Real Case Research Articles

An Integrated Lean and Six Sigma Framework for Improving Productivity Performance: A Case Study in a Spanish Chemicals Manufacturer

In the pursuit of operational excellence and enhanced competitiveness, a wide range of industries have turned to methodologies such as Lean and Six Sigma; however, in the chemical sector, their application is very limited. This paper presents a Lean Six Sigma framework to identify and reduce sources of variability occurring in the final product composition of a Spanish SME fertilizer manufacturer. The company faced important challenges related to product variability, adversely affecting overall productivity. A real-life case of the Lean Six Sigma implementation was conducted over two years, and its applicability and ability to improve productivity performance were thoroughly assessed. The proposed framework successfully integrated Lean and Six Sigma methodologies, i.e., process mapping (value stream mapping), root cause analysis (Ishikawa cause–effect diagram), project management (SIPOC and DMAIC), and statistical process control, and demonstrated practical benefits for the case company by identifying the key variables affecting product variability and determining their optimal levels. A substantial 50% reduction in the variability of several products and a 42% reduction in material preparation time were achieved. These reductions resulted in a 40% reduction in costs associated with product losses and a 54% reduction in costs from raw material losses.

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.

Solar Power Supply for Sensor Applications in the Field: A Guide for Environmental Scientists

The move toward sophisticated sensor networks in ecological applications requires a substantial amount of energy. Energy storage solutions based simply on batteries are often not sufficient to cover the energy needs, so a standalone power supply using solar energy harvesting is generally required. However, designing an appropriate solar power supply without oversizing and avoiding output power disruption all year long is not a trivial task. This paper provides a set of guidelines as well as useful information and advice for environmental researchers and other non-experts to select the right components when designing their own autonomous solar power supply for a range between 10 mW and 10 W. The design steps are compiled into a comprehensive document, free of irrelevant information yet still presenting a general overview of the solar power supply design process, in order to make this task more accessible and understandable for non-experts. The methodology for simple initial dimensioning was carried out and applied to a real-life use case by using the estimated or measured daily consumption combined with free meteorological data of the deployment site provided by various websites. Next, an hourly simulation completed the first sizing. A year of experimental results validated the methodology.

Data Security and Data Protection in Cloud Privacy Systems

In this modern world, the internet has become very influential in our daily lives. The Internet has many uses to solve various problems, one of which is full hardware storage, therefore the Internet provides software-based data, for example, cloud storage applications. The study aims to analyze and evaluate various data security techniques and protection methods used in the cloud computing framework, focusing on vulnerability identification, risk mitigation strategy assessment, and user security awareness improvement. In addition, this study aims to analyze data security techniques and data protection approaches applied to cloud systems. In addition, the study also assesses potential risks and mitigates security threats such as cyberattacks and data breaches. Using qualitative methodologies, the study investigated relevant literature, examined real-life cases of data breaches, and analyzed the effectiveness of security measures such as encryption and multi-factor authentication. The findings show that cloud systems remain vulnerable to cyber threats like DDoS attacks, credential theft, and data breaches due to inadequate security protocols. Key recommendations include adopting encryption, improved multi-factor authentication practices, and encouraging collaboration between cloud service providers and users to strengthen data security. The research contributes by proposing a comprehensive approach to improving data protection in cloud environments, ensuring a secure and resilient infrastructure for users. Implementing these findings will support the development of more secure cloud technologies and increase user confidence in data security practices. It is hoped that the study's results can provide recommendations to improve data security and data protection in cloud systems.

Assessing Psychological Explanations for Jesus’ Post-Resurrection Appearances: A Response to Stephen H. Smith

Abstract In a recent article published in this journal, Stephen Smith acknowledges that bereavement hallucination is an unlikely explanation for Jesus’ post-resurrection appearances, and suggests supplementing it with theories concerning collective delusion, distorted memory, and cognitive dissonance. Our response contributes to the discussion by bringing our expertise in psychology and New Testament studies together to advance interdisciplinary study on this important topic. We show that Smith’s discussion confounds real-life cases and laboratory experiments on memory, and mass psychogenic illness with illusions. Moreover, Smith fails to consider a number of important differences between the case concerning Jesus’ post-resurrection appearances and cases of cognitive dissonance and other psychological theories, which indicate that these theories are not plausible explanations concerning Jesus’ post-resurrection appearances.

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.

Why Bystanders Report Intimate Partner Violence Anonymously: A Qualitative Analysis of Real-Life Cases

Although bystanders can play an important role in combating intimate partner violence (IPV), they also experience unique barriers and challenges that may prevent them from involvement. While anonymous reporting could potentially address some of these barriers, this form of intervention has rarely been studied. Using a thematic analysis of case files ( N = 34 reports), this study investigates bystanders’ reasons to report suspicions of IPV anonymously to a domestic violence hotline. Results indicate that anonymity allowed bystanders to balance the desire to protect the victim(s) from harm with the fear of potential consequences for themselves and/or their relationship with the victim(s).

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.

Deepfake Detection: A Comprehensive Survey from the Reliability Perspective

The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. We identify three reliability-oriented research challenges in the current Deepfake detection domain: transferability, interpretability, and robustness. Moreover, while solutions have been frequently addressed regarding the three challenges, the general reliability of a detection model has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake-related cases in court. We, therefore, introduce a model reliability study metric using statistical random sampling knowledge and the publicly available benchmark datasets to review the reliability of the existing detection models on arbitrary Deepfake candidate suspects. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of the reliably qualified detection models as reviewed in this survey. Reviews and experiments on the existing approaches provide informative discussions and future research directions for Deepfake detection.

For Justice! The Double Burden of Women in Indonesia from the Perspective of The Compilation of Islamic Law

This study investigates the phenomenon of the double burden faced by women in Indonesia through the lens of the Compilation of Islamic Law (Kompilasi Hukum Islam, KHI). The double burden refers to the dual roles that women fulfill, balancing responsibilities within the household and in the public workforce. Utilizing a qualitative approach and document analysis, this research explores how the KHI addresses women’s roles and responsibilities in both spheres. Real-life cases of Indonesian women are examined to assess how interpretations of the KHI impact their experiences. Findings indicate that while the KHI acknowledges women’s participation in multiple facets of life, its implementation is often constrained by patriarchal interpretations that lack gender sensitivity. This has resulted in women bearing disproportionate responsibilities at home and work. The study underscores the need for a more progressive reinterpretation of the KHI to reduce the double burden on women, promoting gender equality and enhancing women’s welfare in Indonesia. These insights are intended to inform strategies for advancing gender equity within the framework of the KHI, contributing to broader social and legal reforms.

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.

Economical-epidemiological analysis of the coffee trees rust pandemic.

Coffee leaf rust is a prevalent botanical disease that causes a worldwide reduction in coffee supply and its quality, leading to immense economic losses. While several pandemic intervention policies (PIPs) for tackling this rust pandemic are commercially available, they seem to provide only partial epidemiological relief for farmers. In this work, we develop a high-resolution spatiotemporal economical-epidemiological model, extending the Susceptible-Infected-Removed model, that captures the rust pandemic's spread in coffee tree farms and its associated economic impact. Through extensive simulations for the case of Colombia, a country that consists mostly of small-size coffee farms and is the second-largest coffee producer in the world, our results show that it is economically impractical to sustain any profit without directly tackling the rust pandemic. Furthermore, even in the hypothetical case where farmers perfectly know their farm's epidemiological state and the weather in advance, any rust pandemic-related efforts can only amount to a limited profit of roughly 4% on investment. In the more realistic case, any rust pandemic-related efforts are expected to result in economic losses, indicating that major disturbances in the coffee market are anticipated.

Identifying the Host Galaxies of Supermassive Black Hole Binaries Found by Pulsar Timing Arrays

Supermassive black hole binaries (SMBHBs) are thought to form in galaxy mergers, possessing the potential to produce electromagnetic (EM) radiation as well as gravitational waves (GWs) detectable with pulsar timing arrays (PTAs). Once GWs from individually resolved SMBHBs are detected, the identification of the host galaxy will be a major challenge due to the ambiguity in possible EM signatures and the poor localization capability of PTAs. To aid EM observations in choosing follow-up sources, we use NANOGrav’s galaxy catalog to quantify the number of plausible hosts in both realistic and idealistic scenarios. We outline a host identification pipeline that injects a single-source GW signal into a simulated PTA data set, recovers the signal using production-level techniques, quantifies the localization region and number of galaxies contained therein, and finally imposes cuts on the galaxies using parameter estimates from the GW search. In an ideal case, the 90% credible areas span 29–241 deg2, containing about 14–341 galaxies. After cuts, the number of galaxies remaining ranges from 22 at worst to one true host at best. In a realistic case, these areas range from 287 to 530 deg2 and enclose about 285–1238 galaxies. After cuts, the number of galaxies is 397 at worst and 27 at best. While the signal-to-noise ratio is the primary determinant of the localization area of a given source, we find that the area is also influenced by the proximity to nearby pulsars on the sky and the binary chirp mass.

Decoherence time control by interconnection for finite-level quantum memory systems

This paper is concerned with open quantum systems whose dynamic variables have an algebraic structure, similar to that of the Pauli matrices for finite-level systems. The Hamiltonian and the operators of coupling of the system to the external bosonic fields depend linearly on the system variables. The fields are represented by quantum Wiener processes which drive the system dynamics according to a quasilinear Hudson-Parthasarathy quantum stochastic differential equation whose drift vector and dispersion matrix are affine and linear functions of the system variables. This setting includes the zero-Hamiltonian isolated system dynamics as a particular case, where the system variables are constant in time, which makes them potentially applicable as a quantum memory. In a more realistic case of nonvanishing system-field coupling, we define a memory decoherence time when a mean-square deviation of the system variables from their initial values becomes relatively significant as specified by a weighting matrix and a fidelity parameter. We consider the decoherence time maximization over the energy parameters of the system and obtain a condition under which the zero Hamiltonian provides a suboptimal solution. This optimization problem is also discussed for a direct energy coupling interconnection of such systems.

Efficient Model Calibration Using Submodels

AbstractGroundwater models tend to become increasingly detailed to accommodate increasing data availability and higher accuracy demands from stakeholders. As runtimes increase almost quadratically with the number of model cells, this makes the models ever more computationally demanding. This high computational demand introduces challenges for the history‐matching (calibration) process as this is an algorithmic process that needs hundreds or thousands of model‐runs to obtain the model sensitivities needed to estimate parameters. Model runs may take hours or days to complete which in fact, is often a reason to discard the history‐matching all together. As a solution, we present a practical approach to use sub‐modeling in combination with parallelization for automatic history‐matching. Therefore a large model is subdivided into smaller models to carry out the sensitivity simulations. With a realistic case the method is elaborated, after which the method is demonstrated in the history‐matching of the transient Dutch National Groundwater Flow model. In this manner the model, which consisted of over 12 million model cells, could be optimized using 416 sub‐models and altogether 2,188 parameters in 1 week. This would take years to complete in a conventional way.

Drug Survival and Clinical Course of Patients with Cancer Treated with Biologic Therapy for Psoriasis.

Background/Objectives: Patients with treated solid tumors (TST) are a highly heterogeneous and difficult-to-treat population due to the risk of disease progression/recurrence or infection. Methods: We conducted an observational, retrospective, single-center study at the Dermatology Clinic of Turin with a focus on the special population of cancer patients with psoriasis treated with biologics. Results: As of July 2023, 52 psoriatic patients with a prior/concomitant history of malignancy had taken biologic drugs. The median age was 67 years, and the median age of cancer onset was 55 years. The most common tumors were gastrointestinal cancer and melanoma. After the tumor diagnosis, 61% received an anti-IL17 drug; 37 patients continued the initiated biologic therapy, while 12 switched drugs due to secondary inefficacy. The estimated biologic DS was 55.6% at 50 months. Evidence suggests that IL-17 is a key pathogenic factor involved in tumorigenesis, resulting in a lower risk of malignancies in subjects managed with IL-17 inhibitors. Similarly, IL-23 plays a role in suppressing innate immunity and promoting tumor and metastases development. This is a consistent real-life case series that support the use of biologic drugs in patients with TST. Conclusions: IL-23 and IL-17 inhibitors, being immunomodulators rather than immunosuppressants, may be a safe option for patients in an active oncological setting and for immune-correlated adverse events.

Uptake of ecosystem service concept in urban blue-green infrastructure planning and decision-making in Russia

ABSTRACT In the scientific community, ecosystem services (ESs) are well-recognized as a promising concept to more actively consider and plan for the multitude of benefits provided by the green environment and thus promote sustainable urban development. Yet, to have an impact on decision-making, the concept must spread from academia to practice. To understand to what extent ES have been adopted in practice and decision-making in Russia, we conducted a series of interviews and an online survey with both practitioners and scientists and complemented it by document analysis. Our results showed that ES are not adequately reflected in urban decision-making and planning processes. Moreover, we found no explicit references to ES in strategic urban planning and landscape development projects, although there are few implicit references in several reviewed documents. Among the identified challenges for better uptake of the concept in practice were many disagreements associated with the lack of a unified terminology, typology, and methods. Stakeholders understand the potential of applying ES valuation in practice, which includes a necessity to develop methodological materials and different tools based on real-life cases. Finally, a list of proposals on possible ways to better include ES in urban planning and in decision-making processes was formed by the respondents.

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.