Prevention of Workplace Injuries and Illnesses

The purpose of this project is to explain ways of preventing injuries and illnesses in the workplace. Safety and health have been major focus in most workplaces because many injuries occur every day. Activities carried out in the workplace can cause direct exposure to physical, or chemical stressors that can lead to acute illnesses or later chronic illnesses if countermeasures were not implemented by employers and no one is monitoring what the employees are exposed to. There are various ways of preventing these injuries including implementing some techniques in the workplace. It is the work of the employer to ensure that every employee who comes to work in the morning must go home with all the fingers and toes. According to the General Duty Clause the worker should be protected. It is the employee\u27s duty to follow the regulations of the workplace in order to be safe at the end of the day. This project will focus on the identification of health and safety hazards within the workplace. Emphasis will be placed on the identification of measures to protect workers from harm

Micro and Small Enterprise Sector and Existing Support System with emphasis on High-Tech oriented Entrepreneurship in Kenya

The significance of Kenya’s micro and small enterprises (MSE) activity has continued to grow since the sector was first brought to the limelight in 1972. In Kenya, it is now recognized that the promotion of the MSE sector is a viable and dynamic strategy for achieving national goals, including employment creation, poverty alleviation and balanced development between sectors and sub-sectors. Together, all this form the foundation of a strong national base and domestic production sector that is central to the government’s vision of achieving a newly industrialized country status by the year 2020.  According to Kenya’s National Development Plan (1997), the MSE sector has been growing in importance both as a source of employment as well as innovative technologies. However, industrial technology development in Kenya is yet to take off. Kenya still relies heavily on imported technology. The plant and machinery that most MSEs use to produce goods and services have little technology (know-how) value. Due to the foregoing, the study reviews the current technological situation of the MSE sector in Kenya to determine the extent of government support services. The study also seeks to analyze how best this support can be delivered to help MSEs develop their technological capacities. The methodology that the study uses to achieve its objectives is documentary analysis and analytical narrative. The main finding of the study is that the major constraint in the MSE sector’s ability to upgrade its existing technological base is lack of national support. This weakness has undermined the development of indigenous labor intensive and local resource-using technologies.  Key Words: micro and small enterprises, technolog

Estimating selected disaggregated socio-economic indicators using small area estimation techniques

In 2015, the United Nations (UN) set up 17 Sustainable Development Goals (SDGs) to be achieved by 2030 (General Assembly, 2015). The goals encompass indicators of various socioeconomic characteristics (General Assembly, 2015). To reach them, there is a need to reliably measure the indicators, especially at disaggregated levels. National Statistical Institutes (NSI) collect data on various socio-economic indicators by conducting censuses or sample surveys. Although a census provides data on the entire population, it is only carried out every 10 years in most countries and it requires enormous financial resources. Sample surveys on the other hand are commonly used because they are cheaper and require a shorter time to collect (Sarndal et al., 2003; Cochran, 2007). They are, therefore, essential sources of data on the country’s key socio-economic indicators, which are necessary for policy-making, allocating resources, and determining interventions necessary. Surveys are mostly designed for the national level and specific planned areas or domains. Therefore, the drawback is sample surveys are not adequate for data dis-aggregation due to small sample sizes (Rao and Molina, 2015). In this thesis, geographical divisions will be called areas, while other sub-divisions such as age-sex-ethnicity will be called domains in line with (Pfeffermann, 2013; Rao and Molina, 2015). One solution to obtain reliable estimates at disaggregated levels is to use small area estimation (SAE) techniques. SAE increases the precision of survey estimates by combining the survey data and another source of data, for example, a previous census, administrative data or other passively recorded data such as mobile phone data as used in Schmid et al. (2017). The results obtained using the survey data only are called direct estimates, while those obtained using SAE models will be called model-based estimates. The auxiliary data are covariates related to the response variable of interest (Rao and Molina, 2015). According to Rao and Molina (2015), an area or domain is regarded as small if the area or domain sample size is inadequate to estimate the desired accuracy. The field of SAE has grown substantially over the years mainly due to the demand from governments and private sectors. Currently, it is possible to estimate several linear and non-linear target statistics such as the mean and the Gini coefficient (Gini, 1912), respectively. This thesis contributes to the wide literature on SAE by presenting three important applications using Kenyan data sources. Chapter 1 is an application to estimate poverty and inequality in Kenya. The Empirical Best Predictor (EBP) of Molina and Rao (2010) and the M-quantile model of Chambers and Tzavidis (2006) are used to estimate poverty and inequality in Kenya. Four indicators are estimated, i.e. the mean, the Head Count Ratio, the Poverty Gap and the Gini coefficient. Three transformations are explored: the logarithmic, log-shift and the Box-Cox to mitigate the requirement for normality of model errors. The M-quantile model is used as a robust alternative to the EBP. The mean squared errors are estimated using bootstrap procedures. Chapter 2 is an application to estimate health insurance coverage in Kenyan counties using a binary M-quantile SAE model (Chambers et al., 2016) for women and men aged 15 to 49 years old. This has the advantage that we avoid specifying the distribution of the random effects and distributional robustness is automatically achieved. The MSE is estimated using an analytical approach based on Taylor series linearization. Chapter 3 presents the estimation of overweight prevalence at the county level in Kenya. In this application, the Fay-Herriot model (Fay and Herriot, 1979) is explored with arcsine square-root transformation. This is to stabilize the variance and meet the assumption of normality. To transform back to the original scale, we use a bias-corrected back transformation. For this model, the design variance is smoothed using Generalized Variance Functions as in (Pratesi, 2016, Chapter 11). The mean squared error is estimated using a bootstrap procedure. In summary, this thesis contributes to the vast literature on small area estimation from an applied perspective by; (a) Presenting for the first time regional disaggregated SAE results for selected indicators for Kenya. (b) Combining data sources to improve the estimation of the selected disaggregated socioeconomic indicators. (c) Exploring data-driven transformations to mitigate the assumption of normality in linear and linear mixed-effects models. (d) Presenting a robust approach to small area estimation based on the M-quantile model. (e) Estimating the mean squared error to access uncertainty using bootstrap procedures

A Study Of Issues And Problems Women Face In Attempting To Pursue Careers In Educational Administration In Kenya

Problem In Kenya, the majority of women in education occupy classroom teaching positions. Educational officers, in their hiring practices, appear to overlook women as resources, thereby depriving the schools of the skills and talents of potentially capable leaders. Students are also denied the role models of female leaders. This study investigates how difficult it is for women to secure professional careers in educational administration in Kenya. Method The population consisted of all the female teachers employed by the Ministry of Education in Kenya, and female Kenyan students in North American universities. A questionnaire was used to collect data from the women teachers selected from five educational levels, namely, university, commercial and technical colleges, secondary and primary schools, and Kenyan students in North American universities. The data collected were analyzed and tested for significant differences related to the women\u27s marital status, age, education, and experience. All 52 items were tested by Chi-square and the alpha level was .05 for all tests. Conclusion The conclusions are given in the order presented in the Purpose of the Study. 1. A majority of women teachers strongly agreed that advanced degrees were the key to administrative positions. 2. It appears that self-confidence to become school administrators was lacking as portrayed in the women\u27s responses. 3. Women teachers believed they would gain self-satisfaction in school administration positions. 4. Culture was recognized as the major barrier as compared to all other factors. 5. Family pressures were also a deterrent to women teachers aspiring to become educational administrators. 6. Kenyan women teachers showed little interest in and commitment to educational administration as a career. 7. Lack of role-models was yet another factor preventing women from pursuing careers in educational administration

Estimating county-level overweight prevalence in Kenya using small area methodology

Public health surveillance of overweight prevalence is essential to assess the extent of the problem, identify regions and groups most affected and inform policy-making. However, the needed reliable data at disaggregated levels is lacking in Kenya. The Kenya STEPwise Survey for Non-communicable Diseases and RiskFactors (KSSNDRF) was nationally representative. It was used to obtain various indicators of non-communicable diseases and risk factors including overweight. However, due to small sample sizes at lower levels like at the county, overweight prevalence estimates are statistically imprecise (i.e., high variance). Therefore, to increase the effective sample size we combine data from the KSSNDRF and the Kenya Population and Housing Census by model-based small area methods. In particular, we fit an arcsine square-root transformed Fay–Herriot model. To transform back to the original scale, we use a bias-corrected back transformation. For this model, we smooth the design variance using Generalised Variance Functions. We compute the mean squared error estimates using a bootstrap procedure. We found that counties within urban areas — including the major towns like Nairobi, Nakuru, Nyeri and Mombasa — have a higher prevalence of overweight compared to rural counties. Although the paper focuses on overweight prevalence in Kenya, the presented method can also be applied to other indicators in developing countries with similar data sources

Small area estimation of health insurance coverage for Kenyan counties

Health insurance is important in disease management, access to quality health care and attaining Universal Health Care. National and regional data on health insurance coverage needed for policy making is mostly obtained from household surveys; however, estimates at lower administrative units like at the county level in Kenya are highly variable due to small sample sizes. Small area estimation combines survey and census data using a model to increases the effective sample size and therefore provides more precise estimates. In this study we estimate the health insurance coverage for Kenyan counties using a binary M‑quantile small area model for women (n=14,730) and men (n=12,007) aged 15 to 49 years old. This has the advantage that we avoid specifying the distribution of the random effects and distributional robustness is automatically achieved. The response variable is derived from the Kenya Demographic and Health Survey 2014 and auxiliary data from the Kenya Population and Housing Census 2009. We estimate the mean squared error using an analytical approach based on Taylor series linearization. The national direct health insurance coverage estimates are 18% and 21% for women and men respectively. With the current health insurance schemes, coverage remains low across the 47 counties. These county-level estimates are helpful in formulating decentralized policies and funding models

Internet of Things security with machine learning techniques:a systematic literature review

Abstract. The Internet of Things (IoT) technologies are beneficial for both private and businesses. The growth of the technology and its rapid introduction to target fast-growing markets faces security challenges. Machine learning techniques have been recently used in research studies as a solution in securing IoT devices. These machine learning techniques have been implemented successfully in other fields. The objective of this thesis is to identify and analyze existing scientific literature published recently regarding the use of machine learning techniques in securing IoT devices. In this thesis, a systematic literature review was conducted to explore the previous research on the use of machine learning in IoT security. The review was conducted by following a procedure developed in the review protocol. The data for the study was collected from three databases i.e. IEEE Xplore, Scopus and Web of Science. From a total of 855 identified papers, 20 relevant primary studies were selected to answer the research question. The study identified 7 machine learning techniques used in IoT security, additionally, several attack models were identified and classified into 5 categories. The results show that the use of machine learning techniques in IoT security is a promising solution to the challenges facing security. Supervised machine learning techniques have better performance in comparison to unsupervised and reinforced learning. The findings also identified that data types and the learning method affects the performance of machine learning techniques. Furthermore, the results show that machine learning approach is mostly used in securing the network

Pricing Unit-Linked Insurance Contracts using Estimated Volatility

This paper develops a model for pricing a unit-linked insurance contract by estimating the volatility. This insurance contract with minimum death guarantee is a contingent claim which implies that a hedging argument can be used to determine the price. In this case, the guarantee strike price does not depend on the current time and the insurer’s liability for a death at a given time is similar to the terminal cash flow of a European put option and we end up with a Black-Scholes like put pricing formula. In this paper, we extend the work of Frantz et al. (2003) by relaxing the assumption that volatility is constant. Keywords: unit-linked insurance contract, premiums, guaranteed minimum death benefi

Comparison of Web Analytics : Hosted Solutions vs Server-side Analytics

The ratability of websites allows the aggregation of detailed data about the behavior and characteristics of website visitors. This thesis examines the value of different web metrics based on the analytics tools used and the behavior of website visitors. The objective is to test and identify key metrics and discuss how they compare between hosted solutions and server-side analytics. The value of the web metrics is evaluated by examining the relationships of the metrics to website conversions. This thesis also shows how different web metrics were analyzed to reveal important characteristics of site visitors and how web metrics can be used to evaluate the effectiveness of the different aspects of a website. This thesis evaluates the performance of Google Analytics (hosted solution) and AWstats Analytics (server-side analytics). The case study examines the metkaweb.fi website by following eight different web metrics during a period of three months. The relationship analysis between the web metrics and the data provided by the two analytics tools used gives an insight into the workings of each tool and why they differ in the results produced. The case study described in this thesis identifies various key web metrics such as unique visits and page view. The sever-side and hosted analytics can be used together as a hybrid solution to tap into the capabilities of both technologies for more accurate reports. In general it is best practice to decide on the web metrics to be analyzed and then select a tool that is best optimized to analyze the metrics to get the most accurate results