Determinants of Choice of Finance by Coffee Farmers in Machakos County

Coffee farming in Kenya has faced numerous challenges over time ranging from land ownership to access to information, cultural beliefs and collateral challenges to acquisition of bank credit. This study aims to establish the determinants of choice of finance by coffee farmers in Machakos County Kenya. The study adopted a descriptive approach which utilized both quantitative and qualitative research methodologies. The study used questionnaires to collect data from a sample of ninety-six (96) respondents. Multiple regression analysis was undertaken to test the relationship between the independent variable (collateral, interest rates, bureaucracy and accessibility to financial institutions) and the dependent variable (choice of finance). The findings indicate that R is 0.726, R2 is 0.527 and adjusted R2 is 0.5905. ANOVA of the data showed that F calculated is greater than F critical (26.361>2.49), indicating that the overall model was reliable in predicting the relationship between the independent variable (collateral, interest rates, bureaucracy and accessibility to financial institutions) and the dependent variable (choice of finance).The study concludes that there was a statistically significant association between collateral, interest rates, bureaucracy and accessibility to financial institutions and selection of funding as the p values 0.039, 0.001, 0.015, 0.011 and 0.018 are less than 0.05 at 5% level of significance. The study recommends that government and financial institutions, as well as other lending institutions, should consider coming up with policies and procedures geared towards catering for specific credit needs of farmers

Plant genetic resources for agriculture, plant breeding, and biotechnology: Experiences from Cameroon, Kenya, the Philippines, and Venezuela

"Local farming communities throughout the world face binding productivity constraints, diverse nutritional needs, environmental concerns, and significant economic and financial pressures. Developing countries address these challenges in different ways, including public and private sector investments in plant breeding and other modern tools for genetic crop improvement. In order to measure the impact of any technology and prioritize investments, we must assess the relevant resources, human capacity, clusters, networks and linkages, as well as the institutions performing technological research and development, and the rate of farmer adoption. However, such measures have not been recently assessed, in part due to the lack of complete standardized information on public plant breeding and biotechnology research in developing countries. To tackle this void, the Food and Agricultural Organization of the United Nations (FAO), in consultation with the International Food Policy Institute (IFPRI) and other organizations, designed a plant breeding and biotechnology capacity survey for implementation by FAO consultants in 100 developing countries. IFPRI, in collaboration with FAO and national experts contracted by FAO to complete in-country surveys, identified and analyzed plant breeding and biotechnology programs in four developing countries: Cameroon, Kenya, the Philippines, and Venezuela. Here, we use an innovation systems framework to examine the investments in human and financial resources and the distribution of resources among the different programs, as well as the capacity and policy development for agricultural research in the four selected countries. Based on our findings, we present recommendations to help sustain and increase the efficiency of publicly- and privately-funded plant breeding programs, while maximizing the use of genetic resources and developing opportunities for GM crop production. Policy makers, private sector breeders, and other stakeholders can use this information to prioritize investments, consider product advancement, and assess the relative magnitude of the potential risks and benefits of their investments." from Author's Abstractplant breeding, biotechnology, public research, Funding, Innovation systems, Capacity building, Biosafety,

<i>Trypanosoma evansi</i>: Genetic variability detected using amplified restriction fragment length polymorphism (AFLP) and random amplified polymorphic DNA (RAPD) analysis of Kenyan isolates

We compared two methods to generate polymorphic markers to investigate the population genetics of Trypanosoma evansi; random amplified polymorphic DNA (RAPD) and amplified restriction fragment length polymorphism (AFLP) analyses. AFLP accessed many more polymorphisms than RAPD. Cluster analysis of the AFLP data showed that 12 T.evansi isolates were very similar (‘type A’) whereas 2 isolates differed substantially (‘type B’). Type A isolates have been generally regarded as genetically identical but AFLP analysis was able to identify multiple differences between them and split the type A T. evansi isolates into two distinct clades

Computational Models Development and Demand Response Application for Smart Grids

This paper focuses on computational models development and its applications on demand response, within smart grid scope. A prosumer model is presented and the corresponding economic dispatch problem solution is analyzed. The prosumer solar radiation production and energy consumption are forecasted by artificial neural networks. The existing demand response models are studied and a computational tool based on fuzzy clustering algorithm is developed and the results discussed. Consumer energy management applications within the InovGrid pilot project are presented. Computation systems are developed for the acquisition, monitoring, control and supervision of consumption data provided by smart meters, allowing the incorporation of consumer actions on their electrical energy management. An energy management system with integration of smart meters for energy consumers in a smart grid is developed

L’apport du contentieux, de la justice transitionnelle et de l’activisme communautaire à l’éradication de l’apatridie au Kenya: réflexion à partir des cas des communautés nubienne, makonde et shona

This article outlines the challenge of statelessness in Kenya and proceeds to focus on two seminal cases filed by the Nubian community against the Kenyan state: one before the African Commission on Human and Peoples’ Rights and the other at the African Committee of Experts on the Rights and Welfare of the Child. Attention then turns to Kenya’s transitionaljustice agenda and its interaction with the plight of stateless persons in Kenya. Through the experiences of the Nubian, Makonde and Shona communities, the article also explores the role of community-led activism infurthering the cause of ending statelessness in Kenya. It concludes with key lessons to be learned from utilising litigation, transitional justice and community-led activism as part of the struggle for the rights of stateless persons in Kenya. It relies on desk-review and research of the Nubian cases, Kenya’s truth commission report and other official inquires, civil society reports, the 2010 Constitution and related laws.Cet article décrit le défi de l’apatridie au Kenya à travers l’examen de deux affaires importantes initiées par la communauté nubienne contre l’État kenyan: l’une devant la Commission africaine des droits de l’homme et des peuples et l’autre devant le Comité africain d’experts sur les droits et le bien-être de l’enfant. L’emphase est mise sur le programme de justice transitionnelle du Kenya et son interaction avec la situation critique des apatrides dans ce pays. Partant des expériences descommunautés nubienne, makonde et shona, l’article explore également le rôle de l’activisme communautaire dans la promotion des activités tendant à éradiquer l’apatridie au Kenya. En conclusion, l’article tire les leçons principales que le recours au contentieux, à la justice transitionnelle et à l’activisme communautaire apporte à la cause des apatrides au Kenya. Différentes méthodes sont mobilisées pour répondre à la question de recherche, à savoir une étude documentaire et des recherches sur les affaires nubiennes, le rapport de la commission de vérité du Kenya et d’autres enquêtes officielles, des rapports de la société civile, la Constitution de 2010 et les lois connexes

Electrochemical immunosensor based on polythionine/gold nanoparticles for the determination of Aflatoxin B1

An aflatoxin B1 (AFB1) electrochemical immunosensor was developed by the immobilisation of aflatoxin B1-bovine serum albumin (AFB1-BSA) conjugate on a polythionine (PTH)/gold nanoparticles (AuNP)-modified glassy carbon electrode (GCE). The surface of the AFB1-BSA conjugate was covered with horseradish peroxidase (HRP), in order to prevent non-specific binding of the immunosensors with ions in the test solution. The AFB1 immunosensor exhibited a quasi-reversible electrochemistry as indicated by a cyclic voltammetric (CV) peak separation (ΔEp) value of 62 mV. The experimental procedure for the detection of AFB1 involved the setting up of a competition between free AFB1 and the immobilised AFB1-BSA conjugate for the binding sites of free anti-aflatoxin B1 (anti-AFB1) antibody. The immunosensor’s differential pulse voltammetry (DPV) responses (peak currents) decreased as the concentration of free AFB1 increased within a dynamic linear range (DLR) of 0.6 - 2.4 ng/mL AFB1 and a limit of detection (LOD) of 0.07 ng/mL AFB1. This immunosensing procedure eliminates the need for enzyme-labeled secondary antibodies normally used in conventional ELISA–based immunosensors

IAT, consumer behaviour and the moderating role of decision-making style: An empirical study on food products

This article discusses the reasons why the study of consumer preferences requires indirect measures. Particularly, the research is focused on the use of the Implicit Association Test (IAT). The main aim of the present research is to verify the usefulness of the IAT in situation of ambivalent attitudes, such as in the food domain. On the basis of the relationship between interest/motivations and visual attention, the first study explores the effect of implicit associations on consumers’ visual behaviour on food labels. Moreover, the predictive and incremental validities of the IAT over traditional self-report measures on subjects’ intention to buy were tested in the specific field of food purchases, where attitudes can be ambivalent. Finally, the role of preference for intuition or deliberation in the decision-making process as a moderator of the relationship between the IAT score and the intention to buy was assessed. The second and the third studies aim to verify the same moderation pattern in real behavioural choices between tasty/healthy foods and between different food brands. Overall, the results (1) show the effect of implicit (and not explicit) associations on the way in which consumers read the information on food packaging; (2) demonstrate that the IAT enhances the understanding of consumer preference, intention to buy, and choices among different products, especially in domains where attitudes could be ambivalent; and (3) support the moderating role of the decision-making style. Overall, the research supports the employment of the IAT in consumer research

Monitorização do impacto da qualidade dos dados e do modelo em aprendizagem automática

Considering the evolution of machine learning algorithms and their use in the dayto- day operations of organizations, it has become necessary to monitor and evaluate their performance in production environments. This dissertation aims to contribute to the existing body of knowledge by offering a perspective focused on monitoring machine learning models during their operational phase. The research approach involves a theoretical exploration followed by the simulation of various errors that cause model degradation in production. In this way, we identify several factors that may go unnoticed when models are in production, such as model bias, data drift, concept drift, and others, and we demonstrate ways to detect them. We conclude that it is imperative to have processes in place for monitoring data and models in production, as well as to highlight Machine Learning Operations (MLOps) as a solution to streamline the deployment, monitoring, and maintenance of a model in production.Considerando a evolução que os algoritmos de Aprendizagem computacional têm tido e o seu uso no dia-a-dia de organizações, tornou-se uma necessidade monitorizar e avaliar a sua execução quando em ambientes de produção. É neste sentido que surge esta dissertação, com o objetivo de contribuir para a base de conhecimento existente, oferecendo uma perspetiva focada na monitorização de modelos de aprendizagem automática durante a sua fase operacional, a abordagem de pesquisa envolve uma exploração teórica seguida pela simulação de vários erros que causam a degradação de modelos em produção. Desta forma, identificamos diversos fatores que podem passar despercebidos quando os modelos estão em produção, como o enviesamento dos modelos (model bias), a deriva de dados (data drift), deriva de conceito (concept drift), entre outros, e demonstramos maneiras de os detetar. Concluímos que é imperativo ter processos em prática para a monitorização de dados e modelos em produção, bem como trazer à luz o Machine Learning Operations (MLOps) como uma solução para agilizar a implementação, monitorização e manutenção de um modelo em produção.Mestrado em Engenharia Informátic