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

This paper presents a novel approach to modeling and controlling a solar photovoltaic conversionsystem(SPCS) that operates under real-time weather conditions. The primary contribution is theintroduction of an uncertain model, which has not been published before, simulating the SPCS’sactual functioning. The proposed robust control strategy involves two stages: first, modifying thestandard Perturb and Observe (P&O) algorithm to generate an optimal reference voltage usingreal-time measurements of temperature, solar irradiance, and wind speed. This modification leadsto determining and linearizing the nonlinear current-voltage (I-V) characteristics of the photovoltaic(PV) array near standard test conditions (STC), resulting in an uncertain equivalent resistance used tosynthesize an overall model. In the second stage, a robust fixed-order H∞ controller is designed basedon this uncertain model, with frequency-domain specifications framed as a weighted-mixed sensitivityproblem. The optimal solution provides the controller parameters, ensuring good reference trackingdynamics, noise suppression, and attenuation of model uncertainties. Performance assessments atSTC compare the standard and robust P&O-MPPT strategies, demonstrating the proposed method’ssuperiority in performance and robustness, especially under sudden meteorological changes andvarying loads. Experiment results confirm the new control strategy’s effectiveness over the standardapproach.

Background: Diagnosing pulmonary tuberculosis (pTB) in children is challenging due to the difficulties in acquiring respiratory specimens, which unspecific and paucibacillary disease presentation, and the lack of sensitive diagnostic assays with non-invasive sample collection methods. As a result, millions of children around the world get tuberculosis (TB) each year, which is a leading cause of morbidity and mortality.
Objective: The aim of this study was to assess the diagnostic accuracy of Stool and Respiratory Sample-based Genexpert MTB/RIF assay from presumptive TB among children in Northwest, Ethiopia.
Methods and Materials: Hospital based cross-sectional with diagnostic accuracy study was conducted on consecutively recruited presumptive TB children. Data were collected by sem-structured questionnaires. Single respiratory (5ml) and 3g stool specimen were collected Lowenstein Jensen (LJ) and Xpert assay. Laboratory SOPs were strictly followed to assure the quality of whole procedures. The diagnostic accuracy of stool Xpert was evaluated against respiratory specimen Xpert, culture and composite reference standards (CRS). Sensitivity, specificity, and predictive values for the stool Xpert assay were calculated with a 95% confidence interval (95% CI) with MedCal statistical software. Data were entered in EPIData V4.2 and exported to SPSS 25 for further analysis.
Results: A total of 557 children were recruited; 510 of whom had complete microbiological results. Overall, pTB was diagnosed in 52/510 (10.2%) of the children with presumptive TB. Of these, only four had microbiologically unconfirmed pTB, were clinically diagnosed with positive response to anti-TB and the remaining 48 were microbiologically confirmed (Positive Xeprt and LJ culture). Stool specimen Xpert had sensitivity of 93.8 %( 95%CI: 82.8-98.6) and specificity of 99.8% (95%CI: 98.7–100) compared to culture; however, the sensitivity of stool was 88.5% (72-95.6) and specificity 100% (99.2-100) when compared to CRS. The Xpert on respiratory specimen had sensitivity and specificity of 95.8 % (85.8– 99.5) and 99.8% (98.7–100) to culture and 92.3 %( 81.4-97.9) and 100% (99.2-100) compared to CRS.
Conclusion: The sensitivity and specificity of Xpert assay for stool specimen is almost similar to that of respiratory specimen. Stool specimen is a highly promising alternative specimen in the diagnosis of pTB in children when respiratory specimen is impossible.


Key words: Diagnostic accuracy, pulmonary tuberculosis, Xpert MTB/RIF, Stool, Children

EXECUTIVE SUMMARY Globally, the amount of time children spend using electronic or digital devices—commonly referred to as screen time are increasingly prevalent. This growing prevalence of screen time among children has raised concerns about its impact on their development. Therefore, this study aimed to explore three key themes: (1) the availability and use of electronic devices in households, (2) factors contributing to excessive screen time among children, and (3) the relationship between screen time and children's cognitive, emotional, and social development. The study was conducted in five selected towns within the East Gojjam Administrative Zone, namely Debre Markos, Bichena, Merto Lemariam, Dejen, and Lumame. For the quantitative data, 845 participants were chosen using multistage sampling techniques. Additionally, qualitative data was collected through interviews with 25 participants, 9 focus group discussions, and the collection of 16 diaries. We collected data by using questionnaires, semistructured interviews, focus group discussions, and diary records. For quantitative data analysis, SPSS version 20 was used. To summarize the demographic characteristics of participants, and describe the availability and use of electronic devices, descriptive statistics such as percentage and mean were used. Multiple regression analysis was used to examine the relationship between the total and device-specific screen time and children’s socioemotional and cognitive development. Qualitative data was analyzed using thematic analysis. The study revealed that children ages 2 to 5 had an average screen time of 244 minutes each day. The two most frequently used electronic devices in this age group are televisions, at 80.4%, and smartphones, at 45.2%. The multiple regression analysis indicated that parental screen time, mother’s employment status, and age were significantly and positively associated with children’s screen time. In conclusion, screen time for children aged 2 to 5 years exceeds the recommended limit. To have proper screen time for both parents and children, it is important to establish and implement clear guidelines.

Knowledge of domestic dog ecology and demography has been recognized as central to the designof an effective rabies control program. The study was conducted to assess owned dogs’ ecologyand demography and to identify predictors associated with dog ownership and rabies occurrencein the Amhara region, Ethiopia.Method: ology: The study employed dog census and questionnaire surveys of 907 householdsselected using a multistage sampling technique from six rural and six urban districts of theAmhara region, Ethiopia. The ecology and demography of owned dogs in the selected areas wererecorded and described using descriptive statistics. Mixed-effect logistic regression models wereused to identify factors associated with dog ownership and rabies occurrence.Results: A total of 6609 dogs were estimated from 42 kebeles in the 12 study districts. The male-to-female ratio of dogs was 1.7:1.0, and the mean age of dogs was 3.2 years. The proportion ofhouseholds who owned at least one dog was 5.9 %. The average number of dogs per dog-ownedhousehold was 1.3. Dog to household ratio was 1.0:13.0, and dog to human ratio was 1.0:48.5.The majority of the dog owners (97 %) keep dogs for home guard and livestock herding. Only 57% of the dogs were confined, and 16 % of them were vaccinated. Ninety-one percent of the dogowners did not practice neutering and spaying for dog population control. Religion, livestockownership pattern, and occupation were associated with dog ownership (p < 0.05). Communityresidence and age of respondents were associated with rabies occurrence (p < 0.05), while zonewas associated with both dog ownership and rabies occurrence at p-value

This study investigates the dynamics of instability among the Amhara people in Ethiopia following the political reform of 2018. It examines the main causes, key actors, phases of instability, and possible remedies. Employing a mixed-method research design, the study collected data through questionnaires, interviews, focus group discussions, and document analysis. Thematic and descriptive analyses were used to interpret the findings. The research reveals that the ongoing instability in the Amhara region since 2018 involves multiple actors and has both historical and contemporary roots. Deep-seated historical contradictions, coupled with a regressive political culture, have contributed to the persistence of unrest. Left-wing ethnically based political parties that perceive the Amhara as historical adversaries—alongside power struggles within the ruling coalition, merged as central drivers of instability. Beyond external political forces such as the Tigray People’s Liberation Front (TPLF) and the Oromo Liberation Army (OLA), internal factions like the Oromo Democratic Party (ODP) also played direct and indirect roles. Additionally, influential activists have further intensified the region’s instability. Factors such as public euphoria following the 2018 reform, the weakening of state institutions, perceived political dominance of the ODP, divisions among Amhara elites, the outbreak of the Tigray war, and shortages of agricultural inputs like fertiliser have collectively fueled the unrest. By categorising the instability into three distinct phases, the study offers a comprehensive understanding of its evolution. Ultimately, it concludes that inclusive political dialogue, mutual understanding among major actors, and national consensus are essential pathways toward restoring stability and peace in the Amhara region.
Keywords: Amhara, Causes, Instability, actors, Ethiopia, Remedies

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 was conducted to estimate the technical, allocative and economic efficiency levels, identify the determinant factors of technical and allocative inefficiencies, Debre Markos City and Sinan District of the Amhara National Regional State, Ethiopia. The data were collected from 397 smallholder sheep fatting farmers in all kebeles in debremarkos and two kebeles in sinan district of the study area. Maximum likelihood techniques were used to estimate a Truncated-Normal Model production frontier. The mean estimated technical, allocative and economic efficiencies were 83.07, 91.84.61 and 76.5 respectively with consecutive SD of .1645 , 0.1490 and 0.1985. The estimated results provide evidence that the sheep fatting farmers are technical, allocatively and economically inefficient. The coefficients estimated from the Truncated-Normal Model stochastic production frontier model show the effect of different inputs on sheep fatting farmers. Ln (fodder) has a positive coefficient of .0481792, significant at the 1% level, meaning that the improvement in feed quality increases read met output. Likewise, Ln (heredsize) showed a highly significant coefficient value of .9518208 significant at the 1%, showing its importance to increase fatting sheep.Also, Ln (cost fodder) has a negative coefficient of 1.628079, significant at the 1% . Likewise, Ln (cost of animal health) showed a highly significant coefficient value of 1.838712significant at 10%. The most vital factors include sex, education, age, family size of household, availability of feed supply, extension and training on significant effect on technical inefficient of small scale sheep fatting farmers. The most vital factors include education, non-farm activities, Total livestock unit, availability of feed supply, family size of the household and training on significant effect on allocative inefficient of small scale sheep fatting farmers.The government and the concerned body should be encouraging sheep fatting farmers to grow, properly harvest and store high-protein fodder on their farms reduce reliance on expensively purchased fodder. The government and the concerned body should be expanding animal health centers to keep animal health for fulfill small scale sheep fatting sheep to bring productive the fatting sheep and improve the livelihood. Extension of the household has been shown to significantly improve technical efficiency by providing advice the way of fatting, how to feed

Method Twenty inactive males (BMI 27.67 ± 0.88 kg/m2 , age 49.15 ± 2.58 years) participated in an eight-week wererandomly assigned to one of three intervention groups (combined (CT), resistance (RT), and aerobic (AT)) exercisemodalities to assess within-subject and between group changes in glycolipid profile. Data were analyzed usingrepeated measures ANCOVA.Result Pre-post mean values of body fat percentage (%BF), area under the curve (AUC), low density lipoprotein (LDL),high density lipoprotein (HDL) and total cholesterol (TC) decreased in all three groups. The main effect of exercisemodality on the AUC (F (2, 26) = 10.577, P = 0.001, η2 = 0.569) was significant. Post-hoc analyses revealed that the RTgroup (-30.653 ± 6.766, p = 0.001) with 11.53% and the CT group (M = -0.896, SE = 3.347, P = 0.015) with 3.79% exhib-ited significantly greater reductions in AUC compared to the AT group. LDL levels showed significant differentbetween groups (F (2, 26) = 6.33, p = 0.009, η2 = 0.442), specially significantly 3.7% lowered in AT (MD = 4.783, SE = 1.563,P = 0.002) and 3.79% lower in CT (MD = 4.57, SE = 1.284, P = 0.008) groups compared to the RT group. AT significantlyreduced TC by 17.716 ± 5.705 mg/dL (p = 0.02) compared to RT, representing a 7.97% decrease.Conclusion Exercise type significantly influences lipid profiles and glycemic control. Notably, both aerobic and com-bined training demonstrated a superior ability to modulate the lipid profile, and resistance training and combinedtraining were more effective in reducing the AUC.Trial registration May, 31st 2024. Registration no: PACTR202405463745521 “Retrospectively registered”.Keywords Glucose tolerance, Lipid profile, Resistance training, Aerobic training and combind training