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The rapid evolution of smart grids, driven by rising global energy demand and renewable energy integration, calls for intelligent, adaptive, and energy-efficient resource allocation strategies. Traditional energy management methods, based on static models or heuristic algorithms, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy wastage. To overcome these challenges, this paper presents ORA-DL (Optimized Resource Allocation using Deep Learning) an advanced framework that integrates deep learning, Internet of Things (IoT)-based sensing, and real-time adaptive control to optimize smart grid energy management. ORA-DL employs deep neural networks, reinforcement learning, and multi-agent decision-making to accurately predict energy demand, allocate resources efficiently, and enhance grid stability. The framework leverages both historical and real-time data for proactive power flow management, while IoT-enabled sensors ensure continuous monitoring and low-latency response through edge and cloud computing infrastructure. Experimental results validate the effectiveness of ORA-DL, achieving 93.38% energy demand prediction accuracy, improving grid stability to 96.25%, and reducing energy wastage to 12.96%. Furthermore, ORA-DL enhances resource distribution efficiency by 15.22% and reduces operational costs by 22.96%, significantly outperforming conventional techniques. These performance gains are driven by real-time analytics, predictive modelling, and adaptive resource modulation. By combining AI-driven decision-making, IoT sensing, and adaptive learning, ORA-DL establishes a scalable, resilient, and sustainable energy management solution. The framework also provides a foundation for future advancements, including integration with edge computing, cybersecurity measures, and reinforcement learning enhancements, marking a significant step forward in smart grid optimization.

Economic stability is crucial for improving export performance, particularly within regional trade blocs such as the Common Market for Eastern and Southern Africa (COMESA). However, empirical studies examining how threshold levels of economic stability affect export dynamics among COMESA member states have been limited. This study explores the threshold effects of key economic indicators, economic growth, trade openness, and inflation, on export performance in COMESA countries using panel data from 2000 to 2022. Employing panel threshold regression analysis, this study investigates the nonlinear relationships between these variables, focusing particularly on the impact of inflation thresholds. The study found an optimal inflation threshold of 3.662%, aligning with the lower end of the global benchmark of 3-7% typically observed in emerging and developing economies. In low-inflation regimes (LnInfl ≤ 3.662), GDP significantly enhances export growth by 0.348% per unit increase, while trade openness yields a smaller contribution of 0.091%. In contrast, high-inflation scenarios (LnInfl > 3.662) show that GDP continues to support exports (0.779% increase), but trade openness negatively impacts export performance by 0.127%. Policymakers need to focus on maintaining inflation under the critical threshold of 3.662%, as this is vital for enhancing the benefits of GDP growth and trade openness on export performance. These insights underscore the necessity of keeping inflation in check to improve export competitiveness. Additionally, prioritizing monetary, fiscal, and institutional stability initiatives will support sustainable growth and strengthen regional trade dynamics within COMESA, as exceeding this inflation limit could impede export potential and regional integration efforts. This study offers valuable insights for developing regions aiming to enhance their trade performance through effective economic policies.

Crop simulation models serve as effective instruments for evaluating the management conditions of irrigation systems. This study aims to simulate maize production to identify optimal irrigation water management strategies under deficit irrigation and moisture conservation practices, utilizing the AquaCrop model. We conducted this research at Woleh irrigation schemes during the 2023/2024 irrigation season in the Wag-himra zone of northern Ethiopia. To check how well the model worked, we used statistical tests such as prediction error (PE), root mean square error (RMSE), index of agreement (D), goodness-of-fit (R2), and the Nash–Sutcliffe coefficient of efficiency (NCE). The model effectively simulated canopy cover, aboveground biomass, and yield across all treatments, evidenced by the high R2 (0.99) and NSE (0.99) values. Furrow-irrigated raised bed planting (FRBP) at 100% and 75% ETc with mulch exhibited the lowest predicted errors and deviations in yield and water productivity. The model effectively predicted maize yield and biomass under full irrigation in FRBP at 75% ETc with mulch. The AquaCrop model serves as a dependable measure of maize crop development and outcomes across different irrigation conditions and mulch types, potentially enhancing sustainable maize productivity in water-stressed areas.

The emergence of electric vehicles (EVs) as key elements in the decarbonization of transportation demands a new class of intelligent infrastructure capable of optimizing charging behavior while maintaining power system stability. This paper proposes a novel Scalable Cloud-Based Continuous Monitoring Platform (SC-CMP) designed to support real-time optimization of microgrid operations, particularly in EV-dense and renewable-integrated environments. By fusing cloud computing, machine learning (ML), and artificial intelligence (AI) with Internet of Things (IoT) data acquisition, SC-CMP enables continuous monitoring, predictive scheduling, and adaptive energy management across distributed power networks. Unlike conventional systems, SC-CMP supports both centralized and decentralized microgrid architectures, providing scalable support for dynamic load balancing, V2G coordination, and resilient energy dispatch. Simulation and validation are performed using a real-world dataset of 3395 EV charging sessions across 105 stations, demonstrating SC-CMP’s superiority over existing AI/ML baselines. Quantitatively, the platform achieves 97.34% predictive accuracy, 96.81% grid stability improvement, 94.5% resource allocation efficiency, 93% scalability, and 95.2% data privacy assurance. These outcomes position SC-CMP as a comprehensive, adaptive, and cost-effective solution for microgrid-oriented EV integration, offering substantial advances in resilient power distribution, renewable energy utilization, and sustainable electric mobility. The platform serves as a foundation for next-generation microgrid control systems that demand real-time intelligence, scalability, and reliability across evolving smart grid landscapes.

Accurate and real-time estimation of the state of charge (SoC) is critical for ensuring the performance, safety, and longevity of lithium-ion batteries in electric vehicles (EVs). This research work presents an Uncertainty-Aware Hybrid Learning Framework for accurate and safe State-of-Charge (SoC) prediction in lithium-ion batteries, particularly under dynamic and thermal-varying conditions during fast charging. The framework integrates multi-fidelity electrochemical observer outputs with a Tuned Multilayer Perceptron (MLP) Regressor, enabling real-time estimation of high-fidelity SoC while quantifying uncertainty using a Negative Log-Likelihood (NLL) loss. Case studies were conducted on a modified dataset simulating sensor faults and thermal distortions. The proposed model achieved a peak R² of 0.9921 and MSE of 0.000021 under clean conditions. Under thermal noise, the retrained MLP maintained strong generalization with R² as 0.9657, MSE as 0.00053, and Prediction Interval Coverage Probability (PICP) of 0.975 with a narrow MPIW of 0.200. Comparative evaluation against Random Forest, Gradient Boosting, and Linear Regression confirms the MLP’s superior adaptability and robustness. This hybrid framework is particularly suitable for deployment in real-time Battery Management Systems (BMS) and offers a foundation for future thermal-aware predictive control in electric vehicles. The proposed framework also highlights the potential of integrating low-complexity observer models with learning algorithms for real-time applications in EV systems.

The Ethiopian highlands are affected by soil erosion resulting in the deterioration of soil properties. To reverse this, different soil and water conservation (SWC) measures were spatially practiced; however, the effect of SWC and slope gradient on soil properties is not well studied in the area. Hence, this study was conducted to evaluate the effects of SWC and slope gradient on selected soil physicochemical properties in Dawnt watershed, northwestern Ethiopia. The treatments were a combination of four different SWC measures on three slope gradients replicated at three sites. Disturbed and undisturbed soil samples were collected from 0–20 cm soil depth, and physicochemical properties were determined following standard laboratory procedures. The laboratory results depict that sand, bulk density, moisture, particle density, porosity, pH, organic carbon (OC), cation exchange capacity (CEC), total nitrogen, and available phosphorus were significantly (P<0.05) affected by SWC measures and slope gradient. High OC (2.44%), CEC (45 cmol (+) kg−1), and moisture (19.55%) were obtained from stone-faced soil bund stabilized with grass (SFSBG) and higher available phosphorus (7.83 ppm) from soil bund (SB), while lower bulk density (1.13 gm/cm3) was obtained from SFSBG. Additionally, higher clay (41.67%) and moisture (19.81%), and lower bulk density (1.14 g·cm−3) were obtained from the lower slope. Higher pH (6.75) and OC (2.89%) were recorded at the lower slope under SFSBG and lower pH and OC (6.03 and 1.02%) at the upper slope with nonconserved. Soil chemical properties, except available potassium, were increased down the slope. The interactions of slope position and SWC measures affect soil texture, pH, organic carbon, and available phosphorus but not affect soil bulk density, moisture content, particle density, total porosity, cation exchange capacity, total nitrogen, and available potassium. In general, the soil properties were improved through integrating conservation practices with multipurpose grass species across the study watershed. Therefore, it is possible to infer that SFSBG measures improve the observed physicochemical soil properties, which urge for the maintenance and the development of SWC measures in the study watershed as well as nearby highlands with similar topographic conditions and agroclimatic characteristics.

Climate change is intensifying soil erosion and sedimentation, particularly in vulnerable regions like the Ethiopian highlands. This study assesses the impacts of climate change on soil erosion and sediment yield in the Beressa Watershed, Upper Blue Nile Basin, Ethiopia, using the Soil and Water Assessment Tool (SWAT). Historical climate data and bias-corrected CMIP6 projections under SSP245 and SSP585 scenarios were used to simulate future conditions. The Mann–Kendall (MK) trend analysis of precipitation and minimum and maximum temperatures revealed increasing trends for both scenarios. The SWAT model showed good agreement between observed and simulated streamflow and sediment data, confirming reliable performance during calibration and validation. The sub-watersheds' Sediment delivery ratio (SDR) value ranged from 0.152 to 0.523. Results indicate a substantial increase in soil erosion and sediment yield under projected climate scenarios, accompanied by significant spatial shifts in hotspot areas. Compared to the baseline, sediment yield is projected to increase by 32% and 27.9% under near- and far-term SSP245, and by 19.2% and 45.4% under near- and far-term SSP585, respectively. The area generating sediment within tolerable soil loss limits decreased by 29.3% in the near term, and by 41.7% and 44.9% in the far term under the SSP245 and SSP585 scenarios, respectively. These findings enhance understanding of climate-driven erosion processes and provide a basis for adaptive soil and water conservation strategies.

PurposeThe aim of this study is to examine the motive of China's and India's engagement in African countries particularly in Ethiopia and to address the land grabbing and debt-trap diplomacy between Ethiopia and the Asian drivers, which creates challenges across the diverse social, political, economic and ecological contexts.Design/methodology/approachThis study utilises both primary and secondary data. The available literature is also reviewed. The primary data were gathered through semi-structured interviews and discussions from (1) several authority offices in Ethiopia, sources close to authorities, information-rich informants, employees and (2) perspectives, perceptions and prospects from individual members of society.FindingsThe study unmasks the win-win cooperation strategy from the perspective of the members of society in Ethiopia, evaluates whether China and India have strings attached or land grabbing motives. The study also shows that whether China's and India's move was deliberate, the implications of debt-trap diplomacy and exploitation in Ethiopia are apparent. Additionally, this study investigated several considerable potential threats to Ethiopia that will persist unless significant measures are taken to control the relations with Asian drivers.Research limitations/implicationsSome of the limitations of this paper pertain to the primary data collection process from the Ethiopian Investment Commission (EIC) and other authorities, which was very challenging because people can be punished for talking to journalists or researchers. Furthermore, some investors were not willing to participate in discussions because they were engaged in areas that are not related to their licenses. Many interviewees were also not willing to disclose their names, and the data are not exhaustive in the number of investment projects covered.Originality/valueThis study provides new evidence on the influence of Chinese and Indian investment, aid and trade on Ethiopia's social, political and economic spheres. Additionally, this study contributes to the ongoing debate on land grabbing and debt-trap diplomacy in Ethiopia.

Soil acidity has become a serious problem in the northern highlands of Ethiopia, limiting land productivity mainly that of crop yield. Research had been done to try to come up with results showing the area was affected by severe acidity, but based on few watersheds and districts in a haphazard way. The objective of this research was therefore to develop a comprehensive soil pH map that covers a broader area of 27 districts covering 2,810,055 ha of land to assess the degree of soil acidity in the Tana Sub‐basin, northwestern highlands of Ethiopia. Using GPS (3‐m precision), 2652 soil point data were collected. Precipitation, elevation, and land use cover were taken into account when creating an interpolated soil pH map. Using adjacent probed pH values, ordinary kriging interpolation (R2 = 0.65; root‐mean‐square error = 0.37125) technique was used to estimate the unmeasured locations, while the spatial pH variability was mapped using the geostatistical (GS+10) software. All Food and Agriculture Organization of the United Nations soil pH categories, with 11.22%, 19.97%, 18.77%, 14.59%, 9.64%, and 6.29% of the soil samples being extremely acidic, strongly acidic, moderately acidic, slightly acidic, neutral, moderately alkaline and strongly alkaline, respectively, were observed. The interpolation result showed a moderate coefficient of variation (10.52%) indicating that the research area had medium variability. In addition, the nugget‐to‐total semivariance ratio ranged from 25% to 75%, indicating moderate geographic dependence. The pH semivariograms best fitted the exponential model, which means the one with maximum (R2 = 0.834) and with minimum residual sum of squares (1.819E‐06) value for mapping of pH. Our result showed that soil pH was inversely dependent on precipitation and altitude, while it varies with land use type. The research result/map could give useful tools for effective integrated land management to minimize soil acidification by policymakers, agriculturalists, and other stakeholder groups. We also recommended future routine updates on the size and distribution of surface soil acidity in the study area. Core Ideas The study area had all FAO soil pH categories, with 314,265, 559,270, 545,946, 525,887, 408,605, 270,074, and 176,295 ha extremely acidic, strongly acidic, moderately acidic, slightly acidic, neutral, moderately alkaline and strongly alkaline, respectively. Cross‐validation showed that ordinary kriging was more efficient than invers distance weighting when interpolating property pH. Our result shows that there was a general decrease in pH as the elevation increases. This may be due to high precipitation. The nugget‐to‐total semivariance ratio (49.9%) which is in between 25% and 75%, indicating moderate geographic dependence. Substantial regional dependence is indicated by the relative nugget effect of pH, which ranges in value from 25% to 75%.

Various soil and water conservation (SWC) practices have been constructed through campaign-based integrated watershed management in Ethiopia since 2011. However, not all SWC measures were implemented across the country and preferred by the farmers equally. Hence, this research aimed to evaluate the trends of community-mobilized SWC practice and farmers’ SWC preference to combat land degradation in Amhara region, Ethiopia. The study utilized primary and secondary data from 92 selected watersheds across 13 zones, 45 woredas, and 1,739 households. Extensive household interviews that include elders, women, and youth, as well as focus group discussions and key informant interviews were conducted and analyzed using descriptive statistics. The result showed that 128,726.28 hectares of gully rehabilitation had been carried out, 4,436,096.3033 hectares of cultivated fields have received SWC measures, and SWC measures were done on 817,104.7 hectares of communal land. Despite these commendable initiatives, it was found that 46% of the community-mobilized SWC structures built on cultivated land have been partially or entirely removed, additionally, 66% of the constructed SWC structures lack support by biological measures. Furthermore, the survey revealed that 20% of the respondents’ land holdings were affected by gullies, and a 43% decrease trend in efforts to combat gully erosion since 2011. This study demonstrates the significance of implementing SWC measures for the sustainability of the watershed. It also underscores the vital role of regular maintenance in enhancing the effectiveness of the structures, along with the imperative need to reinforce the SWC structures using biological measures. Moreover, the research stresses the importance of rehabilitating communal lands through enclosure and improving cropland soil fertility by applying organic compost.