International Journal of Advance Industrial Engineering
type: channel
description: International Journal of Advance Industrial Engineering is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of industrial. engineering.The goal of this journal is to provide a platform for engineers and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of industrial engineering. IJAIE is dedicated to present state of-the-art, high quality, research developments in all areas of industrial engineering research, including applications in industry and services, to a broad audience of academics and professionals. Target areas include manufacturing, operations management, product and process design, information systems, business and strategy, and quality management amongst others. Important fields of industry: Food Technology: Food chemistry& nutrition ,Food microbiology, Food engineering ,Principles of food processing, Food Packaging technology, Food quality systems & Management, Techniques in food analysis, Technology of fruits and vegetables processing, Technology of cereals and oilseeds, Bakery and Confectionary technology , Technology of plantation spices, Technology of milk and milk products, Snack foods Technology , Beverages Technology,Food Biotechnology, Food Additives and Ingredients, Enzymes in food processing, Food physics, Food Toxicology, Principles of Human Nutrition, Food Preservation, Industrial Fermentation, Food Safety and Hygiene Leather Technology: Pretanning operation, tanning operations, post-tanning and finishing operation, footwear technology. Leather Apparel and Goads technology, tilization of animal byproducts and byproducts of meat industry. Fertilizers. Gelatin and glue manufacture from collagen of tannery byproducts. Collagen stability and collagen cross links. New application for animal hides and skins, eco-friendly processes. Textile Technology: Fibre science and technology, yarn engineering, fabric engin…
publisher: MRI Publication Pvt. Ltd.
language: en
publication name: International Journal of Advance Industrial Engineering
issn: 2320-5539
items: Intelligent Datacenter Networks: Integrating Software-Defined Networking, Artificial Intelligence, and Autonomous Network Operations
items: Next-Generation Datacenter Networking: Architectures, AI-Driven Automation, Software-Defined Infrastructure, and Future Research Directions
items: Cybersecurity Challenges in Next-Generation Aircraft and Avionics: A Secure Framework for Aviation Communication and System Protection
items: Artificial Intelligence and ML-Driven Personalized Hearing Aid Manufacturing: A Control-Mapped Governance Framework
items: Artificial Intelligence and SAP ERP in Human Resource Management: Transforming Employee Behavior and Mental Well-Being
items: Machine Learning-Based Decision Support for Stormwater Management and Workforce Optimization in Environmental Engineering
items: Distributed Data Processing Frameworks for Large-Scale Healthcare Analytics
items: Event Streaming Architectures for High-Volume Transaction Processing
items: Performance Optimization Of ETL Pipelines In Distributed Data Warehouse Environments: A Network-Aware Scheduling Approach
items: Navigating Disputes and Arbitration Mechanisms in Construction: Insights from Power & Energy Infrastructure Project Management
items: Quantum Machine Learning: Algorithms and Applications
items: Risk Management in it Program Management: Balancing Cybersecurity, AI Integration and Infrastructure Stability
items: Strategic drivers of digital transformation in small and medium enterprises
items: Digital Transformation Trends in Small and Medium Enterprises: A Systematic Review
items: Securing enterprise LLM gateways & misconfigured proxy exposure detection + mitigation framework for exposed LLM endpoints, API gateways, model download hooks
items: Explainable Artificial Intelligence for Protecting the U.S. Financial System from Sanctions Evasion and Trade-Based Money Laundering
items: Machine Learning Algorithms for Enhancing Predictive Analytics in ERP-Enabled Online Retail Platform
items: AI-Driven Customer Relationship Management: Enhancing Salesforce Efficiency Through Predictive Analytics
items: AI-Driven Agriculture Marketplaces Digitalizing Farm to Retail Supply Chains in UK
items: Revitalizing Agriculture in Conflict Zones: A Case Study on Machinery Restoration in Borno State
items: Leveraging Six Sigma for Continuous Improvement in Petroleum and Chemical Processing
items: Optimizing Fluid Flow and Pressure Control in Industrial Operations: A Case Study
items: Advanced Simulation on Techniques for Predicting Gas Behavior in LNG and NGL Operations
items: Photocatalytic degradation of methylene blue dye using synthesized CoxNi1-xFe2O4nanoparticlesunder direct visible light
items: Department of Physics, Government College University Faisalabad, Pakistan
items: Aneela Shahzad
items: Construction site safety and quality analysis utilizing KHNN Model
items: Effect of magnetic field on germination and early growth of soybean (Glycine max) seed.
items: Recycling textile industry waste materials: fabrication, characterization, mechanical and thermal performance analysis of developed non-woven interlining
items: Experimental Investigation of Thermal Fatigue life of Engine Cylinder Liner

Intelligent Datacenter Networks: Integrating Software-Defined Networking, Artificial Intelligence, and Autonomous Network Operations
type: item
description: The rapid growth of cloud-native applications, large-scale artificial intelligence workloads, and latency-sensitive edge services has placed unprecedented demands on datacenter network infrastructure. In response, the networking industry is converging around three transformative paradigms: Software-Defined Networking (SDN), which decouples control logic from forwarding hardware to enable centralized programmability; Artificial Intelligence for networking (AI4Net), which applies machine learning to traffic optimization, anomaly detection, and capacity planning; and Autonomous Network Operations (ANO), which aspires to closed-loop, self-configuring, self-optimizing, and self-healing network management with minimal human intervention. This paper presents a comprehensive, integrated survey of these three paradigms and their synergistic convergence in modern datacenter environments. We begin by examining the architectural evolution of SDN from early OpenFlow-centric designs to contemporary intent-driven, multi-layer control platforms that govern network, compute, and storage resources holistically. We then survey AI-driven networking techniques, including deep reinforcement learning for traffic engineering, graph neural networks for topology-aware optimization, transformer-based models for network telemetry analysis, and generative AI for configuration synthesis. Next, we analyze autonomous network operations frameworks, covering closed-loop automation architectures, digital twin-based validation, formal verification of network invariants, and trust and explainability requirements for human-machine teaming. We further investigate cross-cutting concerns including programmable data planes, network security automation, multi-domain orchestration, and energy-efficient networking. Finally, we identify open research challenges and chart future directions toward fully intelligent, self-driving datacenter networks. By synthesizing insights from over 80 references spanning academ…
subject: Articles
  topic: section
subject:
  topic: keywords
  value: software-defined networking, artificial intelligence, autonomous network operations, deep reinforcement learning, intent-based networking, network digital twin, programmable data planes, closed-loop automation, self-driving networks, datacenter infrastructure
creator: Jacob Sean
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-30
publication date: 2026-06-30
doi: 10.14741/

Next-Generation Datacenter Networking: Architectures, AI-Driven Automation, Software-Defined Infrastructure, and Future Research Directions
type: item
description: Modern hyperscale datacenters form the computational backbone of the global digital economy, supporting workloads ranging from cloud computing and artificial intelligence to real-time streaming and edge computing. As traffic volumes grow exponentially and application demands become increasingly heterogeneous, traditional datacenter network architectures face fundamental scalability, efficiency, and operational challenges. This paper provides a comprehensive survey of next-generation datacenter networking, spanning four interconnected domains. First, we examine emerging network architectures, including spine-leaf topologies, fat-tree derivatives, dragonfly interconnects, and optical circuit-switched fabrics, evaluating their scalability, fault tolerance, and cost profiles. Second, we analyze the role of artificial intelligence and machine learning in network automation, covering intent-based networking, autonomous traffic engineering, predictive failure detection, and AI-driven security orchestration. Third, we investigate software-defined infrastructure paradigms, encompassing Software-Defined Networking (SDN), Network Function Virtualization (NFV), programmable data planes via P4, and the convergence of network-compute-storage disaggregation. Fourth, we synthesize open research challenges and future directions, including terabit-scale optical switching, quantum networking integration, sustainable energy-aware networking, and the evolution toward self-driving datacenter networks. Through a systematic analysis of over 80 references spanning academic literature, industry whitepapers, and standards documents, this article provides researchers, architects, and practitioners with a unified reference framework for understanding and advancing the state of datacenter networking.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: datacenter networking, software-defined networking, AI-driven automation, network function virtualization, programmable data planes, optical switching, intent-based networking, spine-leaf architecture, disaggregated infrastructure, sustainable networking
creator: Snehal Kumar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-03
publication date: 2026-03-03
doi: 10.14741/

Cybersecurity Challenges in Next-Generation Aircraft and Avionics: A Secure Framework for Aviation Communication and System Protection
type: item
description: The increasing digitalization of next-generation aircraft and avionics has significantly enhanced flight safety, operational efficiency, and communication capabilities. However, the integration of interconnected avionics networks, wireless communication systems, cloud-based maintenance platforms, and intelligent onboard technologies has also expanded the cyberattack surface, exposing aviation systems to sophisticated cybersecurity threats. This study investigates the major cybersecurity challenges affecting next-generation aircraft and proposes a comprehensive multilayer cybersecurity framework designed to improve the protection of critical avionics infrastructure. The proposed framework combines secure communication protocols, system integrity verification, artificial intelligence-based anomaly detection, and automated incident response mechanisms to safeguard aircraft communication networks and flight-critical systems against cyber threats. A risk-based assessment methodology was employed to identify vulnerabilities associated with communication systems, avionics software, and connected maintenance platforms, while simulation-based performance evaluation was conducted to assess the effectiveness of the proposed architecture. The results demonstrate that the integrated framework improves cyber threat detection, enhances system integrity, reduces incident response time, and strengthens the resilience of aviation communication systems while maintaining alignment with established aviation cybersecurity guidelines issued by international regulatory authorities. The study further highlights the importance of adopting layered defense strategies, continuous monitoring, secure software management, and regulatory compliance to address the evolving cybersecurity landscape in modern aviation. The proposed framework provides a practical foundation for enhancing the security, reliability, and operational continuity of next-generation aircraft and avionics systems and offers val…
subject: Articles
  topic: section
subject: Aviation Cybersecurity; Next-Generation Aircraft; Avionics Systems; Aircraft Communication Networks; Artificial Intelligence; Anomaly Detection; Risk Assessment; Secure Communication.
  topic: keywords
creator: Sam Suseelan
rights: Copyright (c) 2025 Sam Suseelan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2022-12-12
publication date: 2022-12-12
starting page: 74
ending page: 85
doi: 10.14741/

Artificial Intelligence and ML-Driven Personalized Hearing Aid Manufacturing: A Control-Mapped Governance Framework
type: item
description: Hearing aid manufacturing has shifted from mass-produced, one-size-fits-many devices toward highly personalized products shaped by digital ear-canal scanning, generative design, additive manufacturing, and machine-learning-tuned digital signal processing. While this shift improves acoustic fit and patient outcomes, it introduces new governance challenges: personalization pipelines depend on AI/ML components whose behavior must remain traceable, verifiable, and compliant with medical-device quality regulations across every stage of production. This paper proposes a control-mapped governance framework that links each stage of AI/ML-driven personalized hearing aid manufacturing — digital impression capture, generative shell design, additive manufacturing, acoustic personalization, quality release, and post-market monitoring — to specific governance controls and applicable standards, including ISO 13485, IEC 60118, IEC 62366, and relevant FDA quality-system regulations. We argue that treating AI/ML governance as a set of controls mapped explicitly onto manufacturing stages, rather than as a separate compliance overlay, allows manufacturers to maintain both innovation velocity and regulatory defensibility. The framework is illustrated through a stage-by-stage control-mapping table and a discussion of algorithm change control, design verification, and post-market surveillance specific to personalized hearing devices. We conclude by examining implementation challenges, including data provenance across scanning devices, validation of generative design outputs against anatomical safety constraints, and the auditability of continuously updated fitting algorithms, and we outline directions for future work on standardized AI/ML governance in personalized medical-device manufacturing.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Hearing aid manufacturing; personalized medical devices; artificial intelligence; machine learning; governance framework; quality management system; additive manufacturing; ISO 13485; digital signal processing.
creator: Suresh Gondu
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2023-12-30
publication date: 2023-12-30
starting page: 1
ending page: 4
doi: 10.14741/

Artificial Intelligence and SAP ERP in Human Resource Management: Transforming Employee Behavior and Mental Well-Being
type: item
description: The integration of Artificial Intelligence (AI) with SAP Enterprise Resource Planning (ERP) systems is fundamentally reshaping the practice of Human Resource Management (HRM), creating unprecedented capabilities for understanding, influencing, and improving employee behavior and mental well-being. As organizations worldwide confront escalating rates of burnout, disengagement, and psychological distress—intensified by post-pandemic hybrid work transitions and accelerating digital transformation pressures—the intersection of AI and SAP HCM presents a timely and powerful opportunity to reimagine the human dimension of enterprise technology. This paper provides a comprehensive, multi-disciplinary review of the current and emerging applications of AI within the SAP ERP HRM ecosystem—encompassing SAP SuccessFactors, SAP S/4HANA Human Capital Management, SAP Analytics Cloud, and the SAP Business Technology Platform (BTP)—for the purposes of behavioral transformation and mental well-being enhancement. Drawing on behavioral science, organizational psychology, and enterprise systems research, we examine five core transformation domains: AI-augmented talent management and behavioral profiling; predictive analytics for mental health risk stratification; generative AI and large language model (LLM) applications in HR service delivery; real-time performance feedback systems and their behavioral impacts; and AI-driven diversity, equity, and inclusion (DEI) analytics. We further analyze the role of SAP Joule—SAP’s generative AI copilot introduced in 2023—in reshaping employee-HR interaction paradigms.
subject: Articles
  topic: section
subject: Artificial Intelligence,
  topic: keywords
subject: SAP ERP,
  topic: keywords
subject: Human Resource Management,
  topic: keywords
subject: Mental Well-Being, SuccessFactors, SAP Joule
  topic: keywords
creator: Kavya Ramachandran
creator: Nadia El- Sayed
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 16
ending page: 21
doi: 10.14741/

Machine Learning-Based Decision Support for Stormwater Management and Workforce Optimization in Environmental Engineering
type: item
description: Urban stormwater management has emerged as a critical environmental engineering challenge in the context of intensifying climate volatility, rapid urbanization, and aging municipal infrastructure. Traditional stormwater management relies on static design standards and historical rainfall statistics that are increasingly inadequate for capturing the non-stationary hydrological dynamics of a changing climate, while the engineering workforce responsible for designing, operating, and maintaining stormwater infrastructure faces parallel challenges of skills shortages, inefficient task allocation, and rising operational costs. This paper presents a comprehensive review and integrated decision-support framework that applies machine learning (ML) techniques to two traditionally siloed domains: stormwater infrastructure management and environmental engineering workforce optimization. We examine ML applications across stormwater forecasting and real-time control, including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) for precipitation-runoff modeling, Random Forest and Gradient Boosting models for green infrastructure performance prediction, and reinforcement learning for real-time control of detention basins and combined sewer overflow systems. In parallel, we review ML applications in environmental engineering workforce management, including predictive models for field crew scheduling optimization, skills-gap forecasting, safety incident risk prediction, and workforce well-being monitoring in high-exposure environmental field roles. We propose an Integrated ML Decision Support Architecture (IMDSA) that unifies hydrological prediction, infrastructure asset management, and workforce allocation within a single optimization framework, enabling municipalities and environmental engineering firms to simultaneously improve flood resilience and operational workforce efficiency
subject: Articles
  topic: section
subject: Stormwater Management, Environmental Engineering, Green Infrastructure, Predictive Maintenance, Climate Resilience, Decision Support Systems, Urban Hydrology
  topic: keywords
creator: Aditi Krishnan
creator: Lukas Andersson
creator: Daniel Okafor
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
license: https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 6
doi: 10.14741/

Distributed Data Processing Frameworks for Large-Scale Healthcare Analytics
type: item
description: The expansion of healthcare information generated by electronic health records, medical imaging systems, genome sequencing technologies, and wearable devices, alongside healthcare information systems, has been a challenge for data storage, management, and analysis. But traditional centralized computing methods can often fall short of efficiency when it comes to processing the vast amount, speed and complexity of health information. Distributed data processing frameworks have become a viable alternative to address the challenges of scalability, parallel processing, fault tolerance, and performance when dealing with large datasets and complex analytics tasks. The capabilities of these frameworks enable the extraction of insightful information from massive and complex healthcare data sets, enabling better clinical care, disease forecasting, personalised care plans, population health and operational optimization. This research aims to discuss the architectures, features and applications of distributed data processing frameworks in the context of large-scale healthcare analytics. It delves into key distributed computing frameworks, such as cluster computing, distributed storage solutions, and parallel analytics tools, each of which plays a crucial role in managing and analyzing healthcare data. The study also analyzes the major applications such as clinical analytics, genomic research, epidemiological surveillance, and real-time patient monitoring. Furthermore, it highlights key challenges related to scalability, interoperability, security, privacy, and resource management in distributed healthcare settings. The results suggest that distributed data processing frameworks offer the computational infrastructure and basis for deriving valuable knowledge from large healthcare datasets, which has the potential to optimize healthcare delivery, research, and organizational processes. The study concludes that continued advancements in distributed computing technologies will play…
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Distributed Data Processing, Healthcare Analytics, Big Data, Distributed Computing, Hadoop, Apache Spark, Healthcare Information Systems, Clinical Analytics, Genomic Data Analysis, Parallel Processing, Cloud Computing, Scalable Analytics.
creator: Anjani Haritha Sannidhanam
rights: Copyright (c) 2026 Anjani Haritha Sannidhanam (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2019-12-30
publication date: 2019-12-30
starting page: 1
ending page: 10
doi: 10.14741/ijaie/v.7.4.01

Event Streaming Architectures for High-Volume Transaction Processing
type: item
description: The rapid growth of digital commerce, financial services, and enterprise applications has led to unprecedented volumes of transactional data, creating significant challenges for traditional processing architectures. Event streaming architectures have emerged as an effective approach for handling high-volume transaction processing by enabling continuous data flow, real-time analytics, and low-latency decision-making. Unlike conventional batch-oriented systems, event streaming platforms process transactions as they occur, allowing organizations to respond immediately to operational events, detect anomalies, and maintain up-to-date system states. A surge of digital commerce, financial services and enterprise applications has generated unprecedented amounts of transactional data, posing enormous challenges to traditional processing architectures. Event streaming architectures have proven themselves to be an effective way to deal with high-volume transaction processing, by providing real-time analytics, low latency decision-making, and continuous data flow. In contrast to traditional batch processing systems, event streaming platforms can process transactions in real time, enabling organizations to quickly act on operational events, identify anomalies, and ensure accurate system states. This research investigates the architectural underpinnings, architectural elements and performance metrics of event streaming systems for high-volume transaction systems. Event producers, message brokers, stream processors, and storage layers are explored to explain their functions as they relate to scalability, reliability and fault tolerance. The study also investigates optimization methods including partitioning, distributed processing, event replay mechanisms and complex event processing to increase throughput and system resiliency. In addition, the use cases in financial services, retail platforms and enterprise data ecosystems are explored, as well as challenges such as data consist…
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Event Streaming Architecture, High-Volume Transaction Processing, Event-Driven Architecture, Real-Time Analytics, Stream Processing, Apache Kafka, Complex Event Processing, Scalability, Distributed Systems, Transaction Management.
creator: Anjani Haritha Sannidhanam
rights: Copyright (c) 2026 Anjani Haritha Sannidhanam (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2022-03-30
publication date: 2022-03-30
starting page: 1
ending page: 8
doi: 10.14741/

Performance Optimization Of ETL Pipelines In Distributed Data Warehouse Environments: A Network-Aware Scheduling Approach
type: item
description: The basic data integration infrastructure in enterprise data warehousing systems is the Extract, Transform and Load (ETL) pipeline. Quite often, as the volume of data in the organization grows and the variety of data sources increases, traditional ETL architectures suffer from serious performance problems due to sequential processing bottlenecks, poor network resource utilization, and limited parallelization of the transformation workload on distributed computing nodes. This paper presents a network-aware ETL scheduling framework that dynamically schedules extraction, transformation, and load tasks among different distributed processing nodes considering the available network bandwidth, the capability of processing nodes, and an estimation of the data transfer cost. The proposed framework embeds SQL Server Integration Services (SSIS) architectural patterns and presents a cost-based task dispatcher which decreases end-to-end execution time of pipelines by streaming data from the data source to the data target in parallel, minimizes the amount of data transferred between nodes by placing transformations in an area of the pipeline where the data is stored in the node, and distributes the workload of the pipeline across the different processing nodes. Experimental tests on enterprise data warehouse workloads show that the average execution time for pipelines is reduced by 31.4% and network resource use is improved by 26.8% compared with a traditional sequential execution of ETL pipelines. The outcomes are relevant to enterprise reporting scenarios with a big number of distributed source systems that need to provide reporting data with high throughput and low latency.
subject: Articles
  topic: section
subject: ETL pipelines; data warehousing; distributed computing; network-aware scheduling; SSIS; query optimization; enterprise reporting; parallel processing
  topic: keywords
creator: Kola Janardhana Naidu
rights: Copyright (c) 2013 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2013-12-30
publication date: 2013-12-30
starting page: 63
ending page: 67
doi: 10.14741/ijaie/v.01.3.4

Navigating Disputes and Arbitration Mechanisms in Construction: Insights from Power & Energy Infrastructure Project Management
type: item
description: In construction project delivery, conflicts are an ongoing issue, especially in power and energy infrastructure construction, where technical complexity, multi-stakeholder relationships, and ambiguities of the contracts often coincide. In the paper, the manager, the cause of construction disputes, and their nature are analyzed, discussing arbitration as the method of choice to solve conflicts in power and energy infrastructure projects. With current case studies used as reference, the paper is going to examine the efficiency of arbitration systems used, the importance of international and regional arbitral institutions, and the practical issues surrounding the costs and enforcing arbitral awards. It also examines the new trending phenomenon, including online arbitration markets and other developments with the incorporation of expert determination, as dispute avoidance and resolution tools. Policy and practice recommendations are found at the end of the paper to ensure readiness in dispute resolution, mitigation of project delivery risks, and better governance of large-scale energy projects in developed and emerging economies.
subject: Articles
  topic: section
subject: Construction disputes, arbitration, energy infrastructure, dispute resolution mechanisms, project management, and the power sector.
  topic: keywords
creator: Oba, Timothy Taiwo
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-01-15
publication date: 2025-01-15
starting page: 36
ending page: 44
doi: 10.14741/ijaie/v.13.1.4

Quantum Machine Learning: Algorithms and Applications
type: item
description: Quantum machine learning (QML) represents a cutting-edge convergence of quantum computing and artificial intelligence, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance machine learning algorithms. By exploiting the computational advantages of quantum systems, QML offers the potential for exponential speedups in data processing, optimization, and pattern recognition tasks, particularly for high-dimensional and complex datasets. This article explores the foundational principles, algorithmic approaches, and practical applications of quantum machine learning across domains including finance, chemistry, drug discovery, optimization, and natural language processing. It discusses quantum-enhanced supervised and unsupervised learning, quantum neural networks, variational quantum algorithms, and hybrid quantum-classical architectures. Additionally, the article addresses challenges related to hardware limitations, noise, scalability, and interpretability, as well as the emerging trends in near-term quantum devices, quantum simulators, and integration with classical AI pipelines. Quantum machine learning holds the promise of revolutionizing computational intelligence, offering transformative capabilities for solving problems that are currently intractable for classical systems.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Quantum Machine Learning, Quantum Computing, Variational Quantum Algorithms, Quantum Neural Networks, Hybrid Quantum-Classical Systems, Quantum Optimization, Supervised Learning, Unsupervised Learning, Quantum Data Encoding, High-Dimensional Data, Quantum Simulation, Quantum AI Applications.
creator: Adewuyi Michael Oluwanifemi
rights: Copyright (c) 2024
date: 2024-06-30
publication date: 2024-06-30
starting page: 9
ending page: 12
doi: 10.14741/ijaie/v.12.2.2

Risk Management in it Program Management: Balancing Cybersecurity, AI Integration and Infrastructure Stability
type: item
description: Risk management is one of the fundamental pillars of IT program management, particularly as organizations navigate the increasingly complex digital landscape of the 21st century. With the growing reliance on digital infrastructures, data, and interconnected systems, the challenges associated with cybersecurity, artificial intelligence (AI) integration, and infrastructure stability are becoming more pronounced. These three components—cybersecurity, AI, and infrastructure stability—are intricately linked, and their balance is essential for ensuring the long-term viability, resilience, and security of an organization’s IT systems. As organizations adopt new technologies and expand their digital footprints, IT managers face the critical task of balancing these elements in order to build robust, future-proof IT programs. The role of cybersecurity in IT program management has evolved significantly over the past few decades. Historically, cybersecurity was often relegated to being a peripheral concern, managed in isolation by dedicated security teams. Traditional approaches primarily focused on perimeter defenses, such as firewalls, intrusion detection systems (IDS), and antivirus software, to keep cyber threats at bay. While these methods provided some degree of protection, they were largely reactive and could only address known threats. As cyber adversaries have become more sophisticated, cyber threats have transcended basic attacks and evolved into more complex and persistent tactics, such as advanced persistent threats (APTs), zero-day exploits, and ransomware attacks. These evolving threats have highlighted the limitations of traditional cybersecurity measures and underscored the need for more proactive, dynamic security solutions. As cyber threats continue to evolve, organizations are increasingly turning to artificial intelligence (AI) to enhance their cybersecurity capabilities. AI, particularly in the form of machine learning (ML) and deep learning (DL), is enabli…
subject: Articles
  topic: section
subject:
  topic: keywords
  value: In this article, the key concepts explored include AI-driven cybersecurity, IT program management, risk management, machine learning (ML), deep learning (DL), cyber threat detection, incident response automation, predictive security, data privacy, AI vulnerabilities, infrastructure stability, data protection regulations, compliance, adversarial attacks, cybersecurity frameworks, IT infrastructure resilience, AI integration, predictive analytics, behavioral analytics, security automation, cloud security, AI system security, risk mitigation strategies, business continuity, cybersecurity strategies, digital transformation, cyberattack prevention, AI model vulnerabilities, automated remediation, continuous learning in AI, security operations management, IT security frameworks, network security, and proactive risk management.
creator: Kumar Saurabh
rights: Copyright (c) 2024
date: 2024-03-17
publication date: 2024-03-17
starting page: 1
ending page: 9
doi: 10.14741/ijaie/v.12.1.1

Strategic drivers of digital transformation in small and medium enterprises
type: item
description: This study explores the strategic drivers influencing digital transformation within small and medium enterprises (SMEs). The purpose of the research is to identify the key external and internal factors that motivate SMEs to adopt digital technologies and reconfigure their business processes in response to changing market dynamics. A systematic review methodology was employed, analyzing existing academic literature, industry reports, and empirical studies to synthesize current knowledge on the subject. The findings reveal that competitive pressure, evolving customer expectations, operational efficiency goals, technological advancements, and regulatory requirements serve as major catalysts for digital transformation in SMEs. The review further highlights that innovation capacity and organizational agility play critical roles in accelerating adoption and sustaining transformation outcomes. The study concludes that digital transformation in SMEs is not driven by a single factor but rather by an interplay of strategic motivations shaped by both market forces and internal organizational priorities. These insights contribute to a clearer understanding of digital transformation pathways and offer implications for SME policymakers, managers, and future research.
subject: Articles
  topic: section
creator: Victor Ogunrinde
rights: Copyright (c) 2023
date: 2023-03-10
publication date: 2023-03-10
starting page: 64
ending page: 68
doi: 10.14741/ijaie/v.11.2.3

Digital Transformation Trends in Small and Medium Enterprises: A Systematic Review
type: item
description: Digital transformation has become a critical strategic priority for small and medium enterprises (SMEs) seeking to enhance competitiveness, resilience, and long-term sustainability in an increasingly digital economy. The purpose of this study is to systematically review and synthesize existing academic literature on digital transformation trends in SMEs, with a focus on adopted technologies, key drivers, barriers, and organizational outcomes. This research adopts a systematic literature review (SLR) methodology, following established review protocols to identify, screen, and analyze peer-reviewed journal articles published in major academic databases. Relevant studies were selected based on predefined inclusion and exclusion criteria, and qualitative content analysis was used to categorize themes and research patterns. The findings reveal that SMEs primarily adopt digital technologies such as cloud computing, e-commerce platforms, data analytics, and Industry 4.0 solutions to improve operational efficiency and market reach. Leadership commitment, digital skills, and external partnerships emerge as key enablers, while limited financial resources, skill shortages, and resistance to change remain significant barriers. Overall, digital transformation is associated with improved performance, innovation capacity, and organizational resilience. In conclusion, the review demonstrates that digital transformation in SMEs is a multifaceted and evolving process that extends beyond technology adoption to include strategic, organizational, and cultural dimensions. The study highlights existing research gaps and offers directions for future research on long-term impacts, contextual differences, and sustainable digital transformation strategies in SMEs.
subject: Articles
  topic: section
creator: Opeyemi Ajayi
rights: Copyright (c) 2023
date: 2023-09-22
publication date: 2023-09-22
starting page: 108
ending page: 114
doi: 10.14741/ijaie/v.11.3.2

Securing enterprise LLM gateways & misconfigured proxy exposure detection + mitigation framework for exposed LLM endpoints, API gateways, model download hooks
type: item
description: The quick process of enterprise adoption of large language models (LLMs) via API gateways, reverse proxies, and orchestration layers has enabled a novel category of security threats related to architecture misconfigured as opposed to model-specific behavior. Open LLM endpoints, permissive proxy settings, and unprotected model download handles are facilitating more and more data leakage, unauthorized inference, supply-chain failure and trust boundary breaches. This paper describes a system level detection and mitigation system framework to secure enterprise LLCM gateways based on the co-design principles of cyber-physical systems, embedded security, and safety-critical architectures. The framework is both based on semantic configuration analysis and runtime monitoring, and secure-by-design architecture patterns to highlight early exposure detection and design-time mitigation of the gateway. The paper shows how the knowledge of cross-domain automotive, IoT, and CPS security can be used to design resilient LLM infrastructure to enable scalable, trustworthy, and governable enterprise AI deployments.
subject: Articles
  topic: section
subject: Huge Language Models; API Gateways; Proxy Misconfiguration; AI Infrastructure Security; System-Level Co-Design; Supply Chain security; Enterprise AI Governance
  topic: keywords
creator: Ankita Sharma
rights: Copyright (c) 2026 International Journal of Advance Industrial Engineering
date: 2024-11-30
publication date: 2024-11-30
starting page: 1
ending page: 15
doi: 10.14741/ijaie/v.12.4.1

Explainable Artificial Intelligence for Protecting the U.S. Financial System from Sanctions Evasion and Trade-Based Money Laundering
type: item
description: The integrity of the U.S. financial system is increasingly threatened by sophisticated sanctions evasion networks and trade-based money laundering schemes that exploit cross-border payment channels and global supply chains. While machine learning techniques have enhanced the detection capabilities of anti-money laundering and sanctions screening systems, their limited transparency poses significant challenges for regulatory compliance, supervisory review, and effective financial intelligence generation. This study proposes an explainable artificial intelligence framework for protecting the U.S. financial system by enabling detection models that not only identify illicit financial risk but also clearly articulate the underlying drivers of that risk. The research develops explainable AI-based anti-money laundering models tailored to U.S.-touch payment and trade finance environments, including cross-border wire transfers, correspondent banking flows, and ISO 20022-aligned messaging. The framework targets two priority threat domains identified in U.S. national-security guidance: sanctions evasion involving restricted jurisdictions and proxy counterparties, and trade-based money laundering linked to supply-chain manipulation, over-invoicing, and under-invoicing practices. Explainability mechanisms are integrated at the feature, counterparty, transaction routing, and documentation levels to generate investigator-readable narratives and examiner-ready audit trails. Empirical results demonstrate that the incorporation of explainable artificial intelligence significantly reduces false-positive alert volumes and shortens investigation time compared to traditional opaque models, while improving the clarity and consistency of suspicious activity reporting. By enhancing transparency, accountability, and operational efficiency, the proposed approach strengthens regulatory trust and supports higher-quality financial intelligence outcomes. These findings underscore the role of expl…
subject: Articles
  topic: section
creator: Pristly Turjo Mazumder
rights: Copyright (c) 2026 International Journal of Advance Industrial Engineering
date: 2024-05-20
publication date: 2024-05-20
starting page: 1
ending page: 8
doi: 10.14741/ijaie/v.12.2.1

Machine Learning Algorithms for Enhancing Predictive Analytics in ERP-Enabled Online Retail Platform
type: item
description: Enterprise Resource Planning (ERP) systems are known to be key to online retail management and its resources and operations where proper demand forecasting and sales projections are needed to be effective and competitive. This paper presents a Convolutional Neural Network (CNN) based prediction model for online retail systems that are ERP-enabled. Online Retail II is used to test the model. The methodology starts with the large-scale data preprocessing that involve cleaning, feature engineering, label encoding, and normalization with the help of Standard Scaler and then the further division into testing and training sets entails. The following methods are used to evaluate and train convolutional neural network (CNN) models: root-mean-squared error (RMSE), R2-score, mean absolute percentage error (MAPE), and mean absolute error (MAE). With an R2 of 94, MAE of 2.277, RMSE of 2.814, and MAPE of 13.72%, the experimental findings show that the suggested CNN outperforms the conventional machine learning models. Additional comparative analysis indicates the superiority of the CNN over the models of Decision Tree, Gradient Boosting and Random Forest, which prove its strength in reflecting complex transaction patterns. The results highlight the opportunities of deep learning in enhancing online retail forecasting using ERP which, in turn, enhances business decision-making, operational effectiveness, and customer satisfaction.
subject: Articles
  topic: section
subject: Predictive Analytics, ERP, Retail Analytics, Machine Learning, Automation, Optimization, Predictive Analytics, Business Intelligence, Sales.
  topic: keywords
creator: Venkata Deepak Namburi
creator: Dinesh Rajendran
creator: Aniruddha Arjun Singh
creator: Vaibhav Maniar
creator: Vetrivelan Tamilmani
creator: Rami Reddy Kothamaram
rights: Copyright (c) 2022
date: 2022-12-30
publication date: 2022-12-30
starting page: 65
ending page: 73
doi: 10.14741/

AI-Driven Customer Relationship Management: Enhancing Salesforce Efficiency Through Predictive Analytics
type: item
description: The use of artificial intelligence (AI) with Customer Relationship Management (CRM) systems has transformed the way organizations operate their sales pipelines, anticipate customer behaviour, and manage the effectiveness of their workforce. This paper looks at the place of AI-based CRM solutions, specifically focusing on predictive analytics as a driver of Salesforce efficiency. Lack of data silos, manual reporting, and inability to utilize forecasting have long been a problem with traditional CRM models. By contrast, AI-powered tools make use of machine learning algorithms, natural language processing, and real-time data to automate the process of scoring leads, enhance sales projections, and customize customer engagement strategies. Based on new evidence regarding the industry, such benefits as enhanced conversion rates, increased retention, and decreased operation costs are mentioned in the discussion. Nonetheless, there are still difficulties, such as the issue of data privacy, the ethical aspect of algorithmic decisions, and compatibility with older systems. The paper also examines the future prospects, including hyper-personalization, conversational AI, and prescriptive analytics, under the overall digital transformation agenda. The study highlights the strategic significance of ensuring that predictive technologies are aligned to human-centric values by critically assessing opportunities and risks in order to optimize the business performance and customer trust. Finally, the analysis shows that predictive AI is not a replacement of human judgment but a complementary tool that can enable organizations to be able to foresee needs, build stronger relationships and maintain a competitive advantage in the rapidly changing digital market.
subject: Articles
  topic: section
subject: AI-driven CRM, predictive analytics, Salesforce efficiency, customer engagement, sales forecasting, digital transformation.
  topic: keywords
creator: Nancy Al Kalach
rights: Copyright (c) 2025
date: 2025-09-10
publication date: 2025-09-10
starting page: 22
ending page: 35
doi: 10.14741/ijaie/v.13.1.3

AI-Driven Agriculture Marketplaces Digitalizing Farm to Retail Supply Chains in UK
type: item
description: The fast digitization of the agriculture industry is changing the dynamics of the farm-to-retail supply chain, and Artificial Intelligence (AI) has become a driver of efficiency, visibility in the process and sustainability. This paper investigates the emergence of AI-driven agricultural marketplaces in the United Kingdom and how predictive analytics, dynamic pricing, automated quality scoring and intelligent logistics are changing agrifood traditional value chains. Using recent case studies, regulatory considerations, and the regulatory drivers of adoption, the researchers detail how online platforms increase the connectivity between farmers and retailers and help them to avoid food wastage, better price realizations, and create resilience when faced with supply shocks. The most significant disadvantages, including data governance, interoperability, shortage of digital skills, and trust-related issues are also highlighted. The paper makes contributions to the current debates about the digitalisation of agriculture by putting forward a UK-specific lens to the topic of the integration of AI-enabled marketplaces into farm-to-retail supply chains, providing implications to policymakers, practitioners, and researchers.
subject: Articles
  topic: section
subject: AI agriculture, digital marketplaces, farm-to-retail supply chains, UK agrifood sector, predictive analytics, traceability, sustainability.
  topic: keywords
creator: Akeem Amusa Akintoye
creator: Cynthia Chidinma Onyenze
creator: Michael Okpotu Onoja
rights: Copyright (c) 2023
date: 2023-06-10
publication date: 2023-06-10
starting page: 57
ending page: 63
doi: 10.14741/ijaie/v.11.2.2

Revitalizing Agriculture in Conflict Zones: A Case Study on Machinery Restoration in Borno State
type: item
description: Agricultural productivity in northeast Nigeria has been drastically affected by armed conflict and Borno State ranks among the worst-affected states. Lack of farmland, mislocation of agricultural populations and destruction or damages of farm equipment(farm machinery) were some of the major results seen after years of violence, which is very important machinery in the big agricultural system. The paper examines how the restoration of machinery can be used to give new life to agriculture in conflict-struck lands by studying Borno State. With the use of qualitative field interviews, secondary data, and policy reports the paper focuses on how public, private and international stakeholders have helped in rebuilding agricultural equipment, and enabling accessibility of mechanized tools by the farmers. Results indicate that selective machinery repair schemes, equipment sharing co-operates, and homegrown repair training have helped to make farms productive and communities stronger. Nonetheless, the issues of insecurity that have lingered on, lousy infrastructure, and retention of spare parts has impeded sustainable recovery. The research highlights why it is so necessary to embed machine restoration into the larger agricultural and post-conflict development plans. It provides policy consideration in expanding an intervention to assist in the process of mechanization, livelihood recovery, and long-term food security in weak environments.
subject: Articles
  topic: section
subject: Agricultural recovery; Conflict zones; Borno State; Machinery restoration; Post-conflict development; Mechanization; Food security; Nigeria; Agricultural resilience.
  topic: keywords
creator: Olatunde Olagunju
rights: Copyright (c) 2020 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2020-12-30
publication date: 2020-12-30
doi: 10.14741/ijaie/v.8.4.1

Leveraging Six Sigma for Continuous Improvement in Petroleum and Chemical Processing
type: item
description: The petroleum and chemical processing industries operate in highly complex, capital-intensive environments where operational efficiency, safety, and environmental compliance are paramount. In such settings, continuous improvement methodologies have become essential to maintain competitiveness and sustainability. This article explores the strategic application of Six Sigma, a data-driven quality management approach originally developed for manufacturing in the context of petroleum refining and chemical production. Drawing from industry case studies, literature reviews, and operational data, the article evaluates how Six Sigma’s DMAIC (Define, Measure, Analyze, Improve, Control) framework can be effectively leveraged to reduce process variability, enhance yield, minimize waste, and drive cost savings. It highlights real-world implementations, such as catalyst optimization, emissions control, and downtime reduction, that demonstrate measurable improvements in performance metrics. The paper also critically examines the challenges of adopting Six Sigma in traditionally conservative engineering environments, including organizational resistance, training demands, and integration with legacy systems. Finally, the article discusses broader industry and policy implications, advocating for targeted capacity-building programs, regulatory incentives, and public-private collaborations to embed continuous improvement practices across the sector. By positioning Six Sigma as a catalyst for operational excellence and innovation, the article contributes to the evolving discourse on quality and sustainability in energy-intensive industries.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Six Sigma, Continuous Improvement, Petroleum Industry, Chemical Processing, Operational Excellence, DMAIC, Process Optimization, Quality Management, Industrial Efficiency, Sustainability, Lean Six Sigma.
creator: Christopher Nkansah
rights: Copyright (c) 2025
date: 2025-02-26
publication date: 2025-02-26
doi: 10.14741/ijaie/v.13.1.2

Optimizing Fluid Flow and Pressure Control in Industrial Operations: A Case Study
type: item
description: Solid control of flow and pressure of fluids is core to efficiency, safety and cost effectiveness of industrial processes within industries like petrochemicals, manufacturing and energy. This paper represents an intensive case study of a medium-sized industrial facility and comes up with resolving inefficiencies in the flow management system and pressure control. The work examines possible approaches to the optimization of fluid transport based on computational fluid dynamics (CFD) simulations, real-time deployment of sensors and system reconfiguration, in order to mitigate system losses. As the results show, the actual improvements in flow-rate stability and energy consumption by flow are impressive, even when insignificant design alterations are made like valve repositioning, changes in diameter of pipes, refined integration of control logic. The figures after the implementation depict that pressure fluctuations have dropped by 22 percent and there is a 15 percent increase in the uptake of pump energy consumption. Another issue presented in the paper is the practicality of the economic and operational aspects of upgrading the current systems with automation technology so that they may be smarter in controlling the pressure. The results can also be used to contribute to the upcoming body of knowledge of this topic, that is, process optimization, and also provide some practical recommendations to engineering professionals who want to optimize the performance of a system without investing a significant amount of capital.
subject: Articles
  topic: section
subject: Fluid dynamics, Pressure control, Industrial optimization, Computational fluid dynamics (CFD), Flow regulation, Process engineering.
  topic: keywords
creator: Maxwell Collins Bawuah
rights: Copyright (c) 2023
date: 2025-01-27
publication date: 2025-01-27
doi: 10.14741/ijaie/v.13.1.1

Advanced Simulation on Techniques for Predicting Gas Behavior in LNG and NGL Operations
type: item
description: When it comes to Liquefied Natural Gas (LNG) and Natural Gas Liquids (NGL) processing plants, the precise forecast of gas behaviour is a key component in the enhancement of the safety, efficiency and reliability of plant operation. Tightly coupled with the large dynamism of thermodynamic properties of natural gas mixtures at cryogenic temperatures and pressures, the standard methods of estimating will not always serve the interests of natural gas mixtures at high pressures and cryogenic temperatures, as they do not capture the nature of complexities in phase nature, composition, and flow dynamics. This paper discusses various highly detailed simulation methods that have become prominent in LNG and NGL practices, which take such extensive modeling like thermodynamic modeling, molecular modeling, and compound process simulators like Aspen HYSYS, and OLGA. The importance of choosing and using suitable equations of the state, the use of dynamic simulation to make predictions in real-time and the inclusion of simulation with a control system to be able to respond toward operations is stressed. The industrial feasibility of the various methods and tradeoffs in computation are also reviewed. Results indicate that the effective use of simulation tools can not only help increase the accuracy of prediction but also achieve proactive decision making in complex conditions of gas processing. This marks the research, the necessity of further development of the simulation structure to meet the changing needs of the operation, varying gas mixture.
subject: Articles
  topic: section
subject: LNG, NGL, Gas behavior, Simulation techniques, Thermodynamic modeling, Process optimization, Thermodynamic modeling, Equations of state, Dynamic simulation.
  topic: keywords
creator: Christopher Nkansah
rights: Copyright (c) 2023
date: 2023-10-23
publication date: 2023-10-23
doi: 10.14741/ijaie/v.11.4.1

Photocatalytic degradation of methylene blue dye using synthesized CoxNi1-xFe2O4nanoparticlesunder direct visible light
type: item
description: A cobalt doped nickel ferrites were prepared in this study. Photo catalysts made of cobalt-doped nickel ferromagnetic materials nanoparticles were utilized to degrade dyes. These nanoparticles were synthesized by the co-precipitation technique. A UV-Vis spectrophotometer was used to determine the band gap. These ferrites had band gap between 2.32eV to 2.20eV. The spinel structure of ferrites was characterized X-ray diffraction (XRD) technique. The shape and average size of synthesized nanoparticles was determined by scanning electron microscopy (SEM). Fourier transform infrared FTIR shows metal-oxygen bond in the range of 400-1000cm-1. The photocatalytic degradation of dye under UV radiation and direct sunlight was studied. Almost 72 to 92 percent methylene blue dye was degraded in first 100 mints owing to the active octahedral to tetrahedral lattice sites and prevent the recombination of electron/hole pair. The photo-degradation of methylene blue (MB) and a few textile dyes was practised to examine the photocatalytic performance of Co-NiFe2O4. It was observed as the most effective photocatalyst for treating sewage.
subject: Articles
  topic: section
subject: Cobalt chloride hexahydrate; Nickel chloride hexahydrate; Spinel ferrites; Methyl blue dye; Photocatalytic activity; Electron/hole pair recombination mechanism
  topic: keywords
creator: Seemab Hussnain
creator: Syed Salman Ali
creator: Muhammad Qadeer Alam
creator: Muhammad Hamad Safder
creator: Atia Roman
creator: Tehreem Fatima
creator: Muhammad Asif
rights: Copyright (c) 2023
date: 2023-09-21
publication date: 2023-09-21
starting page: 100
ending page: 107

Department of Physics, Government College University Faisalabad, Pakistan
type: item
description: This research was conducted to study acoustic noise isolation performance of coco peat and recycled rubber composite material, which has high potential to be used for sound absorbing material. The sample was tested with different composition ratios. The composition ratios varied from 0% to 100 % of coco peat and recycled rubber in the steps of 20%. The samples were manufactured by using mold that was made by using MS sheet with the 5mm thickness. These samples were tested by using DEWE-43 instrument used for finding the sound absorption coefficients. Similarly, simulation was conducted on Ansys software for finding the sound absorption coefficient. For composition 40% of coco peat & 60% of recycled rubber at the various frequencies such as 250,500,750 and 1000 Hz at constant sound pressure source of 100db, the sound absorption coefficient levels achieved were 43.7db, 10.2db, 9.7db and 0.3db respectively for DEWE-43 instruments. These results are better as compared to Baggas panels. These results indicate that manufactured composite panels are promising to be used as sound absorbing material with low cost, light weight, and biodegradable.
subject: Articles
  topic: section
subject: Sound Absorption; Composite Material; Recycled Rubber.
  topic: keywords
creator: A.B. Lingemali .
creator: R. T. Vyavahare .
rights: Copyright (c) 2023
date: 2023-06-21
publication date: 2023-06-21
starting page: 50
ending page: 56

Aneela Shahzad
type: item
description: The synthesis of metal oxide nanomaterials for energy storage and optimization of their electrochemical performance is vital for next-generation energy solutions. This study focused on synthesizing nickel hydroxide nanomaterials using precipitation and microwave methods for use as pseudo-capacitor electrodes. The precipitation method, utilizing starch as a capping agent, produced nickel hydroxide with a maximum specific capacitance of 333 Fg⁻¹. Calcination of nickel hydroxide at elevated temperatures yielded nickel oxide/carbon (NiO/C). The electrochemical performance of NiO/C was optimized at calcination temperatures of 400, 500, and 600 °C, with the 500 °C sample achieving a maximum specific capacitance of 450 Fg⁻¹ at a scan rate of 5 mV·s⁻¹. Structural characterization was performed using X-ray diffraction (XRD), while Fourier-transform infrared spectroscopy (FTIR) investigated functional groups. Morphological analysis was conducted via field emission scanning electron microscopy (FESEM), and elemental composition was assessed using energy dispersive spectroscopy (EDS). Electrochemical properties were evaluated through cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), and electrochemical impedance spectroscopy (EIS).
subject: Articles
  topic: section
subject: Nickel hydroxide; Electrochemical performance; Nanomaterials; Precipitation method
  topic: keywords
creator: Ali Jawad
creator: 1Muhammad Mobin Ul Haq
creator: Abid Munir .
creator: Fatima Khan
creator: Ahmed Sajid Muhammad
creator: Muhammad Aslam Aflatoon
creator: Muhammad Farooq Khan
creator: Nasrullah .
creator: Aneela Shahzad
creator: Abdul Shakoor
creator: Shadab Ghafoor
creator: Muhammad Sohail Ahmad
rights: Copyright (c) 2023
date: 2023-03-20
publication date: 2023-03-20
starting page: 16
ending page: 22

Construction site safety and quality analysis utilizing KHNN Model
type: item
description: Improving construction site safety through effective hazard identification and mitigation is critical. This study aims to predict rework, defects, and associated costs using artificial neural networks and optimization algorithms. Traditional safety planning approaches lack pre-construction hazard analysis. To examine deficiencies, various metrics were analyzed, including rework costs per $1M scope and injury rates. Ineffective safety practices like inadequate training and protection have led to accidents. This work identifies approaches to enhance worker safety performance through hazard identification. Inputs to a neural network model predict rework workers, defects, and costs. Safety execution aims to systematically identify hazards before construction. Model performance using actual data was evaluated. Two soft computing methods - artificial neural network and optimization algorithms - were implemented in MATLAB. Krill herd and grey wolf optimization techniques optimized hidden neuron weights in the neural network structure. Predictions from these algorithms outperformed other existing methods like particle swarm and genetic algorithms. This study provides a framework to quantitatively forecast rework, defects, and associated costs through systematic pre-construction hazard analysis and modeling. The proposed optimizationenhanced neural network models can help construction managers implement targeted safety improvements.
subject: Articles
  topic: section
subject: Krill Herd Neural Network, Particle Swarm Optimization, Grey Wolf Optimization, Work Health and Safety, Artificial Neural Network
  topic: keywords
creator: Muhammad Imran Khan
creator: Qamer Muhammad
creator: Atta Mehroz
rights: Copyright (c) 2023
date: 2023-03-20
publication date: 2023-03-20
starting page: 7
ending page: 15

Effect of magnetic field on germination and early growth of soybean (Glycine max) seed.
type: item
description: Magnetic field is beneficial and essential tool in modern research. Magnetic field is a force that can be made by moving electric charges and magnetic dipoles which apply a power on other close-by moving charges and magnetic dipoles. Magnetic field has numerous applications in our daily life. Nowadays it is used in agriculture to enhance the seed germination, plant growth and yield because it has no toxic effect on environment as need us on soil. In this research work effect of magnetic field on soybean seed (Glycine max) was investigated. The soybean seed was taken from institute of Horticulture science. Non treated seeds were served as control while treated seeds were served as magnetically treated seeds. Seeds before sowing were treated with magnetic field stimulator which is fabricated in department of Physics University of agriculture Faisalabad, by varying magnetic field for different time exposure. The seed was exposed for different time duration and different magnetic field strength. The seeds were grown in three replicates in Petri dishes as well as in pots. The different parameters like germination, seedling growth, seedling fresh weight, seedling dry weight, root length, shoot length, root dry weight, shoot dry weight, leaf area, enzymes activity, proteins content, chlorophyll content and antioxidant activities were measured. It was noticed that the magnetic field effected the seed germination and other parameters. Difference between treated and non-treated plant seeds was checked by using different statistical tests namely RCBD, CRD, and ANOVA.
subject: Articles
  topic: section
subject: Magnetic field, Glycine max, RCBD, CRD, ANOVA
  topic: keywords
creator: Ghafoor Ahmad
creator: Ali Muhammad
creator: Usman Muhammad
creator: Samreena Rasheed
rights: Copyright (c) 2023
date: 2023-04-13
publication date: 2023-04-13
starting page: 1
ending page: 6

Recycling textile industry waste materials: fabrication, characterization, mechanical and thermal performance analysis of developed non-woven interlining
type: item
description: In the last few years, the shortage of natural resources, the energy crisis and environmental issues have threatened the world simultaneously. Furthermore, the manufacturing sector is facing the most vulnerable economic conditions due to high raw material, energy and operating costs. It is critical to emphasize cost-cutting without sacrificing sustainability. Herein, it has been attempted to recycle textile waste material into non-woven interlining. The fabrication process includes the collection of textile waste materials, bleaching the fabric, garneting to loosen fibrous and conversion to nonwoven web form through an aqueous solution of fibrous flock with a mixture of epichlorohydrin as a cross-linker. Three nonwoven interlinings were successfully developed from 100% cotton, 50% polyester/50% cotton and 75% polyester/25% cotton blended ratio of waste fabric. Linear weight, thickness, tear strength, tensile strength, Eb%, bursting strength and TGA of these samples are assessed. Furthermore, we compare the recycled sample cost and investigate the seam performance of developed samples made from various woven twill fabrics. It is observed that the developed samples show satisfactory performance and are cheaper than the current market price, having potential scope in clothing manufacturing as well as offering a sustainable approach to the environment.
subject: Articles
  topic: section
subject: Climate change, recycling, waste materials, textile industry, non-woven interlining, TGA, seam efficiency
  topic: keywords
creator: Md. Washim Akram
creator: Sayed Hasan Mahmud
creator: Shanzida Easmin .
rights: Copyright (c) 2022
date: 2022-12-15
publication date: 2022-12-15
starting page: 56
ending page: 64

Experimental Investigation of Thermal Fatigue life of Engine Cylinder Liner
type: item
description: Thermo mechanical stresses play an important role in the failure of critical parts of many applications like turbines, power plants, hot rolling mills and especially in automotive industry. Thermal fatigue is the major cause of this damage of metals due to thermal cycling. In automotive industry thermal fatigue occurs in engines due to variation in temperature during intake and exhaust stroke. This gradual thermal cycling damage the engine parts especially engine cylinder liner which reduces the service life of an engine. So, it is very important to estimate the thermal fatigue life of engine cylinder liner material. In this research work, thermal fatigue life of engine cylinder liner material grey cast iron has been investigated. A wedge-shaped specimen is prepared from sand casting and used for the experimentation of thermal fatigue. Thermal fatigue experiments are performed between 750C2800C thermal cyclic ranges. Crack initiation and propagation are investigated experimentally on one edge of wedge shape specimen and validate of these propagations of cracks with numerical simulation using ABAQUSTM software.
subject: Articles
  topic: section
subject: Thermal fatigue, thermal stress, mechanical stress, crack initiation, crack propagation, crack saturation
  topic: keywords
creator: Shahwaiz Hassan Shah
creator: Usama Zafar Muhammad
rights: Copyright (c) 2022
date: 2022-12-06
publication date: 2022-12-06
starting page: 50
ending page: 55