Zetech University

Social media has emerged as a transformative force in democratic participation, challenging traditional media gatekeeping and enabling citizen-led political mobilization. This systematic review examines how social media platforms fostered democratic engagement during Kenya's 2024 Gen-Z movement, in which young activists used X (formerly Twitter), Facebook, Instagram, and TikTok to organize protests against the Finance Bill 2024. The movement demonstrated how digital platforms enable rapid information dissemination, coordinate collective action, and amplify marginalized voices beyond traditional media narratives. Through a systematic thematic analysis of secondary literature and documentation of the 2024 protests, this paper reveals that social media facilitated unprecedented direct engagement between citizens and government leaders, including Kenya's first presidential participation in an X-space discussion. However, significant challenges, including misinformation, digital surveillance, state-sponsored censorship, and the rural-urban digital divide, constrained the movement's full potential. The findings suggest that maximizing social media's democratic potential requires prioritizing digital literacy programs, strengthening independent journalism, ensuring equitable access to technology, and protecting digital rights. This study contributes contextually relevant insights into digital activism in African democracies and offers lessons for global democratic engagement in the digital age.

The Atlantic Meridional Overturning Circulation (AMOC) plays a central role in climate by transporting and redistributing recently observed temperature increases to depth, thereby regulating the effective heat capacity of the ocean under global warming. The AMOC is projected to decline in response to climate change and there is broad agreement that the climate consequences of a potential shutdown of this vital ocean circulation are enormous. The Nordic Seas are a dominant contributor to the overturning circulation due to the production of dense waters north of the Greenland-Scotland Ridge which feed into the lower limb of the AMOC. The objectives of ARCTIC-FLOW, an ESA Polar Cluster project, are: 1) to identify the main locations of surface water mass transformation into denser waters; 2) to provide new estimates of water mass transformation and overturning in order to understand the mechanisms driving  surface density changes and their impact on the ocean circulation; 3) to investigate the temporal and spatial scales at which the main processes of water mass formations occur; and 4) to assess the impact of extreme freshening events, with the main focus on different regions of the Nordic Seas. To achieve these objectives, we will construct a new 16-year time series of satellite-derived freshwater and density fluxes for the Arctic and sub-Arctic regions,  obtained by combining SSS, SST and velocity fields from EO observation, along with information of the Mixed Layer Depth. We will then  perform an in-depth analysis of a comprehensive set of in situ measurements in combination with results of model experiments and the new EO-derived time series. In this talk we will present the project and the progress made in generating  the new satellite product. 

Kenya acknowledges the imperative of integrating green finance into its financial systems to combat climate change and promote environmental sustainability. Despite its commitment to a comprehensive green finance strategy, there remains a significant gap in understanding its actual impact. This study assesses Kenya’s National Green Finance Strategy, focusing on its effectiveness in achieving climate goals by mobilizing green investments and influencing financial institutions to adopt sustainable practices. The primary variables studied include the alignment of financial policy and institutions with green finance principles and the mobilization of green investments. Grounded in Stakeholder Theory and the Theory of Sustainable Finance, the study employs a mixed-methods approach, integrating policy analysis, stakeholder interviews, and case studies. Initial findings highlight progress in fostering green finance while exposing deficiencies in regulatory frameworks, financial inclusion, and public awareness. Although the strategy has partially mobilized green investments, financial institutions have not fully embraced green finance principles. Regression analysis demonstrated high explanatory power (R² = 0.9992, Adjusted R² = 0.9962, F-statistic = 330.8, p = 0.04121), confirming the significant role of financial and institutional resources in mobilizing green investments. Granger causality tests revealed potential temporal dependencies between investment inflows and greenhouse gas (GHG) emissions, showing significant self-causality within GHG emissions data. However, GDP and renewable energy share showed no significant effects. The study concludes that addressing gaps in regulatory frameworks, enhancing financial inclusion, and raising public awareness are critical. Implementing these recommendations could fortify Kenya’s green finance mechanisms, contribute significantly to global climate goals, and provide a model for other developing nations striving to align their financial systems with sustainability principles. By tackling these challenges, Kenya can unlock its full potential in mobilizing green investments, fostering institutional change, and driving the transition to a low-carbon economy. The study further establishes that the development of stakeholder engagement frameworks is necessary, actively engaging local governments, the community, and private investors in the design and delivery of green finance projects and ensuring participatory processes underpin the projects, a precept of Stakeholder Theory.

In machine learning, feature selection is of utmost importance for augmenting the predictive capabilities of ensemble models. This paper presents an innovative hybrid framework for selecting features in ensemble models, which combines Rough Set Theory (RST) with Recursive Feature Elimination (RFE), complemented by Association Rule Mining, to enhance interpretability. The suggested method considerably improves ensemble models’ prognostic accuracy and comprehensibility, particularly Random Forests and Gradient Boosting Machines. The framework starts with the RFE process, meticulously eliminating less influential features, and then applies RST to refine the feature set further by eliminating redundancies. This two-phase approach results in a feature set that is optimally reduced yet highly influential. By implementing this hybrid method on ensemble models, significant improvements in predictive accuracy are demonstrated across three diverse datasets: cancer, Pima Indians Diabetes, and a weather dataset from Underground. The accomplished accuracies for these datasets were 0.9663, 0.8793, and 0.8427, respectively, highlighting the proposed approach’s effectiveness. This article also proposes the incorporation of association rule mining to analyze the outcomes of the models. This technique improves the understandability of the models, offering more profound insights into the connections and patterns, thus tackling the difficulty of interpretability in intricate ensemble models. Our empirical analysis confirms the effectiveness of the proposed hybrid feature selection model, representing a significant advancement in the field. The integration of RFE and RST optimizes the feature selection process and bridges the gap in interpretability, offering robust solutions for applications where accuracy and understanding of model decisions are crucial.

Purpose: The aim of the study was to assess the influence of green marketing strategies on consumer behavior. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Research on the influence of green marketing strategies on consumer behavior reveals several key findings. Firstly, consumers increasingly prioritize environmentally friendly products and companies, driven by concerns about sustainability and climate change. Green marketing strategies, such as eco-labeling, eco-packaging, and messaging emphasizing environmental benefits, significantly influence consumer perceptions and purchasing decisions. Studies indicate that consumers are willing to pay a premium for products perceived as environmentally friendly, particularly when accompanied by credible certifications or endorsements. Additionally, consumers tend to exhibit greater brand loyalty to companies perceived as environmentally responsible. However, the effectiveness of green marketing strategies can vary depending on factors such as consumer demographics, product characteristics, and the perceived sincerity of the company's environmental efforts. Overall, the findings underscore the significant impact of green marketing on shaping consumer behavior and highlight the importance for companies to integrate sustainable practices into their marketing strategies to meet evolving consumer preferences and contribute to environmental conservation efforts. Implications to Theory, Practice and Policy: Theory of planned behavior, diffusion of innovation theory and cognitive dissonance theory may be use to anchor future studies on assessing the influence of green marketing strategies on consumer behavior. Businesses should prioritize authentic and transparent green marketing strategies that resonate with consumer values and preferences. Governments and regulatory bodies should enact policies that incentivize and promote green marketing practices among businesses.

The surge in the use of smartphones and internet has coincided with an increase in sexting among the youth. Existing studies have hardly provided comprehensive analyses of perspectives and experiences of Kenyan youth on prevailing social contexts of sexting, particularly the nexusbetween the practice and processes of dating and forming romantic relationships. This study aimed at analyzing how the use of smartphones interrelated with the exchange of sexually inclined messages in daily social life of middle level college (MLC) students in Nairobi, Kenya. The research question that yielded the findings of this article was: how does exchange of sexting messages relate to daily social activities among the selected MLC students? The study was guided by the Theory of Planned Behaviour. The study used a qualitative research design conducted using multiple case study. Urbanized students with diverse characteristics who used smartphones to create and share sexually inclined messages were selected for the study. Non-probability sampling techniques were employed to identify 65 students aged 18-24 years from five MLCs that were purposively selected. In-depth interviews and focus group discussions were used to generate the data, and the ethical issues that were considered included consent, confidentiality and anonymity. The data were audio-recorded, transcribed and thematic coding procedures were used to organize the data into themes and subthemes. The findings revealed that the selected students’ sexting perceptions and practices shaped and were shaped by online and offline sexting activities of romantic partners. The study concluded that most students had misconceptions and harmful perceptions about the role of sexting in romantic relationships. Therefore, the study recommended use of responses that encourage bold and open discussion about sex, sexting and dating between children and adults, and that foster healthy parent-child relationships.

African indigenous restaurants have become a preference for most consumers although their patronage varies, attributed to various push factors such as health, curiosity and variety. This study explored food handlers’ hygiene practices as determinants of customers’ choice of selected African indigenous restaurants’ in Nairobi City County, Kenya. The study adopted a cross-sectional descriptive survey targeting 15 selected African indigenous restaurants. Purposive sampling was used in selecting all supervisors in the 15 African indigenous restaurants. Using Yamane formula, a sample size of three hundred and forty (340) food handlers was obtained from a population of 2250. Proportionate sampling was used in selecting food handlers as their population had different numbers in each of the selected restaurant.  Data collection instruments were a questionnaire, an interview guide and an observation checklist. Qualitative data was ordered, coded and summarized in compilation sheets for easier analysis in addition to inferential statistics. Quantitative data was analyzed using statistical packages for social sciences with levels of significance established using paired tests with a cut-off point of P < 0.05, (95%) confidence and significance levels. Chi square Pearson’s correlation coefficient tests were calculated to identify the correlation between food handlers’ hygiene practices and customers’ choice of restaurants. The findings showed that most restaurant supervisors were well aware of HACCP system although not all of them implemented it. Further, the restaurants do not observe adequate precautions in the entire food production and therefore programs related to HACCP training needed to be implemented in a practical and realistic manner. The study further identified that the general hygiene standards of the restaurants were relatively high although during the time of the visits, some were not clean. The study recommended the public health authorities in the urban centers to educate all restaurant stakeholders on food hygiene requirements and regulations in order for them to adhere as required. The study further recommended that similar studies to be done in other localities, in rural restaurants, and to incorporate more restaurants. Keywords: Hygiene Requirements, Regulations, Training, Awareness, African Indigenous Restaurants.

Abstract Secure storage and secondary use of individual human genome data is increasingly important for genome research and personalized medicine. Currently, it is necessary to store whole genome sequencing information (FASTQ data) itself, which enables detections of denovo mutations and structural variations in the analysis of hereditary diseases and cancer. Furthermore, bioinformatics tools to analyze FASTQ data are frequently updated to improve the precision and recall of detected variants. However, existing secure secondary use of data, such as multi-party computation or homomorphic encryption, only can handle a limited algorithms and usually requires huge computational resources. Here, we developed a high-performance one-stop system for large-scale genome data analysis with secure secondary use of data to the data owner and multiple users with different data access control. Our quantum secure cloud system is a distributed secure genomic data analysis system (DSGD) with “a trusted server” built on a quantum secure cloud, Tokyo QKD Network under the information-theoretically secure. The trusted server will be capable of deploying and running a variety of sequencing analysis hardware, such as GPUs and FPGAs, as well as CPU-based software. We demonstrated DSGD achieved comparable throughput between with and without encryption on the “a trusted server”. Therefore, our system would be ready to be installed to the research institutes and hospitals that makes diagnoses based on whole genome sequencing on a daily basis.

University curriculum all over the world contain a set of learning outcomes to be achieved through different learning and teaching modes. The achievement of the Learning Outcomes is measured using some form of assessment, usually in the form of University examinations. The traditional teaching has been face-to- face, and therefore many examinations have also been conducted in a face to face manner. One of the challenges of any examination setting is cheating amongst University students, which implies that the examinations must be properly invigilated or proctored. Since the introduction of ICT in Education, there have been attempts to introduce Online examinations, although these are yet to gain full traction. In Kenya, the ODEL standards lean more towards blended learning, where each course has to have a face to face element. Many Universities have therefore been conducting their examinations in a traditional setting. The onset of COVID-19 created a different situation as the social distancing and the restricted movement meant that learners could not attend a physical class. The movement to Remote Emergency Teaching created a challenge on how examinations could be conducted and proctored. Several Universities decided to do the teaching but wait for resumption of face to face studies to conduct their exams, and this had a negative impact on the University Almanac. This paper looks at the case of one Private University that decided to complete a whole semester online but also went on to conduct examinations via the Learning Management System. Using a triangulation of Interviews, Observation and Document Reviews, staff and students who had participated in conducting and taking online examinations were interviewed. The online examinations processed was observed and documents and software used in online examinations and proctoring were reviewed. Reorganization of the examination processes, organizational and cultural change management, ICT technical issues, extensive training, software selection and communication were identified as the key requirements for successful online examinations and proctoring to take place. The use of Proctoring Systems that are integrated with the LMS give a shorter learning curve and are easier to learn and use.

Purpose: The general objective of the study was to establish the role of human resource management practices on the performance of health workers in Kiambu County, Kenya.
 Methodology: The study adopted cross sectional research design, using both descriptive quantitative and qualitative techniques. The study targeted managers and workers of four hospitals which were purposefully chosen. The data was collected by administering questionnaires to the respondents and conducting KII to the managers. The data was analyzed using SPSS V27. The data was presented using tables and charts to summarize responses. For further analysis and comparison, the generated quantitative reports were subjected through tabulations, percentages, measure of central tendencies, correlation coefficient, chi square test, and inferential statistics.
 Findings: The study reviewed that training opportunities and performance management practices improves staff motivation and performance (mean of 4.23, and 4.13). Planning (2.66) was biggest hindrances to utilizing training to improve performance. The managers were not well trained on using appraisals, ratings and feedback (2.06) for performance management. The institutions were not embracing performance culture (2.60), and using it for continuous improvement (1.82). Additionally, the study showed that hospitals did not carry out performance measurement regularly (2.81) and the systems for data collection were inadequate (2.43).
 Unique contribution to theory, practice and policy: The study showed that human resource management practices improve health workers performance. Institutions should formulate and review policies to support human resource management practices that improve performance of the health workers