International Journal of Multidisciplinary Engineering In Current Research
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A Study On Ethical Challenges And Bias In Machine Learning Algorithms
In the realm of consumer behaviour analysis, the confluence of machine learning methodologies and abundant data resources has propelled the discipline to new heights. This comprehensive review delves into the latest frontiers of deploying machine learning algorithms to decode the intricacies of consumer behaviour. The investigation initiates with an in-depth exploration of data acquisition and optimization, elucidating the meticulous process of feature engineering, and scrutinizing the practical application of diverse machine learning paradigms, encompassing regression, classification, clustering, and recommendation systems. Furthermore, the discourse encompasses the tangible ramifications of machine learning derived insights, elucidating their utility in optimizing pricing strategies, guiding product development, orchestrating targeted marketing campaigns, and enhancing the Caliber of customer service. It is essential to underscore the complexity of the challenges and ethical considerations intrinsic to the computational analysis of consumer behaviour. In conclusion, this review casts a forward-looking perspective, highlighting the imperative need for an ever-evolving data landscape to adapt to the ever-shifting consumer preferences and market dynamics. This paper serves as a paramount reference for researchers, businesses, and policymakers aiming to harness the multifaceted potential of machine learning in the realm of consumer behaviour understanding and influence. DOI: https://doi-ds.org/doilink/12.2025-55337411
Machine learning, Consumer behaviour analysis, Data optimization, Feature engineering, Ethical considerations
AI-Based Approaches for Predicting Employee Success and Retention
Employee retention has emerged as a critical challenge for organizations globally, with attrition rates reaching 57.3% in 2021 and turnover costs estimated at 200% of an employee's annual salary. This research investigates AI-based approaches for predicting employee success and retention through advanced machine learning algorithms. The study employs a comprehensive methodology incorporating predictive analytics, natural language processing, and classification models to analyze employee behavior patterns. The hypothesis posits that AI-driven predictive models can achieve accuracy rates exceeding 80% in identifying at-risk employees. Results demonstrate that Random Forest and XGBoost classifiers achieved 98.7% and 98.8% accuracy respectively in predicting employee turnover. Key findings reveal that overtime, job satisfaction, monthly income, age, and distance from home are primary attrition predictors. Implementation of AI-powered retention strategies resulted in 15-30% reduction in turnover rates across analyzed organizations, with IBM saving $300 million through predictive attrition models. The study concludes that AI-based approaches significantly enhance retention strategies through early risk detection, personalized interventions, and real-time monitoring capabilities.
Artificial Intelligence, Employee Retention, Machine Learning, Predictive Analytics, Turnover Prediction
Enhancing Liver Disease Detection Using Cloud Computing And Autoencoders In Healthcare Systems
Liver disease is a dangerous medical condition in need of an early diagnosis for proper treatment to prevent complications. This paper presents a prediction framework for liver disease based on Autoencoders and Cloud Infrastructure. The framework commences with the preprocessing of the data through cleaning, augmentation, and Z-score scaling, after which feature extraction from a Deep Belief Network (DBN) and Autoencoder-based classification are performed. The performance of the proposed model is evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Proposed Autoencoders model performs better than the existing methods like Ensemble Method, HGB+MARS+SoftMax and SVC as per the experiment results. The Proposed Autoencoders have excellent 99.45% accuracy, 95.11% precision, 96.31% recall, 97.82% F1-score, and 96.51% AUC-ROC, and they exhibit superior performance in predicting liver disease. The suggested framework, with its cloud infrastructure and deep learning architecture, provides a strong solution for accurate and efficient diagnosis of liver disease.
Liver Disease, Cloud Infrastructure, Deep Belief Network, Autoencoders, Healthcare Systems
Integrated Design And Analysis Of Liquid Pvt Technology For Renewable Energy-Enabled Buildings
The integration of liquid Photovoltaic-Thermal (PVT) technology represents a significant advancement in renewable energy systems for sustainable buildings. This study investigates the integrated design and performance analysis of liquid-based PVT collectors for renewable energy-enabled buildings. The primary objective is to evaluate the thermal and electrical efficiency of liquid PVT systems and their applicability in building energy management. The research employs a quantitative experimental methodology utilizing secondary data analysis from existing studies conducted between 2018-2021, examining various liquid PVT configurations under different solar radiation levels ranging from 500-1000 W/m². The hypothesis proposes that liquid PVT technology significantly enhances overall energy efficiency compared to standalone PV systems. Results demonstrate that liquid-based PVT collectors achieve thermal efficiency of 54.6-58.64%, electrical efficiency of 11.5-13.8%, and combined efficiency of 65-68.4% under optimal conditions. The integration of spiral flow absorbers with water-based cooling systems demonstrated superior performance at mass flow rates of 0.041 kg/s. The findings conclude that liquid PVT technology offers substantial potential for zero-energy buildings, reducing conventional energy dependence by 30-40% while simultaneously generating electricity and thermal energy for building applications.
Liquid PVT Technology, Renewable Energy Buildings, Thermal Efficiency, Electrical Performance, Building Integration
Personalized Learning in Indian Schools through Artificial Intelligence (AI) in Present Scenario
The National Education Policy (NEP) 2020 emphasises the inclusion of contemporary subjects like Artificial Intelligence (AI) in school curricula, at appropriate stages, to foster innovation and digital literacy among students. Personalized learning enabled by Artificial Intelligence (AI) represents a transformative shift in Indian school education, offering adaptive, student-cantered experiences that respond to individual abilities, interests, and backgrounds. With the National Education Policy (NEP) 2020 encouraging technology integration and innovations, AI-driven personalized learning tools have rapidly emerged across urban and rural landscapes. These include intelligent tutoring systems, adaptive platforms, predictive analytics, automatic assessment, and multilingual content delivery. India’s government and private edtech sector have jointly launched initiatives such as SOAR and integrated AI capabilities into the CBSE curriculum to democratize access and raise educational outcomes. Recent data show over 60% of Indian higher education institutions now permit AI-based learning tools, and leading platforms have reached millions of students across diverse settings. Despite strong momentum and clear benefits in engagement, efficiency, and equity, challenges persist regarding infrastructure, teacher training, data privacy, and linguistic inclusivity. This article reviews theoretical foundations, case studies, current progress, and evolving challenges in AI-driven personalized learning in Indian schools. Recommendations emphasize a collaborative, ethical, and scalable approach to ensure inclusivity and maximize the impact of this technological revolution on future generations.
Comparative Review of Silicon-Based and Non-Silicon Solar Cells
Solar photovoltaic (PV) technology has emerged as one of the most critical renewable energy solutions in combating climate change and ensuring long-term energy security. Over the past six decades, silicon-based solar cells have dominated the PV market due to their high efficiency, long-term stability, and well-established manufacturing processes. However, their relatively high manufacturing energy requirements, rigidity, and cost have prompted research into alternative non-silicon technologies such as cadmium telluride (CdTe), copper indium gallium selenide (CIGS), perovskites, organic photovoltaics (OPVs), dye-sensitised solar cells (DSSCs), and quantum dot solar cells (QDSCs). This chapter presents a comprehensive comparative review of these technologies, analysing their material properties, fabrication techniques, performance records, cost structures, environmental implications, and market prospects. The review also discusses the emergence of hybrid and tandem architectures that combine the advantages of different materials to achieve record efficiencies exceeding 33%. The chapter concludes with future research directions and strategies for large-scale sustainable deployment of both silicon-based and non-silicon PV technologies. DOI: https://doi-ds.org/doilink/12.2025-61124448
An Enhanced OBS Core Node Design Incorporating Wavelength Conversion and Deflection Routing Mechanisms
The primary challenge in Optical Burst Switching (OBS) networks is the occurrence of burst contention. To address this issue, techniques such as wavelength conversion and deflection routing are widely used within switch fabrics. This study introduces a mathematical model for a newly proposed OBS core-node architecture. The system’s performance is evaluated by analyzing burst loss probability, relying on steady-state occupancy analysis and a Poisson arrival process. The results of the performance assessment are provided for various average burst arrival rates while considering key node parameters, including wavelength conversion capability and support for deflection routing. DOI: https://doi-ds.org/doilink/12.2025-27862832
burst loss probability, deflection routing
AI in Cybersecurity: Enhancing Digital Defenses with Anomaly Detection and LSTM-Based Threat Prediction
These are the key future areas of research in Applied Artificial Intelligence. In this era of increasingly fast, internet-connected systems, traditional defenses have become inefficient at detecting and responding to various types of sophisticated threats such as malware, phishing, ransomware, distributed denial of service attacks, and more. Such conventional security approaches are not effective with zero-day attacks or new-age evolving attacks. AI-based methods have developed into serious competitors through LSTM networks-an advanced adaptive technology-for continuous improvement of digital defense against anomalous access into network traffic patterns to provide maximum efficiency. To this end, the current research revolves around LSTM-based VAE anomaly detection methodology, utilizing onboard data from live network detection for prediction of future threats. The methodology involves necessary steps that include data pre-processing of Z-score normalization, encoding network traffic into latent representations and then reconstructing the pattern to realize anomaly detection through reconstruction loss and KL divergence. LSTM-based classifier with SoftMax activation is used for threat classification. Testing results indicated improvement in detection accuracy over 90% after 20 epochs of training. This is an AI-driven system that increases real-time monitoring in cybersecurity while reducing false positives associated with it. It future work concentrates on improving the efficiency of the models and embedding them within large-scale security frameworks.
Cybersecurity, Anomaly Detection, Long Short-term Memory - Variational Auto-Encoder, Threat Prediction, AI-Based Security, Network Traffic Analysis
A Study On Synthesis With Respect To Characterizations And Applications Of Various Types Of Nanoparticles And Composites
Nanoparticles and nanocomposites have emerged as transformative materials due to their superior physicochemical, mechanical, catalytic, and biological properties. This study presents a systematic investigation into the synthesis, characterization techniques, and multi-dimensional applications of metallic, metal oxide, polymeric, carbon-based nanoparticles, and hybrid nanocomposites. Emphasis is placed on green synthesis approaches, physiochemical modifications, and structure–property relationships. Characterization tools such as XRD, FTIR, UV-Vis, SEM, TEM, AFM, TGA, DLS, and Raman spectroscopy are examined in detail to understand morphological, structural, optical, and thermal behaviors. The study also reviews cutting-edge applications in catalysis, drug delivery, energy storage, environmental remediation, biosensing, antimicrobial activity, and smart materials. The findings underscore the tremendous potential of nanoparticles and composites in developing sustainable, efficient, and technologically advanced systems.
Nanoparticles; Nanocomposites; Green Synthesis; Chemical Reduction; Sol–Gel Method; Hydrothermal Synthesis; Metal Oxide Nanoparticles; Polymer Nanocomposites; Carbon-Based Nanomaterials etc.
Design Of A Low-Latency Pipelined Fir Filter Through Advanced Critical Path Analysis
A filter with a finite period impulse response that settles to zero in a finite amount of time is known as a FIR in digital signal processing. This is frequently contrasted with IIR filters, which may respond endlessly even if they have internal feedback. In this study, we use fine-grained seamless pipelining to propose a novel hardware design for a very high-speed finite impulse response (FIR) filter. By positioning the pipeline registers both across and in between components, the suggested full-parallel pipeline FIR filter may provide an output sample in a few gate delays. Depending on the throughput need, a suitable pipelining strategy may be created with the help of a detailed critical path analysis at the gate level. Two other designs are also shown in this project, each with a unique trade-off between throughput rate and area. The suggested FIR filters are created to measure the highest throughput while striking a compromise between speed and complexity. Model sim software is used for simulation, while Xilinx is used for implementation. DOI: https://doi-ds.org/doilink/11.2025-32813615
FIR filter, FFA, EKG, DSP, WRT, PP, Carry adder.
Study on Rock Load Variation Considering Weatherability in Bord and Pillar Method of Underground Coal Extraction
In India, bord and pillar is the most commonly used method in the extraction of coal which contributes approximately 98% production from underground. Estimation of the rock load on the roof is important in providing sufficient support systems and mine safety. This research is carried out for the evaluation of rock load variation with weathering, which has been treated as a key parameter in CMRI-ISM RMR classification. A relationship was established between weather ability as determined by slake durability index (SDI) and rock load mobilization due to development drivages in Indian coalfields. The approach combined field technique and geotechnical characterization. Findings indicated that weather ability plays an important role in rock load estimation; for example, first-cycle SDIs are highly inversely correlated to the rock load magnitude. Strong coefficients of determination (R² > 0.85) were found between CMRI-ISM RMR and rock loads for galleries at various widths (3.6-4.8 m). Results have demonstrated that the bulk rock load under weather ability dependent is heavier than that of mechanically dependent, which should be taken into account for support design, especially in moisture-sensitive coal measure rocks, and provide reliable guidance on safety regulation of underground coal mining. DOI: https://doi-ds.org/doilink/11.2025-86468638
Rock loading effect, Weathering, Bord and pillar method, Slake durability index, CMRI-ISM RMR
A Critical Analysis of Thematic Depth, Narrative Techniques, and Cultural Representation in the Writings of Tagore, Bandyopadhyay, Devi, and Basu
This empirical study examines the thematic patterns, narrative techniques, and cultural representations in the works of four distinguished Bengali writers: Rabindranath Tagore, Bibhutibhushan Bandyopadhyay, Mahasweta Devi, and Buddhadeva Basu. Through systematic textual analysis of 120 literary works spanning 1890-1990, this research employs quantitative content analysis, thematic coding, and statistical correlation methods to identify distinctive literary characteristics and evolving cultural paradigms. The study analyzes 45,000 pages of text across multiple genres including poetry, novels, short stories, and essays. Data collection involved manual coding of thematic elements, narrative structure assessment, and cultural motif identification validated through inter-rater reliability testing (κ = 0.87). Results reveal significant variations in thematic focus: Tagore emphasizes philosophical humanism (42%), Bandyopadhyay foregrounds nature and rural life (51%), Devi concentrates on social marginalization (63%), and Basu explores psychological modernism (48%). Narrative style analysis demonstrates distinct temporal structures and point-of-view preferences across authors. Statistical tests including chi-square analysis and ANOVA confirm significant differences in cultural representation patterns (p < 0.001). The findings contribute to comparative literary studies by providing empirical evidence of stylistic evolution in Bengali literature and establishing quantifiable parameters for cross-cultural literary analysis.
Bengali literature, comparative textual analysis, thematic coding, narrative structure, cultural representation, quantitative literary studies, Indian regional literature
Comprehensive Analysis of Preparation Techniques, Structural Features, and Properties in Composite Multiferroic Systems
This empirical study provides a comprehensive exploration of synthesis methodologies, characterization techniques, and fundamental properties in composite multiferroic materials. Multiferroics, exhibiting simultaneous ferroelectric and magnetic ordering, represent a frontier class of multifunctional materials with significant technological potential. Through systematic investigation of 127 distinct composite samples synthesized via solid-state reaction, sol-gel, and hydrothermal methods, we establish critical correlations between processing parameters and resultant multiferroic properties. X-ray diffraction, scanning electron microscopy, and vibrating sample magnetometry analyses reveal distinct phase formation behavior dependent on synthesis route, with optimal magnetoelectric coupling coefficients (up to 18.7 mV/cm·Oe) achieved in samples with controlled interfaces between ferroelectric and ferromagnetic components. Statistical analysis demonstrates significant variations in multiferroic performance across synthesis methods (p<0.001), with hydrothermal processing yielding superior magnetization (1.27±0.11 emu/g) while maintaining comparable ferroelectric properties to conventional techniques. These findings establish comprehensive synthesis-structure-property relationships in composite multiferroics, providing quantitative guidance for optimizing magnetoelectric coupling in next-generation multifunctional devices.
Composite multiferroics; magnetoelectric coupling; synthesis-structure-property relationship; hydrothermal synthesis; phase interface; ferroelectricity; ferromagnetism.
Analysis of Ventilation Requirements Affected by Diesel Machinery in Underground Mines
Underground mines extensively utilize diesel-powered equipment for material handling and transportation, resulting in significant emissions of toxic gases and diesel particulate matter (DPM) that pose serious health risks to miners. This research analyzes ventilation requirements in underground mining operations with a focus on diesel machinery emissions and their control through optimized ventilation systems. The study examines current ventilation standards, emission characteristics, and airflow requirements across various mining jurisdictions. Through comprehensive literature review and data analysis, this research identifies that ventilation systems consume approximately 30-40% of total underground mine energy, with airflow requirements ranging from 0.047 to 0.067 m³/s per kilowatt of diesel engine power depending on regulatory frameworks. The study reveals that diesel equipment contributes over 50% of heat load in deep mines while emitting carcinogenic DPM, carbon monoxide, and nitrogen oxides. Results demonstrate that optimizing auxiliary ventilation systems through computational fluid dynamics modeling and implementing ventilation-on-demand strategies can reduce energy consumption by 31-53% while maintaining acceptable air quality standards. The research concludes that integrated approaches combining improved engine technologies, after-treatment devices, and advanced ventilation control systems are essential for ensuring miner safety and operational efficiency in modern underground mining operations. DOI: https://doi-ds.org/doilink/10.2025-53564516
Underground mine ventilation, Diesel particulate matter, Airflow requirements, Emission control, Computational fluid dynamics
Use of Industrial and Mining Wastes for Backfill in Subsurface Mining Operations
The escalating generation of industrial and mining wastes poses significant environmental challenges globally, necessitating sustainable disposal solutions. This research investigates the utilization of various industrial wastes including fly ash, slag, coal gangue, construction and demolition waste, and mine tailings as backfill materials in subsurface mining operations. The study aims to evaluate the mechanical properties, environmental sustainability, and economic feasibility of waste-based backfill composites. A comprehensive experimental methodology was employed involving laboratory testing of different waste combinations with varying cement ratios, curing periods, and compositional parameters. Results demonstrate that cemented paste backfill incorporating 30% fly ash with 8% cement content achieved uniaxial compressive strength of 4.95 MPa after 28 days curing, meeting mining industry requirements. Statistical analysis of five experimental datasets revealed significant correlations between waste content, binder ratio, and mechanical performance. The findings confirm that waste-based backfills effectively control ground subsidence while promoting circular economy principles. This research contributes to sustainable mining practices by transforming hazardous waste streams into functional engineering materials, simultaneously addressing environmental concerns and improving mine stability. DOI: https://doi-ds.org/doilink/10.2025-87317652
Backfill mining, Industrial waste, Fly ash, Mine tailings, Cemented paste backfill
Optimization Techniques and Performance Assessment of IoT-Enabled WBANs in Healthcare Systems
Wireless Body Area Networks (WBANs) integrated with Internet of Things (IoT) technology have emerged as transformative solutions for continuous healthcare monitoring systems. This research investigates optimization techniques and performance assessment mechanisms for IoT-enabled WBANs in healthcare applications. The primary objective focuses on evaluating energy efficiency, quality of service parameters, and network reliability through advanced optimization algorithms. The methodology encompasses systematic analysis of IEEE 802.15.6 standard implementation, examining multiple performance metrics including throughput, packet delivery ratio, latency, and energy consumption across various network configurations. Experimental results demonstrate that optimized WBANs achieve 35% reduction in energy consumption, 97.5% packet delivery ratio, and latency below 92 milliseconds. The study employs machine learning algorithms, ant colony optimization, and particle swarm optimization techniques for cluster head selection and routing protocols. Statistical analysis reveals significant improvements in network lifetime extending up to 40% compared to traditional approaches. The findings confirm that integrated optimization strategies effectively balance energy efficiency with quality of service requirements, providing robust and scalable solutions for next-generation healthcare monitoring systems.
Wireless Body Area Networks, IoT Healthcare, Energy Optimization, Quality of Service, Performance Metrics
Experimental Analysis of Underground Mine Ventilation System of Indian Metal Mines
Underground mine ventilation systems in Indian metal mines represent critical infrastructure for ensuring miner safety, productivity, and operational efficiency. This experimental study examines ventilation parameters across five major zinc-lead metal mining operations in India, specifically analyzing Rampura Agucha, Rajpura Dariba, Zawar, Sindesar Khurd, and Kayad mines in Rajasthan state. The research investigates airflow distribution, fan performance, air quality parameters, and energy consumption patterns in deep underground metal mining environments. Primary objectives include quantifying ventilation efficiency, assessing thermal comfort conditions, evaluating contaminant dilution effectiveness, and determining optimal fan operation strategies. Methodology employed field measurements, computational fluid dynamics simulations, and statistical analysis of ventilation data collected over twelve months during financial year 2023-24. Results demonstrate that Indian metal mines maintain airflow rates between 1450-2024 m³/sec, with primary ventilation consuming 32-34% of total mine electricity. Temperature regulation challenges persist at depths exceeding 600 meters, necessitating auxiliary cooling systems. Discussion reveals significant energy optimization potential through variable speed drives and ventilation-on-demand systems, achieving 25-31% energy reduction. The study concludes that modernization of ventilation infrastructure, implementation of intelligent monitoring systems, and adoption of international best practices can substantially enhance both safety parameters and operational economics in Indian underground metal mining sector. DOI: https://doi-ds.org/doilink/10.2025-74516576
Underground mine ventilation, metal mines India, airflow optimization, fan efficiency, thermal management
Toward Sustainable Development Through Green Finance
Environmental change, energy constraints, and financial crisis are the three largest threats to the global economy in this era of fast technological advancement.. The reason for this is that governments incur costs associated with environmental deterioration as a result of financial development. To achieve a balance between the market and the natural world, green financing is the way to go. Financial backing for environmentally friendly development practices, sometimes known as "green finance," significantly cuts down on emissions of pollutants that deplete the ozone layer and other harmful air pollutants. If the country wants to enhance its finances, it should invest more in green gardening, green buildings, green security, and other green initiatives. The goal of this study is to survey what is been written on green finance and to speculate on its potential future development in India. DOI: https://doi-ds.org/doilink/10.2025-93865331
Environment; Green finance; Green building; Green projects; Renewableenergy.
A Critical Review of Composite Phase Change Materials in Lithium-Ion Battery Thermal Management
The rapid adoption of lithium-ion batteries in electric vehicles has heightened concerns over thermal safety and performance optimization, particularly the prevention of thermal runaway. Effective battery thermal management systems (BTMS) are therefore essential. This review focuses on composite phase change materials (CPCMs) as advanced solutions for BTMS, examining their thermal characteristics, modification strategies, and performance outcomes. The study evaluates key composite structures such as expanded graphite, metal foams, and nanoparticle-enhanced organic PCMs. Research objectives include assessing improvements in thermal conductivity, efficiency of temperature regulation, and effectiveness in mitigating thermal runaway. Methodologically, the review synthesizes experimental findings from peer-reviewed studies involving thermal property characterization, computational simulations, and performance testing under varied discharge rates. Results show expanded graphite composites deliver thermal conductivity enhancements of 400–1700%, while metal foam composites reduce cell temperature by 4–13 °C. Nanoparticle integration offers moderate improvements of 31–124% depending on concentration. Hybrid systems that couple CPCMs with active cooling provide superior thermal uniformity and safety. The findings confirm that multi-component CPCMs with three-dimensional thermal networks hold strong potential as next-generation BTMS for electric vehicles.
Composite phase change materials, Lithium-ion battery, Thermal management, Expanded graphite, Thermal conductivity, Thermal runaway
Enhancing Asset Reliability through Risk-Based Inspection and Reliability-Centered Maintenance in SAP EAM
Risk-Based Inspection (RBI) and Reliability-Centered Maintenance (RCM) have emerged as critical methodologies for enhancing asset reliability in industrial operations. This research investigates the integration of RBI and RCM frameworks within SAP Enterprise Asset Management (EAM) systems to optimize maintenance strategies and improve asset performance. The study employed a mixed-method approach, combining quantitative analysis of maintenance data from manufacturing facilities and qualitative assessment of implementation practices. Data were collected from 150 industrial facilities across oil and gas, manufacturing, and utilities sectors implementing integrated RBI-RCM approaches through SAP EAM. Results demonstrate that integrated RBI-RCM implementation through SAP EAM reduces unplanned downtime by 45-50%, decreases maintenance costs by 25-30%, and improves asset availability by 35-40%. The hypothesis that synchronized RBI-RCM methodologies within SAP EAM significantly enhance asset reliability metrics compared to traditional maintenance approaches was confirmed. Discussion reveals that successful implementation requires organizational commitment, data quality management, and continuous improvement processes. The findings suggest that combining RBI's risk prioritization with RCM's systematic approach creates a comprehensive asset integrity framework. This research contributes to understanding how digital transformation through SAP EAM enables predictive maintenance capabilities and data-driven decision making in asset-intensive industries. DOI: https://doi-ds.org/doilink/10.2025-21618375
Risk-Based Inspection, Reliability-Centered Maintenance, SAP Enterprise Asset Management, Asset Reliability, Predictive Maintenance
Computational Approaches for Investigating Basic Material Properties of Cobalt-Aluminum Alloys
Cobalt-aluminum (Co-Al) alloys have garnered significant attention as potential high-temperature structural materials due to their exceptional thermal stability, mechanical properties, and oxidation resistance. This study employs comprehensive computational methodologies, primarily density functional theory (DFT) calculations and machine learning approaches, to investigate the fundamental material properties of Co-Al alloy systems. The research objectives encompass determining structural parameters, elastic properties, thermodynamic stability, and electronic characteristics of various Co-Al intermetallic phases including CoAl, Co3Al, and CoAl3. The methodology integrates first-principles calculations using DFT with generalized gradient approximation (GGA) functionals, complemented by high-throughput computational screening and machine learning models for property prediction. Results reveal that Co3Al exhibits superior mechanical stability with calculated elastic modulus values ranging from 180-220 GPa and bulk modulus of approximately 200 GPa. The formation energies demonstrate thermodynamic favorability with negative values between -0.45 to -0.65 eV/atom for stable phases. Electronic structure analysis indicates strong covalent-metallic bonding characteristics contributing to enhanced mechanical properties. The computational predictions show excellent agreement with available experimental data, validating the accuracy of employed methodologies. This comprehensive investigation provides crucial insights into structure-property relationships in Co-Al systems, facilitating accelerated alloy design for advanced high-temperature applications in aerospace and energy sectors.
Cobalt-Aluminum Alloys, Density Functional Theory, Elastic Properties, Computational Materials Science, Intermetallic Compounds
Green Innovations in Water and Wastewater Treatment: Mechanistic Insights and AI Integration
The pursuit of sustainable water purification technologies has catalyzed the emergence of green materials as high-performance alternatives to conventional synthetic agents. Sourced renewably and characterized by their biodegradability and low environmental toxicity, these advanced materials represent a paradigm shift in water treatment. This review highlights the exceptional potential of natural nanomaterials—including cellulose nanofibers, chitosan, alginate, and lignin—as well as their engineered composites, functionalized hydrogels, and aerogels for the highly efficient adsorption of heavy metals, dyes, and persistent organic contaminants. A critical analysis of contaminant removal mechanisms is presented, alongside the application of advanced modeling and optimization techniques. These computational tools are indispensable for predicting material properties, simulating molecular interactions, and tailoring bespoke solutions, thereby accelerating rational design. While challenges in scalability, synthesis cost, and real-world validation remain, this work concludes that the integration of green materials with cutting-edge computational models is pivotal for developing the next generation of efficient, sustainable, and environmentally benign water treatment systems, directly supporting global sustainable development and climate action goals.
Green materials; Sustainable water treatment; Natural polymers; Adsorption mechanisms; Computational modeling; Contaminant removal.
Wire-Arc Additive Manufacturing (WAAM) for the Fabrication of High Fatigue-Resistant Railcar Couplers
Wire-Arc Additive Manufacturing (WAAM) presents a revolutionary approach for fabricating high fatigue-resistant railcar couplers in the Indian railway sector. This study investigates the application of WAAM technology to enhance the fatigue life and structural integrity of railcar couplers compared to conventional casting methods. The primary objective was to evaluate the mechanical properties, fatigue resistance, and cost-effectiveness of WAAM-fabricated couplers. A comprehensive methodology involving experimental design, WAAM fabrication using ER110S-G high-strength steel wire, mechanical testing, and fatigue analysis was employed. The hypothesis proposed that WAAM-fabricated couplers would demonstrate superior fatigue resistance due to refined microstructure and absence of casting defects. Results demonstrated that WAAM couplers exhibited 35-40% improved fatigue life compared to cast steel equivalents, with ultimate tensile strength reaching 850-920 MPa and yield strength of 720-780 MPa. The refined grain structure and elimination of casting defects contributed to enhanced fatigue performance. Discussion reveals that WAAM offers significant advantages including reduced material waste, shorter production cycles, and improved mechanical properties. The study concludes that WAAM technology provides a viable and superior alternative to conventional casting for railcar coupler fabrication, offering enhanced safety, reliability, and economic benefits for Indian Railways.
Wire-Arc Additive Manufacturing, WAAM, Railcar Couplers, Fatigue Resistance, Indian Railways
Study on the Performance of Solar-Evaporative Cooling Technology for Preserving Crops in Tropical Environments
Solar-evaporative cooling technology represents a sustainable solution for reducing postharvest crop losses in tropical environments where conventional refrigeration systems are economically and logistically challenging. This study evaluates the performance of solar-powered evaporative cooling storage systems (SPECSS) for preserving various agricultural crops including tomatoes, mangoes, bananas, and carrots. The research involved comprehensive analysis of temperature reduction, humidity enhancement, and shelf-life extension capabilities under tropical conditions. Results demonstrated that SPECSS achieved temperature depressions of 7.8-15.4°C and relative humidity increases from 44% to 96.8% compared to ambient conditions. The shelf lives of tomatoes, mangoes, bananas, and carrots stored in SPECSS were extended to 21, 14, 17, and 28 days respectively, compared to 6, 5, 5, and 8 days under ambient storage. The cooling efficiency reached 84.7% with optimal pad thickness of 80mm, maintaining chamber temperatures at 25°C with 82.4% relative humidity. The system demonstrated significant potential for reducing the 25-30% postharvest losses typically experienced in tropical regions, providing an economically viable and environmentally sustainable solution for smallholder farmers and rural communities lacking grid electricity access.
Solar cooling, evaporative cooling, crop preservation, postharvest losses, tropical agriculture.
Structural Analysis of CFST and DS CFST Using ANSYS Workbench
Concrete-Filled Steel Tubes (CFSTs) have emerged as efficient structural members in modern construction due to their superior strength, stiffness, and ductility arising from the composite interaction between a steel tube and infilled concrete. However, the depletion of natural river sand, a primary component of conventional concrete, has raised sustainability concerns and prompted the search for alternative fine aggregates. In this context, dune sand, abundantly available in desert regions, offers potential as a sustainable replacement material. This thesis presents a comparative study of the structural performance of conventional CFST and Dune Sand Concrete-Filled Steel Tubes (DS CFST) through finite element analysis (FEA) conducted in ANSYS Workbench. Both CFST and DS CFST models were developed with identical geometry (1000 mm height, 150 mm diameter, 5 mm steel thickness), material definitions, meshing strategies, and boundary conditions to ensure direct comparability. Static structural analyses evaluated maximum deformation and equivalent elastic strain, while eigenvalue buckling analyses assessed critical load multipliers and mode shapes. Results indicated that DS CFST exhibited nearly identical deformation (9.37 × 10⁻⁹ m vs. 9.40 × 10⁻⁹ m for CFST) and slightly lower strain (2.63 × 10⁻⁸ m/m vs. 2.84 × 10⁻⁸ m/m). DS CFST showed a marginally higher buckling load multiplier (4.05 × 10⁵), suggesting improved global stability. The findings confirm that dune sand concrete can replace conventional concrete in CFSTs without compromising performance, while contributing to sustainable construction practices by reducing reliance on river sand. The study provides novel numerical evidence supporting DS CFST as a viable structural solution, particularly suited for sand-rich and desert regions. Limitations include the exclusive reliance on simulations and single geometry and future research should incorporate experimental validation, varied loading conditions, and extended geometrical configurations. DOI: https://doi-ds.org/doilink/09.2025-32841784
Concrete-Filled Steel Tubes, Dune Sand, Finite Element Analysis, ANSYS, Buckling, Sustainable Construction
Bridging Automation and Intelligence: UiPath’s Agentic AI Framework and Robotic Process Automation Platform
UiPath Maestro introduces agentic automation, which goes beyond traditional RPA by handling complex, unstructured tasks with real-time decision-making. While conventional RPA is ideal for repetitive, rule-based tasks, Maestro integrates AI agents, BPM, and process intelligence to optimize dynamic workflows. Users can design processes using BPMN models or speed up creation with Autopilot. Business users build workflows visually, and developers can later integrate RPA bots. Real-time dashboards help monitor performance, fix issues, and reduce delays between teams. UiPath’s low-code platform makes automation accessible, improving speed, accuracy, and cost-efficiency. Maestro enhances this by enabling smarter, adaptive automation for modern business needs.
RPA, UiPath, Low-Code Automation, Business Process Automation, Drag-and-Drop.
Corporate Social Responsibility 2.0: From Compliance to Competitive Advantage
The landscape of corporate obligation is undergoing a profound and irreversible transformation. This paper delineates the critical evolution from traditional, reactive Corporate Social Responsibility—termed here as CSR 1.0—which treated social and environmental concerns as peripheral compliance issues or public relations exercises, to a transformative, strategic paradigm we define as CSR 2.0. We posit that CSR 2.0 is no longer an optional add-on or a charitable sideline but has emerged as a fundamental driver of competitive advantage and long-term, sustainable value creation. It represents a fundamental rewiring of corporate purpose, moving from "doing good" alongside business to "doing good through business." Our analysis begins by deconstructing the inherent limitations of the compliance-based model, illustrating how its siloed, often superficial nature fails to address the root of a corporation's societal impact and leaves it vulnerable to accusations of greenwashing. In contrast, we define the core tenets of CSR 2.0 as deep integration into corporate strategy and operations, a rigorous materiality assessment to focus efforts on the most impactful issues, and purpose-driven innovation that uses societal challenges as a catalyst for new products, services, and business models.
Corporate Social Responsibility (CSR), CSR 2.0, Competitive Advantage, Strategic Integration, ESG (Environmental, Social, and Governance), Creating Shared Value, Stakeholder Theory, Purpose-Driven Business, Sustainable Development, Triple Bottom Line, Brand Equity, Operational Efficiency, Talent Management.
Structural Engineering Insights into the Chenab Railway Bridge Design
The Chenab Railway Bridge, standing at 359 meters above the Chenab River in Jammu and Kashmir, India, represents a remarkable achievement in structural engineering (Kumar et al., 2023). This paper presents comprehensive insights into the design, construction, and engineering challenges of the world's highest railway bridge. The bridge spans 1,315 meters with a main arch span of 467 meters, incorporating innovative steel-concrete composite construction (Singh & Sharma, 2022). The research objectives focused on analyzing structural design parameters, seismic resistance capabilities, and construction methodologies employed. Through detailed analysis of engineering drawings, material specifications, and construction reports, this study employed a mixed-method approach combining quantitative structural analysis with qualitative assessment of construction challenges. The bridge utilizes E410-grade steel with 63mm blast-proof specifications and concrete-filled steel box sections for enhanced structural integrity (Patel et al., 2024). Results demonstrate exceptional seismic resistance up to magnitude 8.0 earthquakes and wind load resistance up to 266 km/h. The innovative arch design with two-ribbed configuration provides optimal load distribution across the challenging Himalayan terrain. This engineering marvel not only connects Kashmir valley with the Indian mainland but also sets new benchmarks for high-altitude railway bridge construction, proving that extreme geographical challenges can be overcome through advanced engineering solutions and innovative construction techniques.
Chenab Bridge, Railway Engineering, Steel Arch Bridge, Seismic Design, Structural Analysis
Advanced M-Dab-Based Ac/Dc Charger With Optimized Conduction Loss And Hybrid Charge–Discharge Strategy For Electric Vehicles
The rapid growth of electric vehicles (EVs) demands charging infrastructures that offer both high efficiency and reliability while addressing grid integration challenges. This work presents a novel high-power density and low conduction loss bidirectional AC/DC charging pile scheme with a hybrid charge–discharge control strategy. The proposed system leverages an optimized power conversion topology to minimize conduction and switching losses, thereby enhancing overall efficiency and reducing thermal stress on components. The bidirectional capability allows not only fast and efficient charging of EV batteries but also controlled discharge back to the grid, supporting vehicle-to-grid (V2G) applications and grid stability. A hybrid control strategy is implemented to balance constant current/constant voltage charging with dynamic discharge management, ensuring battery longevity and reliable energy exchange. Simulation and experimental results validate that the system achieves superior power density, high efficiency, and stable operation under varying load and grid conditions. This approach provides a scalable and practical solution for next-generation EV charging piles, promoting energy sustainability and smart grid integration. DOI: https://doi-ds.org/doilink/08.2025-98635245
Integrated Pq Enhancement System For Industrial And Commercial Grids Using Advanced Power Oscillation Mitigation Techniques
The increasing penetration of solar power in commercial and industrial facilities poses significant challenges to power quality due to intermittency, voltage fluctuations, and power oscillations. This work proposes an integrated approach that combines solar photovoltaic (PV) generation with a battery energy storage system (BESS) to mitigate power oscillations and enhance overall power quality. The BESS is strategically controlled to smooth out variations in solar output, regulate voltage, and maintain grid stability under dynamic load conditions. Simulation and performance analysis demonstrate that the proposed system not only improves power factor and reduces harmonic distortion but also ensures reliable and uninterrupted power supply for commercial and industrial applications. By effectively integrating renewable energy with advanced energy storage, the solution addresses both sustainability and operational efficiency, making it a practical model for modern smart grids. DOI: https://doi-ds.org/doilink/08.2025-33731528
Solar photovoltaic (PV), Battery energy storage system (BESS), Power quality improvement, Power oscillations mitigation, Voltage regulation, Harmonic distortion reductiony.