Metallurgical and Materials Engineering (E-Journal)
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"Sustainability in Indian Banking Sector: Moving Towards ESG Framework"
Get PDFThis study investigates the relationship between Environmental, Social, and Governance (ESG) practices and the financial performance of Indian commercial banks. While prior research has extensively examined ESG impacts in developed economies and Southeast Asia, empirical evidence from India—a fast-growing economy with a distinct banking architecture—remains sparse. Utilizing panel data spanning from 2015 to 2024, the study analyzes ESG disclosures from 30 leading Indian banks and examines their effect on key financial performance indicators, including Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q. By applying the dynamic panel data approach using the Arellano and Bond (1991) Generalized Method of Moments (GMM) estimator, the findings reveal a significant positive relationship between ESG practices and financial performance. Among the three ESG dimensions, governance (G) demonstrates the strongest influence. These results resonate with prior studies from the UK and Malaysia, reinforcing the notion that proactive ESG integration boosts investor trust and operational effectiveness. The study makes a vital contribution to the sustainable finance literature by delivering India-centric empirical insights, which can inform both policy formulation and strategic decision-making in the banking sector
Evaluating The Performance Of Green Facades Using Smart Agriculture In Residential Buildings To Improve Energy Efficiency In The Cairo Region (Case Study Of Dar Misr Buildings)
Get PDFGreen Facades are a recommended sustainable environmental solution for residential buildings in hot climates, providing effective protection from direct sunlight. Plants also contribute to oxygen production through photosynthesis, improving thermal performance and enhancing energy efficiency. This, in turn, mitigates the negative effects of global warming, which contributes to global climate change. This study tackles the issue of excessive energy consumption in Medium-income housing units resulting from the inefficiency of building envelopes. The aim of this research is to formulate a comprehensive methodology for evaluating the energy performance of green walls integrated with smart agriculture technologies on the envelopes of residential buildings. The study assesses energy performance of encapsulated green walls using vertical hydroponic systems and plant growth stimulations in Medium-income housing units in Cairo with an energy efficiency focus on sensitive hot dry climate zones. The study uses Dar Misr residential project as a case study and combines automation and empirical approaches to building system assessment, employing DesignBuilder v7.0 simulation software to evaluate thermal performance and energy efficiency of building envelopes. The findings suggest that the addition of green walls to the outer envelopes of the Dar Misr Medium-income housing units in Greater Cairo enhances energy performance by approximately 29% in comparison to the baseline scenario
AI-Driven Demand and Supply Forecasting Models for Enhanced Sales Performance Management: A Case Study of a Four-Zone Structure in the United States
Get PDFThis study investigates the application of AI-driven demand and supply forecasting models to enhance sales performance management within a four-zone structure in the United States. With increasing market volatility and regional variability, traditional forecasting methods often fail to capture real-time dynamics and cross-zone dependencies. Leveraging machine learning algorithms and data analytics, this case study explores the implementation of predictive models tailored to zone-specific characteristics, seasonal trends, and historical sales data. The research evaluates model accuracy, adaptability, and impact on decision-making efficiency, inventory optimization, and revenue growth. Findings demonstrate that AI-enhanced forecasting significantly improves planning precision, reduces stockouts and overstocks, and aligns sales strategies with localized demand patterns. This paper contributes practical insights for businesses seeking to adopt intelligent forecasting systems in multi-regional operations
AI Techniques for Robust Data Integrity and Security in Adhoc Networks
Get PDFAd-hoc networks are supported by an AI-based framework to enhance robustness as well as secure data transmissions. The Artificial Intelligence framework is made for getting robust and secure ways for data transfer through ad hoc networks. It combines reinforcement learning for optimizing routing in dynamic environments, supervised learning for intrusion detection, and resource management for energy efficiency and improvement in network lifetime. Changes in routing and security according to different conditions like node mobility, traffic patterns, detection of security anomalies will also be done along with such intelligent techniques. Furthermore, advanced techniques for anomaly detection will counteract black hole and denial-of-service attacks. Besides this load distribution and bandwidth allocation would also be performed dynamically for better performance in the system. Experimental results showed enormous improvements over traditional methods in terms of packet delivery ratio, latency, and security resilience of dynamic ad hoc communication
Driver Drowsiness Detection Based on Convolutional Neural Network Architecture Optimization Using Genetic Algorithm
Get PDFDrowsy driving is a major factor in many road accidents, which makes it essential to have dependable real-time detection systems to help keep roads safer. Detection of driver drowsiness presents a novel approach using convolutional neural network (CNN) optimized by a genetic algorithm (GA). The facial features of drivers are examined for the system to classify whether the driver is "Alert" or "Drowsy," thereby issuing warnings to prevent fatigue-related incidents. The Genetic Algorithm optimizes a few critical CNN hyperparameters dynamically, such as the number of layers, filter sizes, and dropout rates. This evolutionary optimization enhances classification accuracy and decreases overfitting in the model, thereby producing a much stronger and more generalizable solution. The CNN model was trained on a set of labeled facial images and tested for performance on a separate set for validity and applicability under real-world conditions. The achieved high accuracy with the optimized system is 91.8% and a billion low inference time of 50 milliseconds per frame suitable for real-time deployment with vehicles. This way, the driver monitoring system opens avenues for efficient and high performance through a smart marriage of deep learning and evolutionary algorithms. The results strongly suggest that the proposed method could be a promising option for enhancing Advanced Driver-Assistance System (ADAS) and thus building safer driving environments
Modeling Of Half-Live Values By QSAR Using Computational Indices And Physico-Chemical Properties For Β-Lactam Structure Containing Drugs
Get PDFIn current study forty-nine compounds of cephalosporin containing β-lactam type core structure containing drugs compounds were selected. The half-life (t1/2) of these drug actions were searched out from various journal publications and other sources of chem-informatics. 2D & 3D chemical structures of these compounds were primed by by a software chem sketch and outputs were saved as a file in a format of mol file. Some 2D and 3D indices and physico-chemical properties were calculated by Dragon software. It was observed that Half-life (t1/2) not strongly correlated with any one individual index out of selected indices and physico-chemical properties, so this is not possible to represent half-life of selected class of drug with simple linear regression obtained in single step regression analysis. Hence for developing QSAR equation/ model for prediction of half-life step wise multiple linear regression (MLR) analysis by means of forward selection method was carried through Microsoft excel software. For stepwise regression analysis excel notes for stepwise regression analysis was followed in which in each step independent variable (indices and physico-chemical properties) were filtered out/in on the basis of lowest P-value. it was observed that 3D-Morse, T(N-S), Harary index, orbital electro negativity at O-atom of carbonyl group, donar sites and density can predict half-life in much better way among others. There occurs a strong correlation (pearson's r2 = 0.887371) of observed values with predicted values calculated by the regression equation model developed by current study
Mineralogical And Microthermometrical Studies On The Gonharan Lead-Zinc Deposit, Daran, Isfahan Province
Get PDFThe Gonahran lead-zinc deposit in Isfahan is located in the central part of the Sanandaj-Sirjan zone and is situated along northwest-southeast trending faults. The primary mineralization is hosted in Cretaceous limestones and occurs as silica-carbonate veins containing sulfides of lead, zinc, and copper. The style of mineralization varies across the deposit. In the western part, it appears as a network of lead veins within a dolomite and calcite gangue. In the eastern section, mineralization occurs as masses, lenses, and pockets parallel to the layering at the contact with metamorphic sandstone and shale. Mineralogical studies show that galena is the main ore mineral, with minor amounts of pyrite, sphalerite, and chalcopyrite. Analysis of fluid inclusions indicates formation temperatures between 160 and 180°C and a salinity range of 5 to 21%. This wide range of temperature and salinity suggests the mixing of hydrothermal and meteoric waters, a process considered crucial for concentrating the ore. Based on this data, the Gonahran deposit is classified as a Mississippi Valley-Type (MVT) deposit, with a minimum formation depth estimated at 80 meters
Human In The Loop Generative AI: Redefining Collaborative Data Engineering For High Stakes Industries
Get PDFHuman-in-the-Loop generative artificial intelligence is reshaping collaborative data engineering for high-stakes in- dustries. These technologies are enabling new levels of speed and scale in data engineers’ decision-making process, by har- nessing AI to generate potential solutions for their review and selection. This approach combines the domain-specific insights and quality-control capabilities of human subject matter experts with the generative AI models’ unprecedented ability to learn from and synthesize billions of data engineering documents such as tweets, blogs, books and manuals. Human-in-the-Loop paradigms have existed since the earliest days of AI development, especially in industrial contexts, yet the greater sophistication demonstrated by the latest generative AI tools poses both new opportunities and new challenges. Presented through the lens of an experienced data engineer working with challenging high- stakes industries such as financial services, health care, phar- maceuticals, aerospace/defence, and industrial manufacturing, this paper explores the practical side of Human-in-the-Loop generative AI. It examines real use cases and provides answers to three key questions: (1) Why does Human-in-the-Loop matter?
(2) How does Human-in-the-Loop work? and (3) What does the future hold for Human-in-the-Loop
Automated Chest X-ray Report Generation Using Attention-Enhanced GoogleNet-LSTM Architecture
Get PDFChest radiography remains a cornerstone of clinical diagnostics, yet its interpretation is time-consuming and dependent on specialized expertise. The growing shortage of radiologists, combined with the increasing volume of imaging exams, often leads to delays and inconsistencies, highlighting the need for automated solutions. In this study, we present an automated framework for generating diagnostic reports directly from chest X-rays. The model uses GoogleNet for visual feature extraction and a long short-term memory (LSTM) network to generate reports. An attention mechanism is incorporated to focus on clinically relevant image regions. The framework was evaluated on the publicly available Indiana University (IU) Chest X-ray dataset, with performance assessed using language-based metrics (BLEU, ROUGE-L, METEOR, CIDEr) and clinical accuracy indicators, such as precision, recall, and F1-score. Results demonstrated that the attention-based architecture outperformed baseline encoder-decoder models, particularly in CIDEr and clinical F1 metrics, suggesting the reports were more fluent and clinically accurate. Attention maps showed alignment with key image areas, such as the cardiac silhouette for cardiomegaly and costophrenic angles for pleural effusion. While limitations were observed in handling rare conditions and occasional generic phrasing, the framework effectively improved the efficiency and consistency of radiology reporting
Simulation Of Flow And Heat Transfer In Channels With Airfoil Obstacles
Get PDFThis study numerically investigates heat and fluid flow in a channel with airfoil-shaped obstacles at low Reynolds numbers. Understanding fluid dynamics in channels with varying obstacle geometries is crucial for applications in aerospace, energy, and thermal systems. The study employs the FLUENT software to simulate two-dimensional, laminar, and steady-state flows using the SIMPLE algorithm for solving the Navier-Stokes and energy equations. The effects of Reynolds number, obstacle arrangement, length, diameter, and Prandtl number on the flow and heat transfer were analyzed through velocity and temperature contours, the average Nusselt number, and drag coefficient. The results reveal that increasing the Reynolds number and obstacle diameter, reducing the obstacle length, and boosting the Prandtl number significantly enhance the average Nusselt number. Additionally, a triangular obstacle arrangement outperforms a rectangular arrangement in optimizing heat transfer performance. These findings highlight the influence of geometric and flow parameters in designing efficient thermal management systems