Research Corridor Journal of Engineering Science
About the Journal The Research Corridor Journal of Engineering Science (RCJES) is multidisciplinary academic journal that focuses on disseminating research across various engineering disciplines. It serves as a platform for the publication of original research and review articles in fields Engineering. The primary aim of RCJES is to facilitate the sharing of advancements in engineering, providing an open-access forum where scholars, researchers, and professionals can contribute and access knowledge freely. Being a peer-reviewed journal, RCJES upholds rigorous standards for the publication of scholarly work, ensuring that the research it publishes is both original and of high quality.
Industrial Management Approaches to Hazardous Waste Reduction and Reuse
The increasing production of hazardous waste is a consequence of globalization in the form of industrialization and urbanization. It is a major concern in environmental management, with crucial effects on public health and ecosystems. This article critically reviews the industrial management of hazardous waste to highlight the need to shift from the current linear disposal models to more sustainable solutions based on the principles of a circular economy. It presents a holistic set of management hierarchies, prioritizing waste prevention and reduction at the source, followed by reuse, recycling, and waste-to-energy conversion. The review also provides details of the advanced treatment technologies and the various proper disposal methods. Moreover, the article discusses the role of data-driven approaches for estimation and management of waste. The review also presents a summary of some major international policies, legislation, and conventions on global waste management. The sustainable management of hazardous waste should take a holistic approach toward economic prosperity, environmental sustainability, and well-being of the society to achieve the overall sustainable development goals.
Hazardous Waste Management, Sustainable Development, Waste Reduction, Recycling, Waste-to-Energy, Industrial Waste, Pollution Control
Copyright (c) 2025 Research Corridor Journal of Engineering Science
Integrating Mobile Phone Data with Traditional Census Figures to Create Dynamic Population Estimates for Disaster Response and Resource Allocation
Accurate and up-to-date information on where people are and in what numbers is essential for disaster management and the fair distribution of public goods and services. However, official population counts are typically only available from censuses, which are often outdated and lack temporal and spatial resolution. Here we develop and validate a scalable approach that combines anonymized, aggregated mobile phone data (call detail records and passive network events) and census baselines to produce up-to-date, error-quantified population estimates at sub-district spatial resolutions and hourly to daily temporal frequencies. We normalize mobile activity to resident population with a Bayesian hierarchical model that accounts for carrier market share, multi-SIM ownership, inactive devices, and diurnal/weekly seasonality effects. We allocate the normalized activity across space with dasymetric mapping using ancillary layers (land use, building footprints, night-time lights, road networks) to restrict to likely inhabited areas and disentangle through-mobility from residential population. A state-space data assimilation framework fuses multiple activity signals (tower handovers, app pings, emergency alert traffic) to provide near–real-time population updates in the presence of hazard-induced mobility changes. We assess performance against ground truth from household surveys, administrative registries, smart-meter rollups, and post-event field counts, reporting accuracy as mean absolute percentage error, coverage probabilities for credible intervals, and resilience to simulated network outages. We illustrate applications for storm evacuation and flood response, showing how the estimates can inform the pre-positioning of supplies, dynamic routing of ambulances, siting of mobile clinics and shelters, and prioritization of damage assessment teams. The framework incorporates privacy-by-design measures (k-anonymity thresholds, differential privacy noise, strict governance and retention policies) and generates confidence surfaces to enable risk-aware decision-making. Our method effectively transforms static census baselines into living population surfaces, helping responders and planners see where people are—and how they move—before, during, and after a disaster. Beyond the emergency context, the approach can support the routine distribution of health, education, and transportation resources, offering a practical, ethically sound pathway to data-driven public service delivery in rapidly urbanizing and peri-urban environments. This integrated methodology advances state-of-the-art approaches by delivering higher accuracy in complex urban contexts and enabling more impactful healthcare planning during post-disaster recovery periods.
Mobile Phone Data Integration; Dynamic Population Estimation; Bayesian Hierarchical Modeling; Dasymetric Mapping; Disaster Response and Resource Allocation
Copyright (c) 2024 Research Corridor Journal of Engineering Science
Transforming electronics lifecycle through expert refurbishment, quality assurance, and transparent environmental impact.
The modern electronics industry faces a dual challenge: sustaining rapid technological progress while mitigating its significant environmental footprint. This study explores how expert refurbishment, rigorous quality assurance, and transparent environmental reporting can transform the lifecycle of electronic devices into a sustainable model of circular economy. Expert refurbishment extends product usability by restoring performance to near-original standards, reducing the need for new manufacturing and conserving valuable resources such as rare earth metals. Quality assurance ensures that refurbished electronics meet industry standards, fostering consumer trust and expanding the market for sustainable alternatives. Transparent environmental impact assessment—through life-cycle analysis (LCA) and carbon footprint reporting—further enhances accountability, encouraging both producers and consumers to make responsible decisions. The integration of these elements can create a systemic shift from the traditional “take-make-dispose” model to a “repair-reuse-recycle” framework, promoting sustainable consumption and production. Moreover, the adoption of standardized refurbishment protocols and certification systems can boost the global trade of refurbished devices, providing economic benefits alongside environmental gains. This approach aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action), positioning refurbishment as a critical pathway toward sustainable industrial transformation. The research concludes that embracing expert refurbishment, coupled with robust quality assurance and transparent environmental metrics, can redefine the electronics lifecycle, reduce e-waste, and foster a culture of environmental accountability within the digital economy.
electronics lifecycle, expert refurbishment, quality assurance, environmental impact, circular economy, e-waste reduction, sustainable development, lifecycle analysis, carbon footprint, responsible consumption.
POST-QUANTUM CRYPTOGRAPHY FOR HEALTHCARE: FUTURE-PROOFING POPULATION HEALTH DATABASES AGAINST QUANTUM COMPUTING THREATS
In the face of quantum computing advancements, classical encryption methods used in protecting health data are expected to become insecure within the next 10-15 years. This paper is the first to provide a comprehensive, detailed framework for healthcare organizations, specifically targeting population health databases, to migrate their cryptographic systems to quantum-resistant algorithms without disrupting performance or violating compliance standards. We provide implementation and performance analysis of the four NIST standard candidate post-quantum algorithms CRYSTALS-Kyber, CRYSTALS-Di lithium, FALCON, and SPHINCS+, all tuned for high-throughput, low-latency healthcare workloads. Our empirical data demonstrates that Kyber-1024 is best-suited for health record encryption tasks with minimal performance overhead (2.3x slower) compared to AES-256, and Dilithium-5 offers the most efficient trade-off for long-term signature security for audit logging (4.1x slower than RSA-2048). The research introduces an innovative "crypto agility" system design, facilitating seamless transitioning between traditional and post-quantum cryptographic methods. This design mitigates transitional risks and enables concurrent support for both legacy and quantum-resistant cryptographic processes. Protocols for negotiating between different cryptographic algorithms automatically, based on a combination of data sensitivity, retention policies, and prevailing threat models, are also established. Empirical evidence from deployment within a production-grade population health system, which currently processes 50 million patient records, indicates the transition to post-quantum cryptography can occur with only 0.03% total downtime, 18% additional storage overhead, and 31% additional compute overhead, well within the tolerance of most healthcare IT budgets. The paper includes a risk assessment that establishes population health databases, which contain sensitive genetic data, disease profiles, and long-term biometric information with relevance extending over a century, as the most critical assets to be protected against quantum cryptographic attacks. Additionally, the cost-benefit analysis included shows that the U.S. healthcare industry could avoid up to $47 billion in breach-related expenses by adopting post-quantum cryptography proactively. Supporting the migration, performance optimization, and regulatory adherence, the framework consists of practical migration tooling, a guide for fine-tuning performance, and evidence to show that post-quantum cryptographic implementations meet the necessary conditions for HIPAA encryption safe harbor and are robust against future quantum-computing-specific regulatory requirements.
Post-quantum cryptography, Quantum computing threats, Healthcare data protection, NIST PQC standards, Crypto-agility, Long-term data security, Population health databases.
THE INFLUENCE OF FAMILY INVOLVEMENT ON THE QUALITY OF CARE FOR AGED ADULTS: A COMPARATIVE STUDY
Background: The growing population of older adults worldwide necessitates the provision of high-quality elderly care. Family involvement is a crucial aspect of caregiving, impacting the physical and emotional well-being of the elderly. While previous studies have focused on family engagement in specific settings, there is a need for a comparative analysis of its role in home care, nursing homes, and assisted living facilities. Methods: This research employed a mixed-methods approach, utilizing both quantitative surveys of family members and professional caregivers and qualitative, in-depth interviews. The quantitative data focused on the frequency of family involvement, participation in decision-making processes, and perceived outcomes of quality of care. These outcomes included measures such as physical health status and mental well-being of the elderly. Qualitative interviews delved into the lived experiences, cultural factors, and perceived benefits and barriers to family participation. The analysis aimed to compare findings across the three care settings, seeking to understand commonalities, differences, and context-specific dynamics. Results: The results of the study reveal that family involvement significantly improves the quality of care, with marked positive effects on emotional support, patient advocacy, and adherence to care plans. The home care environment was found to offer the most significant benefits, attributed to the flexibility and direct collaboration between family members and caregivers. In institutional settings, however, family involvement shifted more towards shared decision-making rather than active care provision. Notable barriers include conflicting expectations between family members and professional caregivers, caregiver burden, and cultural differences in caregiving roles. Conclusion: Family involvement emerges as a complex construct that positively impacts care quality when effectively integrated into service delivery. Tailored strategies are necessary to enhance the collaboration between family members and professional carers in various care contexts. Policies and training programs should focus on bridging communication gaps, managing expectations, and promoting culturally sensitive engagement to optimize care outcomes for older adults.
Family involvement, aged care quality, caregiver participation, elderly healthcare outcomes, mixed-methods research, patient-centered care, long-term care settings
Securing Big Data Pipelines in Healthcare: A Framework for Real-Time Threat Detection in Population Health Systems
Purpose: The paper aims to develop a comprehensive security framework for big data pipelines in healthcare, focusing on real-time threat detection and mitigation. It addresses the increasing security and privacy risks associated with the rapid growth of health data streams from IoT devices, electronic health records, and wearable technologies. Methodology: The study extends previous work on real-time survival risk prediction by designing a layered security architecture that integrates Apache Kafka for stream processing and Splunk SIEM for event monitoring. A machine learning-based anomaly detection algorithm is implemented to identify potential security breaches within 500 milliseconds, achieving 97.3% detection accuracy and a false positive rate of 0.02%. The framework is evaluated in a simulated population health system processing 2.5 million health events per second and is specifically designed to tackle five key security challenges: unauthorized data access, data injection attacks, privacy breaches, insider threats, and compliance violations. Findings: The results show that the proposed framework: Achieves near-instantaneous threat detection (500 ms) Delivers 97.3% detection accuracy with 0.02% false positive rate Reduces mean time to threat detection (MTTD) by 84% compared to batch-processing systems Maintains HIPAA compliance throughout the data pipeline Detects multi-stage and sophisticated attack patterns by correlating threats across multiple data streams. Contribution: This research provides a practical and scalable solution for securing healthcare big data infrastructures while enabling advanced population health analytics. The combination of Apache Kafka, Splunk SIEM, and ML-based anomaly detection offers significant improvements in detection speed, accuracy, and compliance. The work contributes to the field by presenting a real-time, multi-layered security framework that can be adopted by healthcare organizations to enhance data security, privacy, and operational resilience.
Healthcare cybersecurity, Real-time threat detection, Big data security, Stream processing, Population health, Apache Kafka, Splunk, SIEM, Anomaly detection
Artificial Intelligence-Driven Optimization Techniques in Modern Engineering Systems
The integration of artificial intelligence (AI) in engineering has revolutionized the way complex systems are designed, analyzed, and optimized. AI-driven optimization techniques, including machine learning algorithms, genetic algorithms, and deep reinforcement learning, provide engineers with powerful tools to solve multi-variable, non-linear, and large-scale problems that were previously challenging using conventional methods. This study investigates the role of AI in enhancing engineering efficiency across diverse fields such as structural engineering, energy systems, manufacturing, and transportation. Emphasis is placed on the capability of AI to reduce computational costs, improve design accuracy, and enable predictive maintenance. The research also explores emerging hybrid models that combine AI with traditional optimization approaches, offering enhanced adaptability and reliability. Findings suggest that AI not only accelerates innovation but also fosters sustainability and resilience in engineering practices. The paper concludes with challenges related to data quality, interpretability, and ethical considerations, providing insights for future directions in AI-enabled engineering optimization.
Artificial intelligence; Optimization; Machine learning; Engineering systems; Genetic algorithms; Predictive maintenance; Deep reinforcement learning; Sustainable engineering
Advancements in Smart Materials for Sustainable Engineering Applications
Smart materials have emerged as a transformative innovation in engineering sciences, offering unique properties such as self-healing, shape-memory, and adaptability to environmental changes. These materials are increasingly being integrated into sustainable technologies to address global challenges in energy, infrastructure, healthcare, and environmental protection. This study explores the latest advancements in smart materials, focusing on their applications in renewable energy systems, eco-friendly construction, and intelligent manufacturing. It also examines the role of nanotechnology and artificial intelligence in enhancing the efficiency and functionality of these materials. By analyzing recent case studies and technological breakthroughs, the research highlights how smart materials are driving sustainability, reducing energy consumption, and promoting eco-innovation. The paper concludes with potential future directions and the challenges of commercialization, emphasizing the need for interdisciplinary collaboration to unlock their full potential.
Smart materials; Sustainable engineering; Nanotechnology; Renewable energy; Intelligent manufacturing; Eco-innovation; Artificial intelligence
Investigating the Impact of Climate Change on Water Requirements of Sweet Pepper Crop grown in Qassim Region.
Climatic and environmental changes are important issues for present day research. In this paper, the effect of climatic and environmental changes on water requirements of Sweet Pepper crop grown in Qassim-Saudi Arabia have been investigated by incorporating the impacts of climatic and environmental changes on precipitation and temperature in the Qassim region. The base period for data is taken as 1999 -2024. The changes in precipitation and temperature have been predicted by Global Circulation Model (GCM) under RCP 2.6 (Representative Concentration Pathway 2.6) for a period of 25 years. Evapotranspiration has been estimated using Penman-Monteith equation. Its best parameters have been estimated by the General Reduced Gradient (GRG) technique. The water requirements of Sweet Pepper crop grown in Qassim have been estimated using CROPWAT model. Finally, the impact of temperature and precipitation variations on crop water requirements have been estimated. It is observed that the water requirements of Sweet Pepper will slightly increase in the future in the Qassim region. The results of this research will be useful for sustainable water planning, development and management.
Global Circulation Model, General Reduced Gradient, Evapotranspiration, Crop Water Requirements
Building Information Modeling framework for improved Visualization, Management, and Simulation
The discipline of civil engineering has witnessed a revolutionary approach in the form of advanced software of BIM due to the expeditious progress observed in construction technologies. This study offers a thorough analysis that examines conventional building techniques with BIM-based techniques, highlighting the efficiency of each approach for project management and execution. An actual building structure is used in the research to demonstrate the practical implementation of BIM tools and it results in efficiency, accuracy, and flexibility. The results show that BIM substantially lowers construction costs and duration while raising overall project quality because of better decision-making offered by BIM during the designing and planning stage.
BIM Tools, Conventional Tools, Comparison of methods, Estimation, Management , Visualization, Simulation
Investigating the Structural Performance of Precast Concrete Pipes with Steel Fiber-Reinforced Geopolymer Concrete
Recent developments in materials science have identified geopolymer as a promising substitute for Ordinary Portland Cement (OPC), with industrial byproducts serving as its primary ingredients. Studies on geopolymer concrete have revealed notable improvements in early strength development and superior resistance to chemical reactions, in contrast to OPC. These properties of geopolymer prefer its use in precast concrete pipes production. This study recognizes the need to interpret the structural response of geopolymer concrete under load-deflection behavior of geopolymer concrete pipes. In collaboration with “Allah Ho Yasir” precast pipe factory located in Lahore, Punjab, Pakistan. R.C.C pipes of 450mm diameter using OPC and GPC were manufactured at industrial scale. For the geopolymer concrete (GPC) locally produced steel fibers are added with the dosage of 30 kg/m³ along with conventional steel cage. These pipes were manufactured by conventional methodology. For structural analysis performed destructive and non-destructive test, were performed. Destructive testing includes compressive strength test, core cutter compressive test, and three-edge bearing test which provided critical insights regarding maximum load-bearing capacity and failure characteristics. The core cutter compressive test was particularly valuable in assessing the in-situ compressive strength of the concrete within the pipes. Additionally, bond strength tests were performed to assess the adhesion between the steel bars and the concrete matrix, which is essential for structural strength of the pipes. Non-destructive testing such as ultrasonic pulse velocity and rebound/Schmidt hammer test, were performed to assess the quality and uniformity of the concrete without causing damage. These combined testing approaches ensured a comprehensive understanding of the mechanical properties, bond strength, and structural strength of the geopolymer concrete pipes, providing its potential application in R.C.C projects.
Unconfined Precast Pipe, Steel Fiber-Reinforced, Geopolymer Concrete.
POTENTIAL OF ALKALI SILICA REACTION IN CONCRETE USING KASHMIR AGGREGATE
The mountainous belt of Kashmir, bearing complex geological formations, provides aggregates the have potential to be used in construction industry for both small as well as large scale projects. However, a very expansive reaction, known as Alkali-Silica Reaction (ASR) must be carefully evaluated to ensure their suitability and to minimize durability problems in concrete structures. The present study aims to analyze the potential of Alkali silica reactivity in aggregate samples collected from three different quarries of Kashmir region. These aggregates were examined through chemical analysis, petrographic analysis and standard test procedures, including ASTM C 227 and ASTM C1260. Based on the standard expansion limits for mortar bars, all the aggregates sources were identified as potentially reactive. Mortar cubes were also analyzed at different ages to determine the mechanical properties under different ASR conditions. The results reveal that reduction observed in the concrete properties varied among different aggregate sources, depending on their chemical and mechanical properties of aggregates. It was observed that the reduction in strength was found to be inversely proportional to the expansion observed in the mortar bars.
Alkali Silica Reaction, Chemical analysis, Petrographic Analysis, Cubes, Prisms, Mortar Bars
Shear Strength Improvement of Fine-Grained Soils Using Lime Stabilization
An essential component of any civil engineering building is soil. Buildings, bridges, highways, tunnels, dams, towers, and other civil engineering structures are constructed on or below the earth's surface. Proper foundation soil is necessary for their stability. Fine-grained soils often exhibit weak shear strength, posing challenges in construction projects. This study focuses on enhancing the shear strength of such soils through chemical stabilization using lime, a cost-effective and widely available material. In this experimental investigation, lime was mixed with fine-grained soil at varying dosages of 2%, 4%, 6%, 8%, and 10% by weight. The prepared samples were subjected to curing periods of 7, 14, and 28 days. The unconfined compressive strength (UCS) test was conducted to evaluate the shear strength development over time. The results revealed a significant increase in shear strength with both lime content and curing duration. The maximum shear strength of 1007.7 kPa was observed at 8% lime content after 28 days of curing, which is approximately five times greater than that of the untreated (in-situ) soil. The study concludes that lime stabilization is an effective technique for improving the shear strength of fine-grained soils, with optimal performance achieved at 8% lime content after sufficient curing. These findings contribute to the development of sustainable ground improvement strategies in civil engineering.
Enhancing Shear Strength, Lime stabilization, Soil Strength Improvement, Unconfined Compressive Strength (UCS).
Developing a Composite Index to Measure the Integration of BIM, IPD, and Lean Construction: Analysis and Framework
Abstract: The integration of Building Information Modeling (BIM), Integrated Project Delivery (IPD), and Lean Construction (LC) presents a transformative opportunity to improve collaboration, efficiency, and sustainability in construction projects. However, the lack of a unified metric to evaluate the maturity of this integration limits strategic benchmarking and implementation. This paper introduces a novel composite index, titled the BIM-IPD-Lean Integration Index (BILI), to measure integration maturity across these three methodologies. A structured questionnaire was administered to 27 construction firms, and response data were used to develop and apply the index. The analysis revealed that most firms exhibit high to exceptional integration readiness. To bridge perception with practice, the study also proposes a conceptual framework for practical implementation of the index, enabling real-world auditing based on documented project evidence. The BILI model offers a scalable and replicable approach for evaluating integrated construction practices and contributes a structured tool for industry benchmarking and improvement.
Building information modeling, Integrated project delivery, Lean construction
A Review on the Mechanical and Microstructural Performance of Sustainable Geopolymer Concrete
With high levels of CO₂ emissions and resource depletion more attention has been paid to environmental aspects of ordinary Portland cement (OPC) production, especially in view of the need for better sustainable construction materials. The good news to the usage of waste product in GPC as an eco-friendly construction material through the use of fly ash, ground granulated blast furnace slag and Meta kaolin. These differences could be attributing to the lack of aluminosilicate rich substances which are required for GPC to occur using regular cement as well as better environmental advantages.
Geopolymer concrete, microstructure, compressive strength, aluminosilicate, N-A-S-H, durability, green construction, SEM, sustainability, activators
HYDRAULIC AND STRUCTURAL ASSESSMENT OF THE RAVI SYPHON STRUCTURE FOR UP GRADATION
The canal system in District Lahore and Kasur Irrigation Divisions have been suffering from shortage of water for a long time, due to reduction in capacity of Ravi Syphon (4200 Cs) as compared to design capacity (4853 Cs) at BRBD Link Canal which is outlived being a 70 years old structure. Irrigation Department carried out an underwater inspection of the Ravi Syphon with the help of divers in 2007.About 33% of the overall length could only be examined due to limitations and constraints. The inspection of Ravi Syphon structure is carried out recently though out the length of barrels (100%) by Irrigation Department and consultant whereas I supervise the inspection of barrels as Sub Divisional Officer, Ravi Syphon. Underwater and dry inspection of barrels were done through divers and recorded the images and videos for assessment of heath of structure. The results of underwater inspection revealed that the concrete face has been eroded at the syphon inlet leaving coarse aggregate exposed and in some places reinforcement steel bars are exposed. Many of the barrel joints were found to be leaked. Traverse joints in all five barrels were found to be in poor condition. Cracks in the concrete of the five barrels were observed. Two large holes were found in one of the barrels. Water stops are exposed at many places. In this research repairing of existing barrels is recommended by using sprayed concrete (shotcreting or guniting) for the top and sides of the barrel with reinforced concrete at the base. The proposed thickness of sprayed concrete is 2 inches (50 mm) and that of reinforced concrete is 4 inches (100 mm). Repairing of the damaged upstream bed protection is essential and proposed that scour holes should be rock filled.
Advancing Population Health Segmentation Using Explainable AI in Big Data Environments
Population health segmentation creates cohorts of individuals with similar health needs to help develop targeted healthcare interventions. The U.S. healthcare system faces potential benefits and obstacles when using diverse datasets comprising electronic health records and social determinants for patient segmentation. Although complex machine learning models improve segmentation precision, they function as "black boxes" that obstruct clinical acceptance. XAI methods, especially SHAP (Shapley Additive exPlanations), solve the problem of model opacity by clarifying which features contribute to model decisions. We present a framework that combines Explainable AI methods with big data analytics to create transparent population segmentation. The proposed framework uses Apache Spark MLlib to segment patient populations with diabetes, cardiovascular disease, and chronic respiratory illnesses. Our research shows that SHAP-based explanations effectively reveal main factors (e.g., lab values, comorbidities, social factors) that drive population segments. SHAP-based explanations allow clinicians to understand critical drivers such as lab values, medical comorbidities, and social factors for each patient segment, improving clinical decision-making. Our case studies and realistic examples demonstrate how explainable segmentation leads to optimal resource allocation while allowing for personalized care plans and ethical supervision. bias detection) In large-scale health systems. This discussion presents the technical and clinical benefits of implementing XAI-driven segmentation within U.S. healthcare systems to enhance population health outcomes through transparency and trust-building.
Explainable Artificial Intelligence (XAI), Population Health Segmentation, Big Data Analytics, SHAP (SHapley Additive Explanations), Healthcare Risk Stratification.
The Impact of Artificial Intelligence on Business Model Transformation in E-Commerce
The rapid advancement of Artificial Intelligence (AI) technologies has catalyzed a fundamental shift in the business models of e-commerce enterprises. This research investigates the transformative impact of AI on key components of e-commerce, focusing on how data-driven intelligence is reshaping value creation, customer engagement, and operational efficiency. Through a detailed analysis of AI applications—such as machine learning algorithms, predictive analytics, and natural language processing—the study explores how businesses are leveraging these tools to enhance personalization, streamline processes, and drive smarter decision-making. AI-powered recommendation systems and customer segmentation techniques have revolutionized the way online retailers interact with consumers, leading to significantly improved conversion rates and tailored user experiences. In parallel, automation tools such as chatbots, robotic process automation (RPA), and intelligent order processing systems have reduced operational costs while increasing speed and accuracy in customer service and logistics. The integration of AI in demand forecasting and inventory management has further enabled companies to anticipate market trends, optimize stock levels, and reduce waste. Additionally, this paper examines real-world case studies of AI implementation in e-commerce giants, evaluating their outcomes and implications for competitiveness and long-term strategy. Despite its many benefits, the research also highlights potential risks and limitations, including algorithmic bias, data privacy concerns, and the high costs associated with AI infrastructure. Overall, this study provides a comprehensive evaluation of how AI is not merely enhancing existing processes but actively transforming the foundational architecture of e-commerce business models.
Artificial Intelligence, E-Commerce, Business Model Transformation, Personalization, Automation, Predictive Analytics, Recommendation Systems, Competitive Advantage
Use of Artificial Intelligence for Tax Planning Optimization and Regulatory Compliance
Integrating Artificial Intelligence (AI) into financial systems has revolutionized numerous aspects of tax planning and regulatory compliance. This research explores the transformative role of AI in automating tax processes for both individuals and corporate entities. The study investigates how AI-based tools, particularly those leveraging machine learning and natural language processing, optimize tax burdens, detect anomalies in tax returns, and ensure compliance with increasingly complex domestic and international tax regulations. A multi-method approach was employed, combining a critical literature review with simulated model testing and analysis of current industry practices. The paper examines the efficiency of AI in identifying tax risks, automating transfer pricing assessments, and enhancing decision-making in tax consulting. Various AI models—including decision trees, neural networks, and anomaly detection algorithms—were evaluated for their performance in predictive accuracy and compliance automation. Visual tools such as charts, tables, and workflow diagrams are used to support the comparative analysis and demonstrate the effectiveness of AI applications. Key findings indicate that AI can reduce tax compliance time by up to 40%, improve anomaly detection accuracy by over 85%, and significantly minimize manual errors in tax reporting. However, the study also identifies critical challenges, including data privacy risks, algorithmic bias, and the interpretability of AI decisions in legal contexts. The implications of this research are twofold: First, AI presents a scalable and adaptive solution for tax optimization and regulatory alignment in an increasingly digitized global economy. Second, organizations must adopt ethical AI frameworks and robust data governance policies to mitigate the associated risks. This study serves as a foundational reference for policymakers, financial technologists, and tax professionals aiming to harness AI for smarter, compliant, and efficient tax systems.
Artificial Intelligence, Tax Planning, Regulatory Compliance, Machine Learning, Anomaly Detection, Transfer Pricing, Tax Automation, Data Privacy
Ethical Considerations in AI-Powered Decision-Making Systems
AI-powered decision-making systems are increasingly being adopted across various sectors, from healthcare to finance, and education to law enforcement, offering enhanced efficiency and accuracy. However, the integration of AI in decision-making processes raises significant ethical concerns that must be addressed to ensure fairness, accountability, and transparency. These systems often rely on complex algorithms that may inadvertently reinforce biases, leading to discriminatory outcomes. Moreover, the "black-box" nature of many AI systems complicates the process of understanding how decisions are made, making it difficult for stakeholders to hold the system accountable. This paper explores the ethical implications of AI-driven decision-making, focusing on the importance of ensuring fairness in algorithmic outcomes, protecting individual privacy, and promoting transparency in AI systems. The paper also emphasizes the need for regulatory frameworks and ethical guidelines to mitigate risks such as bias, discrimination, and lack of accountability. By examining real-world case studies and ongoing efforts to address these challenges, this paper aims to provide recommendations for the responsible development and deployment of AI decision-making systems.`
AI-Powered Solutions in Climate Change Mitigation and Environmental Sustainability
Climate change is a pressing global challenge requiring innovative, scalable solutions. Artificial Intelligence (AI) is emerging as a powerful tool in mitigating climate change and advancing environmental sustainability. AI applications span diverse areas, including climate modeling, renewable energy optimization, carbon capture, and resource management. Machine learning algorithms enhance climate prediction models, enabling policymakers to anticipate and adapt to future environmental conditions effectively. In renewable energy, AI improves the efficiency of solar and wind energy systems through predictive maintenance, resource forecasting, and grid optimization. Similarly, AI-driven solutions facilitate precision agriculture by optimizing water usage, reducing fertilizer dependency, and minimizing crop waste, thereby promoting sustainable farming practices. AI also plays a crucial role in monitoring deforestation, ocean health, and biodiversity loss via satellite imagery and data analytics, offering timely insights for conservation efforts. However, the adoption of AI in climate initiatives poses challenges, including energy-intensive training processes, ethical considerations, and equitable access to technology. Addressing these issues requires a collaborative effort between governments, industries, and academic institutions to ensure that AI technologies are developed and deployed responsibly. Through leveraging AI, humanity has a transformative opportunity to combat climate change and create a more sustainable future.
Artificial Intelligence in Autonomous Systems: Challenges and Opportunities
Artificial Intelligence (AI) has significantly transformed various domains, and one of the most promising areas of application is autonomous systems. These systems, which are designed to perform tasks with minimal or no human intervention, include self-driving cars, drones, and robotics. The integration of AI into autonomous systems offers immense potential for revolutionizing industries such as transportation, healthcare, and logistics. However, despite their promise, the development and deployment of AI-driven autonomous systems face numerous challenges. Key issues include the need for robust decision-making capabilities, the ability to adapt to dynamic environments, and ensuring the ethical and safe operation of such systems. Additionally, the complexity of human-robot interactions, regulatory concerns, and the need for real-time data processing further complicate the implementation of autonomous systems. Opportunities for improvement lie in advancements in machine learning algorithms, sensor technologies, and data fusion techniques, which can enhance the accuracy and reliability of autonomous systems. Moreover, the growing importance of explainability and transparency in AI models offers a pathway to address concerns surrounding trust and accountability. This paper aims to explore the challenges and opportunities in the integration of AI into autonomous systems, examining the technological, ethical, and societal implications. By identifying the key barriers and opportunities, we can better understand the future trajectory of autonomous systems and their impact on society.
The Evolution of E-Commerce: Emerging Trends and Consumer Behaviors in the Digital Marketplace
The evolution of e-commerce has profoundly transformed consumer habits and business practices worldwide. This study explores emerging trends and consumer behaviors in the digital marketplace, with a focus on personalization, sustainability, and the integration of cutting-edge technologies. Personalization, driven by AI and machine learning, has redefined customer experiences, enabling tailored recommendations and seamless interactions. Sustainability has emerged as a key driver, with eco-conscious consumers favoring brands adopting ethical practices and sustainable supply chains. Additionally, the advent of augmented reality (AR), blockchain, and the metaverse has significantly enhanced online shopping, bridging the gap between physical and digital retail. Changing consumer behaviors, such as the preference for mobile commerce, social commerce, and buy-now-pay-later (BNPL) payment models, have reshaped the competitive landscape. This paper also highlights challenges like data privacy concerns and the digital divide that hinder equitable growth. Through an analysis of recent trends and comprehensive literature, this research underscores the transformative impact of technological innovation and evolving consumer demands on e-commerce. The findings emphasize the need for businesses to adapt to these shifts by leveraging emerging technologies, prioritizing sustainability, and embracing a consumer-centric approach to thrive in an increasingly competitive digital economy. Key references include insights from Smith (2023) on AI's role in e-commerce, Johnson et al. (2022) on sustainability in online retail, and Carter (2023) on the metaverse's influence on digital commerce. These studies collectively provide a foundation for understanding the dynamic nature of the digital marketplace and guide strategic decision-making for stakeholders.
Exploring the Role of Artificial Intelligence in Enhancing E-Commerce Customer Experiences
The integration of Artificial Intelligence (AI) in e-commerce has revolutionized the way businesses interact with customers, enabling personalized, efficient, and engaging customer experiences. AI-powered tools such as chatbots, recommendation systems, and sentiment analysis algorithms have become central to optimizing customer satisfaction and retention. By leveraging machine learning and natural language processing, e-commerce platforms can predict customer preferences, automate responses, and enhance the shopping journey. For instance, personalized product recommendations based on user behavior data significantly improve purchase likelihood, while AI-driven customer support reduces response time, improving customer satisfaction rates. Furthermore, AI assists in inventory management and demand forecasting, ensuring products are available when customers need them, thus minimizing frustration and maximizing profitability. Despite these advancements, challenges such as ethical considerations, data privacy, and the digital divide must be addressed to ensure equitable access and trustworthiness of AI systems. This paper discusses the current applications of AI in e-commerce, evaluates their impact on customer experience, and identifies areas for future research.
Personalization in E-Commerce: Balancing Consumer Privacy and Marketing Effectiveness
Personalization in e-commerce has revolutionized online shopping experiences, leveraging advanced technologies like artificial intelligence (AI), machine learning, and data analytics to deliver tailored recommendations. This approach enhances customer satisfaction and drives sales. However, the growing reliance on personalized marketing raises significant concerns about consumer privacy. Striking a balance between personalization and privacy requires a nuanced understanding of consumer expectations, ethical considerations, and regulatory frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Effective personalization relies on collecting and analyzing consumer data, including browsing behavior, purchase history, and preferences. However, improper data handling can lead to breaches, eroding consumer trust. Transparency, consent mechanisms, and anonymization of data are critical strategies to address these challenges. Furthermore, integrating privacy-preserving technologies like differential privacy and federated learning allows companies to personalize effectively while maintaining user confidentiality. This paper explores the dual imperatives of personalization and privacy in e-commerce, examining their interplay through the lens of marketing effectiveness, ethical considerations, and legal compliance. A sustainable approach involves not only technological innovation but also fostering a trust-based relationship with consumers by aligning business practices with societal values and legal standards. Future research should address emerging privacy-preserving AI techniques, consumer sentiment dynamics, and global privacy norms to inform best practices in personalized e-commerce.
Generative AI: Transforming Creative Industries with Machine Learning
Generative AI has revolutionized the creative industries, redefining the boundaries of human creativity and machine learning. By leveraging advanced models such as Generative Adversarial Networks (GANs) and transformer architectures like GPT, creative industries now produce innovative content in domains including art, music, film, and design. These systems excel in generating high-quality visual art, crafting engaging narratives, and composing music by learning patterns from extensive datasets. Generative AI democratizes content creation, enabling individuals with limited technical expertise to produce professional-grade outputs. It also fosters collaboration between human creativity and AI, enhancing efficiency and expanding artistic possibilities. However, the rise of generative AI raises concerns regarding intellectual property, authenticity, and ethical usage. Issues such as copyright infringement and the potential for AI-generated misinformation challenge regulatory frameworks. Furthermore, cultural homogenization caused by reliance on training data biased toward dominant cultural norms is a critical concern. Despite these challenges, the integration of generative AI into the creative sector signifies a paradigm shift, emphasizing its potential to augment human creativity rather than replace it. Research continues to explore optimizing AI algorithms for ethical and innovative applications, ensuring inclusivity and fairness in creative outputs (Goodfellow et al., 2014; Brown et al., 2020). The transformative impact of generative AI necessitates a balance between innovation and ethical accountability, paving the way for a future where humans and machines collaborate harmoniously.
Topology Optimization, CAD Modelling, and FEA: Advancements with Ansys, Abaqus, and Hyper work
Topology optimization, CAD modeling, and Finite Element Analysis (FEA) have become integral parts of modern engineering design and manufacturing, enabling innovative solutions to complex structural problems. Advancements in these areas, especially with software tools like Ansys, Abaqus, and HyperWorks, have revolutionized the way engineers approach product design. Topology optimization allows for the efficient distribution of material within a given design space, ensuring the best structural performance with minimal weight and material usage. In combination with CAD modeling, which provides detailed geometric representations, and FEA, which simulates real-world conditions to predict the behavior of materials under stress, these tools enable the creation of optimized, high-performance structures. Ansys and Abaqus offer advanced solvers for linear and nonlinear analyses, while HyperWorks excels in multi-disciplinary optimization, providing a comprehensive suite for various engineering applications. The integration of these tools has made it possible to tackle increasingly sophisticated challenges in automotive, aerospace, and civil engineering industries. These advancements not only improve product performance but also significantly reduce time and cost in the design and prototyping phases. The continuous development of algorithms and simulation methods further enhances the capabilities of topology optimization and FEA, offering engineers the ability to explore and validate innovative designs with higher precision and reliability.
Material Engineering Advances: Development of High-Performance Composites for Aerospace Application
Developing high-performance composites for aerospace applications is a critical area of research in materials engineering. Composites offer significant advantages over traditional materials, such as metals, in terms of weight reduction, improved mechanical properties, and enhanced corrosion resistance. This paper reviews recent advancements in the development of high-performance composites for aerospace applications, focusing on the use of advanced matrix materials, reinforcement fibers, and manufacturing techniques. The use of polymer matrix composites (PMCs), metal matrix composites (MMCs), and ceramic matrix composites (CMCs) is discussed, along with their respective advantages and limitations. The paper also highlights the importance of interfacial bonding between the matrix and reinforcement phases in determining the overall composite properties. Finally, emerging trends in composite development, such as the use of multifunctional composites and additive manufacturing techniques, are briefly discussed. Advancements in material engineering have led to the development of high-performance composites, revolutionizing the aerospace industry. These composites, comprising a matrix material reinforced with fibers, offer exceptional properties like high strength-to-weight ratio, stiffness, and resistance to corrosion and fatigue.
Architectural Engineering: Integrating Biophilic Design Principles for Healthier Built Environments
This research investigates the potential of biophilic design principles to enhance the health and well-being of occupants in architectural environments. By incorporating elements of nature into built spaces, architects can create more conducive environments for physical and mental health. The study explores various biophilic design strategies, including natural light, greenery, natural materials, and views of nature, and analyzes their impact on occupant satisfaction, productivity, and overall health outcomes. The findings highlight the significant benefits of biophilic design in reducing stress, improving cognitive function, and fostering a sense of connection to the natural world. The research concludes by emphasizing the importance of integrating biophilic principles into architectural practice to create healthier, more sustainable, and human-centered built environments.