This study comprehensively evaluates the system performance by considering the thermodynamic and exergy analysis of hydrogen production by the water electrolysis method. Energy inputs, hydrogen and oxygen production capacities, exergy balance, and losses of the electrolyzer system were examined in detail. In the study, most of the energy losses are due to heat losses and electrochemical conversion processes. It has also been observed that increased electrical input increases the production of hydrogen and oxygen, but after a certain point, the rate of efficiency increase slows down. According to the exergy analysis, it was determined that the largest energy input of the system was electricity, hydrogen stood out as the main product, and oxygen and exergy losses were important factors affecting the system performance. The results, in line with other studies in the literature, show that the integration of advanced materials, low-resistance electrodes, heat recovery systems, and renewable energy is critical to increasing the efficiency of electrolyzer systems and minimizing energy losses. The modeling results reveal that machine learning programs have significant potential to achieve high accuracy in electrolysis performance estimation and process view. This study aims to contribute to the production of growth generation technologies and will shed light on global and technological regional decision-making for sustainable energy policies as it expands.

Global energy agencies and commissions report a sharp increase in energy demand based on commercial, industrial, and residential activities. At this point, we need energy-efficient and high-performance systems to maintain a sustainable environment. More than 30% of the generated electricity has been consumed by HVAC-R units, and heat exchangers are the main components affecting the overall performance. This study combines experimental measurements, numerical investigations, and ANN-aided optimization studies to determine the optimal operating conditions of an industrial shell and tube heat exchanger system. The cold/hot stream temperature level is varied between 10 ℃ and 50 ℃ during the experiments and numerical investigations. Furthermore, the flow rates are altered in a range of 50–500 L/h to investigate the thermal and hydraulic performance under laminar and turbulent regime conditions. The experimental and numerical results indicate that U-tube bundles dominantly affect the total pumping power; therefore, the energy consumption experienced at the cold side is about ten times greater the one at the hot side. Once the required data sets are gathered via the experiments and numerical investigations, ANN-aided stochastic optimization algorithms detected the C10H50 scenario as the optimal operating case when the cold and hot stream flow rates are at 100 L/h and 500 L/h, respectively.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

The food supply chain in South Africa faces significant challenges related to transparency, traceability, and consumer trust. As concerns about food safety, quality, and sustainability grow, there is an increasing need for innovative solutions to address these issues. Blockchain technology has emerged as a promising tool to enhance transparency and accountability across various industries, including the food sector. This study sought to explore the potential of blockchain technology in revolutionizing through promoting transparency that enable the achievement of sustainable food supply chain infrastructure in South Africa. The study found that blockchain technology used in food supply chain creates an immutable and decentralized ledger of transactions that has the capacity to provide real-time, end-to-end visibility of food products from farm to table. This increased transparency can help mitigate risks associated with food fraud, contamination, and inefficiencies in the supply chain. The study found that blockchain technology can be leveraged to enhance supply chain efficiency and trust among stakeholders. This technology used and/or applied in South Africa can reshape the agricultural sector by improving production and distribution processes. Its integration in the food supply chain infrastructure can equally improve data management and increase transparency between farmers and food suppliers.There is need for policy-makers and scholars in the fields of service delivery and food security to conduct more research in blockchain technology and its roles in creating a more transparent, efficient, and trustworthy food supply chain infractructure that address food supply problems in South Africa. The paper adopted a qualitative methodology to collect data, and document and content analysis techniques were used to interpret collected data. 

Solar energy is a reliable and abundant resource for both heating and power generation. The current research examines how the novel class of nano-embedded Bees wax phase change materials (NEBPCMs) improves heat storage qualities. The synthetic NEBPCMs were subjected to experimental testing using, XRD, Bees wax and Al2O3 FESEM. A typical solar water heating system features a flat plate collector unit incorporating Bees Wax phase change material (NEBPCM) combined with varying concentrations of Al2O3 (0.01%, 0.015%, and 0.02%). The absorber plate surface is coated with a Nano-hybrid coating consisting of Black Paint, Al2O3, and additional Fe3O4 at a 2% concentration. Pure water is frequently used in these solar water heaters (SWH), with performance evaluations conducted using different Bees Wax and Al2O3 concentrations of NEBPCM (Bees Wax + Al2O3). The system’s efficiency is assessed across different flow rates (60, 90, and 120 kg/hr) and tilt angles (15, 30, and 45 degrees). This study aims to examine the feasibility of using PCMs to store solar energy for night time water heating, ensuring a continuous supply of hot water maximum efficiency achieved by using NEBPCM in solar water heater 52.26% at a flow rate of 120 Kg/hr, at angle of 45 degrees and Concentration 0.015%.

Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.