Leptospirosis and melioidosis are emerging tropical diseases that are seriously affecting both human and animal populationsworldwide. The actual incidence and fatal cases of the diseases are underreported due to a lack of awareness of the diseases,underuse of clinical microbiology laboratories test, and limitations of the model. In this paper, a new deterministicmathematical model for the coinfection of leptospirosis and melioidosis with optimal controls is presented. Based on the next-generation matrix approach, the basic reproduction numbers for the coinfection model as well as for submodels are computedto analyze their dynamics behavior. The disease-free equilibrium point of the melioidosis-only submodel is proven to beglobally asymptotically stable when the basic reproduction number (R0m) is less than unity, whereas the existence of its uniquepositive endemic equilibrium is shown if R0m > 1. Based on the center manifold theory, the endemic equilibrium point of theleptospirosis-only submodel is proven to be locally asymptotically stable when the basic reproduction number (R0l ) is greaterthan unity. The disease-free equilibrium point of the full model is locally asymptotically stable whenever the basicreproduction number (R0ml) less than unity. Sensitivity analysis for the basic reproduction number of the model is performedto determine the most influencing parameters on the transmission dynamics of the model. Furthermore, the model wasextended into an optimal control problem by incorporating four time-dependent control functions. Pontryagin’s maximumprinciple was used to derive the optimality system for the optimal control problem. The optimality system was simulated usingthe forward–backward sweep method to show the effectiveness and cost-effectiveness of different optimal control strategies incombating the burden of leptospirosis–melioidosis coinfection. The incremental cost-effectiveness ratio was applied todetermine the most cost-effective strategy. The numerical results revealed that Strategy 6 which implements a combination ofall optimal control measures is the most effective strategy for minimizing the spread of the coinfection of the epidemics,whereas Strategy 1 which implements rodenticide control measure is the most effective when available resources are limited.Keywords: coinfection; cost-effectiveness; leptospirosis; melioidosis; numerical simulation; optimal control; sensitivity analysis

Melioidosis is a significant health problem in tropical and subtropical regions, especially inSoutheast Asia and Northern Australia. Recurrent melioidosis is a major obstacle to eliminatingthe disease from the community in these nations. This work aims to propose and analyzea human melioidosis model with recurrent phenomena and an optimal control model byincorporating time-dependent control functions. The basic reproduction number (𝑅0) of theuncontrolled model is derived using the method of the next-generation matrix. Using theconstruction of a Lyapunov functional, we present the global asymptotic dynamics of theautonomous model in the presence of recurrent for both disease-free and endemic equilibria. Theglobal asymptotic stability of the model’s equilibria shows the absence of a backward bifurcationfor the model in both cases, whether in the absence or presence of relapse. The sensitivityanalysis aims to identify the parameters that have the most significant impact on the model’sdynamics. Furthermore, qualitative analysis of the model’s global dynamics and the changingeffect of the most influential parameters on 𝑅0 are supported by numerical experiments, with theresults being illustrated graphically. The model with time-dependent controls is analyzed usingoptimal control theory to assess the impact of various intervention strategies on the spread ofthe epidemic. The numerical results of the optimality system are carried out using the Forward–Backward Sweep method in Matlab. We also conducted a cost-effectiveness analysis using twoapproaches: the average cost-effectiveness ratio and the incremental cost-effectiveness ratio.
Keywords: Melioidosis model; B.pseudomallei; Recurrent; Global stability; Optimal control; Cost-effective strategy

Executive Summary Background and objectivesDiabetes mellitus (DM) and hypertension (HTN) are leading causes of cardiovascular disease, death, and disability, with a growing burden in developing countries. Early detection is essential, and machine learning (ML) offers powerful tools for predicting diseases risk by uncovering complex patterns in health data. At the same time, the Health Belief Model (HBM) explains preventive behaviors through constructs such as perceived susceptibility, severity, benefits, barriers, self-efficacy, and cues to action. This study integrates ML-based predictive modeling with the HBM to identify individuals at risk of HTN and DM and to better understand the behavioral factors influencing prevention, employing a dataset collected in 2025. Materials and methodsData on DM and hypertension HTN were collected from 1,771 employees of Debre Markos, Injibara, and Bahir Dar universities in Northwest Ethiopia. The cross-sectional survey included demographic, health-related, and behavioral factors, with constructs from the Health Belief Model (HBM) such as perceived susceptibility, severity, benefits, barriers, self-efficacy, and cues to action. RFE was applied to identify the most relevant predictors of DM and HTN. Four machine learning algorithms—Logistic Regression (LR), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and k-Nearest Neighbor (kNN) were developed using theselected features. Model performance was evaluated based on accuracy, precision, recall, the F1-score, and the area under the ROC curve. ResultsThis study found that the ensemble ML models, RF and GBDT, outperformed in predicting HTN and DM, achieving higher accuracy, precision, recall, F1-score, and area under the ROC curve. Analysis of Health Belief Model (HBM) constructs further showed that preventive behaviors were positively associated with perceived susceptibility, severity, benefits, self-efficacy, and cues to action, while perceived barriers were negatively associated. Perceived susceptibility emerged as a significant predictor of HTN and DM, and cues to action contributed to the identification of undiagnosed DM cases. 

ABSTRACT The present study was conducted to assess the potential occurrence of coal deposits in the East Gojjam zone, specifically in Debre Eliyas woreda. It also aims to determine the quality and quantity of coal through field and laboratory techniques. A total of twenty coal samples and twenty rock samples were systematically collected from surface outcrops and analyzed at the Geological Survey of Ethiopia. Major oxides in the rock units were determined using Atomic Absorption Spectrometry (AAS), while coal samples were subjected to Gravimetric, Proximate, and Adiabatic Calorimetric analyses to quantify moisture content, volatile matter, fixed carbon, ash content, and calorific value. The geologic setup of the study area is predominantly characterized by sedimentary rock, like Sandstone, limestone, mudstone, and basaltic rocks. Geochemical analysis of sandstone, mudstone, and limestone samples reveals distinct compositional characteristics that reflect their depositional environments and diagenetic processes, providing valuable insights for resource exploration and geotechnical assessments. A geological map at a scale of 1:25,000 and three coal occurrence maps at a 1:20,000 scale were prepared based on detailed field surveys and laboratory analyses. Chemical analysis of collected coal samples revealed moisture contents ranging from 2.32% to 29.72%, volatile matter from 20.01% to 37.29%, fixed carbon from 7.12% to 31.88%, ash content from 4.27% to 66.07%, and calorific values between 2,323.044 and 9,378.684 Cal/gm. The values indicate that the coal in Debre Eliyas ranges in rank from lignite to bituminous. Across all identified coal-bearing sites, the average seam thickness ranges from 2.35 to 5.13 meters.  The total estimated coal resource of the study area is approximately 2,755,124.83 tons.  Keywords: Debre Elias, Coal Deposit, Economic Potential, Calorific value

ABSTRACT The main objective of the research is to investigate iron deposit by using petrographic, geochemical, XRD and geophysical results. To achieve the desired objective, secondary data compilation and interpretation, field work and post-field work (including petrographic result, geochemical result, XRD and geophysical result analysis) have been conducted. The study area is comprised of both Mesozoic sedimentary rocks and Tertiary - Quaternary volcanic rocks. The sedimentary rocks include sandstone, limestone, and shale, whereas the volcanic rocks are basalt and trachyte. Ternary diagrams of Al2O3-Fe2O3-SiO2 are commonly used to determine the degree of laterization. As laterization progresses increases, silica is leached out of the rock, leaving behind iron oxides.  Fe2O3-rich samples are indicative of higher degrees of lateritization, while SiO2-rich composition experienced weak lateritization (Meyer et al., 2002). Data points for iron ore samples from the study area, were plotted in moderate to strong lateritization field. Hematite, magnetite, goethite and siderite are the primary ore minerals, according to both polished section petrography and XRD investigations. Furthermore, the main gangue phases in the region are anatase, quartz and kaolinite. The mineral concentration is between 20.16 and 71.88% hematite, 7–40% goethite, 1–30 siderite, and 1-3 percent magnetite. Approximately 5–10.5% kaolinite, 3–25% quartz, and 0.5% anatase are among the related gangue minerals. Varying amplitudes of magnetic anomaly signature indicates that the ore body is not evenly distributed along the respective profile across the study area and the ore bodies suspected to be magnetic mineral exist near surface to medium depth which is between 23.33m to 52.5m. Iron occurrence resource estimation was done by a conventional approach methods, such as, resources = A (m2) *T (m) * ρ (g/cm3). As a result the total tonnage of iron resource is about 17,844,964.452 tons. Key words: Iron deposit, magnetic anomaly, geochemical result, geological map, host rock 

Abstract Over the years, agriculture in the Amhara region has faced challenges due to climate variability and change. In this regard, this paper aims to develop and adapt a crop selection dataset model for resilience to climate change. Wheat, maize, and teff data were investigated from the Central Statistics Agency of Ethiopia (CSA). Climate parameter data were taken from reanalysis models, satellites, and in situ sources. An Artificial Neural Network (ANN) model was applied to predict El Niño and La Niña events. The results reveal that the presence of El Niño leads to a reduction in wind speed and an increase in cloud base height (CBH), resulting in a precipitation deficiency. El Niño is weakening the trend winds and enhancing the cloud base height, leading to a deficiency of summer Precipitation. The crop selection model is developed to select a climate change resilience crop. The occurrences of El Niño enhance maize production in the midland farm areas. The average value of the crop selection dataset model parameters (slope ‘n’, intercept ‘a’, and the magnitude of the error ‘ ’) is found to be -1.937, 28.498, and 0.168, respectively, that satisfy the developed model requirements. The average error between the actual crop data and the model value is found to be -0.156 quintals per hectare. In general, the impact of El Niño on teff in middlealtitude areas is affected by 25.36%, while on wheat production in lowland areas is affected by 18.44%. However, maize production in the midland region is enhanced by 21.90%. In this sense, farmers in the middle-altitude areas use vast farmlands for maize during the El Niño phase. In the future, researchers made improvements on the draft crop selection dataset model and tailored it for use across the country via various scenarios. Keywords: Altitude, Artificial intelligence, Climate change, Crop model, El Niño-La Niña 

Abstract Over the years, agriculture in the Amhara region has faced challenges due to climate variability and change. In this regard, this paper aims to develop and adapt a crop selection dataset model for resilience to climate change. Wheat, maize, and teff data were investigated from the Central Statistics Agency of Ethiopia (CSA). Climate parameter data were taken from reanalysis models, satellites, and in situ sources. An Artificial Neural Network (ANN) model was applied to predict El Niño and La Niña events. The results reveal that the presence of El Niño leads to a reduction in wind speed and an increase in cloud base height (CBH), resulting in a precipitation deficiency. El Niño is weakening the trend winds and enhancing the cloud base height, leading to a deficiency of summer Precipitation. The crop selection model is developed to select a climate change resilience crop. The occurrences of El Niño enhance maize production in the midland farm areas. The average value of the crop selection dataset model parameters (slope ‘n’, intercept ‘a’, and the magnitude of the error ‘ ’) is found to be -1.937, 28.498, and 0.168, respectively, that satisfy the developed model requirements. The average error between the actual crop data and the model value is found to be -0.156 quintals per hectare. In general, the impact of El Niño on teff in middlealtitude areas is affected by 25.36%, while on wheat production in lowland areas is affected by 18.44%. However, maize production in the midland region is enhanced by 21.90%. In this sense, farmers in the middle-altitude areas use vast farmlands for maize during the El Niño phase. In the future, researchers made improvements on the draft crop selection dataset model and tailored it for use across the country via various scenarios. Keywords: Altitude, Artificial intelligence, Climate change, Crop model, El Niño-La Niña 

Diluted magnetic semiconductors are a recent research area due to their ability to enhance ferromagneticproperties and facilitate the electrical detection of magnetoresistance and polarization. (In, Mn)As dilute mag-netic semiconductor has potential application in the field of spintronic devices, such as spin field-effectivetransistors, spin laser light-emitted diodes, modern technology, multi-functional devices, green technology,and nanotechnology. For this study, we have considered the RKKY interaction between Mn2+ spins via delo-calized carriers. The effect of manganese ion concentration and applied magnetic field on ferromagnetic dilutedmagnetic semiconductor properties such as dispersion relation, Curie temperature, and reduced magnetization of(In, Mn)As are studied. We have developed a spin-wave model using the Holstein-Primakaff transformation.Based on the developed model, the number of ferromagnetic magnons, dispersion relations, and Curie temper-atures were calculated with or without an applied magnetic field. The reduced magnetization is also calculated.The graph of Curie temperature and magnetization of (In,Mn)As versus temperature with applied field up to 6 Tand manganese ion concentration from 0.01 to 0.1 are plotted. The graph of spin wave dispersion of (In,Mn)Asversus a wave vector with varying manganese ion concentration with and without an applied magnetic field up to6 T. In this study, an InMnAs Curie temperature of 290.68 K is found without an applied magnetic field with a 0.1manganese ion concentration, which is near room temperature. Moreover, with an applied magnetic field of 6 Tat 0.1 manganese ion concentration, a Curie temperature of 342.466 K is found, which is above room temper-ature. Hence, these temperatures are suitable for the field of next-generation spintronic new technology.
Keywords: Magnetization, Spintronics, Curie temperature, Dispersion relation, Wave vector

Recent advances in electrochemistry and electrode surface modification highlight the potential of transitionmetal coordination compounds as effective modifiers. This study presents sodium tetraresorcinolatocuprate(II)(Na₂[Cu(HR)₄]), a newly synthesized compound characterized using UV–Vis, FT-IR spectroscopy, ICP OES, andmelting point analysis. A poly(Na₂[Cu(HR)₄])/GCE was fabricated via potentiodynamic techniques, with cyclicvoltammetry and electrochemical impedance spectroscopy confirming the formation of a polymer film thatenhanced the electrode’s active area and electrocatalytic properties. The developed poly(Na₂[Cu(HR)₄])/GCEwas applied for determination of ciprofloxacin (CPF), an antibiotic prone to resistance issues, that requiresreliable monitoring in pharmaceutical and biological samples. The poly(Na₂[Cu(HR)₄]) modifier significantlyimproved CPF detection by reducing its oxidation potential and increasing current response by eightfoldcompared to unmodified electrodes, suggesting the modifier’s catalytic role in CPF oxidation. Differential pulsevoltammetry (DPV) showed a linear CPF response over concentrations of 1.0 × 10 8 to 4.0 × 10 4 M, withdetection and quantification limits of 2.0 nM and 6.8 nM, respectively. Analysis of commercial CPF brandsshowed 98.05–100.00 % accuracy, while spike recovery rates (99.25–100.40 %) and low interference errors(

This study introduces a highly sensitive electrochemical method for detecting paracetamol (PCT) in pharma-ceutical tablets and human serum samples, utilizing a glassy carbon electrode modified with alizarin red S (poly(ARS)/GCE). PCT is one of the most widely used analgesic and antipyretic drugs; however, its overdose orprolonged use can lead to severe liver and kidney damage. Therefore, the development of sensitive and reliablemethods for monitoring PCT levels in pharmaceutical formulations and biological fluids is crucial for ensuringdrug safety and effective therapeutic monitoring. Characterization of the electrode confirmed that the surfacemodification with a conductive and electroactive polymer film (poly(ARS)) significantly enhanced the effectiveelectrode surface area and reduced charge transfer resistance. Compared to the unmodified electrode, themodified electrode exhibited a well-resolved, irreversible redox peak at a significantly lower potential with asixfold increase in current, highlighting the catalytic efficiency of the modifier toward PCT. The electrochemicalbehavior of PCT was analyzed via cyclic voltammetry and square wave voltammetry, revealing significantlyenhanced sensitivity and selectivity due to the conductive polymer coating. Under optimized electrode conditionsquare wave voltammetric current response of poly(ARS)/GCE showed linear dependence on concentration of0.01–250.0 μM and an ultralow detection limit of 1.0 nM in phosphate buffer solution (pH 7.0). Analyticalapplication on real samples confirmed the method's accuracy, achieving recovery rates of 98.8–100.3 % forpharmaceutical tablets and human blood serum, even in the presence of potential interferents. The developedmethod provides a cost-effective and robust alternative for PCT quantification, with superior performancecompared to previously report electrochemical approaches.
Keywords: Paracetamol, Glassy carbon electrode, Alizarin red S, Cyclic voltammetry, Square wave voltammetry