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This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems. [ABSTRACT FROM AUTHOR]

Visual clarity is significantly compromised, and the efficacy of numerous computer vision tasks is impeded by the widespread presence of haze in images. Innovative approaches to accurately minimize haze while keeping image features are needed to address this difficulty. The difficulties of current methods and the need to create better ones are brought to light in this investigation of the haze removal problem. The main goal is to provide a region-specific haze reduction approach by utilizing an Adaptive Neural Training Net (ANTN). The suggested technique uses adaptive training procedures with external haze images, pixel-segregated images, and haze-reduced images. Iteratively comparing spectral differences in hazy and non-hazy areas improves accuracy and decreases haze reduction errors. This study shows that the recommended strategy significantly improves upon the existing training ratio, region differentiation, and precision methods. The results demonstrate that the proposed method is effective, with a 9.83% drop in mistake rate and a 14.55% drop in differentiating time. This study's findings highlight the value of adaptable neural networks for haze reduction without losing image quality. The research concludes with a positive outlook on the future of haze reduction methods, which should lead to better visual clarity and overall performance across a wide range of computer vision applications. [ABSTRACT FROM AUTHOR]

The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) to improve the bot system's understandability. This research introduces a novel approach for HRI that uses optimized algorithms for input detection, analysis, and response generation in conjunction with advanced neural classifiers. This approach employs deep learning models to enhance the accuracy of input identification and processing efficiency, in contrast to previous approaches that often depended on conventional detection techniques and basic analytical methods. Regardless of the input type, this technique defines cooperative control for assistance from previous histories. The inputs are cooperatively validated for the instruction responses for human assistance through defined classifications. For this purpose, a neural classifier is used; the maximum possibilities for assistance using self-detected instructions are recommended for the user. The neural classifier is divided into two categories according to its maximum comparable limits: precise instruction and least assessment inputs. For this purpose, the robot system is trained using previous histories and new assistance activities. The learning process performs comparable validations between detected and unrecognizable inputs with a classification that reduces understandability errors. Therefore, the proposed technique was found to reduce response time by 6.81%, improve input detection by 8.73%, and provide assistance by 12.23% under varying inputs. [ABSTRACT FROM AUTHOR]

Climate change is causing drastic reduction in crop yields around the globe due to increase in soil salinity, drought and heat stress. Quinoa (Chenopodium quinoa Willd) is regarded as a very significant food security crop considering the climate change scenario. Two quinoa genotypes (Puno and Titicaca) were cultivated on salt affected soil under drought stress with different sowing dates. Compared with early sowing, late sowing combined with salinity and drought stress caused drastic decline in plant growth and grain yield due to imposition of heat stress. Plant biomass and grain yield decreased by 26% and 39% in Puno, and by 34% and 49% in Titicaca under late sowing accompanied by salt and drought stress. Relative water contents and stomatal conductance of leaves declined in the same trend in both genotypes. Shoot Na+ concentration was the highest whereas K+ concentration was the lowest in both genotypes when drought and salt stress were combined under late sowing. Grain minerals (Ca, Mg, Fe, Zn, Cu, K, P, N and Mn) and dietary contents (protein, lipids, carbohydrates and fibre) were decreased more under the combination of salinity and drought for late sowing as compared to early sowing. When salinity and drought stress were combined under late sowing, the contents of H2O2 and TBARS were 1.9 and 2.2‐fold higher in Puno and 2.4 and 2.6‐fold higher in Titicaca, respectively. The oxidative stress was mitigated by enhanced activities of antioxidant enzymes (CAT, SOD and POD) more in Puno than Titicaca. Plant biomass and grain yield were higher in Puno with better grain quality than Titicaca. Hence, this genotype should be cultivated on salt affected soils facing drought and high temperatures. [ABSTRACT FROM AUTHOR]