Guru Nanak Dev Engineering College, Bidar

Pharmaceutical manufacturing generates wastewater with unusual contaminant profiles including extraordinarily high ammoniacal nitrogen concentrations (> 12,000 mg/L) and stream-dependent heavy metal speciation patterns not typically documented in literature. This study documents scientific contributions of segregated treatment effectiveness for high-strength pharmaceutical wastewater, providing novel insights into treatment technology selection mechanisms for diverse contaminant profiles. Three segregated wastewater streams (high-COD, high-TDS, and low-TDS) from an active pharmaceutical ingredient manufacturing facility (100–150 KLD capacity) were comprehensively characterized for 42 physicochemical parameters following IS:3025 and APHA 24th Edition standards. Heavy metal speciation across stream types was analyzed using ICP-OES/ICP-MS (IS:3025 Part-65:2022). Segregated multi-train treatment performance was systematically evaluated to quantify removal effectiveness and elucidate mechanistic pathways for contaminant fate across thermal-oxidative and biological treatment processes. Wastewater characterization reveals unusual pharmaceutical manufacturing signatures: ammoniacal nitrogen of 12,272 mg/L constituting 79

The studies have focused on a bibliometric review of electric vehicle (EV) integration with the grid. It follows a methodical procedure using a pre-established search strategy to examine and analyze previous work on vehicle-to-grid (V2G). There were 21,535 articles found initially focusing on green urban transit. Following the last cleaning, editing, and refining round, 16,457 articles remained for evaluation. The literature written in English is one of the constraints that has been acknowledged. The review looks at data from 1970 to 2023, revealing that the number of research articles about V2G has increased significantly, especially after 2000. The collaborative landscape is shown by a network that includes the top ten organizations in the world. Citation analysis of nations indicates that the United States and China are the leading countries in research on V2G technology. Notable publications and organizations are highlighted in the evaluations of institutions and journals. China showcases its numerous connections through country collaboration networks. Research subjects have evolved, shifting from older ones like “secondary batteries” and “electric vehicles” to newer ones like “charging (batteries),” “smart grid,” and “greenhouse gases.” This shift is evident when one looks at the keyword data thematically. The comprehensive overview of V2G research trends and collaborations, identification of gaps, suggestion of future paths, and overall value as a resource will be an indispensable tool for EV integration researchers, legislators, and industry stakeholders and its contribution to infrastructure.

The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has transformed machining processes, significantly boosting efficiency, accuracy, and sustainability. This systematic review analyzes 182 research articles, categorized into eight thematic clusters using VOSviewer software, based on author keywords from the Scopus database, following the PRISMA framework. These clusters comprise ‘advanced sensing and prognostics,’ ‘machine learning and optimization in manufacturing,’ sustainability group (‘energy efficiency and optimization techniques’, ‘smart and sustainable manufacturing’, ‘neural networks and energy management’), ‘intelligent machining processes,’ ‘advanced algorithms in machining,’ ‘lubrication and tool wear management,’ ‘CNC and deep learning applications,’ and ‘digital twins. A critical literature review of each cluster was conducted to identify key trends, challenges, and developments in AI, ML, and DL applied in machining operations. The vital results are presented in table format. The review reveals that AI-driven machining has significantly enhanced predictive maintenance, real-time process monitoring, and energy optimization, resulting in a reduction of machining energy consumption by up to 20

Data-driven prioritization of photovoltaic (PV), battery, and converter technologies is crucial for achieving sustainability, efficiency, and cost-effectiveness in the increasingly complex domain of hybrid renewable energy systems (HRES). Conducting an in-depth and systematic ranking of these components for solar-based HRESs necessitates a comprehensive multi-criteria decision-making (MCDM) framework. This study develops as the most recent and integrated approach available in the literature. To ensure balanced and objective weighting, five quantitative weighting techniques, Entropy, Standard Deviation, CRITIC, MEREC, and CILOS, were aggregated through the Bonferroni operator, thereby minimizing subjective bias while preserving robustness. The final ranking was executed using the measurement of alternatives and ranking according to compromise solution method (MARCOS). Subsequently, comparative validation was conducted across eight additional MCDM methods, supplemented by correlation and sensitivity analysis to evaluate the consistency and reliability of the obtained results. The results revealed that thin-film PV modules (0.7108), hybrid supercapacitor batteries (0.6990), and modular converters (1.1812) emerged as the top-performing technologies, reflecting optimal trade-offs among technical, economic, and environmental performance criteria. Correlation analysis (ρ > 0.9 across nine MCDM methods) confirmed the stability of the rankings. The results establish a reproducible decision-support framework for designing sustainable hybrid systems. These technologies demonstrated superior thermal stability, cycling endurance, and system scalability, respectively, thus laying a foundation for more sustainable and resilient hybrid energy system deployments. The proposed framework provides a reproducible, transparent, and resilient decision-support tool designed to assist engineers, researchers, and policy-makers in developing reliable low-carbon components for the realization of future carbon-neutral energy infrastructures.

Unconfined Compressive Strength (UCS) is a critical parameter in geotechnical engineering, influencing soil stability, foundation design, and load-bearing capacity. Traditional UCS prediction methods, such as Multiple Linear Regression (MLR), often struggle to capture the non-linear relationships inherent in mixed soil compositions. This study evaluates the effectiveness of Artificial Intelligence (AI)-based models, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), in predicting UCS for clay-dominant and sand-dominant soils. A dataset of 100 soil samples from six geographically diverse regions across India was analyzed, incorporating key soil parameters such as clay content, sand content, liquid limit, plasticity index, and curing period. The models were assessed using R2, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Prediction Interval (PI), and Index of Agreement (IOA). Among the AI models, RFR outperformed others with an R2 of 0.93 (training) and 0.84 (testing), a20 accuracy of 95