Evaporative cooling is a widely adopted technology for various applications, including industrial processes, HVAC systems, building cooling, and microclimate regulation. It is known for its cost-effectiveness, energy efficiency, and environmental friendliness compared to conventional refrigerants. With buildings accounting for a significant portion of global energy use, enhancing cooling technology efficiency is critical. This review explores recent advancements in evaporative cooling technologies, particularly those involving desiccants, membranes, and hybrid systems, such as air-mediated indirect evaporative cooling (AMIEC) and water-mediated indirect evaporative cooling (WMIEC). These innovations address traditional challenges like maintenance, efficiency fluctuations, and ambient condition dependencies, while offering sustainable alternatives to ozone-depleting refrigerants. The review delves into the principles and classifications of evaporative cooling, detailing both direct and indirect methods using air and water as cooling media. Factors influencing the efficiency and cost of these systems, such as materials for water evaporation interfaces and design improvements, are discussed. Enhanced evaporative cooling techniques, including desiccant sorption and membrane-assisted cooling, are highlighted for their potential to improve performance in humid environments. Furthermore, the review examines the performance metrics of evaporative coolers, such as cooling capacity, energy efficiency ratios (EER), and effectiveness. Case studies and performance analyses of modified evaporative coolers demonstrate significant energy savings and increased efficiency. Despite challenges like high humidity sensitivity and maintenance needs, ongoing research and development are paving the way for more robust and efficient designs. The integration of evaporative cooling with other technologies, such as vapor compression and solar energy, holds promise for future advancements. In conclusion, evaporative cooling represents a sustainable and efficient alternative to traditional cooling methods, with significant potential for reducing global energy consumption and environmental impact. Continued innovation and addressing current limitations will enhance its applicability and effectiveness, positioning evaporative cooling as a key technology in the future of energy-efficient cooling solutions.

In the Sixth Generation (6G) mobile system, the importance of security increases even more in the communication system. One of the potential technologies of 6G is the Network in a Box (NIB). The 6G-enabled NIB is a multi-generational, easily and quickly installable technology used for communication. It is based on both hardware and software. The main features of a 6G-enabled NIB include low latency and a high level of flexibility. In addition, it provides connectivity services to the applications used in unusual situations such as battlefields or natural disasters in the industry. However, most of the applications used in the 6G-enabled NIB are not appropriately secured. There are chances of several active and passive attacks due to the insecure channel. Therefore, a novel remote user authentication and key management scheme is presented in this paper. This scheme is the modified and improved version of UAKMS-NIB and is renamed as the improved User Authentication and Management Scheme to secure the 6G-enabled NIB (iUAKMS-NIB) that can be used in industrial applications. Hence, the proposed scheme provides the best security solution against the possible attacks on the 6G communication system. The analytical results show that the proposed scheme performs better compared to the existing schemes.

This study focuses on synthesizing Niobium pentoxide (Nb₂O₅) thin films on silicon wafers and quartz substrates using DC reactive magnetron sputtering for NO₂ gas sensors. The films undergo annealing in ambient air at 800 °C for 1 hr. Various characterization techniques, including X-ray diffraction (XRD), atomic force microscopy (AFM), energy-dispersive X-ray spectroscopy (EDS), Hall effect measurements, and sensitivity measurements, are employed to evaluate the structural, morphological, electrical, and sensing properties of the Nb₂O₅ thin films. XRD analysis confirms the polycrystalline nature and hexagonal crystal structure of Nb₂O₅. The optical band gap values of the Nb₂O₅ thin films demonstrate a decrease from 4.74 to 3.73 eV as the sputtering power is increased from 25 to 75 W. AFM images illustrate a progressive increase in particle size ranging from (41.86) to (45.56) nm, with varying sputtering power between 25 and 75 W. Additionally, EDS analysis validates the rise in Nb content, increasing from 12.2 at. % to 20.1 at. %, corresponding to the increase in sputtering power. Hall effect measurements show that all films exhibit n-type charge carriers, and increasing sputtering power leads to decreased carrier concentration and enhanced mobility. The gas sensor's sensitivity, response, and recovery time were evaluated at various operating temperatures. The NO₂ sensor exhibited an optimal sensitivity of 28.6% at 200 °C when the sputtering power was set to 50 W.

The blistering pace of generative and foundational AI models being deployed in 2024 and 2025 is transforming experiences in education, healthcare, science, sustainability and business. This narrative review consolidates findings from the 50 most cited peer reviewed publications in this time frame, providing a cross-cutting overview on the state of development, the application and the challenges concerning technology. We start by discussing the architectural origins behind both large language models, multimodal generators, as well as domain-specific foundation models including SpectralGPT and scGPT. Then, we evaluate their application on vertical-industrial applications of academic teaching, clinical diagnosis, supply chain operation, and environmental monitoring. The review discusses also important ethical and societal issues, including fairness, explainability, AI angst, and academic responsibility. We also discuss lingering technical challenges including hallucination, data privacy, and availability barriers despite recent progress made. Finally, we discuss some of the emerging frontiers that this work opens and their exciting implications, including controllable generation, symbolic-neural integration, and divergence between open and proprietary model ecosystems. This citation-motivated review provides a timely snapshot of how the most influential research is leading the development of generative AI across domains.

Accurate identification of photovoltaic (PV) cell and module parameters remains a fundamental yet challenging task, particularly as model complexity increases from five to nine unknown parameters. In this study, the parameter extraction problem is rigorously formulated as a nonlinear optimization task and addressed using a novel hybrid metaheuristic algorithm, termed the Shuffled Frog Leaping–Shuffled Complex Evolution (SFL-SCE) method. The proposed approach synergistically integrates the population-based social learning mechanism of the Shuffled Frog Leaping Algorithm (SFL) with the robust global search and refinement capabilities of Shuffled Complex Evolution (SCE), thereby achieving an effective balance between exploration and exploitation. The SFL-SCE algorithm minimizes the root-mean-square error (RMSE) between measured and simulated current–voltage characteristics and is systematically applied to three widely used PV technologies: the RTC-France silicon solar cell, the polycrystalline Photowatt-PWP201 module, and the monocrystalline STM6-40/36 module. For each device, parameter identification is performed under one-diode, two-diode, and three-diode modelling frameworks, encompassing increasing levels of physical fidelity and computational complexity. Experimental data are employed throughout to ensure practical relevance and robustness. The performance of the proposed algorithm is comprehensively evaluated against its constituent algorithms (SFLA and SCE) as well as several state-of-the-art hybrid optimization techniques reported in the literature. Comparative results demonstrate that SFL-SCE consistently achieves superior accuracy, enhanced reliability, and faster convergence, as evidenced by lower minimum, mean, and maximum RMSE values, reduced standard deviation, and improved convergence behavior across all test cases. These findings confirm the effectiveness of the proposed hybridization strategy and establish SFL-SCE as a powerful and reliable tool for high-precision PV model parameter identification.

In today's world with the considerable rise in the energy consumption of Energy Systems, the necessity to intelligent and sustainable management of energy has surfaced as a key requirement for energy efficiency and effective data-driven decision making. In recent years, AI and ML techniques have proven to be a promising way to predict energy consumption patterns, yet most of the existing solutions are black-box models, which lack interpretability. This limitation reduces trust in the users and lowers the likelihood of implementing AI solutions in the energy sector that promote sustainability. This study suggests an Explainable Artificial Intelligence Framework for Sustainable Energy Management and Data-Driven Decision Making to offer correct and clear energy analysis, combining machine learning based prediction with Explainable AI techniques. The proposed framework compares the performance of three models of machine learning namely Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) with an energy consumption dataset that features environmental and operational parameters. The performance of the models implemented are assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R²) of regression. Two methods of explaining the contribution of features and individual prediction results, namely, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) are embedded to explain the individual prediction outcomes and feature contribution in an enhanced transparency of the model. The results of the experiments confirm that the developed models have a good prediction performance, with the XGBoost model being the most successful approach for forecasting. Moreover, the explainability analysis reveals the most influential factors on energy consumption and gives valuable insights for optimization of sustainable energy. The proposed framework aims to balance predictive accuracy with interpretability of the models by converting traditional AI-based energy prediction models into transparent decision support models. The developed approach can help the energy manager and stakeholders to understand the consumption pattern, optimize the operational of the system and contribute in taking the initiatives for sustainable consumption.