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Agriculture holds a crucial position in maintaining livelihoods and securing food sources, particularly in nations such as Ethiopia, where a substantial portion of the population depends on agricultural pursuits. However, meeting the growing demand for food production amidst population growth presents considerable challenges. Recent advancements in technology, particularly in the areas of Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) offer promising solutions to address these challenges. This paper explores the potential of integrating ML, DL, and IoT technologies in agriculture to revolutionize the sector. By harnessing data-driven insights, farmers can make informed decisions regarding crop management, soil health, and weather patterns, leading to optimized resource allocation and increased productivity. Moreover, IoT devices enable the real-time monitoring and control of agricultural operations, enhancing sustainability and productivity. Despite the opportunities presented by these technologies, there are also challenges to overcome, such as data quality, connectivity issues, and the need for farmer education. However, with concerted efforts and investment, Ethiopia and other agricultural regions can unlock the full potential of ML, DL, and IoT technologies to ensure food security, alleviate poverty, and drive economic development. This review paper offers perspectives on the present status, challenges, and future possibilities regarding the integration of ML, DL, and IoT in agriculture. It underscores the transformative potential of these technologies within the sector. [ABSTRACT FROM AUTHOR]

Hybrid oils ensure multifaceted technological (self‐lubricating and favorable fatty acid properties), and sustainable (environmental, economic, and societal) benefits towards biodiesel conversions. The hybrid oils (Hydnocarpus wightiana oil and waste cooking oils [40:60 v/v]) were synthesized to methyl ester with alkaline catalyst sodium hydroxide through the base‐transesterification process. The resulting hybrid oil methyl ester (HOME) is 96.68%. The crude oil (CO) and its HOME underwent characterization using gas chromatography–mass spectrometry, Fourier transform infrared spectroscopy, and hydrogen‐1 nuclear magnetic resonance spectroscopy. The physicochemical properties of crude oils and hybrid biodiesel were analyzed and compared to pure diesel. Different blends of biodiesel–diesel, including binary (20% HOME + 80% diesel [D]), ternary (60% D + 20% HOME + 20% heptanol [H]), and quaternary (20% HOME + 20% H + 10% CO + 50% D) blends, were tested in a single‐cylinder compression ignition (CI) engine under various load conditions to assess their performance, emissions, and combustion properties. Experimental findings indicate that the addition of heptanol to diesel or hybrid biodiesel (ternary blend) enhances brake thermal efficiency, reduces brake‐specific fuel consumption, and leads to longer ignition delays, resulting in higher internal combustion pressure and thermal energy release rates compared to the quaternary blend. Additionally, compared to pure diesel, the ternary blend exhibits decreased emissions of carbon monoxide (CO) and hydrocarbon (HC), with a slight increase in nitrous oxide (NOx) and carbon dioxide (CO2). Notably, the ternary blend emerges as a distinct alternative biodiesel blend suitable for direct use in CI engines without requiring any engine modifications. [ABSTRACT FROM AUTHOR]

This study investigates the impact of cooling methods on the electrical efficiency of photovoltaic panels (PVs). The efficiency of four cooling techniques is experimentally analyzed. The most effective approach is identified as water-spray cooling on the front surface of PVs, which increases efficiency by 3.9% compared to the case without cooling. The results show that water-spray cooling raises the PV's temperature to 41°C, while improving its average daytime efficiency to 22%. Air-cooling, water-cooling in the tubes behind the PV, and aluminum oxide-water nanofluid cooling in the tubes behind the PV improve efficiency by 1.1%, 1.9%, and 2.7%, respectively. The findings highlight the potential of water-spray cooling as a cost-effective and efficient method to enhance PV efficiency and contribute to the global effort towards renewable energy. [ABSTRACT FROM AUTHOR]

Metal–air batteries have higher theoretical specific energies than existing rechargeable batteries including Li-ion batteries. Among metal–air batteries, the Na–O2 battery has gained much attention due to its low discharge/charge overpotentials (∼100 mV) at relatively high current densities (0.2 mA/cm2), high electrical energy efficiency (90%), high theoretical energy density, and low cost. However, there is no information reported regarding the effect of CO2 contamination in non-aqueous Na–air batteries. Density functional theory has, here, been applied to study the effect of low concentrations of CO2 contamination on NaO2 and Na2O2 growth/depletion reaction pathways and overpotentials. This was done on step surfaces of discharge products in non-aqueous Na–air batteries. Adsorption energies of CO2 at various nucleation sites for both step surfaces were determined, and results revealed that CO2 preferentially binds at the step valley sites of (001) NaO2 and 1 1 ¯ 00 Na2O2 surfaces with binding energies of −0.65 eV and −2.67 eV, respectively. CO2 blocks the step nucleation site and influences the reaction pathways and overpotentials due to carbonate formation. The discharge electrochemical overpotential increases remarkably from 0.14 V to 0.30 V and from 0.69 V to 1.26 V for NaO2 and Na2O2 surfaces, respectively. CO2 contamination is thus drastically impeding the growth/depletion mechanism pathways and increases the overpotentials of the surface reaction mechanism, hampering the performance of the battery. Avoiding CO2 contamination from intake of gas and electrolyte decomposition is thus critical in development of Na–air batteries. [ABSTRACT FROM AUTHOR]

The utilization of solar energy, an abundant and renewable resource, shows great promise. Various technologies have been employed to capture this vast energy potential. In this study, the effectiveness of natural dye-sensitized solar cells (DSSCs) was explored through experimental and computational methods. Organic dyes derived from specific plant species were examined, with a solvent mixture of ethanol, methanol, and tetrahydrofuran used for extraction. A UV–vis spectrophotometer was utilized to measure the sensitizers' optical characteristics. The study employed density functional theory (DFT), implemented in Gaussian 09 W software, to perform computational calculations. The B3LYP method and a basis set of 6-31G++(d,p) were chosen for optimizing the geometries and energetics of dye molecules. The DFT results indicated that the dye molecules had a bandgap in the range of 2.16–2.38 eV. The photochemical performance of all four fabricated DSSC-based devices was evaluated to be between 0.33% and 1.04%, and the Euryops pectinatus sensitizer demonstrated the highest efficiency of 1.04% among the studied sensitizers. [ABSTRACT FROM AUTHOR]

The precise assessment and evaluation of global solar radiation (GSR) is crucial for designing effective solar energy systems. However, in developing countries like Ethiopia, the cost and maintenance of measuring devices are inadequate. As a result, researchers have explored alternative methods such as empirical models to estimate GSR. This article proposes using artificial neural networks (ANN) to predict daily and monthly averaged horizontal GSR (HGSR) around Fiche town of Ethiopia, using various network types. The input variables were divided into training (70%) and testing (30%) sets to evaluate the network types, with the sigmoid function used as the activation function at the hidden layer and a linear function for the output layer. The predicted mean daily and monthly HGSR ranges from 3.282 kWh/m²/day to 6.967 kWh/m²/day and 4.628 kWh/m² to 6.613 kWh/m² respectively. The values obtained were compared to those provided by NASA observation data and were found to be within acceptable limits. Statistical metrics of MAPE, MSE, and RMSE show that CFBP, FFBP, LR, and EBP are better network types for estimating mean daily HGSR, while EBP, FFBP, CFBP, and LR are better for estimating mean monthly HGSR. Overall, all network types of ANN accurately predicted the mean daily and monthly HGSR. In general, the findings of this study indicated that the location had promising solar energy for producing electricity and for various uses. [ABSTRACT FROM AUTHOR]

Due to a lack of solar radiation measurement instruments in various regions across the world, particularly in Ethiopia, many empirical models have been developed to estimate global solar radiation. Reliable global solar radiation infor- mation is essential for the design and development of solar energy systems.Thus, the aim of this paper is to investigate the feasibility of using Artificial Neural Network (ANN) to predict mean daily global solar radiation (GSR) and to compare the performance of ANN and empirical models based on sunshine in estimating mean daily global solar irradiation on a horizontal surface.For the ANN model, network inputs were daily data for a number of days, average daily data (sunshine hours, maximum temperature, minimum temperature, wind speed, relative humidity, and pressure), and while daily averaged global solar radiation was the network output.After training, the model had R values of 0.891 for training, 0.932 for validation, 0.920 for testing, and 0.903 for all, with validation performance of 0.0797.The results of the ANN model were compared to the empirical equations using statistical error evaluation (MBEANN = 0.0005, RMSEANN = 0.3310, and R2ANN = 0.7998).The agreement between the values of NASA and estimated values reveals that the ANN exhibits good performance in studying daily GSR. The result also shows that the mean daily GSR is varying between 3.675KWh/m² (August-1) and 6.935 KWh/m² (April-19) in the study site. [ABSTRACT FROM AUTHOR]

Clean energy and environmental pollution are two key concerns of modern society and are pivotal necessities for the economic, social, and sustainable development of the world. Today around 80% of energy is generated using nonrenewable resources and fossil fuels (oil, gas, coal) which ultimately results in hazardous global emissions. As a clean substitute for fossil fuels, hydrogen has emerged as a promising and renewable energy resource. Utilization of this energy resource requires the development of active, stable, low‐cost environmentally friendly techniques. Water splitting electrolysis is a method for producing clean and efficient hydrogen using an environmentally benign technique that is currently at its most mature stage. Electrolysis is attracting ever‐increasing attention, as it is a promising electrochemical device for hydrogen production from water due to the high conversion efficiency and relatively low energy input required when compared to thermochemical and photocatalytic methods. This paper will outline the need, performance, and insight of anion exchange membrane (AEM) electrolyzer. Recent developments in the design and preparation of AEM. New strategies for activity, stability, and efficiency improvement of AEM. Membrane types, and factors affecting AEM performance in an electrolyzer. This review also discusses the effects, operating characteristics, and energy consumption of electrocatalysts in the AEM electrolyzer. Hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) pathways and mechanisms in acidic and alkaline media. This study seeks to provide a detailed overview of recent accomplishments in the field of the hydrogen economy, particularly electrolysis, to inspire further research and development to address the technology's obstacles. [ABSTRACT FROM AUTHOR]

AbstractThe aim of the current study is to examine the effect of an ellipse-protruded twisted strip on thermo-hydraulic performancein a circular tube. When elliptically protruded twisted tape tube is compared to plain twisted tape tube, the deviation is between 30% and 33%. The modeling results of Nusselt number vs Reynolds number of the plain twisted tape tube with actual data show a variance of 9.5%. A plain tube has a friction factor of one, whereas a plain twisted tape tube has seven times the friction factor. An elliptical protruding twisted tape tube has 1.5 times the thermal performance factor of a regular twisted tape tube. A correlation between the Nusselt number and the friction factor for the elliptical protruded twisted tape domain has been given; the expected and numerical results are 7.5–8.5 and 6–8% of each other. [ABSTRACT FROM AUTHOR]

High theoretical capacity, high thermal stability, the low cost of production, abundance, and environmental friendliness are among the potential attractiveness of Li 2 MnSiO 4 as a positive electrode (cathode) material for rechargeable lithium-ion batteries. However, the experimental results indicated poor electrochemical performance in its bulk phase due to high intrinsic charge transfer resistance and capacity fading during cycling, which limit its large-scale commercial applications. Herein, we explore the surface stability and various lithium-ion diffusion pathways of Li 2 MnSiO 4 surfaces using the density functional theory (DFT) framework. Results revealed that the stability of selected surfaces is in the following order: (210) > (001) > (010) > (100). Moreover, the Wulff-constructed equilibrium shape revealed that the Li 2 MnSiO 4 (001) surface is the most predominant facet, and thus, preferentially exposed to electrochemical activities. The Hubbard-corrected DFT (DFT + U , with U = 3 eV) results indicated that the bulk insulator with a wide band gap ( E g = 3.42 eV) changed into narrow electronic ( E g = 0.6 eV) when it comes to the Li 2 MnSiO 4 (001) surface. Moreover, the nudged elastic band analysis shows that surface diffusion along the (001) channel was found to be unlimited and fast in all three dimensions with more than 12-order-of-magnitude enhancements compared with the bulk system. These findings suggest that the capacity limitation and poor electrochemical performance that arise from limited electronic and ionic conductivity in the bulk system could be remarkably improved on the surfaces of the Li 2 MnSiO 4 cathode material for rechargeable lithium-ion batteries., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)