This study investigates and compares the seismic performance of structures with different types of plan irregularities, emphasizing the effects of the simultaneous application of bidirectional ground motion components. C-, L-, and T-shaped buildings, as well as a regular square-shaped building, are analyzed under site-specific recorded PEER ground motions in accordance with seismic code requirements. Structural responses, including interstory drift, story displacement, base shear, floor acceleration, and torsional irregularity are evaluated using modal analysis and nonlinear time-history analysis, considering both simultaneous and non-simultaneous application of bidirectional ground motion components. Three site-specific ground motion records with varying peak PGAs are used for nonlinear dynamic analysis and seismic response evaluation of horizontally uneven buildings. The results indicate that the flexural periods of all analyzed buildings are comparable; however, buildings with plan irregularities exhibit significantly longer torsional periods. The simultaneous application of bidirectional ground motions substantially amplifies structural responses, particularly in irregular buildings. For the regular building, top-story displacement and interstory drift increase by approximately 5
Electric Vehicles (EVs) have emerged as a pivotal solution in the global transition toward sustainable transportation, offering significant reductions in greenhouse gas emissions and dependence on fossil fuels. The growing adoption of EVs is anticipated to significantly influence electrical power distribution networks. As EV adoption accelerates worldwide, their integration into existing electrical distribution networks poses considerable challenges, particularly concerning load demand, voltage stability. Recent research has increasingly focused on understanding and mitigating these impacts, through a wide array of analytical, simulation-based, and data-driven methods both with and without the implementation of network management strategies. However, a comprehensive, integrated synthesis of these studies linking technical findings with mitigation strategies and practical implications is still limited. Additionally, there is a lack of a thorough review consolidating the existing research on this subject, which is essential for understanding the scope of prior studies. Motivated by this gap, this review paper provides a critical and consolidated examination of the current literature on the impacts of EV on distribution networks. The paper's objective is to assess the comprehensive effects of EV integration and evaluate mitigation strategies across three core domains: network management, EV and Distributed generation (DG) coordination, and regulated charging solutions. The paper also introduces the role of artificial intelligence in enabling predictive, adaptive, and scalable solutions. It highlights key methodologies, data sources, modeling approaches, and the efficacy of various solutions while identifying prevailing research gaps and future directions, including real-time co-simulation, techno-economic assessments, and hybrid planning frameworks. Key outcomes of this study include a unified framework for understanding EV interactions with the grid and distribution network, a comparative analysis of mitigation approaches, and a set of recommendations for advancing research and deployment strategies. These findings aim to inform stakeholders about the latest advancements and support decision-making. The findings are particularly beneficial for utility providers, network planners, policymakers, charging infrastructure developers, and researchers seeking to develop robust, data-informed strategies for managing EV impacts and guide them in addressing unresolved challenges.
Perovskite solar cells (PSCs) have emerged as a promising photovoltaic technology due to their high efficiency, tunable bandgap, and low-cost fabrication. However, challenges such as lead toxicity, suboptimal architecture, and defect-induced recombination continue to hinder the large-scale commercialization of PSCs. In this study, a comprehensive numerical analysis of an FTO/STO/CH3NH3SnI3/NiO/Au device was performed using SCAPS-1D, complemented by PVSyst modeling to assess module-scale performance. Critical parameters-including absorber and ETL thickness, absorber doping density, intrinsic recombination coefficients, defect densities, and resistive losses-are systematically investigated. The device architecture achieves a simulated power conversion efficiency of 30.72 % (Voc= 1.2159 V, Jsc= 28.39 mA/cm2, FF = 89.00 %) under AM 1.5G illumination, approaching the Shockley-Queisser limit for its 1.3 eV bandgap. Sensitivity analysis reveals that the CH3NH3SnI3 absorber and its interface with STO are the most defect-sensitive regions, where excessive defect densities drastically reduce efficiency, while NiO and other interfaces remain defect-tolerant. Optimal performance is further linked to low radiative (<= 10-13 cm3/s) and Auger (<= 10-33 cm6/s) recombination, as well as minimal series resistance (<= 1 S2 cm2) and high shunt resistance (>= 105 S2 cm2). PVSyst simulations confirmed the scalability of the device architecture to a 72-cell module, while underscoring the need for thermal management to mitigate Voc and PCE losses at elevated temperatures. These results highlight that precise control of structural parameters, defect passivation-especially at the absorber/ETL interface-and resistive loss minimization can enable high-efficiency, environmentally benign PSCs with performance nearing the theoretical efficiency limit.
Additive manufacturing (AM) promises more freedom in design than ever with maintaining consistent and reliable mechanical behavior across processes and materials being a significant challenge. Recent discoveries in machine learning (ML) provide effective data assistance to forecast mechanical properties e.g., tensile strength, porosity, and hardness by revealing non-linear process-structure–property interrelationships. This is a systematic review of the developments published since January 2021 until June 2025, according to the guidelines of PRISMA, in order to assess the status of ML-based property prediction in AM. Thirty peer-reviewed articles were reviewed in metals, polymers, and composites manufactured through such processes as laser powder bed fusion, directed energy deposition, fused deposition modelling, and vat photopolymerization. We benchmark widely used algorithms like artificial neural networks, support machine learning, random forests, and new hybrid or physics inspired models with regard to their data demands, validation schemes, predictive capability and constraints. The specific focus is made on the lack of datasets, the generalizability of models, their interpretability, and their connection with on-site monitoring. The review also suggests a conceptual pipeline of integrating ML into AM pipelines, which covers data acquisition, feature engineering, model training, and deployment. Shedding light on how ML can be used to enhance predictive reliability, quality assurance, and process optimization in additive manufacturing, this article can inform researchers and practitioners with systematic information on the recent theory and support the development of the research field by bridging research gaps.
Lead-free double halide perovskites, specifically Cs₂TlAX₆ (A = As, Sb; X = Cl, Br, I), offer key advantages over lead-based versions, including strong optical absorption, high structural and thermal stability, superior carrier mobility, tunable band gaps, non-toxicity, and cost-effectiveness. Their mechanical, electrical, optical, and thermal properties were investigated using DFT with the PBE functional under ambient and hydrostatic pressures. Stability was confirmed through formation enthalpy, tolerance factor, and elastic constants. Pugh’s and Poisson’s ratios suggest these compounds are generally ductile (except Cs₂TlAsBr₆), with pressure-enhanced machinability, reduced friction, and increased plastic strain. Band structure analysis using the GGA-PBE approximation shows the direct band gap semiconducting nature of Cs₂TlAX₆ (A = As, Sb; X = Cl, Br, I), where the band gap values are ranging from 0.957 to 1.697 eV. However, the TB-mBJ functional efficiently moderates the GGA-PBE underestimation, yielding refined band gaps between 1.22 and 2.17 eV—values which are more efficient solar applications. Pressure-induced band gap tuning highlights potential for optoelectronic device applications. Their narrow band gaps and strong absorption make them suitable for solar cells, while high infrared reflectivity and low thermal conductivity indicate potential as thermal barrier coatings (TBCs).
