse’s position as the tenant can be protected by ensuring that he is given an opportunity to state in writing one month to the expiry of the lease agreement whereas a tenant he wants to continue using the premise of not. According to Manuel (2009), the Landlord and Tenant Act of 1954 requires a landlord to renew the contract of the lease with tenant on request. Therefore, the landlord cannot give the property to another tenant as long as the current tenant is in need of the property. Thus, Epharesse’s can be protected by making sure that the covenant states that at the expiry of the lease period, he will be at liberty of renewing the lease agreement or not and should do so in writing.

References

Crosby, N., & Hughes, C. (2009). Monitoring the 2007 Code for leasing business premises.

Dupuis Jr, J. H. (2007). Case Law Update. Ann. Inst. on Min. L.54, 1.

Manuel, J. S. (2009). Recent Developments in the Law. Ann. Inst. on Min. L.56, 1.

Memo of Advice to Liquidator of the Client Company

June 25, 2017

From:

To: Nicola Adkins

Subject: Advice to the Liquidator of the Client Company

Date:

 

The liquidator should ensure that the payment made by trustee through electronic transfer into the Insolvency Services Account (ISA) is accompanied by the details that identify the liquidator. As a liquidator Nicola should ensure that the details also contain the individual or trustee processing the payment as well as the estate to be credited (Tolmie, 2013). From the case, it is evident that Coppacad Ltd has been paying commissions to secure Unwins that amounts to 5% in the past five years. However, instead of the commission reaching Unwins, it has been going to Redgate, an intermediary company that introduced Coppacad to Unwins. There is a possibility the £100, 000 paid may not have reached the Unwins since the investigations do not show any transfer of money from Redgate to Unwins. This memo presents key factors that Nicola, a liquidator should consider based on the results of the company search.

CLAIMS THAT MIGHT ARISE AGAINST REDGATE:

It is the role of liquidator to note all claims that may arise against Redgate and carry out thorough investigation. There is the likelihood of the claims to be filed against Redgate if the payments made by Coppacad do not reach Unwins. The matter may be based on the purchase of £260, 000 apartments, when the earning is about £20, 000 a year. The allegations may be based on the clarification of the source of the funds used to purchase the three bedrooms flat. Bearing in mind that Redgate does not have any other property, there is the possibility of using the money being paid to Unwins to pay off the WLF interest for £200, 000 loans. It is worth to note that according to the Insolvency Regulations 2000, the payment can only be paid through electronic transfer after identifying the bank account ID as well as the name of the estate of the trustee. According to Tolmie (2013), the payments out of ISA can be made through electronic transfer to the trustee upon the agreement through relevant requisition form. In this case, the discretion is retained by the secretary of state to determine the mode of payment to create room to solve both technical and practical problems that may occur during the payment process.

The fact that Redgate is under one shareholder and director raises questions regarding the legitimate; Nicola has a role in finding more information on the details presented by the company when the agreement was made between Unwins and Coppacad. This will determine whether the details presented by the company are valid. Based on Tolmie (2013) argument, if Coppacad continues paying through Redgate through electronic means without establishing whether the money is reaching Unwins, there is a possibility of not acquiring the intended investment since from the company search results shows that Redgate may run bankrupt, leaving the trustee’s vacancy among between the two companies. However, Coppacad will not be able to account for the funds paid to the Redgate in its financial statements. It is important for Nicola to understand that whenever the creditor contracts with a liquidator through a trustee, the trustee plays a role of an agent of the trust so as to ensure that the trust is liable to sue or get sued. In this case, if Redgate is closed down it will be difficult for Coppacad to claim business possess if Unwin argues otherwise.

As a liquidator, Nicola should understand that trust is not a legal entity or principle such that it can have an agent. Necessary measures should be taken to carry more company search about Redgate since trust is not artificial as the enterprise; hence there are no ultra vires rules that can apply to it if the company does not exist. According to Milman (2017), the Redgate acted as a trustee and its action cannot influence the memorandum of association’s capacity because of protection against the third party’s good faith. Therefore, Redgate has an obligation as a trustee enforceable against it by Coppacad based on contract principles.

CLAIMS AGAINST HARRIET:

Harriet may be accused of director’s duty breach since he is the sole owner of the Redgate Company that participated in the deal. In any deal, the director is expected to act in good faith and not contrary with the company’s interest. Since the Harriet is the sole shareholder of Redgate, he should be ready to relieve himself from the breach if it was ultra vires. With reference to Milman (2017) argument, the contractual liability was incurred in the Redgate’s as well as the power as a liquidator. Hence Harriet has the right out of the trust fund, and hence he is entitled to pay appropriate amounts out of the trust fund.

Coppacad may file a case against Harriet’s right of identity; this may happen since the liability of Redgate to Coppacad is improperly incurred due to lack of capacity. This may arise if Harriet was not empowered by the trust instrument or by law when entering into the contract. Again, there might be a lack of due authentication, meaning that Harriet’s power to contract with Coppacad may not have been practiced in compliance with the internal procedure requisite to its exercise. This may happen if the agreement was made by only Unwins and Coppacad when unanimity was required without obtaining consent from Harriet, or they did not meet before issuing a contract. As claimed by Milman (2017), at the individual level, if the documents presented by Harriet during the agreement were incorrect, he is liable of a lawsuit as the director of the company.

HOW SUCCESSFUL LITIGATION MAY ACCRUE CREDITORS OF UNWINS:

The liquidator may file a case claiming the bankruptcy of Redgate to ensure that its debts to Unwins are covered as a way of settling their debts with Coppacad after successful litigation. This process will preserve the Unwins’ business as a going concern. Based on Chapter 11 Reorganization, the bankruptcy courts will determine if the suggested litigation has a success possibility. However, Section 1129 of the Bankruptcy Code articulates factors to be considered by the court when examining if the debtor will successfully consummate the proposed plan of litigation. On the other hand, the court may find out that Nicola’s plan is not feasible because of different reasons. Based on Tribe & Hunt (2013) argument, if the agreement was between various creditors, hence non-bankruptcy litigation between Unwins and its creditors such as Cumbria Bank may not succeed. On the other hand, if the litigation is pending, the court may suspend the judgment till the investigation is carried out on the current status of the creditor.

In most cases, the court may deny the reorganization plan even after successful litigation. However, there should be evidence from the debtor to convince the court that the claims in the ongoing litigation are strong enough to be considered feasible. However, if the creditors of Unwins believe that it qualifies to be denied an automatic stay, they may file a motion under Section 362d. Tribe & Hunt (2013) asserted that the case is allowed to conduct preliminary hearing accompanied by the final proceedings of the motion regarding the relief from stay; they can as well consolidate Unwins and Redgate and hold a single proceeding. The preliminary hearing is held within 30 days after the filing of automatic stay motion. However, Unwins may be indebted to the trust by its creditors because of improper transaction it conducted with Coppacad through Redgate.  If Redgate does not have enough cash to pay Unwins, it is crucial for the Unwins to claim for the payment.  The key issue is that Redgate stood into the shoes of Coppacad and is not able to pay for the amount to whom Unwin gave the credit. However, Coppacad cannot claim any money from Redgate before having a good deficiency on the accounts between Unwins and Redgate.

The creditors have an obligation to pay in if the trust fund can pay out the amount of the contract between Unwins and Redgate. This only occurs if there is not a problem resulting from the contract. However, in our case, there seem to be big issues regarding the financial position of Unwins due to its negative capital; this may be attributable to the amount that has not yet been paid by the debtors. According to Hayton (1997) arguments, it is hard for the creditors to fund its operations since it is not in a position to pay back based on the current financial position. However, there is the likelihood that Redgate is holding the fund paid by Coppacad till the receipt issued by Unwins is due. However, since Unwins is acting as a trustee for its creditors, the creditor’s right cannot be subrogated to favor the trustee’s right of indemnity if the contractual right is inadequate. The creditors, in this case, may not be able to satisfy the need to finance Unwins if the litigation against Redgate was successful.

The company search results show that there is the likelihood that Redgate is a company that is owned solely by Harriet. However, being the sole director and shareholder, the liquidator should determine if Harriet submitted wrong information during the registration of the company as well as during the contract between his company and Coppacad. The financial results of Redgate indicate that there is the likelihood that the litigation will be a success because of its features of being bankrupt. The success of litigation would influence the decision made by Unwins’ creditors such as Cumbria bank since the current financial position shows that it is not able to pay the debt.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

Hayton, D. (1997). Rights of Creditors Against Trustees and Trust Funds. TOLLEYS TRUST LAW INTERNATIONAL, 11, 58-59.

Milman, D. (2017). Personal Insolvency Law, Regulation and Policy. Routledge.

Tolmie, F. (2013). Corporate and personal insolvency law. Routledge.

Tribe, J. P., & Hunt, S. J. (2013). Insolvency bonds: history, policy and substance. Browser Download This Paper.

 

 

 

 

   

Behavior Analysis

June 24, 2017

Guest: Dr. Laura Kenneally

Important Information Learned

Behavior analysis is a scientific study concerned with immense principles of behavior and learning. There are two primary areas that studies in behavior analysis tend to explore, and they are; applied behavior analysis and experimental behavior analysis. Experimental behavior analysis is basically the science of discipline and has over the years accumulated a great form of well- respected and substantial research literature information on how behavior is learned, acquired, and changes over a particular period of time. Applied behavior analysis on the other hand has been developed around a systematic approach that influences socially important behavior of some sort through identifying environmental variables that are reliably related as well as production of techniques in behavior change that utilize those findings.

In recent years, the practical application of the methods and principles of behavior analysis has emerged as a helpful technique to many different kinds of learners in their bid to acquire different skills and techniques that span across mastery of new languages to acquisition and adoption of healthier lifestyles. Since the 1960s, therapists and psychologists have applied behavior analysis methods and principles to assist children who are suffering from autism and other related developmental disorders of such caliber. It has emerged as a very successful mode of treatment as children have been seen to react positively to the method. Earlier techniques on the use of applied behavior analysis, has often involved adults instigating directions to the patients, while some over the years of development have allowed children to take the lead. The techniques have been applied on classrooms as well as at home during dinners and other family gatherings or at playgrounds so as to ensure that conducive environment is provided for the patients to be able to endure maximum concentration as well as learn and grasp what is instilled. The conducive environment also assist the patients to react positively and in a fast way,

Some of the professionals prefer one on one therapy sessions in which they interact with their patients and deem it to be a faster way of instilling behavior analysis techniques and methods to the participants, but it is also said that group interactions may be useful at times.

How the Information Will Assist Me

I have come to learn that applied behavior analysis is actually quite effective in many ways and especially in treatment of autism. In the world today, applied behavior analysis is recognized globally as the most effective treatment of autism according to Dr. Laura Kenneally. In the United States, it has been endorsed by several state and federal agencies as well as the vastly recognized U.S. Surgeon General and the New York State Department of Health. A stunning number of people living in autism have been viewed to react positively to applied behavior analysis methods and techniques, in which they live a happy life and productive in equal regards. I have learnt that practitioners have acknowledged the fact that the principles and techniques that applied behavior analysis presents, has fostered and enhanced basic skills that conform to learning, imitating, and listening to the people living with autism. It has also assisted them in grasping complex skills such as having a conversation, understanding the reasoning and understanding of another person, as well as reading and understanding. It is therefore conclusive to say that applied behavior analysis is a very helpful tool in the world today.

Reference:

https://www.healthylife.net/RadioShow/archiveADA.htm

Professional Reflection

June 22, 2017

Professional Reflection

In these readings the value of indigenous education in Australian context has been very well explained.  I experienced a lot of new information regarding the system of education and historical perspectives of the Aboriginals and the Torres Strait Islander in the Australian society.

Important information that I have acquired from these studies is about the Social justice and closing the gap as a very important practice in Australia. As an educator, I have been enlightened on why it is important to organize the system of education to improve on equity and equality. I have covered information about why policy making, frameworks and plans are important in steering the Indigenous education. Effective Policy making is what can only lead to the achievement of equity and equality in the administration of education in Australia. I think that this knowledge is implicative in the understanding of the native cultures and practices. Knowledge and value are the two most important factors that promote the welfare of every single community (Harrison &Sellwood, 2016).

I think that the readings have offered me with an  insight over why the system of education is supposed to be flexible in offering the indigenous communities with varied opportunities that accommodate their beliefs, norms and values. In every given situation, the education system is supposed to accommodate cultural opinions and values of the society (Dealtry, Perry & Dockett, 2017).

I understand that there are varied measures that the government is supposed to develop and direct the curriculum in the country. These studies have offered me with an overview of how the historical changes have shaped or influenced the contemporary Aboriginal society in terms of education matters. This subject has enabled me to understand over why it matters a lot to inculcate these historical experiences in the content (Education council, 2015).

The content in this subject has strong message on cultural diversity and has importantly helped me in understanding concepts related to cultural diversity and why the indigenous culture is important in developing concrete curriculum. These modules contain information about Dreaming permeates, Scared and secular life of the indigenous communities (Harrison $ Sellhood, 2016).

Further readings in this subject have helped me to develop an insight over the eco-management techniques of the indigenous people. Land issue in the native Australia shaped the native communities way of life but failed to change their economic practices ( Milgate, 2016).

I am confident that these studies have helped me in administering key important issues about family relationships among the aboriginals. This is an insight over the differences between the kinship ties between the Europeans and the native Australian community. Kinship is about relationships and responsibilities of the communities incorporated in the ancestral ties (Sveiby & Skuthorpe, 2006).

Also I am able to teach more about the frontier period. The frontier period is defined with violence and the European settlement where the indigenous people fought greatly to retain their rights over the land (Milgate, 2016).

As a teacher, I think I will be able to demonstrate the knowledge and skills of the indigenous history and particularly in terms of the assimilation, segregation and dispossession to the class. What I have understood is that the socio-cultural, socio-political and different historical practices in the Australian context led to the growth of the indigenous educational participation.

Lastly, my teaching practice was positively impacted after going through these modules. I have gained competency over the value of knowledge on the principles of social justice, self-determination and the patterns of reconciliation among the indigenous communities. Recognition of the socio-cultural, socio-political and changes over the history are the factors that have impacted on Australians and Indigenous people’s participation (Harrison $ Sellhood, 2016).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Reference

Harrison, N. & Sellwood, J. (2016). Learning and teaching in Aboriginal and Torres Strait Islander Education (3rd ed.). Melbourne, Vic: Oxford University Press.

 

Dealtry, L., Perry, B., & Dockett, S. (2017). A social justice view of educators’ conceptions of Aboriginal children starting school In N. Ballam, B. Perry & A. Garpelin (Eds.). POET: Pedagogies of Educational Transitions: European and Antipodean research. Switzerland: Springer.

 

Education Council. (2015). National Aboriginal and Torres Strait Islander EducationStrategy 2015. Canberra, ACT: Education Council.

http://scseec.edu.au/site/DefaultSite/filesystem/documents/ATSI%20documents/DECD__NATSI_EducationStrategy.

Harrison, N., & Sellwood, J. (2016). Teaching about the Stolen Generations. In N. Harrison & J. Sellwood (Eds.), Learning and teaching in Aboriginal and Torres Strait Islander Education (3rd). Melbourne, VIC: Oxford University Press.

 

Milgate, G. (2016). Building empowering partnerships between schools and communities.In N. Harrison & J. Sellwood (Eds.), Learning and teaching in Aboriginal Education (3rd). Melbourne: Oxford University Press.

 

Sveiby, K., & Skuthorpe, T. (2006). Treading lightly: The hidden wisdom of the world’s oldest people (Chapter 5: Knowledge Economy, pp. 72-94). Crows Nest, NSW: Allen & Unwin.

Strategic Analysis: Caterpillar Inc.

June 20, 2017

1.      Introduction

1.1  Corporate Profile: Caterpillar Inc

Caterpillar Inc is “the world’s leading manufacturer of construction and mining equipment, diesel and natural gas engines, industrial gas turbines, and diesel-electric locomotives” (Caterpillar 1). It operates two main segments: Machinery, Energy, & Transportation segment and Financial Products segment.

Under Machinery, Energy, & Transportation segment, it has three main sub-segments: Construction Industries, $15.6 billion; Resource Industries, $5.7 billion; and Energy & Transportation, $14.4 billion (Caterpillar 29). The segment generated consolidated revenue totaling $35.8 billion (excluding eliminations) in fiscal 2016 (Caterpillar 29).

Financial Products segment generated $2.8 billion (excluding corporate eliminations) in fiscal 2016 (Caterpillar 29). The group had $38.5 billion in consolidated sales and revenues and posted a net loss of $59 million (Caterpillar 70). The bulk of group’s business representing 46.6% of consolidated revenue is undertaken in North America.

2.      Financial Analysis

Financial analysis “involves the selection, evaluation, and interpretation of financial data and other pertinent information to assist in evaluating operating performance and financial condition of a company” (Drake and Fabozzi 243). Some of the tools involved include ratios analysis as well as evaluation of individual financial statements like income statement.

2.1  Income Statement

The income statement outlines the “firm’s revenues and expenses over a period of time” (Berk and DeMarzo 28). In fiscal 2016, total sales and revenues decreased by 18.0% from $47.0 billion in fiscal 2015 to $38.5 billion (Caterpillar 70). Total operating costs reduced by 12.0% from $43.2 billion in fiscal 2015 to $38.0 billion in fiscal 2016 (Caterpillar 70).

Caterpillar achieved gross profit of $9.6 billion in fiscal 2016 compared to $12.9 billion in fiscal 2015 which was reduction of 25.2% year-over-year (Yahoo Finance para.2). The lower level of gross profit was largely driven by higher reduction of 18.0% in total sales and revenues compared to a reduction of 15.3% in cost of goods sold.

Total sales and revenues declined at a greater pace than total operating costs. The difference of 6.0% impacted operating profit performance. It declined by 86.8% from $3.8 billion in fiscal 2015 to $498 million in fiscal 2016 (Caterpillar 70). Consolidated profit before tax also declined significantly after deducting interest expenses.

The massive reduction in consolidated sales was largely due to weakness in commodity prices as well as general global economic weakness. It meant that sales were weak across all operating segments. There was marginal change in total operating expenses except for reduction in cost of goods sold and goodwill impairment charge that was included in fiscal 2016.

It resulted in lower decrease in total operating expenses compared to the reduction in total sales and revenue. There was marginal change in interest expenses and other income which resulted in 95.9% decline in profit before taxes. After deducting provision of taxes of $192 million, the group achieved a net loss after tax of 53 million (Caterpillar 70).

2.2  Statement of Cash Flow

The statement of cash flows “utilizes information from the income statement and balance sheet to determine how much cash the firm has generated, and how that cash has been allocated, during a set period” (Berk and DeMarzo 31). The major items are: cash flow from operating activities, cash flow from investment activities, and cash flow from financing activities.

Total cash flow from operating activities decreased by 16.0% year-over-year in fiscal 2016 which was also lower by 17.2% compared to fiscal 2014 (Yahoo Finance para.4). There was significant change in net income impacting on cash flow generated from operating activities. However, there was a reduction in liabilities and increase in inventory that boosted income.

There was cumulative reduction of 51.5% in total cash flow used in investment activities from fiscal 2014 (Yahoo Finance para.4). Capital expenditure increased while additions to finance receivables were cancelled by collections from finance receivables. There was modest increase in investment which resulted in overall decrease in cash flow used in investment.

Total cash flow used in financing activities decreased by 19.6% in fiscal 2016 compared to fiscal 2015 but was higher by 3.9% in fiscal 2016 when compared to fiscal 2014 (Yahoo Finance para.4). There was an increase in dividends paid, increase in net borrowings, and significant reduction in purchase of stock resulting in low financing costs.

The effect of exchange rate differences was lower in fiscal 2016 at a loss of $28 million compared to a loss of $169 million and $174 million in fiscal 2015 and 2014 respectively (Caterpillar 75). The net impact was an increase in cash and cash equivalents of $708 million in fiscal 2016 compared to a decrease of $881 million in fiscal 2015 (Caterpillar 75).

2.3  Financial Ratios

Financial ratios make comparison between different bits of financial information (Drake and Fabozzi 244). The main financial ratios that assess the operating performance and financial conditions of firms are: liquidity ratios, asset management (activity) ratios, debt (financial leverage) ratios, and profitability ratios (Drake and Fabozzi 245).

2.3.1  Liquidity Ratios

Liquidity ratios compare short-term liquidity by utilizing assets that are readily convertible into cash relatively easily (Drake and Fabozzi 247). Current ratio compares current assets to current liabilities. Caterpillar Inc’s current ratios were 1.31 and 1.22 in fiscal 2015 and fiscal 2016 respectively compared to 2.13 and 2.06 for Deere & Company.

It implied that in fiscal 2016, Caterpillar Inc had $1.22 in current assets compared to every $1.00 in current liabilities; a ratio of 1.22:1. It was a reduction compared to 1.31 in fiscal 2015 largely due to lower inventories. The main competitor, Deere & Company had significantly higher current ratios at 2.06: 1 in fiscal 2015 and 2.13: 1 in fiscal 2016.

Quick ratio excludes inventories (considered as the most illiquid current assets) in the computation that is similar to current ratio (Drake and Fabozzi 251). Caterpillar Inc had quick ratios of 0.84:1 in fiscal 2015 and 0.83:1 in fiscal 2016 compared to Deere & Company’s 1.88:1 in fiscal 2015 and 1.95:1 in fiscal 2016 implying that Caterpillar Inc is underleveraged.

Essentially, Caterpillar Inc had $0.83 in current assets (excluding inventories) compared to every $1.00 in current liabilities. However, Deere & Company had a higher level of current assets compared to current liabilities. Caterpillar Inc’s leverage is below the theoretical minimum of 1:1 that is recommended but it is only considered as a guide (Dyson 223).

2.3.2  Asset Management Ratios

Asset management ratios are considered as activity or efficiency ratios that indicate the benefits from specific assets or entirety of total assets (Drake and Fabozzi 255). Inventory turnover compares the rate of conversion of inventory into goods that are sold. Caterpillar Inc had inventory turnover of 3.08 and 3.09 in fiscal 2015 and fiscal 2016 respectively.

Comparatively, Deere & Company had inventory turnovers of 5.06 and 5.14 over a similar fiscal period. The implication was that in fiscal 2016, cash conversion from inventory to sales occurred 3.09 times at Caterpillar Inc compared to Deere & Company’s 5.14 times. In essence, Caterpillar Inc underperformed Deere & Company in both fiscal years.

Asset turnover illustrates the number of times that the firm’s value of total assets is generated in revenues (Drake and Fabozzi 257). Caterpillar Inc’s asset turnovers were 0.58 and 0.50 in fiscal 2015 and fiscal 2016 compared to Deere & Company’s 0.48 and 0.46 over the same period. In 2016, Caterpillar Inc generated $0.50 in revenue for every $1.00 in total assets.

2.3.3  Debt Ratios

Debt or financial leverage ratios assesses the financing position within the firm (Drake and Fabozzi 258). Debt/equity ratio measures “how the company finances its operations with debt relative to the book value of its shareholders’ equity” (Drake and Fabozzi 259). Caterpillar Inc had debt/equity ratios of 1.70 and 1.74 in fiscal 2015 and fiscal 2016 respectively.

Comparatively, Deere & Company had debt/equity levels of 3.53 and 3.64 respectively in fiscal 2015 and 2016. It implied in fiscal 2016 that Caterpillar Inc had $1.70 in debt for every $1.00 in shareholders’ equity. Thus, Deere & Company was comparatively overleveraged with respect to long-term solvency utilizing more debt relative to shareholders’ equity.

It had interest coverage of 7.78 in fiscal 2015 and 2.46 in fiscal 2016 compared to 5.09 and 3.91 for Deere & Company in fiscal 2015 and fiscal 2016 respectively. Generally, higher interest coverage is preferred (Drake and Fabozzi 260) which Caterpillar Inc had in fiscal 2015 while Deere & Company had better coverage of 3.91 times in fiscal 2016.

2.3.4  Profitability Ratios

Profitability ratios make an assessment of management’s ability to control expenses (Drake and Fabozzi 253). The net margin excludes all expenses including production, operating, and financing costs. Caterpillar Inc had net margins of 4.47% in fiscal 2015 and (0.17%) in fiscal 2016. The negative position in fiscal 2016 was due to a net loss during that year.

Comparatively, Deere & Company had net margins of 6.72% and 5.72% in fiscal 2015 and 2016 respectively. Therefore, Deere & Company outperformed Caterpillar Inc during both fiscal years. Return on Equity (ROE) and Return on Assets (ROA) are factors of net income and the high the net income, the higher ROEs and ROAs.

Thus, in fiscal 2016, the loss that was reported in net income resulted in negative position for ROE and ROA for Caterpillar Inc. Deere & Company had slight decline in net margin implying a reduction in net profit that also impacted its ROE and ROA. Generally, Deere & Company outperformed Caterpillar Inc in all the three measures.

3.      Company Analysis

3.1  Industry/Sector

 

3.2  Market Position

 

3.3  Market Share

 

3.4  Market Structure

 

 

 

 

4.      International Exposure

4.1  International Markets

 

4.2  Operating Model

 

4.3  Currency Issues

 

 

 

 

 

 

 

 

 

 

 

5.      Strategic Issues & Potential Solutions

5.1  Strategic Issues

 

5.2  Potential Solutions

 

 

 

 

 

 

 

 

 

 

 

 

 

Conclusion

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Works Cited

Berk, Jonathan, and Peter DeMarzo. Corporate Finance, 3rd Edition. Boston: Pearson Education, Inc. 2014. Print.

Caterpillar. Building Better: Caterpillar 2016 Annual Report. Peoria: Caterpillar Inc. 2017. Print.

Drake, Pamela Peterson and Frank, J. Fabozzi. The Basics of Finance: An Introduction to Financial Markets, Business Finance, and Portfolio Management. Hoboken: John Wiley & Sons. 2010. Print.

Dyson, John, R. Accounting for Non-Accounting Students, 8th Edition. Harlow; Pearson Education Limited. 2010. Print.

Morningstar. Caterpillar Inc, CAT: Key Ratios. Morningstar. 2017a. Web. 2017.

Morningstar. Deer & Co, DE: Key Ratios. Morningstar. 2017b. Web. 2017.

Yahoo Finance. “Caterpillar Inc. (CAT): Financials: Income Statement”. Yahoo Finance. 2017a. Web. 19 June 2017.

Yahoo Finance. “Caterpillar Inc. (CAT): Financials: Cash Flow”. Yahoo Finance. 2017a. Web. 19 June 2017.

 

 

 

 

Appendix

Figure 1: Comparative Financial Ratios: Caterpillar Inc and Deere & Company

  Caterpillar Inc. Deere & Company
  2016 2015 2016 2015
Liquidity
Current Ratio

Quick Ratio

1.22

0.83

1.31

0.84

2.13

1.95

2.06

1.88

Asset Management
Inventory Turnover

Asset Turnover

3.09

0.50

3.08

0.58

5.14

0.46

5.06

0.48

Debt (Financial Leverage)
Debt/Equity

Interest Coverage

1.74

2.46

1.70

7.78

3.64

3.91

3.53

5.09

Profitability
Net Margin

Return on Equity (ROE)

Return on Assets (ROA)

(0.17%)

(0.48%)

(0.09%)

4.47%

13.32%

2.58%

5.72%

22.97%

2.63%

6.72%

24.54%

3.25%

 

Source: Morningstar (para.1) and Morningstar (para.2)

 

Management Discussion

June 20, 2017

Management Discussion

Methods used in an organizational selection process

According to Robbins & Judge (2017), various selection methods can be utilized in an organizational selection process. Robbins & Judge (2017), suggests that some of the recognizable methods can be as outlined below;

Preliminary screening method

This method constitutes the HR screening of resumes and application to evaluate the applicants who hold the required experience for the vacant positions. The method requires the HR to identify the sufficient applicants for a further preliminary screening interview (Robbins & Judge, 2017). This method is perceived as effective since it narrows the selection of the applicants who qualify for the basic requirements for the vacant position.

Telephone interviewing method

This method is applied as a first-round interview. Robbins & Judge (2017), observes that telephone interviews present the HR managers with an opportunity to gauge the interest of the candidates. Also, the method is perceived as one that saves time and resources when compared to face-to-face interviews.

In-Person interviews method

Another method applied in the selection process relate to the in-person interviews. In this method, HR managers select a few applicants from a pool of candidates in the preliminary screening or telephone interviews (Robbins & Judge, 2017).

Cultural fit selection method

In this method, HR managers select applicants who are best suited to the organizations’ culture hence the method reference to cultural fit selection. The method is usually carried out in the third-round interviews where the HR manager is interested in identifying the best candidate who can fit into the culture of the organization (Robbins & Judge, 2017).. It is perceived to not constitute any objective criteria and is rather a subjective analysis.

Vetting Candidates method

This method involves the HR managers dispatching a conditional job offer (Robbins & Judge, 2017). It involves institution of background checks on applicants to ascertain their qualifications in regard to the provided information.

How conflict within an organization can be helpful or harmful

According to Robbins & Judge (2017), conflict within an organization can be categorized as one that can bring negative or positive results. One of the most recognizable negative results that are a product of organizational conflict relates to the decrease in productivity of the affected workforce. Conflict can cause the workforce to become frustrated in circumstances where a solution is not arrived at. This may cause adverse stress that in turn affect their professional and personal output. Mental health concerns is another aspect of negatives brought about by conflict within an organization. In cases where members of an organization are put on extended stress as a result of conflicts, they may suffer from mental complexities such as headaches, loss of appetite, and to some extent, they may become unapproachable. Another negative effect of such conflicts in an organization relates to sub-optimization. Extreme conflicts result into loss of focus related to organization’s objectives by the concerned parties. Additionally, wastage of resources and time constitute to a negative result of conflicts within an organization. Other negative consequences are the loss of workforce and violence in cases where mediation between conflicting parties is not instituted.

Nonetheless, there are pros that are interlinked with conflict within an organization. Robbins & Judge (2017), suggests that one of the most recognizable positives of such conflicts is the improvement of future communications between the conflicting units. The conflict between members brings them to a common understanding within an organization. More so, conflict helps members or departmental units to share and respect opinions and hence help them work as a unit. Another positive of conflict within an organization refer to the acceleration of change. Conflict stimulates modification of operation procedures and policies from within. Other pros related to conflict within an organization include; the congruence of objectives, spurring of innovations, and inspiring creativity.

Challenges faced by the Human Resource managers in contemporary, global environment

In the modern global business environment, there is a wide range of challenges faced by the human resource management. These challenges stem from the complexity of managing a globalized workforce, directing corporate strategic decision-making units, positioning the organization for future ventures, and keeping the organization within laws (Robbins & Judge, 2017).

In a globalized workforce, the HR managers are faced with the challenge of establishing strategies that involve the adaptation to recruitment and retention of a dynamic workforce (Robbins & Judge, 2017). To counter this challenge, the HR managers require analytics and a comprehensive skill set that include metrics on gender and other diversities to plan for an all-round workforce. Another challenge of faced by the HR managers that is linked to the management of a globalized workforce involve the laying of the groundwork for complexities constituted in hiring, management, and integration of a globalized workforce. Other challenges include; preparation for fresh, global, and modern leadership, the identification of skills at the ground level, and maintenance of a comprehensive relation with regulations and hiring Acts.

In the challenge of directing corporate strategic decision-making processes, HR managers are faced with the complexity of consolidating the human capital risks and opportunities. This implies that in strategic decision-making the managers are required to incorporate the decisions of the human capital that operating from abroad by reviewing the risks and opportunities in strategic decision-making. More so, managers are faced with the challenge of balancing societal and corporate cultures while ensuring that there is an evident promotion of diversity. Additionally, HR managers are tasked with making the business case for Corporate Social Responsibility (Robbins & Judge, 2017). This challenge requires the managers to solve complexities such as when local practices or labor laws conflicts with organization’s policies related to Corporate Social Responsibility.

 

 

 

 

 

 

 

References

Robbins, S. P., & Judge, T. A. (2017). Organizational behavior. Boston: Pearson.

 

Artificial Intelligence Project

June 20, 2017

 

 

Intelligent Health Maintenance System

Predictive Health monitoring and Maintenance based on Cloud Computing and IOT Architecture using Machine Learning

 

 

 

 

 

 

 

 

 

 

 

 

Contents

Chapter 1: Introduction. 6

Problem Definition: 6

Background: 8

Strategy: 9

Research Objectives: 10

Research Limitations: 11

Chapter 2: Literature Review.. 13

Chapter 3: Research Methodology. 18

Chapter 4: End-To-End Architecture For Enabling Artificial Intelligence Through Stream Analytics  20

Stream Analytics: 23

Machine Learning: 24

The Microsoft Azure Machine Learning Studio: 26

Training And Scoring Models: 28

Heart Disease Prediction: 29

Chapter 5: System Requirements. 34

Functional Requirements: 37

Non-Functional Requirements: 42

Project Issues: 44

Sysml Requirements Diagrams: 47

Diagram 1: Operating Environment: Indoor And Outdoor 47

Diagram 2: Type Of Alarm Depending On The Movement: 48

Use Cases: 49

Business Use Cases: 49

Product Use Cases: 55

Adapt Case: Enhancing Data Analysis And Decision Making For Fall Detection Using Machine Learning Algorithms. 60

Chapter 6: Conclusion And Recommendation. 64

References: 67

Appendices: 80

Appendix 1: 80

Appendix 2: 84

Appendix 3: 85

Ploting Correlation Matrix. 86

Binary Classifier Creation. 86

Model Accuracy Evaluation. 86

 

 

 

Chapter 1: Introduction

Problem Definition:

Ever since the advent of smart devices which can be accompanied with humans in mobile forms and be connected to the internet, medical industry has been dreaming of a comprehensive integrated system through which health can be monitored and diseases can be prevented using the data from health monitoring devices. Human health has been at the mercy of doctors and health practitioners, due to which errors are probable in diagnosis of any medical condition. The capability of cloud computing with its ability to handle big data and web of smart devices can give a way for predictive diagnosis which can possibly eliminate human error in prescribing medicines for diseases and as a preventive mechanism for future health threats.

The modern healthcare facilities generate tremendous amounts of data which is handled through conventional in house e-healthcare systems. However, such systems does not have the capability to integrate and process data streams in the form of big data from many different dimensions and sensory devices distributed in hospital all the way to the patient’s home. Healthcare facilities use the traditional relational database management system (RDBMS), which is limited in its processing power and scope to make sense of big data which requires processing in real time such that appropriate steps can be taken before the problem jumps out of reach. In addition to the stated limitation, as mentioned before the new healthcare system is shifting from hospital-centric or career-centric to patient-centric, where patients are to be monitored periodically throughout the day and they themselves along with medical professionals can be empowered to make best decision by taking in view the processed information.  The volume and speed at which there is a requirement of organising the sensor data to make accurate prediction is unprecedented for traditional RDBMS. The big clusters coming in data streams from patient’s home need to be streamed and analysed in real time with seamless connectivity.

With an increase in the use of Internet-of-Things, the data generated from every patient comes in clusters of big data, which is extremely useful for making health situation and cure related prediction if it can be processed and analysed timely. It is very difficult to make sense of data streams in real-time through manual analysis of health care professionals; hence, highly complex computing algorithms are the only possible methods to abstract and make sense of clusters using historical data and enable machine learning of the system.

The system shall cover all the required technicalities which are appropriate to design a cloud Computing based intelligent health monitoring and prevention system for the management and usage of health care data that will use predictive analytics to assess patient’s health condition and suggest appropriate solutions to both patients and health professionals from remote locations along with suggesting ways to prevent a possible health condition in the future. In recent past many systems have been proposed but they all have been designed without the capability of completely eliminating human involvement in drug prescription and use web of devices to monitor health and diagnose according to trends through machine learning at the same time. In the above stated lines, the system is intended to provide web based user friendly platform to patients in order to empower them to monitor their health condition or symptoms, have a deeper insight into the symptoms through the use of recommendations and analytics enabled by machine learning algorithms; receive health professional validated recommendations and keep track of their health on a visual website.

 

 

Background:

In the coming decades, the ageing of population and the revolution of Internet-of-Things (IoT) are among the megatrends that will reshape the world dramatically (National Information Council 2008). According to the department of Economic and Social Affairs of United Nations (2009), the rise of the persons above 60 have been steady during the recent years, which has incremented to 115 in 2009 from 9% in 1950; which according to the recent trends can go as high as 25% in 2050. The aging population rings bells for the already struggling healthcare systems around the world, which lack the robustness to handle huge influx of patients in the coming years. According to National Bureau of statistics (2011) developing countries such as China are even more disposed to this problem. In addition to the rise in population of elderly the overall population in the developing countries is increasing which is partly because of a higher mortality rate; combined with the increasing vulnerability of human health caused by pollution and other artificial ways of everyday lifestyle, every individual is much more disposed to viruses and disease. The best way to handle this rising problem is to introduce ways that have the capability to encourage preventive measures such that any disease can be prevented. At the same time the healthcare system shall have the capability to reach maximum number of individual and monitor their health to enable a transformed ecosystem for healthcare powered by the modern technologies. The future health care services shall be transformed towards patient centricity than the previous criteria of career centricity. Cloud computing provides the required infrastructure which can make the healthcare system completely patient-centric and transparent with a greater degree of reliability and robustness. The machine learning capabilities of the cloud infrastructure and the integration of Big Data analytics can allow the system to predict trends in the patient’s health and prevent any disease by suggesting the appropriate measures based on reliable accumulated data. Such system can be realized through te interjunction of Cloud computing, Data Science for acquiring, processing and managing data for stakeholders in Healthcare.

The current healthcare system is headed towards home-centricity, targeted for 2030 through hospital centric model which is currently dominant. According to Koop et al. (2008,29): Technology will transform our medical system by bringing technology directly into the home. The enabling technology is in two directions: Cloud computing and internet of things. IoT enables the cyber-physical system controlled by cloud infrastructure which has the capability to remotely process information and deliver it to the smartphones of the patients and healthcare professionals. The system has the capability to integrate devices and applications for communication, sensing, information and networking management, identification, through any media and device, anywhere anytime; morevover, the system can be made fully intelligent through the integration of predictive analytics and for the very first time in the history a system much more capable than human mind dedicated to health monitoring and maintenance can be realized and implemented (European Commission Information Society 2008, 1009).

 

Strategy:

The study is focused on utilizing trans-disciplinary approach to probe into the latest computational infrastructure of Cloud and IoT devices to enable Intelligent Health Maintenance System (IHMS), which can monitor current health condition and potentially prevent health deterioration and disease through use of predictive analytics to prescribe preventive medicine based on big data clusters acquired from web of monitoring devices. The system is aimed at catering the in-house health monitoring of the elderly along with providing a health maintenance system for health conscious individuals by acquiring data such as Blood Pressure, Heart Beat Rhythms, Blood Sugar levels, Body Temperature, Physical Activity, indoor air pollution etc. The trends in data acquired for each individual shall be processed through cloud based predictive analytics algorithms using Software as a Service (SaaS) such as Apache Spark and IBM Watson Service. The user generated data shall be compared with cleansed data in clusters through using historical and present trends (predictive analytics) and the trends in the data shall be matched to predict a health condition. The system shall be artificially intelligent given that it will learn with each data sample and will store greater clusters for matching.

The research study is divided into two major stages: first stage aims at collecting data for constructing framework of the system using literature, online courses (MOOCS), and publications of industry leader. The aim here is to probe the technological advancements in multiple computing and health care domains which can make the system truly intelligent through machine learning. In the second stage, the collected secondary and primary data is used to define requirements of the system along with construction of complete end-to-end architecture of the system. The business case for the system is also constructed. The system archtecture modules of the system include: IoT Hubs, Events Hubs, Stream Analytics, Machine learning, Storage and Networking, Web APIs and Business Community Strategy. Special consideration shall be given for the solution of the following issues: Selection predictive analytics platform for accuracy and efficiency; Data Handling issues; Network system security; Ethical and privacy issues.

Research Objectives:

  • To understand the application of machine learning and predictive analytics in cloud based systems.
  • To investigate the needs and requirements of a comprehensive Intelligent Health Monitoring System (IHMMS) using Secondary (content analysis) data and exploit platforms to enable machine learning.
  • To define the system architecture of IHMS through the use of requirements defined using the collected information.
  • To Investigate and define the solutions to technical problems in acquiring and processing data in real time and using predictive analytics through Cloud computing.
  • To identify and provide solutions of possible challenges and obstacles in the implementation of IHMS on a larger scale for consumer market.

 

 

Research Limitations:

This research will be completed using primary and secondary data. This means that there will be primary data collection for the purposes of this study from online survey. However, the use of primary data does not mean that resources are not required. The study will require one to use and internet connection and subscribe or buy some publications over the internet. While most of the publications being sought are likely to be offered free of charge, some may require subscriptions to get access to their content.

The study also aims to utilize data from online courses which are mostly free but researcher will have to purchase few resources for the course work. The study also aims at developing a prototype of the IHMS, which requires use of cloud services as Platform-as-a-service (PaaS); hence, the researcher will have to subscribe to cloud services which might be very costly.

Due to the limitations pertaining to the hardware implementation and collection of appropriate data for predictive analytics the system cannot be fully tested with real time data acquisition.

The study is aimed at exploiting the most recent trends in the technology and healthcare, however, such an approach has many constraints given that much of the research and development in the field is not openly published and is constantly being updated and new standards are introduced.

 

 

 

 

 

 

Chapter 2: Literature Review

Yu et al. (2016) proposed an intelligent system based on cloud computing software as a service approach for handling big data in health care. The focus is on building an efficient platform based on web services in order to simplify the procedures of health monitoring and assessment. The system realized uses big-data analytical approaches to provide reliable recommendations through the comparison of cleansed data with the generated by the user of the system. The repository of cleansed data clusters are acquired by the researchers from the IBM Watson and Apache Spark services. The system provides web based user friendly platform to patients in order to empower them to monitor their health condition or symptoms, have a deeper insight into the symptoms through the use of recommendations and analytics enabled by machine learning algorithms; receive health professional validated recommendations and keep track of their health on a visual website. Yu et al. (2016) provided a one-click solution to all the patients which can potentially solve the problem of inefficient diagnosis and delayed medical attention.

Yamamoto et al. (2014) defined a system through the use of cloud computing to manage big data for connected smart cities. The researchers demonstrated ways to retrieve data from “Scallop4SC”-a smart city platform-of the connected smart houses. The researchers utilized HBase for variety of log data and Hadoop to big-data logs. The study concludes that different data processing and mining methodologies are suitable for different sizes of data acquired.

Jiang et al. (2014) constructed a system to cater the transfer of data for Healthcare big-data systems. They used wearable devices to acquire the data and constructed an intelligent Information Forwarder. They focused on constructing the system for the healthcare of elderly. They provided platforms which allow for centralized analysis of distributed monitoring.

Li et al. (2014) defined a system for sensory data acquired through health monitoring devices and proposed a community based wiki-health as a big data platform. The proposed platform defies a new ontology for data information management which includes ways of analyzing, retrieving, tagging, storing, searching and comparing data acquired from health sensors. The system allows individual users to access health-related knowledge discovery through internet. A similar system was proposed by Kang et al. (2016) which is constructed to cater acquired sensory data of supply chain from Electronic Product Code Information Services (EPCIS) using MOngoDB.

Mohammed et al. (2014) focused on building a mobile application for android platform for healthcare domain, which aims at acquiring the sensory information from connected internet of things and processing the data using cloud computing. The name of their application is “ECG Android App” with an aim to provide users a platform to visualize their current health condition through their Electro Cardiogram (ECG) waves in real time in comparison with historical data. A specific medical cloud is also integrated for the uploading of the user’s data anonymously in order to build repository of cleansed samples. The user data is also uploaded to the personal centralized cloud of the user which can be accessed by medical professionals after permission of the user in order to assess and diagnose their health condition. The researcher presented a comprehensive infrastructure for healthcare industry based on integrating many different technologies: signal processing, communications protocols, IOIO microcontrollers, efficient and secure systems for data sharing, signal processing, central cloud and data base management system. The focus of their study is to construct a comprehensive system architecture and design which can enable the integration of cloud and IoT for healthcare industry. The figure below demonstrates the layered architecture of the proposed patient monitoring system by Mohammed et al. (2014).

Figure 1: Patient monitoring system system architecture including all layers (Mohammed, Thakral, Ocneanu, Jones, Lung, & Adler, 2014)

Song et al. (2012) used android smartphone application and hardware to construct a solution for body monitoring system. They focused to solve the problem of unreliability and limited scope of previously available body monitoring system by utilizing the smartphone technology. The information related to the user’s acquired data can be accessed through the smartphone screen. The researchers integrated multiple technologies in the system design: electrocardiogram acquisition sensor, brainwave capture sensor, temperature detection, smartphone application, and network transmission. They further tested the efficacy of the proposed system in real world cases.

Lee et al. proposed the “Concinnity” system which acquires data from multiple sensors and uses a repository along with workflow system to process and interpret the information. They proposed a cloud platform-as-a-service to manage the sensory data and demonstrated the integrated architecture for the system. They integrated the data input portal where the users of the system can contribute their data to the big data repository. The data that can be uploaded includes lifestyle information, sensory data, well-being etc.

The Health-IoT technologies and services are promising to address the challenges faced by the health care sector. However, literature on the subject shows limited work on the integration of the available technologies to mechanize an automated holistic health care and prevention system for general consumers.

The literature review is providing deep insights into the technological requirements of designing a system that can be truly intelligent and incorporate machine learning. The focus is on the cloud computing elements enabling machine learning capabilities of the system; hence multiple domains have been analyzed, such as predictive analytics, machine learning, big data analytics etc.; using different research instruments. The primary research instrument used is the analysis of articles in peer reviewed journals.

The literature on the subject area shows that there is a need for an intelligent health monitoring and maintenance system which can acquire multiple health related factors through mobile devices and use predictive analytics to suggest preventive medicine along with suggestions for the patient’s decision regarding their healthcare. However, the literature lacks a complete cloud based solution, which can integrate live streams of data acquired through the web of IoT and use Cloud SaaS to for stream analytics and machine learning. Such a system can have unlimited processing power and an ecosystem of patients can allow the system to be much more intuitive than any other system presented in the literature given that this system shall have the capability of expansion by using greater number of measurements to derive a conclusion.

 

Chapter 3: Research Methodology

 

The research study utilizes content analysis and desk based research strategy with an inductive approach by first distilling the appropriate work in the literature and then constructing thresholds to assess the efficacy of the a Cloud Based HealthCare system.

The dependence on literature as major form of data extraction has its pros and cons, when using the content analysis technique; such challenges are identified by Kalenga (2015), which has been considered throughout the study as benchmark to objectify the information. In order to limit the possible sources of anomalies the study inducts triangulation of key findings through primary data collection from online survey (Kratina et al., 2015).

The aim of the method used is to precipitate qualitative information under a theme followed for the analysis. The quality of the published reports is also assessed based on the standards followed in the industry. Bryman and Bell (2015) indicated that the principal aim of cross-sectional survey research designs revolves around the collection of quantifiable data concerning the same research subject with a view of detecting any patterns of association within the secondary data and the triangulation of key findings from multiple sources.

 

 

The research study utilizes content analysis and desk based research strategy with an inductive approach by first distilling the appropriate work in the literature and then constructing thresholds to assess the efficacy of the a IoT and Cloud Based HealthCare system.

The dependence on literature as major form of data extraction has its pros and cons, when using the content analysis technique; such challenges are identified by Kalenga (2015), which has been considered throughout the study as benchmark to objectify the information.

The aim of the method used is to precipitate qualitative information under a theme followed for the analysis. The quality of the published reports is also assessed based on the standards followed in the industry.

 

 

 

 

 

 

 

 

 

 

 

 

Chapter 4: End-to-End Architecture for enabling Artificial Intelligence through stream analytics

The end to end solution of the system comprises of the five architectural layers as shown and explained in table below. However, our main focus is on the “Real-Time Analytics” Layer, where the machine learning algorithm for the acquired streams of data are implemented on the data coming from the IoT hubs from heart beat monitors and other devices attached to the patients. For the purposes of predictive analytics the acquired data is first cleansed and then compared with historical logs of events recorded from multiple data sources. The algorithm is defined in a way such that the system is self-learning and self-optimizing.

Table 1: Architecture layers used for the system model.

 

The real-time event processing reference architecture is dependent on a number of different Azure PaaS capabilities to support end-to-end analytics scenarios. The following diagram and subsequent sections describe the various stages of the data flow lifecycle within this architecture, as well as the components required to support each of these stages, and the interaction between each of these components. The roles that each of the services illustrated above can support tends to blur across the layer boundaries. For example, SQL Database provides both a storage capability as well as a query engine for returning records to a user. Also, Event Hubs provides storage for a pre-defined period of time. This diagram is intended to illustrate each of the services being mapped to their primary role within this architecture.

 

 

The reference architecture used for the study is selected for building scalable and secure solutions that are device-centric which encapsulates the processing required for conducting predictive analysis and integrating the cloud infrastructure as required by the system proposed. The solution can be implemented on public, hybrid or private cloud as per the requirements of the system. The architecture enables a smooth flow of information between the connected health monitoring devices and line-of-business assets to the cloud infrastructure for the processing, analysis, control and business process integration. Figure 1 provides a high-level conceptual architecture which can be used as a reference model for the study and can be modified to serve specific identified purposes. The architecture is also aimed at catering the distributed nature of sensory instruments through a centralized cloud based processing system. The gateway is used to connect the device which has dedicated processing capabilities; it facilitates bidirectional communication with the back-end system and provides endpoints for the connected devices. The basic backend comprises of components for data collection, business logic, visualization, analytics, device discovery and registration.

 

 

Our main concern for the health monitoring system is to define the analytics layer for the system which shall be capable of streaming the live data and applying machine learning algorithms using third party data sources for enabling the system for artificial intelligence in disease discovery and prevention diagnosis. For this system the most appropriate module for streaming and analysing the data is the Azure Stream Analytics. After streaming the data the system utilizes the cortana studio for the implementation of the machine learning algorithm and the newly structured data is managed and stored using a combination of Azure Blob storage and SQL database.

There are two technologies that support the real-time analytics layer of this event processing reference architecture. Azure Stream Analytics acts as the linchpin of this reference architecture by providing the engine capable of temporal analytics over moving data streams. The second technology in the real-time analytics layer is Azure Machine Learning, which is a predictive analytics service capable of consuming either a single record made up of multiple columns (sometimes referred to in the context of machine learning as “features”) via request / response API, or consuming a file for asynchronous batch scoring. Given the real-time scope of this document, the role of Azure Machine Learning will be based on the request / response method only.

Additionally, Apache Storm is positioned in this diagram as a reference as to where it aligns to Azure Stream Analytics. Each of these platforms support similar event processing scenarios, however there are a number of differences in their supported languages and interoperation capabilities.

The figure below provides the reference architecture on which the system shall be based. The main module of concern for this study is the “Analytics and Machine Learning” component of the data processing, analysis and management section.

Figure 3:  Cloud based IoT solution Reference Architecture.

The technical details of the data acquisition devices will be provided and the embedded systems used as well as the sensing devices will be elaborated.

 

 

Stream Analytics:

Azure Stream Analytics ships with a pre-built adapter for Event Hubs, which significantly reduces the time to market for delivering solutions since developers do not need to develop their own custom adapters for the streaming engine. Additionally, and perhaps more significant for enabling accelerated solution deployment, Azure Stream Analytics provides a SQL-like language that allows database developers with existing SQL skills to transition to this platform very quickly. This language is very similar to T-SQL, which is the primary database language for SQL Server, however it contains a superset of functions that support temporal operations such as applying sliding, hopping or tumbling time windows to the event stream.

Azure Stream Analytics supports two different types of inputs, either stream data or reference data, and two different input data sources, either Event Hubs or files from Blob Storage. Only Blob Storage is supported for consuming reference data into the stream. Event streams can be consumed from either Blob Storage (in which case all events already exist in a file) or from Event Hubs whereby data is arriving in real-time. In the instance that data is being consumed from Blob Storage, the file being referenced must contain a timestamp record in a supported time

format for enabling Stream Analytics to perform temporal analysis. Whilst it is possible to stream

a file without a timestamp field that would not enable temporal queries, the value of the

Solution is somewhat diminished unless there is a specific scenario that requires this

Functionality. The following diagram represents the different data ingestion options for Stream

Analytics.

Machine Learning:

Azure Machine Learning provides a cloud based platform for mining data to identify trends and patterns across large scale information sets. By “learning” from historical events and trends, machine learning can publish a model that can be leveraged for determining predictions in real-time based on incoming event data. Combining the real-time rules based processing capabilities of Azure Stream Analytics with the real-time predictive analytics capabilities of Azure Machine Learning can help businesses rapidly deploy highly scalable data solutions to support complex information challenges.

Machine learning models that have been developed, trained and tested, are deployed as web services that can be called from applications. A request to a model is made asynchronously, either by submitting a single record containing a number of columns, or by submitting a file that contains a number of records to be scored. For the purposes of real-time analytics, the single record request / response method is used for submitting an incoming event to the machine learning model.

One of the considerations for constructing a single record feature set to submit to machine learning models is that the incoming event stream may not contain all of the data required for the model schema. For example, telemetry that is transmitted from equipment may contain data about the performance characteristics, but it will likely not contain all of the attributes of that equipment as it is repetitive and will bloat the data transmission. Equipment attributes, such as age may be highly influential features in a real-time machine learning model and therefore need to be appended to the record prior to calling the model.

The following diagram illustrates how Azure Machine Learning can make use of streaming data to enable real-time prediction.

The diagram above does not make an assumption on which action the application will perform

once the prediction has been received back from the machine learning service. There are a

number of possibilities here, one of which may be that the application calls a notification service

(Azure notifications are beyond the scope of this document) to send an alert to a device. Other

alternatives may include writing the prediction result to a SQL database for consumption into

reporting analytics, or initiating a broader automated workflow.

A single solution may contain multiple models that need to be trained for predicting different

outcomes based on the same or similar input data. Whilst this diagram depicts a single machine

learning model, an actual implementation may comprise multiple models being called from a

single orchestrating application, including scenarios where the response from one model may

result in the triggering of another model request in a sequential manner.

 

The Microsoft Azure Machine Learning Studio:

 

The sample data to test the algorithm is used for the cancer detection system. For the coding and scripting R script is used in the MS Azure Machine Learning Studio.

 

The Appendix 3 shows the complete R script implementation from the start till the end of the process for cancer prediction using cleansed data set for training and testing. The implementation of the system is conducted using the jupyter server. The AzureML package is installed by default on the jupyter server.

First of all the data set is loaded through the data storage layer. In order to make the system intuitive and intelligent the algorithm first trains the system. The clusters acquired with results are splited into the positives and negatives; which are further splited into halves for training and testing purposes. The further identified features of the cancer patients are also added individually into the dataset. The figure below defines the process of splitting and cleaning the data to be used for training and testing.

 

The data samples are then joined with the additional features column to make four independent data sets: Positive training examples, positive test examples, negative training examples, negative test examples. After that the test samples and training samples for negative and positive sets of data are joined.

The final cleansed training data set is then acquired for further analysis. The data is further grouped into bins and columns are selected to execute the following R script for training the model.

# replicate positive examples 93 times

 

dataset <- maml.mapInputPort(1)

 

data.set <- dataset[dataset[,1]==-1,]

pos <- dataset[dataset[,1]==1,]

for (i in 1:93) data.set <- rbind(data.set,pos)

row.names(data.set) <- NULL

 

maml.mapOutputPort(“data.set”)

 

 

 

Two individual data sets are then trained: firstly the dataset with edited metadata and secondly the data set without the editing of meta data.

After that the four data sets as shown in figure below are trained and scored.

Training and scoring models:

 

The models are then trained in four different sections using the “Train Model” module of the Microsoft Azure Machine learning studio. Then the patients IDs are used to test the trained model. The result output is executed using the following R script: Given in the appendix 1.

 

Training and evaluation of regression models is then conducted to check which regression model is most appropriate for the acquired data set. The following four regression models are used to train the data: Decision Forest regression, Boosted Decision tree regression, Poisson Regression, and Neural network regression. The rows for the scores of the models are then added and the following Rscript is used for the execution of the results for declaration of best model to be utilized: Given in Appendix 2.

 

 

 

 

Heart Disease Prediction:

A similar heart disease prediction training model is implemented in the MS Azure Machine Learning studio and is published. The diagram below gives the demonstration of algorithm.

Figure 2: Heart Disease Prediction Alorithm.(Published at: https://gallery.cortanaintelligence.com/Experiment/Intelligent-health-Maintenance-system)

The regulatory and ethical requirements revolve around the data handling procedures deployed in the proposed technological solution. Major stakeholder is the Data Protection Agency, responsible for ensuring the validity of the system in the light of Data Protection Act 1998 (Sophie et al. 2012). The proposed technological solution for the fall detection system deploys sensor nodes for data collection to monitor activity of the patient continuously. The data acquired shall be transmitted to the base station for further analysis and to be used by the stakeholders for assessment and retrieval. The stored data for retrieval include the patient’s complete medical information along with all the confidential details required for analysis by health professional. The system shall utilize the localization details of the patient at all times; which might be a source of unethical intervention in the personal lives of the patients if the data is not handled professionally by the stakeholders. According to the proposed technological solution for the system, second party (base station correspondents) shall be given complete access to the patient’s medical data  and third party (relatives) shall be given limited amount of access to patient’s data (Yi et al. 2014). For ensuring the alignment of the system with the rules and regulations applicable, following procedures and regulations are considered (Sophie et al. 2012):

  • APMS contract: arrangements made under section 83(2) of the 2006 Act for the provision of primary medical services.
  • The oversight mechanism of the National Health Service Commissioning Board.
  • Confidentiality and disclosure of information: General Medical Services (GMS) Code of Practice.
  • General Medical Council (2009) Confidentiality Code of Practice.

Given the vulnerability of the data acquired at the base station servers, it is very important that complete network security architecture shall be implemented; with cyber security layers deployed at multiple levels (Boric-Lubecke et al. 2014). A complete network security infrastructure shall be designed to ensure the security and integrity of the data, and make the system resistant to third party intrusions.

 

 

 

 

Chapter 5: System Requirements

  • The Client, the Customer and Other Stakeholders

The Client: Hospitals

The Customer: Elderly persons above age of 65 years and their families.

Other Stakeholders: Doctors, Nurses, Carers, Investors, Emergency Response correspondents, Data Protection Agency, and Medical Association.

 

  • Users of the Product

Hospital Personnel: Doctors, Nurses, Carers, Correspondents and Management.

Elderly Persons

Relatives of the Elderly

 

 

Project Constraints

  • Requirements Constraints

The project poses many technological and ergonomic constraints; due to the extent of requirements and limitations, posed by the required flexibility of application.

  • The system shall be reliable at multiple levels to increase the level of trust for the stakeholders: consistency in fall detection, accuracy of unusual behaviour detection, accuracy of coordinates acquisition using GPS, appropriate battery life for 18 hours of non-stop operation, availability of assistance at all times, and consistency in extent of access of information for every stakeholder (Salem et al. 2013).
  • The system shall use complete cloud computing solution for databases and real time networking which shall be secure and free of latency in VOIP.
  • The system must adapt and evolve to minimize errors and enhance accuracy by integrating machine learning algorithms.
  • User interface must be simple and easy to use; satisfying usability demands for the elderly.
  • The wearable device must be easy to handle and lightweight to ensure that the elderly persons can comfortably operate it, avoiding non-usage and refused-usage behaviours. The design must be non-intrusive and maximally discrete (Patel et al. 2012).
  • The collected data must be organized and available in a way to make sense for professional and non-professional users (Marschollek et al. 2012).
  • The number of nodes for end to end data acquisition and communication must be minimized (Lim et al. 2015).

Technological constraints:

  • The device must continuously monitor movement and localization of the user to perform real-time and transparent evaluations which shall be used to make alarm triggering decision using threshold algorithm.
  • The system shall take into account constraints and limitations posed by wearable device and smartphone integration, due to limited battery life (Lim et al. 2015).
  • The size of the sensor node must be minimized to ensure non-intrusive usage (Patel et al. 2012).
  • Data must be available for updates and real-time evaluation continuously; so, the system shall make use of internet of things for connecting servers to the user nodes (Salem et al. 2013).
  • Naming conventions and definitions

GPS: Global Positioning System

GPRS: General Packet Radio Service

WSN: Wireless Sensor Network

WBAN: Wireless Body Area Network

Sensor Node: The wearable device equipped with accelerometer and gyroscope.

Data Acquisition: Acquiring data and tagging it with appropriate relevance for understanding and retrieval.

Data Fusion: Using data from different sources to predict the outcome.

Medical Database: The medical history of the patient maintained at the base centre.

Internet-of-things: A web of sensors and computer devices connected to each other through internet.

Machine Learning: Intelligent agent algorithm which performs changes in the thresholds of the system to enhance the effectiveness of the detection system automatically.

Wearable device: The necklace designed to be worn by the elderly patient, which transmits sensor data to the wireless transmitter and mobile phone.

 

  • Relevant facts and assumptions
  1. The most important assumption is about the usability of wearable device: The assumption is ‘normal usage’ behaviour of the elderly towards wearable device, that is, the person is wearing an emergency wireless transmitter like wrist watch, a necklace or a pendant permanently during the whole day time.
  2. The correspondents at the base centre are available at all times for monitoring activity of the elderly patients and providing assistance in case of fall event detection or abnormal movement detection.

 

Functional Requirements:

  • The scope of the work

The scope of work for the system extends from customized hardware designs to the integration of hardware with customized software at multiple levels. Data-acquisition and Data-fusion shall be performed using real-time evaluations in the database servers with the aid of threshold and machine learning algorithms using cloud platform of Microsoft Azure. The system shall use GPS, GPRS, WSN and WBAN technology to transmit collected sensor data to the database servers and website in real-time (He et al. 2012). The updated information regarding activity and patient condition must be available at an online platform; which shall be accessed by all stakeholders for analysis and updates.

Azure’s predictive analytics services, including Machine Learning, Cortana Analytics and Stream Analytics, will be used for health intelligence. Doctors will be able to make smarter decisions, improve customer service and uncover new business possibilities from structured, unstructured and streaming Internet of Things data.

  • For IOT data handeling and control the system shall use: Azure IOT Hub, Stream Analytics
  • For networking requirements of the system: Azure DNS, CDN, ExpressRoute and Virtual Network.
  • For customer interaction: Business SAAS apps

 

 

Some major requirements of the system include:

  1. Fall Detection System:
  • Automatic emergency response infrastructure:

Alarm in case the elderly person comes out of the care home and garden area;

Alarm in case of no movement for a set period of time; and

Alarm after detection of sudden acceleration and fall.

  • Manual emergency alarm and communication:

The elderly person using the system shall be able to trigger an emergency alarm voluntarily, bypassing the automatic system. Furthermore, the elderly person shall have an option of communicating with the correspondent at base centre at any time.

  • False Alarm Designation:

He elderly person shall be able to respond immediately to a false alarm by signalling a false alarm designation immediately (Salem et al. 2013).

  1. User Localization:

The system must be capable of tracking the position of the elderly at all times. GPS technology along with GSM shall be used to localize the user.

  1. Real-time and transparent evaluation of the movement:

The movement pattern must be evaluated by the system in real-time using the kinematical activity data-with the use of accelerometers; the movement divided into three types: low, medium and high. Abnormal activity must be detected and an alarm shall be triggered in response (Yi et al. 2014).

  1. Bidirectional Voice communication:

Both the correspondent and the elderly person shall have the flexibility of communicating with each other at all times.

  1. Access to battery level information:

The battery level of the devices must be visible to all stakeholders through online platform (Razzaque and Dobson 2014).

  1. Machine Learning Algorithms for data mining and decision making using real time sensor data

The system shall be capable of learning new behaviours and adjusting itself in real time using data server storage and real time data acquisition. Situations such as false alarm trigger and non-detection of fall shall be utilized by smart agents to enhance the algorithm (Kangas et al. 2014).

  1. Indoor data transmission
  2. Outdoor Data transmission

Outdoor data transmission shall be conducted using android phone application connected to the wearable device via Bluetooth connection as mechanised by Casilari et al. (2015)

  • The scope of the product

The product must be able to use a small web of internet of things in the form of sensor nodes and connect it to the internet for real-time data acquisition and decision making. The product shall be based on multiple hardware devices to be used by the elderly, a data acquisition and decision making system to trigger emergency response, an online platform to be used by professionals and relatives of the elderly to analyse and update information, and a data-server to store and interpret information online. Different UCI Software shall be designed: for, the sensor node device; the smart phone; the online platform; MySQL databases; and emergency response centres (Yi et al. 2014).

 

Some of the major requirements for the product include:

  • The product must be cost effective.
  • The product must be easy to handle and discrete.
  • The product must be adaptable.
  • The product must have the flexibility of customization.
  • The product must maximally aid in management.
  • The product must have minimal power requirements.
  • The product must have long battery life.

 

  • Functional and data requirements

The data should be acquired and transmitted in real time with adequate amount of transparency to the online database and the system shall use Microsoft Azure (open, flexible and enterprise grade platform). The online database then can be accessed by the stakeholders for analysis and updates. The updates must be available at online and call centre platforms, which shall use cloud services and data storage facilities.

 

The collected data shall go through a process of data-fusion to integrate sensory information from different nodes. The movement information along with the sensory data from the wearable device shall be available in real-time for the analysis of professionals (Yi et al. 2014). The data collected shall be used by intelligent software agent to predict if an unusual activity has occurred. The unusual activity shall be categorized into three different ways by the algorithm automatically at the base station (call centre) to predict which emergency response alarm must be triggered (Akbar et al. 2015).

 

The patient information available at the online platform must have different level of access for different stakeholders, depending on their privileges defined by the management.

 

Firstly, the data shall only be available online to the registered users and each registered user shall be assigned a different privilege by the management.

 

Secondly, the relatives of the patient should be able to access the real time sensory data being collected by the sensors but shall not be allowed to access all the comments delineated by the professionals.

Thirdly, an activity report shall be generated once a day and its comparison shall be made by normal activity to sense any possible dangers. This shall be done using data-mining algorithms implemented on the acquired data.

 

Fourthly, the medical professional shall be given complete access to the history and reports generated by patient’s activity. They shall be allowed to amend the data and add their comments and diagnosis online.

 

Non-functional Requirements:

  • Look and Feel Requirements

The product shall utilize a minimalist design and interface standard. The wearable sensor node shall be made as attractive as possible with minimum number of buttons so as to make it less noticeable.

 

  • Usability and Humanity Requirements

The wearable device must be easy to handle and lightweight to ensure that the elderly can comfortably operate it-avoiding non-usage and refused-usage behaviours. The design must be non-intrusive and maximally discrete.

 

The system shall be dependable enough to induce a sense of security and confidence in the patients which can possibly add to its worth and users will deviate less from normal usage.

 

  • Performance Requirements

The system shall be reliable at multiple levels, to increase the level of trust for the stakeholders: consistency in fall detection, accuracy of unusual behaviour detection, accuracy of coordinates acquisition using GPS, appropriate battery life for 18 hours of non-stop operation, availability of assistance at all times, and consistency in extent of access of information for every stakeholder (Akbar et al.2015).

 

 

 

  • Operational and environmental Requirements

The system shall be capable of operating in all sorts of environment. To ensure the operations in variable environments the wearable device must be water proof, shock resistant, shall be capable of operating in extreme temperatures.

  • Maintainability and Support Requirements

A base station shall be mechanized with a purpose of collecting data through wireless transmission, storing data in the data base, using data-mining and data fusion algorithms to trigger emergency response, and a call centre to respond to the emergency needs and communicate with the patient (Kapadia et al. 2015).

 

The medical professional along with other authorized users of the online platform must have access to the battery level of the fall detection sensor node. The system must trigger an alarm in case the battery needs recharging.

 

  • Security Requirements

The data acquired through sensor nodes must only be available to users with authorized access and strict measures must be taken to ensure security of the data at the base station. Complete security requirements protocol is established in Part 2.

 

  • Cultural Requirements

Given that the sensor node is a wearable device; it must be designed such that it is not detectable while the user interacts with other people, so that peers do not pass judgmental remarks.

 

  • Legal Requirements

UK Data Protection Rules and Regulations are applicable for the security of personal information of the patients.

The medical professional using the data and providing services shall be fully qualified for the jobs and should have a professional education.

For detailed legal and ethical requirements kindly see Part 2.

Project Issues:

  • Open Issues

The issues are all covered in the requirements constraints section of the template.

  • Off-the-Shelf Solutions

 

The indoor and outdoor operation:

To enable the system to collect data and transmit it in real time at any place the patient might be, a triple node bidirectional transmission mechanism shall be designed.

 

For indoor application a wireless transmission device is connected to the wearable sensor.

 

For outdoor fall detection a mobile phone shall be used, connected to the wearable sensor device through Bluetooth technology (Casilari et al. 2015).

 

The data collected shall be retained and stored at the call centre to be available for data mining in order to detect unusual activity.

 

Proposed Technological solution:

The proposed technological solution of the system is mechanised taking into close consideration the requirements mentioned in the Volere Template.

The system shall utilize the same infrastructure for fall detection. However, for indoor operations system shall utilize the PERS (personal emergency response system) infrastructure which consists of a wearable device and a base station used for data transmission to the base centre and mobile communication (Casilari et al. 2015).

Personal Emergency Response System shall comprise of three nodes:

  • The wearable device
  • The wireless transmitter remotely connected to the database centre using PERS systems.
  • A mobile device used for communication with the base centre in case of emergency outdoors- the use of built-in tri-accelerometer of smart phone along with GPS data transmission system shall be utilized for fall detection (He et al. 2012).

 

The data collected shall be analysed in real time using threshold algorithms to initiate emergency response infrastructure as proposed by Phu et al. (2015). The data fusion shall be done over the internet through utilizing MySQL database integrating nodes of the system in a web of IoT. The issues as delineated by Kapadia et al. (2015) shall be considered to implement end to end medical informatics.

For the implementation of the hardware infrastructure an INGA wireless sensor node shall be used (Kapadia et al. 2015). The node comprises of an accelerometer, a gyroscope and a barometric pressure sensor. Accelerometer shall transmit the primary data acquired to detect abnormal behaviour. The wireless sensor node is equipped with an IEEE 802.15.4 compatible radio transceiver, which is widely used in the area of Wireless Sensor Networks (WSNs) and Wireless Body Area Networks (WBANs) (Lim et al. 2015).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SysML REQUIREMENTS DIAGRAMS:

 

Diagram 1: Operating Environment: Indoor and Outdoor

Figure 1: SysML requirements diagram: Operational environment.

 

 

Diagram 2: Type of alarm depending on the movement:

Figure 2: SysML requirements diagram: Alarm.

 

 

 

 

 

 

USE CASES:

  • Use Case 2:

Overview:

Health professional analyses and updates patient information from the online platform.

Name: Update Medical reports through online platform.

Identifier: BC2

 

Actors:

Primary Actor: Medical Professionals

Supporting Actor: Online Reception Software Application

 

Preconditions:

  1. The medical professional has authorization of access and can amend the reports of patients.
  2. The system is running online with database maintained at the call centre.

 

Flow of events:

  1. The health professional access the online reception software application using web browser.
  2. The system prompts with the user-interface asking for log-in information. The health professional enters his user name and password.
  3. The system directs the professional to the available database according to his/her privilege of amendment and provides access to reports of the patients generated in the past. The system saves the activity log, tagged with recent activity of the user which can be retrieved at the call centre by Age Concerns management.
  4. The health professional uses the options on the interface to access the reports of the patient under observation. The system prompts with search for the patient number to access relevant data. The system interface also guides the professional to analyse real-time data received from the movement and fall detection devices.
  5. The health professional enters the unique patient ID in the search box.
  6. The system compares the privileges allowed to the user for the specific patient number.
  7. The system directs the user into the profile of the patient, from where the user can access previously generated reports and amend them accordingly.
  8. The user selects the report to be studied, using the date of the report generated.
  9. The user analyses the report and adds his suggestions and diagnosis along with messages for different stakeholders.
  10. The system tags the generated data with the name and ID of the health professional. It updates the database immediately and sends a notification to the stakeholders in their corresponding accounts. The carers and call centre professionals get the instructions generated by the system to act according following the advice of health professional.

 

Postconditions:

The database is updated for every user in real-time.

 

Alternative Course:

  1. The unique patient ID entered by the health professional does not allows privileged access to the user, to study and amend the details.

5.1. System shows a warning message indicating that the user was denied access to the desired information and the user must contact the management team if he/she has detected an unusual behaviour.

5.2. User contacts the support team at the call centre, using Voice-Over-IP communication option at the website and delivers a request for the change in privilege for the patient.

Figure 4: Business Use Case Diagram 2.

 

Product Use Cases:

  • Use Case 1: Alarm activation system by the elderly without fall detection

Overview:

The elderly patient feels a need to talk to the correspondent at the call centre bypassing the emergency alarm response.

Name: Update Medical reports through online platform.

Identifier: PC1

 

Actors:

Primary Actor: Elderly Patient

Primary Actor: Correspondent

Supporting Actor: Call centre software

 

Preconditions:

  1. The elderly person is wearing the necklace (sensor node): Normal Usage.
  2. The correspondent is available at the call centre.
  3. The wireless indoor link to the call centre is working and transmitting real time data to the call centre database.

 

Flow of events:

  1. The elderly patient detects an unusual health condition.
  2. The patient uses his/her pendant to press the button available for direct communication.
  3. The correspondent responds by contacting the patient through wireless GSM transmitter if he is indoors.
  4. The patient refuses to answer but presses the button on the pendant indicating emergency medical assistance required.
  5. The correspondent sends the message to the emergency response team which in turn show up at the door of elderly person using his localization information.
  6. The patient responds to the queries and receives the required emergency medical treatment from nurses.
  7. The patient answers the question if he/she wants doctor at his home or if he is willing to go to the care centre.

 

Postconditions:

The emergency team reaches the location on time.

 

Alternative Course:

  1. The patient is outdoor on the road.

3.1. The data transferred to the base-centre is through the mobile phone.

  1. The patient answers the mobile phone and communicates his situation.

4.1. The correspondent analyses the situation and predicts the intensity of emergency.

 

Figure 3: Business Use Case Diagram 1.

Figure 3: Business Use Case Diagram 1.

Figure 5: Product Use Case Diagram 1.

 

  • Use Case 2: Information access by the relatives

 

Overview:

Relatives of the elderly use the online platform to check the condition and activity of the elderly person.

Name: Relatives accessing online system platform.

Identifier: PC2

 

Actors:

Primary Actor: Relatives of the elderly

Supporting Actor: online system platform

 

Preconditions:

  1. The relative of elderly person has authorization of access to the information of the patient.
  2. The online system is updated from the base centre.

Flow of events:

  1. The relative of the elderly person uses the website URL to access his/her authorized account
  2. The website opens the login page and asks for the login information of the person.
  3. The user enters the username and password assigned by the management.
  4. The user selects the option for checking the patient’s medical condition.
  5. The user analyses the information and prints the data to keep the record to him.
  6. The user chooses the option to check comments by the medical professional.
  7. The user adds queries to the message board which will be answered by the health professional.
  8. The user logs out of the account.

 

Postconditions:

The relative uses the information to serve the elderly better.

 

Alternative Course:

  1. The relative does not have authorized access to the patient’s information anymore.
  • The log-in page requests the relative to talk to call centre for help.

 

Figure 6: Product Use Case Diagram 2.

 

Adapt Case: Enhancing data analysis and decision making for fall detection using machine learning algorithms

Overview:

Adaptation of better algorithm for unusual behaviour detection using false alarm data and the data acquired in case a fall occurred but the system failed to trigger the alarm.

 

Name: Use machine learning intelligent agent to enhance the reliability of the system.

Identifier: AC1

 

Actors:

Primary Actor: Intelligent software agent

Primary Actor: Elderly person

Supporting Actor: Call centre software

 

Preconditions:

  1. The elderly person experienced a fall but the alarm was not triggered at the base centre.
  2. The elderly person did not experience a fall but a false alarm was triggered at the base centre.

 

Flow of events:

  1. The elderly patient experiences an unusual health condition and experiences a fall.
  2. The system uses the data but does not comprehend the behaviour as a fall event.
  3. The elderly person uses the bypassing mechanism, to trigger a manual alarm.
  4. The intelligent agent stores the data acquired before the manual alarm to add to the data base as an example of a fall event.
  5. The intelligent agent adjusts the algorithm to adapt to the latest example data stored for fall event.

 

Alternative Course:

  1. The elderly patient does not experience an unusual health condition or a fall and a false alarm is triggered at the base centre.
  2. The system uses the data and comprehends the behaviour as a fall event.
  3. The elderly person uses false alarm button to indicate that an actual fall did not occurred.
  4. The intelligent agent stores the data acquired before the false alarm to add to the data base as an example of false fall event.
  5. The intelligent agent adjusts the algorithm to adapt to the latest example data stored for fall event.

 

 

 

Figure 7: Adapt Use Case Diagram.

 

 

 

 

 

 

Chapter 6: Conclusion and Recommendation

Ethical issues, Data Encryption and Privacy

Encryption shall be done over all communication channels from sensor node transmission to the PERS transmitter as well as Internet, ISP-based connections etc. Confidential data shall be kept encrypted on the workstations and data base station (Al Ameen et al. 2012). Strict change controls shall be used. Field level file change history shall be maintained. Digital signatures of creator and checker shall be acquired in real time.

Some important considerations for network and data security infrastructure include (Kapadia et al. 2015):

  • Firewalls-internet connection
  • Integrated smart card access control at the data base station
  • Encryption-Application specific
  • Database Security-Proprietary, DBMS-specific, RBAC (role based access control).
  • Authentication- User ID and password based with limited smart card pilots

 

To ensure ethical disclosure of patient’s medical data the following issues shall be taken into consideration:

  • The patient shall be informed of the data usage purposes: for direct clinical care (assessment by health professionals); and for secondary uses (access by the relatives). As according to the requirements online database shall be maintained for frequent access by the stakeholder to patient’s medical data. The patient shall be given access to online database website to change the privilege of access for any second party under the UK National Health Service (NHS), ‘The confidentiality and Disclosure of Information Directive 2013’ (2013).
  • The health professionals shall take into consideration the Data Protection Act 1998 and consider each patient as a unique case study. The online base station website shall delineate the implied rules from the Act to ensure that the doctors consider them before upholding any sort of data usage. Data Protection Act 1998 legislates the following:
  1. Medical data shall be discarded after its necessary usage is completed.
  2. The data required for different purpose shall be disclosed differently, to minimize disclosure amount.
  3. The stakeholders must ensure safety and security in data handling and storage.
  4. Information shall be retained about the place of data storage; the oversight mechanism shall assure the implementation of security and contractual agreements.
  5. A written assurance shall be provided to the stakeholder to outline the type and intensity of data disclosure, with a surety that the data will not be disclosed to a third party.
  • The secondary usage shall be maximally restricted to disclosure of effectively anonymised, pseudonymised or aggregated data. Such as the correspondent at the base station shall not be given access to patient’s personal information such as name, ID or picture. The emergency infrastructure shall use a unique code name provided to each patient. Section 251 of the NHS Act 2006 provides statutory basis for patients to restrict the release of medical data to a second or third party disclosure.
  • The stakeholders shall retain a key to code data and convert it from anonymised to specified form as advised by General Medical Council (2009) Confidentiality, GMC, London p.30.
  • The express consent of the patient is not required under the ‘Confidentiality and disclosure of information Directions 2013’, which delineates statutory basis for bypassing consent where it is not practical to hide data tagging; such as, disclosures relating to financial and management arrangements of NHS (regulations 2004).
  • A formal data sharing protocol shall be published on the base centre website.
  • The health professional shall have an approval of a written protocol delineating the structure of their trials for the patient as instructed by the report ‘Staff care: how to engage staff in the NHS and why it matters’ published by The Point of Care Foundation (2014). The correspondent shall stick to asking about specified and approved set of questions when contacting the elderly in case of emergency. All clinical trials shall be authorised by ethics committees working under NHS as part of the Health Research Authority’s National Research Ethics Service (NRES).

 

 

 

 

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Discrimination and Employment Law

June 20, 2017

Introduction

Diversity is a common practice at any given workplace since individuals originate from different backgrounds due to the differences in their areas of specialization. The level of exposure differs from one individual to the other hence creating an environment that exhibits various practices as also noted by Roberts and Mann (2015). It is important for one to understand the roles played by each part in the process of engagement where the employer and the employee have a significant role to play in their different positions. This paper focuses on the case where an employee had an issue with the kind of language that was used at the workplace hence taking legal action against the employer as a way of trying to solve the problem.

Elements of the case

Freedom of speech is a critical right to human beings especially when at the workplace since they need to communicate with one another. Nickerson et al. (2014) observed that the practice is expected to be conducted maturely by respecting the interests of other people within the same environment. In this case, a plaintiff must present a prima facie case to avoid dismissal of the case or an unfavorable directed verdict. As presented by Reeves, it is evident that her male counterparts behaved in a manner that suggested discrimination based on gender since their choice of words was not friendly to the female workers. The allegations implied that there was a lack of respect by C.H Robinson since its employees could not consider the presence of female gender when discussing sensitive issues and used vulgar language. Reeves assumed that it was the responsibility of the management to stop such culture and encourage a situation whereby each person is respected and allowed to contribute towards the growth of the company.

Basis of the court’s ruling

When a case is presented before the court, due processes are observed when seeking for justices during the judgment stages. Pateman (2014) also observed that the practice advocates for proper analysis of the case using the evidence presented by both parties involved in the case. The move helps in developing effective decision-making process where the judge has enough evidence to decide the nature of the case. In the case presented by Reeves, the court ruled against her expectations since her case did not provide convincing evidence to implicate the employer. When employees interact, it is difficult for the employer to dictate the kind of conversation that should be pursued. It is important to note that the other employees had the freedom to interact and share information without being monitored on what they should speak when together as friends. Law must be observed when prosecuting an individual to ensure the crime is recognized as a punishable under the stipulated terms and conditions during the engagement process.

For an employer to be liable when a case is reported, the evidence is derived from the kind of terms and conditions of engagement between the employer and the employee. The evidence was not presented by Reeves hence making her case weak since the allegations cannot be used to make a fair judgment, especially when ruling against the employer. It is possible that the situation has been the same long before Reeves’ arrival hence making it a culture that did not just target Reeves. Such information makes it difficult for the case to be ruled for Reeves since the case lacked the evidence of sexual discrimination as Reeves might want to imply in the case.

Observation of timeframe is critical practices when engaging in legal battles since there are different categories of cases that have a different timeframe of reporting such cases. Once the timeframe elapses, it is possible for the case to be nullified based on technicality issues. Reeves did not consider the 180 days stipulated in the law when launching a complaint against an employer hence making her case to be ruled against her expectations.

Possible liability

The state and federal laws do not present a similar treatment for the independent contractors and the employees. This is based on the provisions of Title VII of the Civil Rights Act which categorizes work place harassment as an unlawful act once it is based on a person’s race, sex, religion, color as well as national origin. It is common for employees to be held responsible for an independent contractor’s behavior as indicated in the recent ruling (Santos v. Puerto Rico Children’s Hospital). In this case, it is evident that employees need to be protected to ensure that they are not liable for crimes that they have not committed. As much as independent contractors are not protected under the anti-discrimination laws, it is the responsibility of the employer to ensure that the third party who is the employees is prevented from being harassed in any particular way. In this state, if the situation was to be considered to be independent contractor versus an employee, C.H Robinson could possibly be held responsible for sexual harassment and could face the lawsuit.

Conclusion

Discrimination is a sensitive practice that must be controlled at the workplace to ensure employees are protected by the law as noted by Megarry (2014). It is common for individuals to act out of ignorance and exhibit discriminative actions without knowing the impacts created by their actions. Individuals should follow the right procedures when presenting a case against any party since the law is meant to protect the right of every person.

 

References

Megarry, J. (2014, December). Online incivility or sexual harassment? Conceptualizing women’s
experiences in the digital age. In Women’s Studies International Forum (Vol. 47, pp. 46-

55). Pergamon.

Mitchell, K. J., Ybarra, M. L., & Korchmaros, J. D. (2014). Sexual harassment among

adolescents of different sexual orientations and gender identities. Child abuse & neglect,

38(2), 280-295.

Nickerson, A. B., Aloe, A. M., Livingston, J. A., & Feeley, T. H. (2014). Measurement of the

bystander intervention model for bullying and sexual harassment. Journal of adolescence,

37(4), 391-400.

Pateman, C. (2014). Sexual contract. John Wiley & Sons, Ltd.

Roberts, B. S., & Mann, R. A. (2015). Sexual harassment in the workplace: A primer. Akron Law

            Review, 29(2), 5.

 

 

Implement the following tasks using ORACLE SQL*Plus

June 20, 2017

–a

SELECT industryTitle, MAX (jbs.nums) AS Most_Popular_JobType
FROM
(
SELECT j.industryID AS indID, COUNT(*) AS nums
FROM job j
) jbs
INNER JOIN industry i ON j.industryID=i.industryID GROUP BY Most_Popular_JobType

 

–b
SELECT jobDescription, cj.EndDate – cj.StartDate As Duration
FROM contract_job cj
INNER JOIN jobs j ON cj.jobID=j.jobID ORDER BY Duration DESC
FETCH FIRST 1 ROWS ONLY;

 

 

–c

SELECT b.ABNNumber,b.BusinessName
FROM job j INNER JOIN industry ind ON j.IndustryID=ind.IndustryID
INNER JOIN business_industry bi ON ind.IndustryID=bi.IndustryID
INNER JOIN business b ON bi.ABNNumber=b.ABNNumber
LEFT JOIN adjacent_suburb asb ON b.BusinessPostcode=asb.Postcode
LEFT JOIN business b2 ON asb.AdjacentPostCode=b2.BusinessPostcode
WHERE j.jobID=11

 

 

–d

SELECT c.ClientNo,c.ClientName
FROM client c
INNER JOIN equipment_hire eh ON c.ClientNo=eh.ClientNo
WHERE MONTHS_BETWEEN(TRUNC(sysdate),eh.PickUpDate)>3

 

–e

SELECT MAX(ReviewRating) As “Highest rated business”, MAX(ReviewRating)/5 As “Highest rating out of 5”, MIN(ReviewRating) As “Lowest rated business”, MIN(ReviewRating)/5 As “Lowest rating out of 5”
FROM jobs j
INNER JOIN review rv ON j.jobID=rv.jobID
INNER JOIN business b ON j.SelectedBusinessABNNumber=b.ABNNumber

 

–f

SELECT c.ClientNo,c.ClientName, SUM(Amount) AS “Total Job Expense”,(DropOffDate-PickUpDate)*150 As “Total Equipment Hiring Expense”
FROM client c
LEFT JOIN equipment_hire eh ON c.ClientNo=eh.ClientNo
LEFT JOIN corporate_client ccl ON c.ClientNo=ccl.ClientNo
LEFT JOIN contract_job cjb ON c.ClientNo=cjb.CorporateClientNo
LEFT JOIN job j ON cjb.JobID=j.JobID
LEFT JOIN invoice inv ON j.JobID=inv.JobID
WHERE DropOffDate IS NOT NULL
GROUP BY c.ClientNo

 

–g

 

SELECT e.EquipmentID,e.EquipmentName,Count(*) As total_Hires
FROM equipment e
INNER JOIN equipment_hire eh ON e.EquipmentID=eh.EquipmentID
GROUP BY e.EquipmentID
ORDER BY total_Hires DESC
FETCH FIRST 2 ROWS ONLY;

 

 

–h

SELECT b.ABNNumber,b.BusinessName,ContactName
FROM freelancer_business fbz
INNER JOIN business b ON b.ABNNumber=fbz.ABNNumber
INNER JOIN elite_member em ON fbz.EliteMemberID=em.EliteMemberID
LEFT JOIN seminar_attendies sa ON em.EliteMemberID=sa.EliteMemberID
WHERE SeminarID IS NULL

 

–i
SELECT b.ABNNumber,b.BusinessName
FROM business b
INNER JOIN quotation q ON b.ABNNumber=q.ABNNumber
LEFT JOIN job j ON q.JobID=j.JobID
WHERE SelectedBusinessABNNumber IS NULL

 

–j

SELECT SeminarTitle, SeminarDateTime, count(EliteMemberID) As Attendees
FROM seminar s
INNER JOIN seminar_attendies sa ON s.SeminarID =sa.SeminarID
WHERE TO_CHAR(SeminarDateTime)>”12:00″ AND TO_CHAR(SeminarDateTime)<“22:00”
ORDER BY Attendees DESC

 

 

 

2.

–a
CREATE OR REPLACE PROCEDURE industry_details(
induID IN VARCHAR2,
o_UnionTitle OUT UnionTitle%RowType,
o_ABNNumber OUT b.ABNNumber%TYPE,
o_BusinessName OUT b.BusinessName%TYPE

)

IS
BEGIN
SELECT UnionTitle,b.ABNNumber, b.BusinessName
INTO o_UnionTitle,o_ABNNumber, o_BusinessName
FROM trade_union tu
INNER JOIN industry i ON i.UnionID=tu.UnionID
INNER JOIN business_industry bi ON i.IndustryID=bi.IndustryID
INNER JOIN business b ON bi.ABNNumber=b.ABNNumber
WHERE IndustryID= induID;

 

End;
/

 

—-b
CREATE OR REPLACE PROCEDURE industry_details(
CliID IN VARCHAR2,
o_UnionTitle OUT UnionTitle%RowType,
o_ABNNumber OUT b.ABNNumber%TYPE,
o_BusinessName OUT b.BusinessName%TYPE

)

IS
BEGIN
SELECT UnionTitle,b.ABNNumber, b.BusinessName
INTO o_UnionTitle,o_ABNNumber, o_BusinessName
FROM trade_union tu
INNER JOIN industry i ON i.UnionID=tu.UnionID
INNER JOIN business_industry bi ON i.IndustryID=bi.IndustryID
INNER JOIN business b ON bi.ABNNumber=b.ABNNumber
WHERE IndustryID= induID;

 

End;
/

 

3.

Implement the following tasks using ORACLE SQL*Plus

June 20, 2017

–a

 

SELECT industryTitle, MAX (jbs.nums) AS Most_Popular_JobType

FROM

(

SELECT j.industryID AS indID, COUNT(*) AS nums

FROM job j

) jbs

INNER JOIN industry i ON j.industryID=i.industryID GROUP BY Most_Popular_JobType

 

 

–b

SELECT jobDescription, cj.EndDate – cj.StartDate As Duration

FROM contract_job cj

INNER JOIN jobs j ON cj.jobID=j.jobID ORDER BY Duration DESC

FETCH FIRST 1 ROWS ONLY;

 

 

 

 

–c

 

SELECT b.ABNNumber,b.BusinessName

FROM job j INNER JOIN industry  ind ON j.IndustryID=ind.IndustryID

INNER JOIN business_industry bi ON ind.IndustryID=bi.IndustryID

INNER JOIN business b ON bi.ABNNumber=b.ABNNumber

LEFT JOIN adjacent_suburb asb ON b.BusinessPostcode=asb.Postcode

LEFT JOIN business b2 ON asb.AdjacentPostCode=b2.BusinessPostcode

WHERE j.jobID=11

 

 

 

 

–d

 

SELECT c.ClientNo,c.ClientName

FROM client c

INNER JOIN equipment_hire eh ON c.ClientNo=eh.ClientNo

WHERE MONTHS_BETWEEN(TRUNC(sysdate),eh.PickUpDate)>3

 

 

 

–e

 

SELECT MAX(ReviewRating) As “Highest rated business”, MAX(ReviewRating)/5 As “Highest rating out of 5”, MIN(ReviewRating) As “Lowest rated business”, MIN(ReviewRating)/5 As “Lowest rating out of 5”

FROM jobs j

INNER JOIN review rv ON j.jobID=rv.jobID

INNER JOIN business b ON j.SelectedBusinessABNNumber=b.ABNNumber

 

 

–f

 

SELECT c.ClientNo,c.ClientName,  SUM(Amount) AS “Total Job Expense”,(DropOffDate-PickUpDate)*150 As “Total Equipment Hiring Expense”

FROM client c

LEFT JOIN equipment_hire eh ON c.ClientNo=eh.ClientNo

LEFT JOIN corporate_client ccl ON c.ClientNo=ccl.ClientNo

LEFT JOIN contract_job cjb ON c.ClientNo=cjb.CorporateClientNo

LEFT JOIN job j ON cjb.JobID=j.JobID

LEFT JOIN invoice inv ON j.JobID=inv.JobID

WHERE DropOffDate IS NOT NULL

GROUP BY c.ClientNo

 

 

–g

 

 

SELECT e.EquipmentID,e.EquipmentName,Count(*) As total_Hires

FROM equipment e

INNER JOIN equipment_hire eh ON e.EquipmentID=eh.EquipmentID

GROUP BY e.EquipmentID

ORDER BY total_Hires DESC

FETCH FIRST 2 ROWS ONLY;

 

 

 

 

–h

 

SELECT b.ABNNumber,b.BusinessName,ContactName

FROM   freelancer_business fbz

INNER JOIN business b ON b.ABNNumber=fbz.ABNNumber

INNER JOIN elite_member em ON fbz.EliteMemberID=em.EliteMemberID

LEFT JOIN seminar_attendies sa ON em.EliteMemberID=sa.EliteMemberID

WHERE SeminarID IS NULL

 

 

–i

SELECT b.ABNNumber,b.BusinessName

FROM business b

INNER JOIN quotation q ON b.ABNNumber=q.ABNNumber

LEFT JOIN job j ON q.JobID=j.JobID

WHERE SelectedBusinessABNNumber IS NULL

 

 

 

–j

 

SELECT SeminarTitle, SeminarDateTime, count(EliteMemberID) As Attendees

FROM seminar s

INNER JOIN seminar_attendies sa ON s.SeminarID =sa.SeminarID

WHERE TO_CHAR(SeminarDateTime)>”12:00″ AND TO_CHAR(SeminarDateTime)<“22:00”

ORDER BY Attendees DESC

 

 

 

 

 

 

2.

 

–a

CREATE OR REPLACE PROCEDURE industry_details(

induID IN VARCHAR2,

o_UnionTitle OUT UnionTitle%RowType,

o_ABNNumber OUT b.ABNNumber%TYPE,

o_BusinessName OUT b.BusinessName%TYPE

 

)

 

IS

BEGIN

SELECT UnionTitle,b.ABNNumber, b.BusinessName

INTO o_UnionTitle,o_ABNNumber, o_BusinessName

FROM trade_union tu

INNER JOIN industry i ON i.UnionID=tu.UnionID

INNER JOIN business_industry bi ON i.IndustryID=bi.IndustryID

INNER JOIN business b ON bi.ABNNumber=b.ABNNumber

WHERE IndustryID= induID;

 

 

End;

/

 

 

 

—-b

CREATE OR REPLACE PROCEDURE industry_details(

CliID IN VARCHAR2,

o_UnionTitle OUT UnionTitle%RowType,

o_ABNNumber OUT b.ABNNumber%TYPE,

o_BusinessName OUT b.BusinessName%TYPE

 

)

 

IS

BEGIN

SELECT UnionTitle,b.ABNNumber, b.BusinessName

INTO o_UnionTitle,o_ABNNumber, o_BusinessName

FROM trade_union tu

INNER JOIN industry i ON i.UnionID=tu.UnionID

INNER JOIN business_industry bi ON i.IndustryID=bi.IndustryID

INNER JOIN business b ON bi.ABNNumber=b.ABNNumber

WHERE IndustryID= induID;

 

 

End;

/

 

 

3.

 

CREATE OR REPLACE TRIGGER restrict_changes

BEFORE DELETE OR INSERT OR UPDATE ON review

FOR EACH ROW

WHEN (NEW.ReviewID > 0)

BEGIN

IF :old.jobID = NULL THEN

raise_application_error(-20015, ‘You can’t Give a review’);

END IF;

 

END;

/