Real Case Research Articles

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.

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.

The role of hubs and economies of scale in network expansion

Freight distribution often operates on the basis of consolidation, which is achieved through the use of hub facilities that allow for economies of scale. Freight networks need to be expanded to meet future demand, to cater for new markets, and to accommodate trends in global supply chains, for which strategic decisions need to be made. These decisions mainly entail the number and location of new hubs to be established. As network expansions require significant capital, striking a balance between the benefits afforded by the new hubs and the expansion costs is crucial. This paper investigates a hub network expansion problem where the configuration of the resulting network is determined by the trade-off between the fixed costs of locating new hubs and new links, as well as routing costs of shipping commodities, and the cost reductions achieved through economies of scale, without imposing a predetermined network structure. This paper also describes a mixed integer programming formulation of the problem and a Benders decomposition algorithm that uses several enhancement techniques to efficiently solve the model to optimality. The application of the algorithm on a real-life case study arising in the expansion of the Indonesian freight transport network yields several managerial insights. In particular, expanding the network with additional hubs and links can yield substantial cost savings, averaging at 47.6%, although at the expense of an increase in the length of the commodity paths. Failing to operate the network at the selected level of economies of scale can result in an increase in the routing costs by up to 58.8%. Expanding the network with no additional hubs leads to a rise in total costs of up to 20.9%. Finally, lower economies of scale leads to an increase in the length of commodity paths, with the routing cost being identified as the most sensitive parameter.

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.

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.

8.A. Scientific session: Engage, co-create, collaborate, transform: uniting practice and research for health innovation

Abstract Background Public health practitioners are increasingly challenged by theoretical, political, and contextual changes and innovations. To ensure that innovations are sustainable, it is important not only to include stakeholders (e.g. end-users, implementers, policy-makers), but also to actively involve them in the development, implementation and transformation of public health innovations. Participatory action research, co-creation and stakeholder engagement are proven methods to co-produce effective innovations, strengthen existing networks and promote implementation by increasing fit and applicability in practice. In this workshop, our overall aim is to bring together practitioners and researchers to improve mutual understanding and adapt practices in development, implementation and transformation of innovations. The objectives of the workshop are: 1) to increase participants’ understanding of the circumstances under which stakeholder involvement is most meaningful, and 2) to increase participants’ awareness of tools, strategies or theories to overcome challenges in stakeholder involvement. Workshop format The workshop consists of two parts: four interactive presentations that introduce important development steps of public health innovation and a problem-based discussion using a real-life case that builds upon the presentations. Interactive presentations describe (1) Theory of Change (ToC) approach for active collaboration with stakeholders, (2) co-creation of a serious game for and with healthcare providers, (3) visualising collaboration through Social Network Analysis to improve implementation, and (4) enhancing scale up with PRACTIS and Design Thinking. During the presentations, interactive tools are used to help identify the characteristics of the workshop participants (e.g. work setting, type of stakeholders), as well as their interests and needs (e.g. challenges faced, methods used). During the second part of the workshop, a health innovation is chosen from a number of pre-selected innovations from European contexts (e.g. BOB campaign, Schools4Health, LETHE) that aligns most with participants’ background and interests. Next, the main goal and known challenges within these innovations are introduced, which aids participants to formulate intermediate goals using the ToC approach. Then, they answer questions and make decisions for the discussed stages of innovation development (e.g. “Which gaps exist in the current network and how can we address them to improve collaboration?”, ‘What scalability challenges exist in this innovation and how can PRACTIS help identify them?”). In answering these questions, participants will share their knowledge and expertise with each other, while the chair facilitates the discussion. We are flexible in the format of the challenge-based discussion; depending on the number of attendees, we may work with a plenary discussion or smaller groups that work independently under the supervision of 1 or 2 facilitators each. Key messages • Non-linear approaches are needed to take into account the complicated and changing systems in which health innovation takes place. • Theory of change, co-creation, social network analysis, PRACTIS and design thinking offer practical guidance for stakeholder involvement in specific phases of health innovations.

The Illusion of Grandeur: Parallels Between the Great Gatsby and We Work’s Rise and Fall

This paper explores the striking parallels between F. Scott Fitzgerald's The Great Gatsby and the real-life case of WeWork under CEO Adam Neumann. The analysis demonstrates how literature can provide valuable insights into contemporary business phenomena by examining themes such as illusion versus reality, the pursuit of unattainable dreams, and the eventual collapse of facades. The comparison underscores the timeless relevance of literary narratives in understanding and interpreting real-world scenarios, particularly in the context of business ethics, leadership, and corporate governance.

An event tree-based distance transform algorithm for simultaneously determining mountain railway alignments and station locations

The concurrent optimization of railway alignments and station locations (RA&SL) is a complex task, especially for the main railway lines in mountainous regions. The intricate coupling constraints between RA and SL are very challenging for the conventional RA&SL search methods, which consider limited solution types to generate an optimized RA&SL solution. To solve this problem, an event tree-based distance transform (ET-DT) algorithm is proposed. The study area is first divided into grid cells, followed by a preprocessing step to identify infeasible areas for station locations. Next, an event tree (ET) is constructed to represent all the possible solution types during the RA&SL design process. Afterward, in the ET-DT method, a total of eight types of RA&SL search operators are integrated to generate diverse RA&SL alternatives. Ultimately, the proposed method is applied to a realistic case with significantly-undulating terrain. Results show that ET-DT generates 2.8 times more solutions than a contemporary DT-based method (PreDT) and further improves solution quality through an 8.7% reduction in construction cost compared to the best manually-designed alignment, while PreDT only achieves a 5.7% reduction.

NAMAH—An Innovative Tele-ECHO Mentoring Program to Foster Well-being Among Physicians

Background: The current study aimed to develop and implement the National Assistance in Mental Health for Health Care Providers (NAMAH) module, which focused on wellness and building resilience for a cohort of physicians. Methods: The NAMAH module is a 12-week tele-mentoring program leveraging videoconference technology that uses the ECHO (Extension of Community Healthcare Outcome) HUB and SPOKE and consists of real-life case discussions and a brief didactic. The module’s content was developed after iterative feedback from experts and incorporated suggestions from healthcare providers (HCPs) following a needs assessment. A pre and post-design was used to assess the impact of the module on psychological distress using the self-reported Kessler Psychological Distress Scale (K10) and burnout using the Maslach Burnout—Inventory-Human Services Survey (MBI-HSS) among the 32 physicians who participated. Results: There was a significant decrease in the mean scores before (19.5 ± 6.27) and after (17.38 ± 6.23) the NAMAH module ( p < .05) in the psychological distress as measured by K 10 with a Cohen’s d of 0.41 (95% CI: 0.05–0.77). There was also a significant decrease in the mean scores after the intervention in the emotional exhaustion and depersonalization domain of the MBI-HSS with a medium effect size (Cohen’s d of 0.65) and large effect size (Cohen’s d of 0.94), respectively. Conclusion: The findings from this pilot study lay a foundational framework, encouraging further exploration, research, and scaling-up of such interventions to enhance mental health among physicians and HCPs.

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.

A Realistic Case Confirming the Significance of Clinical Pharmacist Role Inside Community Pharmacists

The Article Abstract is not available

Quantifying marine growth on pipelines with archival remotely operated vehicle footage – a case study

Abstract. Marine growth can substantially affect the hydrodynamic properties of subsea infrastructure. The ability to accurately quantify this growth is essential for designing and maintaining subsea structures. Traditionally, quantifying marine growth has been done by applying simple ratio equations putting in relationship the known distance of an object and its length observed in the image to the length of an unknown object only observed in an image. This method is very sensitive if measurements are not taken in the same plane. Hence, this study aims to establish a method for creating a 3D model of a subsea pipeline segment based on Structure from Motion (SfM). The proof of concept is done using a case study. For this study, Remotely Operated Vehicle (ROV) footage from a 2019 inspection survey is utilised. The scenario in which the procedure is tested represents a difficult, but realistic test case. Despite the lack of metadata, video quality and obstructions (e.g. watermarks), meaningful results were produced. The SfM workflow included masking obstructions (e.g. watermarks), and an iterative camera alignment procedure to generate 3D models, enabling the measurement of marine growth using either the models or ortho images created. While the resulting models were sufficiently accurate to provide meaningful measurements for harder growth types, the method faced challenges in accurately modelling softer growth types, suggesting the need for further refinement. Nevertheless, SfM was proven to be a more reliable method of measuring biofouling than the ratio approach that been traditionally used.

Procedural fairness in automated asylum procedures: Fundamental rights for fundamental challenges

In response to the increasing digitalization of asylum procedures, this paper examines the legal challenges surrounding the use of automated tools in refugee status determination (RSD). Focusing on the European Union (EU) context, where interoperable databases and advanced technologies are employed to streamline asylum processes, the paper asks how EU fundamental rights can address the challenges that automation raises. Through a comprehensive analysis of EU law and several real-life cases, the paper focuses on the relationship between procedural fairness and the use of automated tools to provide evidence in RSD. The paper illustrates what standards apply to automated systems based on a legal doctrinal analysis of EU primary and secondary law and emerging case law from national courts and the CJEU. The article contends that the rights to privacy and data protection enhance procedural fairness in asylum procedures and shows how they can be leveraged for increased protection of asylum seekers and refugees. Moreover, the paper also claims that asylum authorities carry a new pivotal responsibility as the medium between the technologies, asylum seekers and their rights.

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.

A new methodology for evaluating the neighbor discovery time in schedule-based asynchronous duty-cycling wireless sensor networks

Duty cycling is a fundamental mechanism for battery-operated wireless networks, such as wireless sensor networks. Due to its importance, it is an integral part of several Medium Access Protocols and related wireless technologies. In Schedule-based Asynchronous Duty Cycle, nodes activate and deactivate their radio interfaces according to a pre-designed schedule of slots, which guarantees overlapping uptime between two neighbors, independent of the offset between their internal clocks, making communication between them possible. This paper presents a new methodology for evaluating the Neighbor Discovery Time (NDT) of Schedule-based Asynchronous Duty Cycle. Differently from previous methodologies, it accounts for the possibility of the slots in the schedules of the two neighbors not being perfectly border-aligned — an unrealistic assumption in practice. By means of simulation, we show that not taking this under consideration can lead to an overestimate of the NDT by a factor of 2 depending on the particular scenario, thus justifying the importance of our work.•We propose a new subslot-based methodology for computing the NDT of a wakeup schedule used for asynchronous duty cycling.•It replaces the traditional slot-based methodology, by dividing slots into subslots, allowing for the analysis of non-integer clock offsets between nodes, and further allowing mathematical models to consider the more realistic continuous-time case.•Our validation data shows that the slot-based methodology may overestimate NDT by a factor of up to 2, making the proposed subslot-based methodology much more precise.

Meningkatkan Motivasi Belajar Melalui Problem Based Learning Pada Materi Perkelahian Pelajar, Narkoba Dan Khomar Kelas Xi Akl Smk Negeri 3 Seluma

This study aims to improve student learning outcomes on the topics of fighting, drugs, and khamr at SMK Negeri 3 Seluma by applying the Problem-Based Learning (PBL) method. PBL was chosen for its ability to encourage active student engagement and the application of knowledge in real-life contexts. The research uses a classroom action research design involving two cycles, with each cycle consisting of planning, implementation, observation, and reflection.The subjects of the study are tenth-grade students at SMK Negeri 3 Seluma, with a focus on teaching materials related to fighting, drugs, and khamr. Data were collected through observations, interviews, and pre- and post-implementation learning tests. The results indicate that the implementation of the PBL method significantly improved students' understanding of the taught material. Students showed improvements in their ability to analyze real-life cases, make better decisions, and increase their awareness of the impacts of fighting, drugs, and khamr.Based on these findings, it can be concluded that the Problem-Based Learning method is effective in enhancing student learning outcomes on social and health-related topics. The study recommends a broader application of the PBL method across various teaching materials to improve students' critical thinking skills and awareness in the context of health and social education.