Wollo University

Cocoa (Theobroma cacao L.) bean colour is a central quality attribute affecting chocolate appearance, consumer acceptance, and market value, yet the mechanistic basis of colour variation across genotypes and processing conditions remains poorly understood. While previous studies have explored fermentation and genotype effects separately, the integrated influence of chemical composition, anthocyanin content, and mineral profiles on cocoa bean pigmentation has not been systematically investigated. This study aimed to elucidate the relationships between cocoa bean colour, biochemical composition, and fermentation across 38 diverse genotypes, including germplasm accessions and cultivated varieties. Colour attributes were quantified using CIELAB parameters (L*, a*, b*, chroma, hue angle), biochemical properties were measured for fat, total phenolics, and essential minerals (phosphorus, sodium, iron, magnesium, calcium), and anthocyanins (cyanidin, delphinidin, pelargonidin, malvidin) were profiled in fermented samples via LCMS. Hierarchical clustering and correlation analyses were employed to link genotype-specific biochemical traits to colour outcomes. Results revealed significant genotype-dependent variation, with EQX78, ARF 22, and MO 20 exhibiting superior colour characteristics, while some genotypes displayed inherently dark brown beans. Germplasm accessions showed optimal colour properties, whereas cultivated varieties had enhanced biochemical content. Fermentation reduced L* and hue angle while increasing a*, b*, and chroma, corresponding to intensified reddish-brown pigmentation, and cyanidin and delphinidin were identified as key anthocyanins driving colour changes. Correlations indicated that fat and mineral contents significantly modulate colour expression, providing mechanistic insights into pigmentation dynamics. These findings highlight the multifactorial nature of cocoa bean colour and establish a foundation for breeding and post-harvest strategies to optimize visual and nutritional quality. Future studies integrating genomics and controlled fermentation are warranted to develop predictive models for cocoa bean colour and to enhance the quality and market value of chocolate products.

The full potential solar photovoltaic (PV) energy is not utilised by the mining industry due to heavy dust deposition and harsh operating condition. This study aims to quantify the seasonal impact of dust deposition on PV performance and determine the optimal cleaning frequency for optimize the performance of solar PV Panel in an operational mining condition. Additionally, an innovative automated dry dust cleaning system is designed, developed, and validated in the active mining field to evaluate its operational efficiency and durability. A comprehensive study of twenty-six week was conducted to monitor the variations in dust deposition density and output energy of PV panel across three distinct seasonal phases. Among these, pre-summer phase showed the highest dust deposition density of 5.98g/m2 and due to this maximum output power (Pmax) of dusty panel reduce by 63.50%. This phase experienced an intense mining activity and dry weather condition. Therefore, a cleaning schedule of 3 to 4 days is recommended to effectively utilise the potential of PV panel. The other two phases such as dry winter and early monsoon experienced comparatively lower average reduction of 35% to 40% in maximum output power. A moderate cleaning scheduled of 6 to 7day is recommended for these two phases Further, the developed dust cleaning system is validated in the mining field under three different environmental phases and an average recovery of 40% in Pmax is obtained across three phases. This presents the effectiveness of developed dust cleaning system under real operating condition. The outcome of this research highlights the need of dynamic cleaning schedule for effective utilisation of solar energy in mining industry. The integration of smart dust sensors and AI based predictive modelling of developed cleaning system for sustainable utilisation of solar energy in mining and allied industries.

This review revealed only modest and statistically non-significant differences in the prevalence of sciatic nerve variations across broad racial and continental groups. In contrast, substantial variation was observed at the regional level, with particularly high prevalence rates in East Asian countries, specifically China and Japan. These findings suggest that regional factors contribute more to the observed variations than racial factors.

In this work, we approximate the NTFK-S equation, a model that describes a number of chemical and physical phenomena, including hydrodynamics, reaction-diffusion systems, and long waves on the boundary of viscous fluids. We use a hybrid approach that combines the Adomian decomposition method with the generalized integral transform to get an approximate analytical solution. We solve multiple numerical examples, including three different cases, to show the effectiveness and applicability of both methodologies while confirming the precision and dependability of the suggested strategies. The outcomes demonstrate the usefulness and practicality of the integral transform and the Adomian decomposition approach in analyzing the behavior of nonlinear models. Convergence and error analysis are verified. Numerical simulations confirm the method’s effectiveness. This study advances fractional calculus in nonlinear applications and uses 2D/3D plots to illustrate the impact of the fractional parameter, plotted using MATLAB R2016a, enabling clear visualization of the obtained results.

A hyperglycemic crisis represents a critical metabolic emergency with potentially fatal consequences and significant health complications. These crises account for approximately 1% of hospital admissions for individuals with diabetes. While hospital admission is essential for providing the intensive care and inpatient services necessary to sustain life, prolonged hospital stays are a major risk factor for increased morbidity and in-hospital mortality among patients admitted for hyperglycemic crises. Therefore, this study aimed to determine the prevalence of prolonged hospital stays and associated factors among hyperglycemic crisis patients admitted to public hospitals in Ethiopia between July and November 2022. This research utilized a hospital-based multicenter prospective observational study design over a period of five months. Patient data were gathered through interviews and chart reviews. The duration of hospital stay was assessed by following the patients from admission to discharge. The collected data were systematically cleaned, coded, and entered into Epi Data Manager version 4.6, subsequently exported to the Statistical Package for the Social Sciences (SPSS) version 25.0. Multiplestep-wisebackward logistic regression was employed to identify factors influencing the length of hospital stay. The strength of associations between dependent and independent variables was examined using a 95% confidence interval (CI), adjusted odds ratio (AOR) and a p-value of less than 0.05. This study included a total of 213 patients with hyperglycemic crisis. A mean age of participants was 40.37 ± 16.87 SD. About 58.2% (95% CI: 51.3-64.9%) of admitted patients were hospitalized for longer than a week, and the mean of hospital stay was 7.21 days. Age of older than 45 years [AOR:6.0,95%CI:2.27-15.93], female gender [AOR:2.76,95%CI:1.4-5.42], type 2 diabetes [AOR: 3.78,95%CI:1.36-10.48], and hypernatremia [AOR:4.31,95%CI: 1.1-16.98] were factors associated with prolonged hospital stays. The findings from this study underscore significant factors contributing to prolonged hospitalization in patients experiencing hyperglycemic crises. These group of patients need more emphasize and tailored management strategies to potentially mitigate the duration of hospitalizations and improve overall healthcare outcomes.

Understanding the dynamic changes in land use and land cover (LULC) and the forces behind them is crucial for developing effective and sustainable land management strategies. However, the limited availability of high-resolution, socio-economic LULC assessments constrains a comprehensive understanding of the human-environment interactions driving these changes. This study analyzes and quantifies the extent and nature of LULC changes and their driving factors in the Gacheb watershed, Southwestern Ethiopia, spanning from 1984 to 2020. The maximum likelihood classifier (MLC) in the earth resource data analysis system (ERDAS) was used to conduct LULC change classification for Landsat imagery from 1984, 2002, and 2020. The study uses in-depth interviews with key informants to understand the factors driving LULC changes. The study identified five primary LULC categories: agroforestry, cultivated land, forest, settlement, and shrubland, with an overall kappa classification accuracy of 88.6% to 93%. The findings show a decrease in shrubland by 10.2% and forest land by 6.6%, while cultivated land and settlements expanded by 9.5% and 7.9%, respectively over the study period. During the entire evaluation period, LULC underwent a substantial transformation, with 7105 ha (48.5%) experiencing change. Meanwhile, 7540 ha (51.5%) remained unchanged. The major identified driving forces of LULC change were population growth, wood extraction for fuel and charcoal, cultivated land expansion, and urban and infrastructure development. Therefore, it is crucial to develop land-use policies and management strategies to control the uncontrolled expansion of cultivated land and settlements.