University of Embu

Ordinary Portland cement (OPC) remains the most common binder used in construction and accounts for nearly 7%‐8% of the world’s CO 2 emissions due to calcination of limestone and high energy use during clinker production. Reducing OPC content using SCMs is one of the major strategies to achieve sustainable construction. Stone‐cutting dust, coming from quarrying activities and having a very fine particle size with oxide composition, has come out recently as a prospective SCM. Its chemical and mineralogical composition may vary widely depending on the type of parent rock: granite and basalt dusts, rich in silica and alumina, in contrast with marble and limestone dusts, which are mainly composed of crystalline calcium carbonate with low pozzolanic activity. This review aims to compile and discuss the published works concerning the use of SCD as a partial replacement of OPC, providing detailed information about its physicochemical, mechanical, and durability performance. Studies showed that 10%–25% replacement of OPC with SCD results in improvement in compressive strength (10%–20%), reduction of water absorption, and refinement of the pore structure due to the filler effect and limited pozzolanic reactivity. However, the obtained results are still inconsistent because of varying stone type, mix design, and curing conditions. Moreover, some reports show losses of strength or reduced workability when higher replacement levels were applied or in the case of using SCD enriched in carbonates. SEM, XRD, and FTIR studies demonstrated microstructural densification and secondary C–S–H in silica‐rich systems, while calorimetric and long‐term durability studies remain limited. Overall, SCD has the potential to be a promising sustainable supplementary cementitious material; however, its performance is strongly dependent on mineralogy and processing conditions. It is recommended that a standardized testing framework should be developed in order to allow better comparability between the various published studies and to guide safe and effective application in low‐carbon cementitious systems.

<p>There is a global concern about promoting peaceful coexistence in school learning environments. Interpersonal conflicts in schools can lead to academic difficulties and violent interactions among the students. The academic performance among students in Embu East Sub-County has not been satisfactory and has become an issue of concern. The study conducted a quantitative correlational study to determine the relationship between interpersonal conflicts and students’ academic performance. A correlational research design was used, involving a sample of 357 form 2 students sampled through simple random sampling. Data were collected using the interpersonal conflict questionnaire and document analysis. The items in the questionnaire yielded a Cronbach’s alpha coefficient value of 0.759. Data were analyzed using inferential statistics, including Pearson’s correlation and simple linear regression analysis. The findings revealed a weak negative and statistically significant relationship between interpersonal conflicts and academic performance among the students (β=-0.152<sup>**</sup>, p=0.008). The study concludes that interpersonal conflict engagement among students leads to poor academic performance. Therefore, schools should prioritize integrating programs that build interpersonal and social skills among students to improve interpersonal relationships and academic performance.</p>

Remote Sensing (RS) images capture spatial-temporal data on the Earth's surface that is valuable for understanding geographical changes over time. Change detection (CD) is applied in monitoring land use patterns, urban development, evaluating disaster impacts among other applications. Traditional CD methods often face challenges in distinguishing between changes and irrelevant variations in data, arising from comparison of pixel values, without considering their context. Deep feature based methods have shown promise due to their content extraction capabilities. However, feature extraction alone might not be enough for accurate CD. This study proposes incorporating spatial-temporal dependencies to create contextual understanding by modelling relationships between images in space and time dimensions. The proposed model processes dual time points using parallel encoders, extracting highly representative deep features independently. The encodings from the dual time points are then concatenated and passed through Long Short Term Memory (LTSM) layers and a decoder. The output from the LSTM is then concatenated with that from the decoder in a space-time feature fusion. This optimizes information representation of spectral, spatial and temporal details, in RS images before further analysis of change. This approach aims to maximize authentic information while reducing noise interference by introducing the context of change. Compared to conventional CD methods, the proposed technique achieves higher overall accuracy of 97.4%, an F1 Score of 89% and an intersection over union (IoU) of 86.7 when evaluated on the EGY-BCD dataset. The results demonstrate the potential of incorporating spatial-temporal dependencies in CD tasks for RS images.

Optimal diabetes mellitus (DM) control is associated with improved cardiometabolic markers. Interventions such as peer-led education and support offer a community-driven approach to enhance self-management and mitigate cardiometabolic risks associated with diabetes, even though they are not well understood in Kenya. This study sought to determine the effectiveness of a 12-months peer-led diabetes education and support on cardiometabolic markers among adults with type 2 DM. A total of 132 participants with type 2 DM were allocated to intervention (n = 66) and control (n = 66) groups. The intervention group was subjected to a structured peer-led diabetes self-management education and support (DSMES) twice a month for 12 months. The control group was provided with standard care. Outcomes, including glycated hemoglobin (HbA1c), random blood glucose (RBG), blood pressure (BP), and anthropometrics, were monitored at baseline and at the end of the study, and compared across the study groups. Peer-led DSMES achieved significant improvements in glycemic outcomes in the intervention group with the RBG being lower (M = 7.56 vs 9.22 mmol/L; P = .008; d = 0.48), and HbA1c being substantially reduced (M = 7.36% vs 8.95%; P < .001; d = 0.78). Blood pressure showed small but non-significant reductions in the intervention group (systolic: d = 0.30, P = .090; diastolic: d = 0.27, P = .117). There were however no significant differences observed in weight, Body Mass Index and waist hip ratio (all P > .60; Cohen's d < 0.10). This is one of the novel peer-led DSMES evaluation demonstrating the feasibility and effectiveness of a peer support system in enhancing self-management, and thus control of DM-related cardiometabolic risks in low-resource settings. However, longer follow-ups, larger populations and multiple study sites are recommended to realize the broader effect of peer-led interventions on cardiometabolic health and universal health coverage.

Graduates in mismatched careers with their personalities often feel dissatisfied and less productive. This research aims at assessing occupational congruence and occupational satisfaction of graduates in Kenya, informed by Holland’s theory. Objective of the study is to investigate the mediating effect of congruence on the relationship between Holland’s personality types and occupational satisfaction. A longitudinal cohort research design was adopted, involving participants from a 2012 study, with 76 respondents participating. Data was collected using Holland's Self-Directed Search (SDS) 4th Edition questionnaire and an interview schedule. Logistic regression was used to examine the mediating effect of congruence on the relationship between personality types and occupational satisfaction. Results indicated that the mediating effect of congruence was statistically significant at (p0.05). This study provides additional empirical evidence for the educational institutions, career counsellors, and employers for policy making. Additionally, Holland’s classification tool can be used for career guidance to align students withsuitable academic fields and ensure congruence of learners with academic programmers, and that of employees with their careers resulting to employeesatisfaction.

Stock price prediction and volatility modeling are important for making financial decisions, especially in emerging markets like the Nairobi Securities Exchange (NSE). This study examines how well the Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models perform in forecasting stock prices and modeling volatility. The ARIMA (2,1,0) model was selected as the best fit using the Akaike Information Criterion (AIC), showing strong performance in capturing long-term price trends. However, an analysis of the residuals showed signs of volatility clustering, meaning ARIMA alone could not capture short-term fluctuations. To solve this, the study added a GARCH (1,1) model, which effectively captured changing volatility and improved prediction accuracy. The combined ARIMA-GARCH model reduced the Root Mean Squared Error (RMSE) from 3.1211 to 2.5786, demonstrating the value of including volatility modeling in financial time series. The results highlight the need for strong statistical models in emerging markets, where stock prices are often affected by external shocks and market inefficiencies. This research offers useful insights for investors, policymakers, and financial analysts by supporting better risk management and more accurate forecasting. Future studies could expand the model to include more stocks, macroeconomic data, and machine learning techniques to further improve results.

Surface water provides crucial support to the aquatic ecosystem and humans in numerous ways. However, its quality is paramount in determining its impact on the health of the aquatic ecosystem and humans. Currently, the malady of pollution arising from anthropogenic activities poses a significant threat to the quality of most rivers. The quality of water in the Kapingazi river in Embu County has received limited attention which is a concern that needs to be addressed. This study therefore, sought to investigate the status of water quality in river Kapingazi. The study analyzed 96 samples from four locations in a span of eight months which combined the dry and wet seasons of the year. Parameters measured in-situ included turbidity, pH, total dissolved solids (TDS), temperature, dissolved oxygen (DO) and electrical conductivity (EC) while ex-situ parameters were phosphates, nitrates and concentrations of heavy metals: iron and manganese. Results showed that Water Quality Index (WQI) during the drought period was74.05, suggesting that the water quality is only recommended for agricultural and industrial applications. During wet season, the water quality index was 89.67, reflecting a poor status, as more contaminants were likely introduced through surface runoff. Overall, the WQI averaged to 88.02. This study concludes that water in River Kapingazi is not suitable for human consumption; therefore, appropriate treatment is essential prior to its use. Similarly, the findings indicate that both rainy and drought periods significantly affect water quality, presenting challenges for its use for various purposes. It is recommended that the allocation of resources towards water treatment facilities and regulation of pollution sources should be enforced to ensure the safety of river water for diverse applications.

Balagha ni mkakati ambao hutumika katika mawasiliano ili kushawishi hadhira lengwa. Utafiti huu ulilenga kuchanganua matumizi ya balagha katika hotuba za wagombea wawili wa urais, wakati wa makongamano yao ya kitaifa ya wajumbe nchini Kenya, mwaka wa 2022. Wagombea hao walikuwa William Samoei Ruto na Raila Odinga. Utafiti uliongozwa na nadharia ya Balagha ya Aristotle. Nadharia hii, ilisaidia mtafiti katika kutambua matumizi ya balagha, maadili, kigusahisi na mantiki kama yalivyothibitika katika hotuba nne zilizowasilishwa. Data ya utafiti huu ilikusanywa kutoka kwa rekodi rasmi za hotuba hizo kutoka kwa mtandao wa YouTube. Mtafiti alitumia mbinu ya kimakusudi kuangazia matumizi ya balagha katika hotuba zilizotolewa na wagombea wawili wakuu wa urais. Hii ni kutokana na uzoefu wao wa muda mrefu katika siasa na nafasi yao ya kipekee katika uchaguzi mkuu wa mwaka 2022. Data ilichanganuliwa kwa njia ya kimaelezo kwa kufuata mihimili ya nadharia ya balagha. Matokeo ya utafiti huu yalidhihirisha matumizi ya aina tatu kuu za balagha ambazo ni; mantiki, kigusahisi na maadili. Utafiti uligundua kuwa balagha ilitumika hasa kujenga imani, kugusa hisia, kuonyesha uwezo wa uongozi, na kueleza sera zao. Kazi hii inatarajiwa kuwa na mchango muhimu kwa usomi katika uwanja wa lugha na mawasiliano, mawasiliano ya kisiasa na kusaidia wanajamii kupata maarifa ya kuwawezesha kung’amua ghiliba za wagombea. Utafiti ulihitimisha kuwa balagha ina jukumu muhimu katika mawasiliano ya kisiasa na ushawishi wa wapiga kura. Hata hivyo, balagha inafaa itumike kwa ujuzi na si kwa lengo la kuidanganya hadhira lengwa bali kwa kutumia urazini na maadili katika azma ya wagombea