Real Case Study Research Articles

A Bi-level programming approach to design a sustainable supply chain network under uncertainty: a real-life case study

A Bi-level programming approach to design a sustainable supply chain network under uncertainty: a real-life case study

CyberCrime Against Women In India

Cybercrime in India have become one of the biggest issues in recent times especially when we are in the era where people post their day-to-day activities from their morning coffee to their dinner. The most disturbing cybercrimes though are often against women. Crimes in general, against women are increasing day by day but the most traumatizing crime against women would be cyber-crime. Cybercrimes are not only crimes that were once done physically but now online like eve-teasing, bullying, harassment and blackmail but they have gone to extreme level like hacking of personal data, cyber pornography, cybersex trafficking, cyber sexual defamation and morphing being the most frightening cybercrime of them all. This research paper aims to explain how several types of cybercrime against women in India are increasing at an alarming rate and how well the legal systems work to protect women against them. This research paper will include various reputed cases that made a huge impact in making laws for Cybercrime against women Utilizing a qualitative approach, the article examines key legislation, including the Information Technology Act and relevant provisions of the BNSS , alongside an analysis of real-life case studies.

An Integrated Multi-Product Biodiesel and Bioethanol Supply Chain Model with Torrefaction Under Uncertainty

This study presents an integrated supply chain network model for biodiesel and bioethanol production, incorporating torrefaction under uncertain conditions related to the establishment of new facilities. The proposed mixed-integer linear programming model aims to minimize the total cost of the supply chain while maximizing social objectives such as reducing unemployment. To solve the bi-objective model, a three-stage approach is employed: first, uncertain parameters are defuzzified; second, the augmented epsilon-constraint method is applied to generate a set of efficient Pareto-optimal solutions; and third, robust optimization is used to handle real-world uncertainties, such as disruptions caused by natural disasters and sanctions, ensuring feasibility under different scenarios. The study considers various stages of the supply chain, from feedstock cultivation to processing, transportation, and distribution. A real-life case study in Iran is used to evaluate the effectiveness of the proposed model, highlighting that biodiesel and bioethanol supply chains are interrelated, particularly at the cultivation stage, where each crop impacts the other. In this regard, Kermanshah, Isfahan, Chahar Mahal & Bakhtiari, Khorasan North, Kohgiluyeh & Boyer-Ahmad, and Lorestan are identified as the most suitable provinces for second-generation plant cultivation. Additionally, Azerbaijan East is identified as the best location for a bioethanol refinery, while Tehran and Markazi are the optimal choices for biodiesel refineries. This integrated approach offers a novel solution that prevents impractical overlaps in land use, providing a comprehensive, sustainable, and socially beneficial framework for bioenergy supply chain management.

Enhancing disaster resilience: optimizing debris management for end-of-life buildings amidst uncertainty

ABSTRACT This study develops a Stochastic Mixed Integer Linear Programming (SMILP) model to optimize the reverse logistics network for debris generated from proactive demolition of end-of-life buildings. Unlike most existing research, this study addresses proactive strategies for the Mitigation stage of disaster management. Our model identifies the optimal number of landfilling areas and sorting facilities, factoring in uncertainties in debris quantity and material quality. It incorporates environmental constraints, such as mandatory sorting processes and recycling thresholds. Multiple scenarios are considered, each with unique parameter values and occurrence probabilities, with the overall objective of minimizing net costs across all scenarios. A realistic case study is used to illustrate the model, demonstrating its capacity to reduce post-disaster recovery costs, improve operational efficiency, and balance financial and environmental considerations. This study offers insights for decision-makers, advocating proactive end-of-life buildings’ management as a disaster preparedness and sustainable practices.

IoT Integration of Failsafe Smart Building Management System

This research investigates the energy consumption of buildings managed by traditional Building Management Systems (BMSs) and proposes the integration of Internet of Things (IoT) technology to enhance energy efficiency. Conventional BMSs often suffer from significant energy wastage and safety hazards due to sensor failures or malfunctions. These issues arise when building systems continue to operate under unknown conditions while the BMS is offline, leading to increased energy consumption and operational risks. The study demonstrates that integrating IoT systems with existing BMSs can substantially improve energy efficiency in smart buildings. The research involved designing a system architecture prototype, performing MATLAB simulations, and a real-life case study which revealed that IoT devices are effective in reducing energy waste, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems and lighting. Additionally, an auxiliary bypass system was incorporated in parallel with the IoT system to enhance reliability in the event of IoT system failures. Preliminary findings indicate that the integration of IoT systems with traditional BMSs significantly boosts energy efficiency and safety in smart buildings. Simulation results reveal an hourly average power savings of 36.8 kw with the integrated failsafe model for all scenarios. This integration offers a promising solution for advancing energy management practices and policies, thereby improving both operational performance and sustainability in building management.

Investigating the Factors Affecting Customer Trust in E-Commerce

In the rapidly evolving landscape of e-commerce, the foundation of customer trust has emerged as a critical determinant of success for online businesses. This article delves into the multifaceted nature of trust in e-commerce, identifying and analyzing key factors that influence consumer confidence and loyalty. Through a comprehensive examination, we highlight the importance of website design and usability, security measures, customer reviews and testimonials, customer service and support, and the impact of brand reputation and influencer endorsements. The discussion extends to actionable strategies for e-commerce businesses to build and maintain customer trust, emphasizing the need for continuous improvement and adaptation to changing consumer expectations and technological advancements. Real-life case studies provide insights into successful and unsuccessful attempts to foster trust online, underscoring the lessons learned. The article concludes by reflecting on the dynamic nature of trust in e-commerce, urging businesses to stay vigilant and proactive in creating a trustworthy shopping environment. This analysis serves as a valuable resource for e-commerce businesses aiming to navigate the complexities of building trust in the digital age.

Enhancing end-of-life product recyclability through modular design and social engineering optimiser

Amidst the thriving landscape of manufacturing, the vision of sustainability in Industry 5.0 is becoming increasingly significant. The implementation of recycling represents a crucial step in the pursuit of sustainability, particularly in light of the mounting challenge posed by the proliferation of end-of-life (EOL) products. Addressing this challenge, we propose a novel Design for Modular Recyclability (DFMR) approach aimed at facilitating the recycling of EOL products. Our study develops a multi-objective optimisation model with a focus on maximising green recyclability and independence while minimising aggregation. We introduce an innovative Social Engineering Optimiser (SEO) to simulate behavioural patterns in complex environments, aiding in identifying and implementing effective strategies for optimal or near-optimal results in diverse scenarios. The practical effectiveness of the proposed models and algorithms is demonstrated by applying them to a real-life case study in an internal combustion engine, followed by performance comparisons with existing well-established multi-objective optimisation algorithms. The findings of our study demonstrate the efficacy of the proposed DFMR model, offering a novel approach for decision-makers to undertake EOL product recovery. This contributes to the further exploration of complex and promising paths towards sustainable manufacturing and green production that are more in line with Industry 5.0.

Leveraging artificial intelligence in Regulatory Technology (RegTech) for financial compliance

Abstract. The rapid development of Regulatory Technology (RegTech) has introduced new methods to handle and simplify the compliance and regulatory issues surrounding financial services and products. Combining artificial intelligence (AI) with this trend is an effective way of strengthening financial services, making them safer and more convenient. The current AI technologies incorporated within RegTech applications improve the efficiency of compliance with rules and regulations, and contribute to the development and implementation of smart technology, including automated regulatory reporting, proven and trustworthy KYC processes, and reduced compliance costs. This paper presents the application of intelligent technology, such as machine learning, deep learning, natural language processing, and smart regulatory frameworks, to build modern, intelligent workflows, dynamically control policies, and instantaneously monitor transactions. We employ empirical economic data, evidenced by realistic case studies, to demonstrate the benefits brought on by intelligent technology to financial institutions, including quicker compliance with regulatory regimes, decreased operational risk, and increased transparency and accountability. Through analysing figures offered in various cases, we show the transformed compliance landscape created by RegTech, how it becomes a solution for the diverse challenges affecting financial sectors on a global scale, and how is it going to impact the whole field of financial services in the future..

Climate-Driven Sustainable Energy Investments: Key Decision Factors for a Low-Carbon Transition Using a Multi-Criteria Approach

Currently, the need for a clean transition has made the upscaling of sustainable energy investment projects imperative. This paper addresses the increasing importance of sustainable energy investment projects in the context of climate change and the urgent need for a global energy transition. Given the complexity of decision-making in this field, a multi-criteria decision-making (MCDM) approach is employed to assess the main criteria considered by project developers and financial institutions. Using the Analytic Hierarchy Process (AHP) method, eight criteria are identified and evaluated. Results highlight differing priorities between project developers and investors, emphasizing the need for adaptable approaches to accelerate sustainable energy investments. The study underscores the importance of understanding diverse stakeholder preferences and priorities in formulating effective strategies and managing associated risks to effectively promote sustainable energy projects. Future research should focus on real-life case studies and policy assessments to further enhance the understanding of sustainable energy investment dynamics.

Methodology for comparative assessment of battery technologies: Experimental design, modeling, performance indicators and validation with four technologies

An increasing number of applications with diverse requirements incorporate various battery technologies. Selecting the most suitable battery technology becomes a tedious task as several aspects need to be taken into account. Two of the key aspects are the battery characteristics under temperature variations and their degradation. While numerous contributions using tailored assessment methods to evaluate both aspects for a particular application exist in the literature, a general methodology for analysis is necessary to enable a quantitative comparison between different technologies. We propose in this paper a novel methodology, based on performance indicators, to quantify the potential and limitations of a battery technology for diverse applications sharing a similar operational profile. A quantification of phenomena such as the influence of high and low temperatures on the battery, or the effect of cycling and state of charge on battery aging is obtained. In pursuit of these indicators, an experimental procedure and the fitting of aging model parameters that allow their calculation are proposed. As an additional outcome of this work, a general aging model that allows comprehensive analysis of aging behavior is developed and the trade-off between experimental time and accuracy is analyzed to find an optimal experimental time between 2 and 4 months, depending on the studied battery technology. Finally, the proposed methodology is applied to four battery technologies in order to show its potential in a real case-study.

Optimal independent but coordinated operation of interconnected power, natural-gas, and district-heating distribution systems

Traditionally, power, natural-gas, and district-heating distribution systems have been planned and operated independently. However, the increasing presence of distributed natural-gas-fired generating units, heat pumps units, power-to-gas facilities, and combined heat and power units create an interdependency where the operational decisions of any system affect the operation of the other systems. We analyze centralized and decentralized operations of the three distribution systems and discuss under which conditions centralized and decentralized operations are equivalent. We find this equivalence by replacing each system operation problem with its optimality conditions and comparing them with the optimality conditions of a centralized operation. Using a realistic case study, we quantify the gains resulting from different levels of coordination. Equilibria, i.e., joint optimal solutions for the three systems, are found using an iterative convexification technique, which is novel in the context of this paper. An advantage of the proposed model lies in its potential to expand across a range of interconnected energy systems, which in turn, enables the identification of the equivalences between centralized and decentralized operations.

Bayesian Learning Based Elitist Nondominated Sorting Algorithm for a Kind of Multi-objective Integrated Production Scheduling and Transportation Problem

This paper focuses on a kind of multi-objective integrated production scheduling and transportation problem (MIPSTP), which is encountered in many real-life industrial processes. MIPSTP contains a production stage for processing products and a transportation stage for transporting products. In MIPSTP, we consider a dedicated integration of distributed scheduling and transportation regarding the two stages, arising from a practical project for a cooperative iron and steel company. The objective of MIPSTP is to minimize the total completion time of each factory and total transportation cost. To solve the problem, a Bayesian learning-based elitist nondominated sorting algorithm (BLENSA) is proposed. The primary highlights of this work are two-fold: 1) new solution structures of MIPSTP and 2) novel search model of BLENSA. For the solution structures, we propose for the first time the mathematical description of MIPSTP, accounting for several features in applications and so is different from conventional scheduling problems. For the search model of BLENSA, we propose a Bayesian learning-based probability model to learn valuable knowledge about nondominated solutions. Thereby, BLENSA combines the Bayesian learning-based probability model to produce a candidate population (CP) and the nondominated sorting method to generate a main population (MP). Next, we propose a greedy competition strategy to construct MP from CP and MP for the next generation. Results of experiments on 57 test instances and a real-life case study demonstrate the effectiveness and practical values of BLENSA.

A production-inventory model to optimize the operation of distributed energy resource networks in a rolling horizon

The recent advancements in energy production, storage, and distribution are creating unprecedented opportunities in the field. Major consumers can benefit from the implementation of distributed energy resource networks capable of generating electricity or heating from sources, often renewable ones, in close proximity to the point of use, rather than relying on centralized generation sources from power plants. In this paper, we introduce a pioneering model designed to determine the optimal set of energy commands in a distributed energy resource network, minimizing operational costs in a time horizon. Indeed, we propose an innovative mixed-integer linear programming formulation rooted in the production-inventory models commonly employed in aggregate production planning. The system integrates diverse energy generation sources, storage facilities, and demand points, encompassing both electric and heating commodities. The optimum of the model is achieved for all analyzed instances of the test library (2 scenarios-20 instances) in an exceptionally short time, outperforming other approaches previously presented in the literature. We employed the Gurobi optimizer to solve the model, obtaining rapid responses that ensure real-time decision-making and facilitate effective control of the distributed energy resource network within a three-days' rolling horizon, as discussed in a simulated real-life application case study. Indeed, the proposed model solves in less than 1s, enabling near-instantaneous decision-making. This swift solution time surpasses any known references in the field, effectively shifting the bottleneck in DER network operation from the decision-making process to the forecasting of demand and weather conditions. While forecasting typically requires a minimum of 15min, our approach suggests that a reduction in this forecasting time could further enhance the control system's response time, given the model's ability to deliver optimal solutions almost immediately. The real-time availability of optimal solutions allows for the seamless incorporation of stochastic elements into the control loop via a rolling horizon process.

Exact optimization of inter-array dynamic cable networks for Floating Offshore Wind Farms

Optimization of inter-array dynamic cables for Floating Offshore Wind Farms (FOWFs) using three integer linear programs and a heuristic is presented. Design optimization of fixed-bottom offshore wind is a challenging research problem but the presence of dynamic components in FOWFs adds new complexity — as the Floating Offshore Wind Turbine (FOWT), the support structure including the station-keeping system, and the floating power cables all experience dynamic movement in reaction to wind, wave and even current forcing. In this study, dynamic modeling for the response of this system is first carried out to assess the risk of potential mechanical interference between movable elements. Subsequently, safety zones constraints are defined in the optimization to ensure minimally safe conditions for operation of the combined FOWT/support-structure/cables system. Likewise, additional constraints including maximum thermal limits, tree topology without branching, and others are incorporated. The programs follow an incremental approach. Model 1 proposes a simple way to avoid mechanical interference, Model 2 adds variables modeling mooring lines anchoring, and Model 3 increases the degrees of freedom through addition of the positioning of the touchdown point where the dynamic and static sections meet at the seabed. The applicability is illustrated through realistic case studies for a reference FOWF in Europe. Results show that: (i) Modern branch-and-cut solvers are able to solve Model 2 getting the global optimum in seconds, and (ii) further cost refining can be obtained after wrapping Model 3 in the heuristic, using Model 2 as the initial design, decreasing the cost of this layout by around 1.5% in few hours through a nonrectilinear topology.

Multimodal transport route selection: An integrated fuzzy hierarchy risk assessment and multiple criteria decision-making approach

Selecting multimodal transport routes is a complex problem due to the presence of various, often conflicting decision-making attributes, such as transportation cost and time, diverse risks with hierarchical relationships both within and across groups, decision-makers’ preferences, and numerous possible options. To address this problem, a novel decision-making approach is proposed, integrating the best-worst method (BWM), fuzzy hierarchy risk assessment (FHRA), and additive ratio assessment (ARAS). This approach decomposes the route decision-making problem into a hierarchy structure and determines the optimal solution through three distinct phases. In the first phase, BWM assigns relative weights to various criteria within the hierarchical structure, ensuring that the decision-makers’ preferences are accurately reflected. In the second phase, FHRA evaluates qualitative risks from the lowest to the highest hierarchy levels. This process involves calculating risk magnitudes at the lowest hierarchy level and progressively recalculating them as they ascend to the highest level of the hierarchy. The multimodal transport cost-model provides transportation cost and time. In the final phase, ARAS synthesizes all the decision-making data to rank all possible multimodal alternative routes based on utility scores. The realistic case study validates the practical applicability and effectiveness of the proposed approach. The findings indicated that the proposed methodology effectively identify the most appropriate multimodal transport route as the best compromise choice. This study contributes new knowledge regarding a new hybrid decision-making framework that combines multiple criteria decision-making methods with fuzzy hierarchy risk analysis, thereby enhancing the design of transportation plans to align with organizational goals and customer requirements.

Advancing Analytical Excellence: A Comprehensive Exploration of ICH Q14 in the Lifecycle of Analytical Procedure Development and Optimization

This research article addresses the complexities of analytical process development and optimization in line with the principles set out in the Commission's Effectiveness of the Requirements for Medicinal Products for Human Use (ICH) Q14 guidelines. Focusing on the pharmaceutical and chemical industry, this article describes the early stages of development in terms of sustainable lifestyles. The first study explores the important role of analytical procedures in ensuring product quality and regulatory compliance, introduces ICH Q14 and explains its scope and objectives. The fundamental principles of audit development are reviewed in depth, including risk assessment, design, and the interaction between Audit Objectives (ATP) and Methodology. Operational Development Region (MODR). Concepts such as Design of Operations (DoE) and Quality by Design (QbD) are under scrutiny for their development and optimization. Real-life case studies demonstrate the application of Q14 principles and demonstrate successful examples of effective methods and continuous improvement in drug screening, drug purity testing and environmental analysis. Looking ahead, this article explores how new technologies, potentially innovative regulations and international cooperation will shape the development landscape. Challenges such as the complexity of new compounds and the need for sustainable practices are addressed and the need for business professionals to be flexible and innovative is emphasized. In summary, this research article can serve as a general guide for professionals in the pharmaceutical and chemical industry, providing insight into the content, eight owners, and use of development analysis. Using the principles defined in ICH Q14, community auditors can investigate the changing landscape and make further progress on quality assurance.

Modular Microgrid Technology with a Single Development Environment Per Life Cycle

The life cycle of a microgrid covers all the stages from idea to implementation, through exploitation until the end of its life, with a lifespan of around 25 years. Covering them usually requires several software tools, which can make the integration of results from different stages difficult and may imply costs being hard to estimate from the beginning of a project. This paper proposes a unified platform composed of four modules developed in MATLAB 2022b, designed to assist all the processes a microgrid passes through during its lifetime. This entire platform can be used by a user with low IT knowledge, because it is completed with fill-in-the-blank alone, as a major advantage. The authors detail the architecture, functions and development of the platform, either by highlighting the novel integration of existing MATLAB tools or by developing new ones and designing new user interfaces linked with scripts based on its complex mathematical libraries. By consolidating processes into a single platform, the proposed solution enhances integration, reduces complexity and provides better cost predictability throughout the project’s duration. A proof-of-concept for this platform was presented by applying the life-cycle assessment process on a real-case study, a microgrid consisting of a photovoltaic plant, and an office building as the consumer and energy storage units. This platform has also been developed by involving students within summer internships, as a process strengthening the cooperation between industry and academia. Being an open-source application, the platform will be used within the educational process, where the students will have the possibility to add functionalities, improve the graphical representation, create new reports, etc.

Revolutionizing Cybersecurity with AI: Predictive Threat Intelligence and Automated Response Systems

The sophistication and breadth of cyber threats are continuously expanding, making it more difficult for traditional security measures to keep up. Artificial intelligence is revolutionizing cybersecurity by equipping businesses to proactively counter threats with automated reaction systems and predictive threat intelligence. Data analytics, behavioral analysis, and machine learning enable AI-powered systems to anticipate cyber assaults, enabling more efficient and rapid threat detection. By automating reaction mechanisms and mitigating threats in real-time, AI systems can minimize human error and maximize damage mitigation. AI techniques, such as anomaly detection, predictive modeling, and real-time threat analysis; data privacy, ethics, and the risks of hostile attacks are among the subjects covered, as are the benefits and drawbacks of utilizing AI in cybersecurity. This article provides the framework for future intelligent, automated cyber defense methods and illustrates how AI may alter cybersecurity using real-life examples and case studies.

Amplified squeezed states: analyzing loss and phase noise

Abstract Phase-sensitive amplification of squeezed states is a technique to mitigate high detection loss, which is especially attractive at 2μm wavelengths. We derived an analytical model proving that amplified squeezed states can mitigate phase noise significantly. Our model discloses two practical parameters: the effective measurable squeezing and the effective detection efficiency of amplified squeezed states. A realistic case study includes the dynamics of the gain-dependent impedance matching conditions of the amplifier. Our results recommend operating the optical parametric amplifier at high gains because of the signal-to-noise ratio’s robustness to phase noise. Amplified squeezed states are relevant in proposed gravitational wave detectors and interesting for applications in quantum systems degraded by the output coupling loss in optical waveguides.

The Implementation of Case Based Learning in Physics Learning at The Collage: A Systematic Literature Review

Abstract Physics learning at the college level using the Case-Based Learning approach offers an interactive method through the analysis of real cases or situations relevant by integrating theory with practical applications and enhancing a deep understanding of concepts. The objective of this research is to analyze literature studies on Case-Based Learning in physics education at the university level. This study employs the Systematic Literature Review (SLR) method. Data collection was carried out by documenting and reviewing related articles published between 2014-2024. Based on the search results, a total of 80 relevant articles were obtained, which were then filtered based on inclusion criteria to yield 10 articles used as primary studies. Each article was coded, and then analyzed based on the skills developed from the research findings. The results of the systematic literature review indicate that Case-Based Learning in physics education effectively enhances students’ analytical and problem-solving skills. This approach facilitates the integration of theory and practical applications through real-life case studies, enabling students to develop their critical and analytical thinking skills.