The Welfare Cost of Inflation in Kenya

The article aims to identify an appropriate money demand function that describes the Kenyan money market, then employ it to approximate the welfare cost. The empirical estimation uses quarterly data sets from 2000 (2000:01) to 2014 (2014:03). The empirical results reveals that, the appropriate money demand function that fits the Kenyan data is the semi-log model, which gives the welfare cost estimate of between 0.041 and 0.103 percent, for the inflation band. The results are consistent with literature and smaller when regression techniques are employed to derive the elasticities as opposed to using Lucas (2000) specifications. We conclude that the target inflation band maybe appropriate, however, the welfare cost estimates interest rate distortions, and any reduction may lead to welfare gains. Keywords: Kenya’s welfare cost of inflation, ARDL Model, VECM Mode

Advancing understanding and modeling of climate processes for provision of deterministic climate information for sustainable development in Kenya and Eastern Africa

The implications of climate variability and emerging climate change make East Africa particularly vulnerable region due to dependence of most socio-economic activities on highly variable climatic variables like rainfall which has relatively low predictability. Dynamical climate modelling for both operational climate information services like seasonal outlooks, and long-term projections has made notable improvements since 1990s. Models are the only tools for projecting the long-term future climate alongside provision of short-term information for planning and management of climate sensitive socio-economic activities like rain-fed agriculture and water resources. Using rainfall and moist circulation evaluation results, this study illustrates the “UM HadGEM-GC2” model give good indications of processes which quantify climate extremes namely floods and droughts over East Africa. Among the most important processes revealed in this study, vertically integrated moisture flux, which embraces both horizontal moisture transport into or out of East Africa with sufficient moist-air depth or dry atmospheric column are crucial mechanisms for occurrence of floods and droughts in Kenya and East Africa. Knowledge products like these can translate into mitigation and adaptation decisions in water resources, agriculture and food security. To model developers, processed based model evaluation outcomes like these reveals what physics and dynamics attributes to focus on in the formulation of next generation models and development of evaluation metrics

A Machine Learning Approach

Mutemi, A., Bação, F. (2023). The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach. Journal of Engineering, 2023. https://doi.org/10.1155/2023/8557487Due to the difficulties inherent in diagnostics and prognostics, maintaining machine health remains a substantial issue in industrial production. Current approaches rely substantially on human engagement, making them costly and unsustainable, especially in high-volume industrial complexes like fulfillment centers. The length of time that fulfillment center equipment failures last is particularly important because it affects operational costs dramatically. A machine learning approach for identifying long and short equipment failures is presented using historical equipment failure and fault data. Under a variety of hyperparameter configurations, we test and compare the outcomes of eight different machine learning classification algorithms, seven individual classifiers, and a stacked ensemble. The gradient boosting classifier (GBC) produces state-of-the-art results in this setting, with precision of 0.76, recall of 0.82, and false positive rate (FPR) of 0.002. This model has since been applied successfully to automate the detection of long- and short-term defects, which has improved equipment maintenance schedules and personnel allocation towards fulfillment operations. Since its launch, this system has contributed to saving over $500 million in fulfillment expenses. It has also resulted in a better understanding of the flaws that cause long-term failures, which is now being used to build more sophisticated failure prediction and risk-mitigation systems for fulfillment equipment.publishersversionpublishe

Fiscal Policy Reaction Function for Kenya

This article investigates how the Kenyan government responds to debt portfolio disturbances by estimating Kenya’s fiscal reaction function. The empirical estimation uses monthly data sets from the seventh month of 2000 (2000:07) to the third month of 2014 (2014:03). The empirical results indicates that, the Kenya government faces difficulties in responding to short term positive debt shocks, given its fiscal policy reaction is contradictory to theory expectation, hence a severe fiscal adjustment may be inevitable in the future. Keywords: Kenya’s fiscal reaction function, Quantile Regression, ARDL Mode

Treatment of Kenya’s Internet Intermediaries under the Computer Misuse and Cybercrimes Act, 2018

Kenya has this year enacted the Computer Misuse and Cybercrimes Act, 2018. This article reviews the Act from the perspective of internet intermediaries, with a view to establishing the impact the Act is expected to have on intermediaries’ operations. The article outlines key concerns regarding the Act’s provisions in respect of obligations and liabilities of intermediaries, particularly with regard to obligations to support state agencies. Recommendations are made for how the Act could be amended to cater more optimally to both state and intermediary concerns.African Academic Network on Internet Policy (AANoIP)CA201

Fiscal Policy Reaction Function for Kenya

This article investigates how the Kenyan government responds to debt portfolio disturbances by estimating Kenya’s fiscal reaction function. The empirical estimation uses monthly data sets from the seventh month of 2000 (2000:07) to the third month of 2014 (2014:03). The empirical results indicates that, the Kenya government faces difficulties in responding to short term positive debt shocks, given its fiscal policy reaction is contradictory to theory expectation, hence a severe fiscal adjustment may be inevitable in the future. Keywords: Kenya’s fiscal reaction function, Quantile Regression, ARDL Mode

A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces

Mutemi, A., & Bacao, F. (2023). A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces. Scientific Reports, 13(1), 1-16. [12499]. https://doi.org/10.1038/s41598-023-38304-5Organized retail crime (ORC) is a significant issue for retailers, marketplace platforms, and consumers. Its prevalence and influence have increased fast in lockstep with the expansion of online commerce, digital devices, and communication platforms. Today, it is a costly affair, wreaking havoc on enterprises’ overall revenues and continually jeopardizing community security. These negative consequences are set to rocket to unprecedented heights as more people and devices connect to the Internet. Detecting and responding to these terrible acts as early as possible is critical for protecting consumers and businesses while also keeping an eye on rising patterns and fraud. The issue of detecting fraud in general has been studied widely, especially in financial services, but studies focusing on organized retail crimes are extremely rare in literature. To contribute to the knowledge base in this area, we present a scalable machine learning strategy for detecting and isolating ORC listings on a prominent marketplace platform by merchants committing organized retail crimes or fraud. We employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviors and transactions on the platform. The proposed framework combines bespoke data preprocessing procedures, feature selection methods, and state-of-the-art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings in this context. Our best detection model obtains a recall score of 0.97 on the holdout set and 0.94 on the out-of-sample testing data set. We achieve these results based on a select set of 45 features out of 58.publishersversionpublishe

Systematic Literature Review

Mutemi, A., & Bação, F. (2023). E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review. Big Data Mining and Analytics, 1-27. https://doi.org/10.26599/BDMA.2023.9020023The e-commerce industry's rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, machine learning, and cloud computing have revitalized research and applications in this domain. While machine learning and data mining techniques are popular in fraud detection, specific reviews focusing on their application in ecommerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of machine learning algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key machine learning and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.publishersversionepub_ahead_of_prin

Phytochemical Screening and Antimicrobial Properties of Allium sativum Against Lactobacillus

The objectives of this study were to extract phytochemical components of Allium sativum and screen the phytochemical composition of allium extracts for bioactivity against Lactobacillus. The methanol extract of Allium sativum was obtained from a dried sample of garlic, was screened for phytochemical composition and tested for antimicrobial properties against probiotic bacteria lactobacillus. Antimicrobial analysis was done using agar well diffusion method where different concentration of garlic extract were tested against lactobacillus. The experiment was arranged in 3 replicates according to 4 treatments of different extract concentrations and in the control experiment the bacterial were grown without extract. The result of the phytochemical screening revealed the presence of alkaloids, saponins, cardiac glycosides, steroids, and flavonoids in garlic, but tannins were absent. The antibacterial activity of the extracts against the test lactobacillus showed inhibitory effect where different concentrations showed different inhibitory activities. This review goes over some relevant research that has already been done in this area where garlic has been tested for antimicrobial activities against numerous human pathogens. It therefore lays a ground for new research in testing allium varieties for antimicrobial activities against human resident microbes like lactobacillus that may be subject to susceptibility on these antimicrobial natural products

Combined in silico approaches towards the identification of novel malarial cysteine protease inhibitors

Malaria an infectious disease caused by a group of parasitic organisms of the Plasmodium genus remains a severe public health problem in Africa, South America and parts of Asia. The leading causes for the persistence of malaria are the emergence of drug resistance to common antimalarial drugs, lack of effective vaccines and the inadequate control of mosquito vectors. Worryingly, accumulating evidence shows that the parasite has developed resistant to the current first-line treatment based on artemisinin. Hence, the identification and characterization of novel drug targets and drugs with unique mode of action remains an urgent priority. The successful sequencing and assembly of genomes from several Plasmodium species has opened an opportune window for the identification of new drug targets. Cysteine proteases are one of the major drug targets to be identified so far. The use of cysteine protease inhibitors coupled with gene manipulation studies has defined specific and putative roles of cysteine proteases which include hemoglobin degradation, erythrocyte rupture, immune evasion and erythrocyte invasion, steps which are central for the completion of the Plasmodium parasite life cycle. In an aim to discover potential novel antimalarials, this thesis focussed on falcipains (FPs), a group of four papain-like cysteine proteases from Plasmodium falciparum. Two of these enzymes, FP-2 and FP-3 are the major hemoglobinases and have been validated as drug targets. For the successful elimination of malaria, drugs must be safe and target both human and wild Plasmodium infective forms. Thus, an incipient aim was to identify protein homologs of these two proteases from other Plasmodium species and the host (human). From BLASTP analysis, up to 16 FP-2 and FP-3 homologs were identified (13 plasmodial proteases and 3 human cathepsins). Using in silico characterization approaches, the intra and inter group sequence, structural, phylogenetic and physicochemical differences were determined. To extend previous work (MSc student) involving docking studies on the identified proteins using known FP-2 and FP-3 inhibitors, a South African natural compound and its ZINC analogs, molecular dynamics and binding free energy studies were performed to determine the stabilities and quantification of the strength of interactions between the different protein-ligand complexes. From the results, key structural elements that regulate the binding and selectivity of non-peptidic compounds onto the different proteins were deciphered. Interaction fingerprints and energy decomposition analysis identified key residues and energetic terms that are central for effective ligand binding. This research presents novel insight essential for the structure-based molecular drug design of more potent antimalarial drugs