Archives of Current Research International

Aim: The aim of this study is to investigate the influence of corona virus disease and other external factors on growth of accommodation and restaurant services (ARS) in Kenya.

Study Design: The study employed quantitative research design involving quarterly time series data from quarter 1 of 2014 to quarter 1 of 2020. The data set was obtained from Kenya National Bureau of Statistics (KNBS).

Methodology: The study employed unrestricted vector autoregression to investigate the changes in the growth of accommodation and restaurant services.

Results: Results indicated that COVID-19, professional, administrative and support services, construction and past ARS growth at 1 to 3 lags influences growth of ARS in Kenya negatively. On the other hand, real estate growth, time trend, tax on products, other services, education, manufacturing, information and communication and past growth in ARS at lag 4 influences growth in ARS sector positively. It was also noted that growth in agriculture and transport and storage do not influence growth of ARS in Kenya.

Conclusion: In conclusion, COVID-19, professional, administrative and support services, construction and past ARS growth, real estate growth, time trend, tax on products, other services, education, manufacturing, information and communication are the main determinants for the growth of ARS in Kenya.

The study evaluated the income and postharvest losses of yam in Nasarawa State Nigeria from 2014 to 2018. The study employed the use of cross sectional design. The study was conducted in Nasarawa state, Nigeria between October 2019 and June 2020. Data for the study were collected using multistage sampling technique from a sample of three hundred and eighty seven (387) respondents. The result showed that majority of the respondents (322) 83%, were male, and within the age range of 31-40 years 158(41%) followed by the age bracket of 41-50: 97(25%); 323(84%) of the respondents were married with average household size of 8.0 persons per household while 87% of the respondents have had one form of formal education or the other. The spearman correlation matrix showing the relationship between the income and post-harvest losses of the farmers further revealed that there is a significant relationship between income and postharvest losses of yam farmers in the studied area. Coefficient of determination (R square) value was 0.922 with a P value of 0.00. The result reveals a significant and strong positive correlation betweenthe income and post-harvest losses of the farmers. The study thus concluded that most of the respondents were high income earners but suffer post-harvest losses throughout yam production chain in various measures. The extent of losses differs from farmer to farmer but does not differ in the communities sampled in the study area.

Smoking of Cannabis sativa (Marijuana), a known psychoactive substance may result in side effect on living cells of the system. The aim of this study is to assess the complete blood count of Cannabis sativa smokers and make comparison with non-smokers in Ekpoma, Edo State. A total of one hundred subjects were recruited for this study which consist of Fifty (50) Cannabis sativa smokers and fifty (50) non Smokers which served as control. A complete record of medical and smoking history was obtained for each subject, with the use of the questionnaire. The samples obtained were taken to the laboratory for analysis using Sysmex KX auto analyzer. The result showed that the levels of WBC, RBC, granulocytes, lymphocytes, monocytes, Haemoglobin, packed cell volume and platelet levels were significantly lower (p<0.05) in cannabis smokers as compared to the control. The MCV, MCH, MCHC, RDW, PDW and MPV values of cannabis smokers were significantly different (p<0.05) as compared to the control subjects. In conclusion, this study showed that haematological parameters of cannabis smokers differed significantly from non-smokers.

A nation’s GDP is an important index reflecting development in economy and incomes. This paper uses the annual data of Nigeria’s GDP from 1981 to 2019 as the research data. An Augmented Dick Fuller test was used to test for stationarity of the data and was seen to be stationary at the second differencing. ARIMA (1, 2, 1) was identified as an appropriate model using Eviews 11 software after comparing the AIC values. The Ljung-Box test of the Residual satisfied that the model was adequate and was used to forecast the out of sample data. And with a Theil inequality of 0.022008, the model forecasting ability is deemed be a good.

UAVs performance is improved, in addition to GPS, they can find routes with artificial intelligence and various techniques, and can fly autonomously. The global UAV market was US$ 25.59 billion in 2018. It is estimated that the market will reach US$70.28 billion by 2029. The first three UAV manufacturers were DCL (USA), Parrot (France) and Yuneec (PRC) and are now being produced in many countries. Aircrafts are exposed to various risks: an aerodynamic stall due to inconsistent sensor readings and inadequate response, hitting a flock of geese or flying objects, pilot being unable to prevent the plane repeatedly nosediving despite following procedures. Therefore, procedures issued by aviation agency for aircraft type certification require an aircraft manufacturer (“applicant”) to demonstrate that its design complies with all applicable agency’s regulations and requirements. Safety risk management is a key component of a safety management system and involves fundamental activities such as identifying safety hazards, and assessing the risks and mitigation. Risk management is an integral component of safety management and involves some essential steps.Take into account any current mitigation measures and assess the seriousness in terms of the worst possible realistic scenario. The risk assessment considered five operational environments; remote, rural, suburban, urban, and congested. Reliability, availability, maintainability, and safety assessment are important study in the development of UAVs. This kind of study is mandatory to increase the reliability of the UAV, its availability, and to reduce repair and maintenance costs.

Aims: While the biomechanical factors causing musculoskeletal disorder (MSDs) are well defined, the psychosocial dimension of MSDs is complex and affected by personal, organizational and environmental elements. There is an impetus to understand how psychosocial risk factors contribute to the manifestation of MSDs and systematically present the strengths and constraints of various instruments in assessing psychosocial risk factors.

Study Design: This article critically reviews various models linking psychosocial constructs to MSDs and various instruments for evaluating psychosocial risk factors.

Methodology: This article reviews the most pertinent literature which presents and discusses the connection of psychosocial domains with the development of MSDs as well as the common instruments used for evaluation of psychosocial factors.

Results: It shows an intricate connection between psychosocial and biomechanical aspects compounded by organizational, individual and environmental factors. Psychosocial factors particularly psychological demand, decisional latitude, level of social support and work organization result in stress which produces strain and physiological deterioration hence MSDs. Different psychosocial risk factors have also been found to evoke MSDs in different body parts. Overarching cognitive and psychological aspects consisting of work demands, maneuver margins and work recognition have also been proposed in examining the psychosocial dimension of MSDs. Instruments for measuring psychological constructs are often subjective and rely on personal reflections. Some instruments assess multiple psychosocial factors while other assess specific attributes. There instruments share a common shortcoming of treating risk factors across different workplaces as homogenous.

Conclusion: Refinement of the instruments and development of sector-specific instruments are beneficial for more reliable evaluation of psychosocial risk factors.