Real Case Research Articles

White collar crime identification in India: A Critical study

White-collar crime poses a significant challenge to the economic and social fabric of India, as it involves non-violent financial offenses committed by individuals or organizations in positions of trust and authority. This research paper critically examines the landscape of white-collar crime identification in India, shedding light on the types of offenses prevalent, the mechanisms employed for detection, and the complex challenges faced by law enforcement and regulatory bodies. The study begins with an exploration of the various types of white-collar crimes observed in the Indian context, including fraud, corruption, embezzlement, money laundering, insider trading, and cybercrimes. Real-life case studies are presented to illustrate these offenses and the methods employed by perpetrators. Detection mechanisms are a central focus of this paper, with an in-depth analysis of financial audits, forensic accounting techniques, and digital monitoring tools used to identify and investigate white-collar crimes. The role of technology, particularly artificial intelligence and data analytics, is examined in enhancing detection capabilities.

Artifice and animality in The Tempest: Imagining Caliban

The article discusses William Shakespeare's Caliban in the context of early modern knowledge culture and its representation of the ‘monstrous’ in wonder cabinets and works of natural history. According to Stephano, Caliban is ‘a present for any emperor’ (2.2.69). This connects him to the real-life case of Pedro Gonsalvus, who became just such a ‘present’ given to the French king in 1547. His face and body overgrown with hair, Gonsalvus and subsequently his equally hirsute children became marvels at several European courts. Their case shows the same intermingling of artifice and animality that went into the creation of Caliban and shaped his afterlife in the theatre.

Artificial neural network-based water distribution scheme in real-time in long-distance water supply systems

ABSTRACT Hydraulic models of long-distance water supply systems are usually used to regulate valves and pumps to realize the expected water distribution. Establishing and calibrating the hydraulic model is time-consuming and requires many engineering parameters, which are usually uncertain. This paper proposes a metamodel based on artificial neural networks (ANNs) to replace the computationally costly hydraulic model. The metamodel is designed to bypass the modeling and calibration processes of the hydraulic model and directly estimate the target state of valves and pumps to realize real-time water distribution. The proposed approach uses the water levels of reservoirs and the flow demands of water plants as input data to the ANN. The metamodel's output prescribes the opening of regulating valves and the speed of pumps. A realistic case study is presented to validate the accuracy and efficiency of the approach. The results show that ANN is feasible as a state predictor to realize real-time water distribution in practical water supply projects.

Capacity Degradation Assessment of Lithium-Ion Battery Considering Coupling Effects of Calendar and Cycling Aging

Capacity degradation of lithium-ion batteries largely determines the cost, performance and environmental impact of various products such as renewable energy production systems, portable electronics, and electric vehicles. Degradation assessment is thus of great importance for prognostic and health management of batteries, which ensures a stable production and operation. There are typically two leading sources contributing to the degradation of a lithium-ion battery, namely, cycling aging during charge/discharge cycles and calendar aging during idle states. However, most existing studies on degradation assessment either only consider a single source or ignore the coupling of these two sources, which makes the evaluation inefficient. A common observation in practice is that a battery tends to degrade faster when it experiences charge/discharge cycles continuously for a longer time. To capture this feature, this paper introduces the concept of the cumulative uninterrupted cycling duration (CUCD), which is defined as the consecutive operation duration since a battery ends the idle state. The CUCD can also help to model the coupling between the calendar and cycling aging. This paper then proposes to model the drift rate of cycling aging as a function of the CUCD and the functional form is determined with the monotonic spline. We estimate the model parameters by maximizing the likelihood function. Hypothesis testings toward the significance of the coupling between two aging sources and the monotonicity of the drift rate function under cycling aging are also provided. The effectiveness and the superiority of the model are validated using numerical and real case studies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study aims to characterize the degradation of lithium-ion battery simultaneously considering calendar and cycling agings and the coupling effect. Unlike previous research that only supplied experimental results to qualitatively illustrate the existence of the coupling effect between these two aging sources, this study provides procedures to quantitatively test the significance of such an effect. In addition, previous research ignored the coupling effect in the degradation modeling as it is difficult to capture, this study propose the concept of CUCD to characterize it. The CUCD is also introduced as a stress factor for cycling aging, which is based on the fact that a battery will degrade faster when it experiences charge/discharge cycle continuously for a longer time. Hypothesis testing is also provided to illustrate this monotonicity. To deal with another challenge that the expert knowledge of the functional form between cycling aging and CUCD is unavailable except for the monotonicity, a nonparametric method with the shape-restricted spline is adapted. The estimation procedures for the model parameters are also presented.

Configuration of multi‐shaper Time‐Sensitive Networking for industrial applications

AbstractIEEE 802.1 Time‐Sensitive Networking (TSN) has proposed several shapers, for example, time‐aware shaper (TAS, 802.1Qbv), asynchronous traffic shaping (ATS, 802.1Qcr), credit‐based shaper (CBS, 802.1Qav), and cyclic queuing and forwarding (CQF, 802.1Qch). The shapers have their advantages and disadvantages and can be used in isolation or in combination to address the varied timing requirements of industrial application streams. There is very limited work on how to analyse and configure shaper combinations. The authors are interested in the configuration optimisation of multi‐shaper TSN networks, targeting the TAS + CBS, TAS + ATS, and TAS + Multi‐CQF combinations. The authors first propose multi‐shaper integration approaches, focusing on a novel iterative delay analysis for TAS + ATS, an approach to integrate TAS and CQF by placing constraints on TAS scheduling as well as the TAS and CBS integration. We formulate the combinatorial optimisation problem of configuring multi‐shaper TSN networks, which consists, for example, of the routing of streams, the assignment of streams to the egress port queues, and the synthesis of gate control lists for TAS. Then, the authors propose a solution based on a simulated annealing metaheuristic. The proposed solutions are evaluated on large realistic test cases, up to tens of thousands of streams and devices.

A Review on Fire Accidents in Electric Vehicles

Abstract: Electric vehicles are replacing internal combustion engines drastically because of high performance and efficiencies compared to internal combustion engines. Weight and cost of production are also very low compared to ICE's. Nowadays electric vehicle usage is high and some of the EVs are catching fire because of various external factors. In this study, we are going to identify the factors for fire accidents in Electric vehicles by following real-life case studies and discuss some safety measures to prevent the vehicles from catching fire.

Simulation-driven design of smart gloves for gesture recognition

Smart gloves are in high demand for entertainment, manufacturing, and rehabilitation. However, designing smart gloves has been complex and costly due to trial and error. We propose an open simulation platform for designing smart gloves, including optimal sensor placement and deep learning models for gesture recognition, with reduced costs and manual effort. Our pipeline starts with 3D hand pose extraction from videos and extends to the refinement and conversion of the poses into hand joint angles based on inverse kinematics, the sensor placement optimization based on hand joint analysis, and the training of deep learning models using simulated sensor data. In comparison to the existing platforms that always require precise motion data as input, our platform takes monocular videos, which can be captured with widely available smartphones or web cameras, as input and integrates novel approaches to minimize the impact of the errors induced by imprecise motion extraction from videos. Moreover, our platform enables more efficient sensor placement selection. We demonstrate how the pipeline works and how it delivers a sensible design for smart gloves in a real-life case study. We also evaluate the performance of each building block and its impact on the reliability of the generated design.

Robust well-balanced method with flow resistance terms for accurate wetting and drying modeling in shallow water simulations

Hydrodynamic simulations in shallow water environments require careful consideration of the Wetting and Drying (WD) processes, which poses challenges to accurately modeling moving boundaries. This study introduces a novel method called the flow resistance method (FRM), which builds upon the foundation of the Negative-Depth Method (NDM) to tackle the intricacies of the moving boundary problem. Inspired by the Navier-Stokes/Brinkman (NSB) model from porous media theory, FRM incorporates a continuous function related to additional flow resistance that is proportional to the flow velocity. This approach facilitates a seamless transition between the exposed bed and fluid area wherein the additional flow resistance becomes 0 within the fluid area and approaches infinity in the exposed bed. Consequently, FRM adeptly and implicitly manages the moving boundary problem, causing a rapid decay of flow velocity to 0 in the exposed bed. In order to test the performance of FRM, four typical numerical experiments were conducted, along with an examination of a real-life case. Accuracy, robustness, and computational efficiency were assessed as key performance indicators. The simulations demonstrate that FRM adeptly tracks the moving water front, yielding precise results. Furthermore, when compared to established methods such as the Element Removal Method (ERM) and NDM, FRM exhibits broader applicability and achieves significant enhancements in the key performance indicators. These findings underscore the promising potential and broad applications of FRM in the field.

A Mathematical Model for Integrated Disaster Relief Operations in Early-Stage Flood Scenarios

When a flood strikes, the two most critical tasks are evacuation and relief distribution. It is essential to integrate these tasks, particularly before the floodwater reaches the vulnerable area, to minimize loss and damage. This paper presents a mathematical model of vehicle routing problems to optimize an integrated disaster relief operation. The model addresses routing for both the evacuation and relief distribution tasks in the early stages of a flood, aiming to identify a minimal number of vehicles required with their corresponding routes to transport vulnerable individuals and simultaneously distribute emergency relief. The new model incorporates several features, including vehicle reuse, multi-trip and split delivery scenarios for evacuees and emergency relief items, uncertainty in evacuation demands, and closing time windows at evacuation points. Due to the complexity of vehicle routing problems, particularly in large-scale scenarios, the exact approach for obtaining optimal solutions is time-consuming. Therefore, we propose the use of a metaheuristic algorithm, specifically a modified genetic algorithm, to find an approximate solution for the proposed model. We apply the developed model and modified algorithm to various simulated flood scenarios and a real-life case study from Indonesia. The experimental results demonstrate that our approach requires fewer vehicles compared to standard models for similar scenarios. Moreover, while the exact approach fails to find optimal solutions within a reasonable timeframe for large-scale scenarios, our new approach provides near-optimal solutions in a much shorter time. In smaller simulated scenarios, the modified genetic algorithm obtains optimal or near-optimal solutions approximately 92.5% faster than the exact approach.

Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study

The successful implementation of predictive maintenance for offshore wind farms suffers from a poor understanding of the consequential short-term impacts and a lack of research on how to evaluate the cost-efficiency of such efforts. This paper aims to develop a methodology to explore the short-term marginal impacts of predictive maintenance applied to an already existing preventive maintenance strategy. This method will be based on an analysis of the performance of the underlying predictive model and the costs considered under specific maintenance services. To support this analysis, we develop a maintenance efficiency measure able to estimate the efficiency of both the underlying prediction model used for predictive maintenance and the resulting maintenance efficiency. This distinction between the efficiency of the model and the service results will help point out insufficiencies in the predictive maintenance strategy, as well as facilitate calculations on the cost–benefits of the predictive maintenance implementation. This methodology is validated on a realistic case study of an annual service mission for an offshore wind farm and finds that the efficiency metrics described in this paper successfully support cost–benefit estimates.

Multi-objective optimization using improved NSGA-II for integrated process planning and scheduling problems in a machining job shop for large-size valve.

This paper studied an integrated process planning and scheduling problem from a machining workshop for large-size valves in a valve manufacturing plant. Large-size valves usually contain several key parts and are generally produced in small-series production. Almost all the parts need to be manufactured in the same workshop at the same time in the plant. Facilities have to handle various items in one order, including different models, sizes, and types. It is a classical NP-hard problem on a large scale. An improved NSGA-II algorithm is suggested to obtain satisfactory solutions for makespan and manufacturing costs, which involve large optimization parameters and interactions. A two-section encoding method and an inserting greedy decoding method are chosen to enable the algorithm. The dynamic population update strategy based on dynamic population update and the adaptive mutation technique depending on the population entropy changing rate are selected for enhancing both the solution quality and population diversity. The methodology was successfully implemented in a real-life case at a major valve machining workshop operated by Yuanda Valve Company in China. By taking into account realistic factors and restrictions that have been identified from a real-world manufacturing setting, this technique aids in bridging the knowledge gap between present IPPS research and practical valve production implementations.

Assessing the benefits of approximately exact step sizes for Picard and Newton solver in simulating ice flow (FEniCS-full-Stokes v.1.3.2)

Abstract. Solving the momentum balance is the computationally expensive part of simulating the evolution of ice sheets. The momentum balance is described by the nonlinear full-Stokes equations, which are solved iteratively. We use the Picard iteration and Newton's method combined with Armijo step sizes and approximately exact step sizes, respectively, to solve these equations. The Picard iteration uses either no step size control or the approximately exact step sizes. We compare the variants of Newton's method and the Picard iteration in benchmark experiments, called ISMIP-HOM experiments A, B, E1, and E2. The ISMIP-HOM experiments consist of a more realistic domain and are designed to test the quality of ice models. For an even more realistic test case, we simulate the experiments E1 and E2 with a time-dependent surface. We obtain that approximately exact step sizes greatly reduce the necessary number of iterations for the Picard iteration and Newton's method with nearly no increase in the computation time for each iteration.

Transitioning complex socioeconomic modeling to informed and visualized decision-making: A tightly coupled planning support system

The integration of urban models into decision-making remains challenging, primarily attributed to the complex nature of urban mechanisms. This study develops a PSS with sufficient methodological complexity to examine socioeconomic dynamics while simultaneously delivering modeling results in a manner that is understandable to non-expert stakeholders for informed decision-making. This paper tests, applies and implements the system in a real-life case at a fine scale collaborating with Sangamon County Regional Planning Commissions. It explores regional socioeconomic mechanisms across economic sectors addressing multicollinearity, projects potential consequences of an investment plan, and develops a web-based PSS tool for their informed decision-making. Our results identify how each economic sector behaves differently concerning location choices for future developments impacted by the investment, necessitating the use of differentiated planning approaches tailored to each sector. Findings underscore the value of assessing the heterogeneity of dynamic land development with spatial explicitness for informed decisions. We advocate for the delivery of this modeling via an interactive PSS platform, fostering responsive and collaborative dialogues. This paper contributes not only to the development of an integrated modeling framework but also to the planning literature, bridging the gap between complexity and accessibility in decision-making.

Analisis Resepsi Isu Pelecehan Seksual Terhadap Pekerja Perempuan Pada Film Bombshell

A real-life case at a well-known media company, Fox News in 2016, which was later made into a movie titled "Bombshell." This incident sparked the researcher's interest in exploring the audience's reception of the issue of sexual harassment of female workers presented in the movie. This incident sparked the researcher's interest in exploring the audience's reception of the issue of sexual harassment of female workers presented in the movie. Therefore, this study involved several female informants who work in various fields with diverse educational backgrounds and occupations. By using Stuart Hall's reception theory analysis, this research aims to understand and position the audience towards the messages contained in the movie "Bombshell" (2019). The research method applied is descriptive qualitative with a constructivist paradigm. Data collection was done through in-depth interviews to get in-depth perceptions from each informant. The results showed that the four informants were in The Dominant Hegemonic-Code Position. Although there are differences in answers to some questions per scene, this variability is influenced by the diverse work and life backgrounds of each informant, including education and employment. Thus, this difference provides a broader context in evaluating the audience's reception of the issue of sexual harassment raised in the movie "Bombshell" (2019).

Spacelord : Private and Secure Smart Space Sharing

Space sharing services like vacation rentals and meeting rooms are being equipped with smart devices such as cameras, door locks and many other sensors. However, the sharing of such devices poses privacy and security problems, as there is typically no clear control transfer between owners and users. In this article, we propose Spacelord , a system to time-share smart devices contained in a shared space privately and securely while allowing users to configure them. When a user stays at a space, Spacelord ensures that the smart devices contained in it run code and configurations the user trusts while removing pre-installed code and configurations. When the user leaves the space, Spacelord reverts any changes the user has introduced to the smart devices, deletes any remaining private data and lets the owner take back control over the devices. We evaluate Spacelord for two realistic space-sharing cases—smart home and coworking meeting room—and observe smart space provisioning delays of ~82sacross three different platforms. Moreover, the average runtime overhead of our system varies from 7.8% to 11.8% across different hub hardware, running native applications.

A game-theoretic approach to mitigate charging anxiety for electric vehicle users through multi-parameter dynamic pricing and real-time traffic flow

While price-based demand response is universally recognized as crucial for managing electric vehicle charging load, the academic literature has explored diverse mechanisms for its implementation. Prior research has revealed that applying load management schemes based on price-based demand response programs results in higher scheduling costs for low or constant-energy consumers. In addition, the inherent uncertainties involved in arrival, service requirements, market pricing and user behaviours have posed serious challenges in managing real-time charging operations. To handle these challenges, this work has addressed the charging problem at the scheduling level by analysing arrival, waiting, service and departure requests. This work proposed a mathematical model to coordinate charging to minimize the total charging cost and time for a given number of vehicles. We have developed an advanced charging algorithm and introduced a multi-leader multi-follower Stackelberg game. This strategic model ensures a Nash equilibrium, aligning the interests of consumers (vehicles) and providers (charging stations), ensuring a fair and balanced charging system. Where, charging load demand, real-time pricing, charging location, and vehicle types are used as input parameters. In the first stage, the real-time pricing obtained from the Spanish Electricity network is used to schedule the charging load, whereas the second stage is dedicated to using the proposed multi-parameter pricing for customer satisfaction and reduced cost. The results demonstrate the superior performance of our proposed scheme compared to existing approaches. Finally, we present a real-life case study to illustrate the practical effectiveness of the proposed techniques. The study underscores the potential of our approach in real-world scenarios, offering a promising avenue for enhancing efficiency and user experience in charging systems.

A column and row generation approach to the crowd-shipping problem with transfers

Crowd-shipping is a last-mile delivery concept in which commuters pick up and deliver parcels on their pre-existing paths. In urban areas, crowd-shipping circumvents problems that traditional last-mile delivery systems suffer from, such as road congestion and lack of parking spaces, especially if more sustainable modes of transport are utilized, like bikes or e-bikes. Using transfers between crowd-shippers allows for expanding the service area and improving the overall performance. However, as this requires synchronization over space and time, it makes the problem more complex. In this work, we develop a model that can encompass fully heterogeneous crowd-shippers and parcels. Thereby, it allows for both direct time-synchronized transfers as well as intermediate storage at designated parcel lockers. We design a column generation algorithm to solve large-scale realistic instances to optimality. We extend the problem to allow crowd-shippers to carry multiple parcels at the same time and for this, we extend the algorithm to simultaneous column and row generation. We evaluate the performance of our algorithm as well as the potential of crowd-shipping with transfers on a realistic case study of a bike-based crowd-shipping system in Washington DC. Our methods solve realistic instances with 1000 crowd-shippers and 1000 parcels within minutes. The results show that a gain in revenue and service level of 30% can be obtained by allowing transfers. By letting part of the population of crowd-shippers carry two or three parcels at the same time, the revenue and service level can be further increased by 30 to 50%. Maximum locker capacities are shown to be reasonable and are the highest in areas where there is a large gap between the moment when parcels are dropped off and when they are picked up from parcel points, which are mainly in the city center.

A qualitative exploration of e-cigarette prevention advertisements' effectiveness among college students in China.

The rapid growth of e-cigarette usage among youth and young people has emerged as a significant public health concern. It is imperative to initiate effective vaping prevention campaigns and undertake relevant research to address this pressing issue. This research seeks to identify effective video advertisements to deter young people from starting to use e-cigarettes. It aims to offer evidence-based insights and recommendations for creating communication materials and designing messages for youth e-cigarette prevention efforts. College students aged 18-24 years (n=40) participated in focus groups within this qualitative study. After viewing four stimulus videos, participants discussed what they perceived as effective and ineffective video characteristics, as well as suggestions for future videos. Effective video characteristics included the use of real-life testimonials, displaying specific health hazards, revealing harmful chemical ingredients and the deceptive nature of flavors, and positively perceived effectiveness. Participants generally found that videos with strong visual impact and graphics were more engaging and that approaches using fear and emotion were more effective. Ineffective characteristics included complex and exaggerated information, lack of empathy and irrelevance, insufficiently specific information, extreme and death-themed content, industry messages, as well as preachy tones, animations, metaphors, dull formats, excessive length, and scenes of e-cigarette use. Developing anti-e-cigarette campaign materials for youth necessitates target audience-focused qualitative research. This helps in deeply exploring and identifying effective themes and messages, as well as video characteristics and details while avoiding ineffective or even misleading messages and themes from young people's perspectives outside the United States. Future development of e-cigarette prevention videos for Chinese college students may consider incorporating localized real-life testimonial cases to convey specific harms, including self-efficacy information, and utilizing fear and emotional appeals.

Prediction-based decomposition optimisation for multi-portfolio supply chain resilience strategies under disruption risks

ABSTRACT This paper focuses on the design of supply chain (SC) risk mitigation and recovery strategies during long-term disruptions caused by the COVID-19 outbreak, which affect both suppliers and plants. Consequently, concurrent disruptions in supply and production are observed, which vary in duration and result in time-varying reductions in supply and production capacity. To cope with long-term disruptions, a modified multi-portfolio approach that integrates simulation and predictions is proposed to develop efficient mitigation and recovery plans. This approach involves selecting primary and recovery supply and production portfolios concurrently. To achieve this objective, time-dependent mixed integer programming (MIP) models that incorporate preparedness and recovery measures are developed to optimise SC operations. A prediction-based decomposition optimisation method is proposed to solve MIP problems and coordinate supply and production portfolios under disruptions and uncertainties. Furthermore, a heuristic approach is established to provide a comprehensive solution process. Finally, computational experiments and comparative analysis are conducted on a real-life case study. The results demonstrate that the proposed modelling and optimisation methods can effectively address disruptions and improve SC resilience. In addition, the developed models and approaches have the potential to serve as decision-making tools in SC management during disruptions.

A hybrid decision support framework for planning a risk-based audit engagement

Internal audit engagement planning is a fundamental component of internal audit activities, and a poor plan can impair the audit quality. However, studies on internal audit engagement planning have limited exposure in the literature. To the best of our knowledge, a comprehensive and robust model has not been established to address the decision-making problems associated with the audit engagement planning process. To fill these gaps, an integrated multi-stage framework is presented for developing a risk-based audit engagement plan. The designed framework includes three stages to carry out the decision procedure. First, a combined method of best worst method and weighted probability-impact score is used to evaluate the risk exposure. Second, risk assessment results are integrated into the weighted multi-choice goal programming (WMCGP) model for selecting the audit scope and allocating budgeted audit hours. Third, WMCGP model assigns audit staff to the selected audit scope to satisfy auditor preference and fitness. Two real-life case studies illustrate that the proposed framework is a useful tool for selecting the critical audit scope and deploying appropriate resources. The research results outperform the manual procedures, enabling the audit team to use constrained resources more effectively and efficiently.