The development of energy-efficient technologies at all stages of a building’s life cycle is essential to achieving sustainable development goals. The object of the study is climate-adaptive façade structures with air gaps in the form of a Trombe wall and a double-skin façade. Cases using phase-change materials (PCM) and photovoltaic modules (PV) in climate-adaptive structures are analyzed separately. The research method is aimed to review and analyze the energy-saving potential from integrating the Trombe wall or double-skin façade in buildings. The work systematizes full-scale, physical, and mathematical experiments. Articles from Scopus and Web of Science systems from 2001 to 2022 inclusive were subject to consideration. The article presents a statistical analysis given by the scientific community on the current topic’s dynamics. The study’s significance is characterized by a lack of knowledge on the behavior of the mentioned façade systems in various climate zones and for different buildings types. The results have shown that comprehensive studies on the investigated systems are significant and can serve for further designs and energy efficiency improvements. For the first time, a scientometric analysis of articles on the topic “Climate-adaptive façades” was compiled.

This study examines the relative effectiveness of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Linear Programming (LP) in optimizing hybrid energy microgrids. Drawing upon empirical data derived from the study, the research explores many facets, including economic efficacy, environmental viability, and microgrid robustness. The use of GA showcases a significant 10% decrease in overall system expenses, highlighting its efficacy in augmenting economic feasibility. PSO diligently tracks, attaining an 8% decrease, while SA and LP make significant contributions but provide somewhat lesser cost reductions at 7% and 6%, correspondingly. Within the domain of renewable energy integration, GA and PSO have emerged as frontrunners, with remarkable advancements of 12% and 10%, respectively. SA and LP provide commendable contributions, demonstrating their effectiveness in optimizing the usage of renewable energy sources inside the microgrid, as seen by their respective increases of 8% and 7%. The environmental factor, as quantified by the decrease of carbon emissions, highlights the commendable efficacy of GA and PSO, resulting in significant reductions of 15% and 12% respectively. SA and LP provide praiseworthy environmental efforts, achieving reductions of 10% and 8% respectively. The resilience index highlights the strength of GA and PSO in assessing the resilience of the microgrid, with GA showing an increase of 0.05 and PSO showing an increase of 0.04. SA and LP make a significant contribution, with increments of 0.03 and 0.02, underscoring the potential of evolutionary and swarm-based methodologies to bolster the microgrid’s resilience against disturbances. Scenario analysis effectively brings unpredictability into the operational environment of the microgrid, continually showcasing the remarkable flexibility of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) over a wide range of situations. SA and LP demonstrate consistent efficacy but with somewhat reduced flexibility. Statistical evaluations provide compelling evidence confirming the exceptional efficacy of GA and PSO in improving microgrid metrics. Ultimately, this research provides valuable perspectives on the intricate trade-offs between various optimization techniques, empowering decision-makers to choose strategies that align with specific microgrid objectives. Moreover, it contributes to the wider discussion on resilient, sustainable, and economically feasible energy infrastructures.

Thermal insulation materials used in power and industrial systems must maintain high performance under extreme environmental conditions. Among such materials, aerogel and basalt fiber are widely applied due to their low thermal conductivity and ease of installation. However, over time, these materials are susceptible to degradation, which can significantly impair their insulating efficiency and increase energy losses. Despite their importance, the long-term behavior of these materials under realistic climatic stressors has not been analyzed enough. This study investigates the degradation of thermal insulation performance in aerogel and basalt fiber materials subjected to complex atmospheric stressors, simulating long-term outdoor exposure. Aerogel and basalt fiber mats were tested under accelerated aging conditions using an artificial weather chamber equipped with xenon lamps to replicate full-spectrum solar radiation, high humidity, and elevated temperatures. The results show that the thermal conductivity of aerogel remained stable, indicating excellent durability under environmental stress. In contrast, basalt fiber insulation exhibited a deterioration in thermal performance, with a 9–11% increase in thermal conductivity, corresponding to reduced thermal resistance. Computational modeling using COMSOL Multiphysics confirmed that aerogel insulation outperforms basalt fiber, especially at temperatures exceeding 200 °C, offering better heat retention with thinner layers. These findings suggest aerogel-based materials are more suitable for long-term thermal insulation of high-temperature pipelines and industrial equipment.

This research examines the optimization of the deployment of electric vehicle (EV) charging stations by using machine vision technology, which involves analyzing real-time data and geographical information. Geospatial data analysis reveals prospective sites for charging stations by considering population density and accessibility to roads, hence identifying regions with increased demand for electric vehicle charging. The assessment of electric vehicle (EV) traffic patterns highlights the ever-changing charging requirements at various times and places, underscoring the need of flexible deployment techniques. Furthermore, evaluating the costs of implementing the deployment and the capabilities of charging, it becomes apparent that there are compromises to be made between the initial expenditures of installation, the amount of power generated, and the quantity of charging stations. These trade-offs are essential for optimizing resources. The usage study of charging stations using machine vision reveals variations in the number of available charging points at different stations and the need for adaptive resource distribution timestamps, techniques. The examination of percentage change reveals notable fluctuations in population density, installation costs, and the availability of charging points. This information is crucial for making well-informed decisions about the deployment of charging infrastructure. Combining machine vision insights with geographical and traffic analyses presents a promising method to create data-driven strategies for the placement of EV charging stations. This approach addresses the changing needs of electric mobility and provides guidance to stakeholders for efficient and flexible charging solutions.

The phase transition and grain refinement features of a Ti-6Al-4V alloy are investigated in this study using in-situ high- temperature Electron Backscatter Diffraction (EBSD). The objective of the experiment was to detect the phase transition from Body-Centered Cubic (BCC) to Face-Centered Cubic (FCC) systems at temperatures ranging from 770 to 900 degrees Celsius. Grain refinement techniques such as recrystallization twin generation, Kernel Average Misorientation (KAM), and low-angle grain boundary creation were the focus of this study. According to our research, phase transformation maps taken at 770°C, 810°C, 840°C, and 900°C reveal that a discontinuous recrystallization process was responsible for the transformation and recrystallization. The results of this investigation provide support to the theory that high-temperature treatments may enhance the microstructure of additively made Ti-6Al-4V alloys. At temperatures of 900 °C, the grain structure was evenly polished, and an almost complete phase shift to a face-centered cubic (FCC) shape was observed.