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Accurate short-term solar radiation forecasting is essential for the reliable integration of photovoltaic systems into modern power grids, particularly in regions characterized by strong climatic heterogeneity. This study proposes a Climate-Aware Hybrid Kolmogorov–Arnold Network (CA-HKAN) framework for forecasting hourly Global Horizontal Irradiance (GHI) under diverse atmospheric regimes. The framework integrates an intrinsically interpretable spline-based Kolmogorov–Arnold Network with a feed-forward neural network through a deterministic switching mechanism governed by Extreme Value Theory (EVT). EVT is employed to derive climate-specific clearness-index thresholds, which are scaled to delineate stable and volatile irradiance regimes. These thresholds deterministically activate the interpretable spline-based component under physically stable conditions, while a neural network fallback is engaged during volatile or extreme atmospheric states. The proposed approach is evaluated using hourly meteorological and irradiance data from five climatically distinct regions in Saudi Arabia, representing desert, coastal, mountainous, and transitional environments. Experimental results demonstrate that the proposed CA-HKAN framework achieves predictive accuracy competitive with modern deep learning baselines, such as CNN–BiLSTM models, across all regions while maintaining physical consistency, including non-negativity and realistic irradiance bounds. Compared with standalone models, the hybrid approach offers a favorable balance between accuracy, robustness, and transparency. Ablation analyses further confirm the complementary roles of the hybrid components and the effectiveness of EVT-based regime control. Overall, the CA-HKAN framework provides a practical and interpretable solution for climate-aware solar radiation forecasting, supporting trustworthy deployment in sustainable energy systems operating under heterogeneous and non-stationary atmospheric conditions.

High-risk pregnancies are associated with increased anxiety and stress. These elevated stress levels can negatively impact both maternal and fetal health. This study aims to explore the impact of artificial intelligence as emotional support tool in reducing anxiety and stress among high-risk pregnant women. The study employed a quasi-experimental design with pre- and post-intervention. A convenient sample of 300 high-risk pregnancies women participated in this study . This study was conducted at Al-Zahraa Hospital in New Damietta, Damietta Governorate, Egypt. Three tools were used for data collection, socio demographic data and Obstetric Data, Stress and Anxiety Scale and AI Usability and Perceived Emotional Support Scale. Results: results demonstrated a statistically significant decrease in pregnancy-related anxiety and stress scores following the four-week intervention period. Specifically, the mean total score dropped from 54.05 ± 5.99 before the intervention to 10.15 ± 3.82 after using the AI chatbot (p < 0.001). Conclusion: The present study concluded that AI emotional support tools can effectively reduce stress and anxiety in women with high-risk pregnancies and chatbot achieved high usability scores, indicating that participants found it easy to access, functional, and responsive. Recommendations: Based on our findings, we recommend integrating AI emotional support tools into standard prenatal care for high-risk pregnancies. Future efforts should focus on developing ethical guidelines and adapting these technologies to meet diverse patient needs across different healthcare settings.Trial registration: Clinical trial registration number (PACTR202601891625550), the date of registration (05/1/2026).

Cloud computing delivers resources and services through virtual machines on a pay-as-you-go basis. The allocation of storage space to users is usually determined by means of open allocation mechanisms that cannot guarantee an efficient allocation. Current allocation mechanisms do not consider user requests when making provisioning decisions. In other words, they assume that the storage spaces are fixed. In this study, we propose an algorithm for allocating storage spaces based on the requests of users. We present a unified storage allocation scheme (USAS) for cloud computing. USAS is a dynamic storage allocation framework for unlimited, limited, and free users. Our proposed approach is based on a storage partitioning policy, and we have compared our proposed scheme with open storage scheme and fixed storage scheme with common partition. We show through simulation study that USAS dynamically allocates space for different user requirements for all traffic loads.

An imperative requirement of a modern machining system is to detect tool wear while machining to maintain the surface quality of the product. Vibration signatures emanating during machining with a single point cutting tool have proven to be good indicators for the tool’s health. The current research undertaken utilizes vibration signatures while turning EN9 and EN24 steel alloy to predict tool life using Artificial Neural Network (ANN). During initial meager experimentation, tool acceleration during machining was recorded, and the width of the flank wear at the end of each run was measured using Tool Makers Microscope. The recorded experimental data is utilized to develop the neural network with the variation of operating parameters and corresponding tool vibration with measured tool flank wear. The endeavor undertaken for the development of ANN flank wear prediction model was effective with a regression coefficient of 0.9964. The proposed methodology of indirect measurement of tool wear is efficient, economical for the machining industry to predict tool life, which in turn avoids catastrophic tool failure.

In the prospect of finding a lightweight and wear-resistant materials, researchers have considered aluminium-based metal matrix composites (MMC), as aluminium has a wide variety of applications but possesses low wear resistance properties. To enhance the wear resistance of aluminium alloys, ceramic particles are reinforced. In this endeavour, commercially available aluminium alloy is reinforced with 2, 4 and 6 wt% of silicon carbide (SiC) and Vanadium pentoxide (V2O5) powder to improve its wear resistance. The intensity of reinforcement in the matrix was uniform, and the Scanning Electron Microscope image showed the grain refinement and grain boundary of the MMC's. Wear tests were performed for L16 array set, uncertainty analysis of wear measurement is evaluated, and data were used to develop Artificial Neural Network (ANN) model. The efficient ANN model with a regression coefficient of 0.999 was used to make predictions for remaining sets. Experimental and predicted wear results were analysed; it is observed that higher wt% reinforcement of V2O5 increased wear resistance of aluminium compared to SiC. The methodology adapted using ANN for prediction of wear using meagre experimentation, will lay a path for tribologists to predict the wear of novel metal matrix composites in their endeavour of finding wear-resistant materials.