Real Case Study Research Articles

Optimization of Well Locations and Trajectories: Comparing Sub-Vertical, Sub-Horizontal and Multi-Lateral Well Concepts for Marginal Geothermal Reservoir in The Netherlands

Scaling up the direct use of geothermal heat in urban areas comes with the challenge of enabling the development of projects in geological settings where geothermal reservoir flow properties may be poor, resulting in low well flow performance. Cost-effective field development strategies and well designs tailored to such reservoirs can ensure the deliverability of geothermal energy in economic terms. This study presents a framework based on computer-assisted optimization to support practitioners in selecting the most suitable well concept for the exploitation of such marginal geothermal reservoirs. The proposed methodology is illustrated in a real-life case study of a geothermal development prospect in an urban area in The Netherlands, where the performance of sub-vertical, sub-horizontal and multi-lateral wells is compared. The obtained results indicate that the techno-economic performance of the geothermal doublet can be significantly improved by optimization, for all considered well concepts, and that, despite the importance of selecting the well concept, well location is still the main determinant of an effective field development strategy. The sub-horizontal and multi-lateral well concepts appear to be the most suitable for the target case study, outperforming the sub-vertical doublets, with a higher expected net present value and a lower economic variability risk for the multi-lateral solution.

A sustainable and resilient humanitarian relief chain network design for distributing assembled relief items dynamically considering perishability, under disruption

Purpose Due to mitigate against natural disasters like earthquake and to distribute relief items, designing humanitarian relief chain networks is an attentional issue. Agile and efficient distribution of relief items after occurring a disaster is significant, especially when some of the relief items are perishable. Therefore, the purpose of this paper is to create a resilient and integrated decision-making structure to distribute relief items at demand points, considering two dimensions of sustainability, under disruption. Design/methodology/approach This study developed a mixed-integer nonlinear mathematical model to handle the pre- and post-disaster planning when a disaster occurs. The represented model has two objective functions: minimizing weighted unmet demand and total costs. Therefore, to convert this multi-objective problem into a single objective one, the e-constraint method was applied. Findings The main results showed that considering some resilience strategies has a significant effect in reducing the weighted amount of unmet demand and saves the total costs. More precisely, considering resilience strategies results in a 60% reduction in total unmet demand and 11% reduction in total pre-positioning costs. On the other hand, reducing the maximum response time with applying resilience strategies is another achievement of the present study. For these reasons, the use of these strategies can reduce people’s pain and suffer from natural disasters. In general, the application and effectiveness of sustainability dimensions and resilience strategies in the introduced humanitarian relief chain network were analyzed. Practical implications To verify the applicability of this study, this model is applied on a probable real-life case study in Tehran. Finally, some managerial insights are discussed to help humanitarian organizations, managers and stakeholders to make better decisions to reduce negative effects of natural disasters. Originality/value This paper introduced a two-stage stochastic mathematical model for designing a resilient humanitarian relief chain network under disruption, at pre- and post-disaster stages. Also, economic and social dimensions of sustainability are considered in this study. Moreover, assembling perishable and im-perishable relief items as relief kits, dynamically is a main contribution of this research.

Lessons Learned from Four Real-Life Case Studies: Energy Balance Calculations for Implementing Positive Energy Districts

Positive Energy Districts (PEDs) are integral to achieving sustainable urban development by enhancing energy self-sufficiency and reducing carbon emissions. This paper explores energy balance calculations in four diverse case study districts within different climatic conditions—Fiat Village in Settimo Torinese (Italy), Großschönau (Austria), Beursplain in Amsterdam (Netherlands), and Lunca Pomostului in Reşiţa (Romania)—as part of the SIMPLY Positive project. Each district faces unique challenges, such as outdated infrastructure or heritage protection, which we address through tailored strategies including building renovations and the integration of renewable energy systems. Additionally, we employ advanced simulation methodologies to assess energy performance. Simulation results highlight the significance of innovative technologies like photovoltaic-thermal (PVT) systems, application of demand-side actions, and flexible grid usage. Furthermore, mobility assessments and resident-driven initiatives demonstrate the critical role of community engagement in reducing carbon footprints. This study underscores the adaptability of PED frameworks across varied urban contexts and provides actionable insights for scaling similar strategies globally, supporting net-zero energy targets.

Quantifying the Impact of Pavement Diversity at the Network- Level: A Heuristic-Based Approach for Pavement Management

Departments of transportation (DOTs) are increasingly utilizing pavement management systems for the efficient maintenance and preservation of the existing roadway system. Few studies to date, however, have considered sources of variation beyond pavement deterioration nor the implication of investing in multiple pavement materials. The goal of this study is to implement a heuristicbased model in a realistic case study so that one can understand the implication of (a) cost variation and (b) the consideration of multiple paving materials and designs on the performance of a roadway network. The model is implemented in four different scenarios for a set of 3,000 pavement segments in the state of Virginia. The four scenarios include a deterministic case using asphalt and concrete and three probabilistic scenarios that utilizes either asphalt, concrete, or both materials. From the case studies, two interesting results emerge. First, incorporating not just more pavement designs, but also including alternative paving materials, in the pavement management model increases the overall performance of the system. Second, incorporating cost uncertainty increases the advantage of investing in alternative materials, something that the current paradigms do not consider.

Treatment quality with and without an electronic cognitive aid for emergencies in anaesthesia (eGENA) : The DANGER pilot study part 2 in randomized controlled in-situ emergency simulations

The electronic cognitive aid for emergencies in anesthesia (eGENA) is an app that offers digital support in anesthesiological emergency situations as a cognitive aid tool via checklists for memory and making decisions. The eGENA was published by the German Society of Anesthesiology and has been implemented in the emergency management of the anesthesiological team of the clinic in Potsdam, Germany. The primary endpoint was to observe the influence of eGENA on the anesthesiological emergency management on the subjective feeling of assurance as well as on quality of treatment and, therefore, patient safety. All employees in the anesthesia department (nursing staff and physicians) took part in the initial implementation of eGENA. The implementation phase covered crew resource management (CRM) principles and eGENA use as well as 10case studies that were discussed with help from eGENA. Afterwards in arandomized controlled simulation study, realistic case studies were processed and evaluated. In this, 18cases were handled by 9groups with 4persons in each group. Treatment during these simulations was assessed using apredetermined 20-point evaluation form and 10resuscitation-related and 10case-related points were awarded. Significance tests were carried out using the Wilcoxon-test (significance levelp < 0.05) and two evaluations were completed by the attendees at the beginning and the end of the eGENA implementation process. Scenarios1 and2 showed comparable overall scores (14.9vs. 16.3points out of 20, not significant). Higher case-associated scores (7.6 vs.5.6out of 10, p = 0.03) and higher total scores were achieved with the help of eGENA (16.9 vs. 14.3 out of 20, p = 0.02). Resuscitation-associated scores did not differ significantly (9.3vs.8.8 of 10, p = 0.1). During eGENA use for cases the execution of the algorithm-based resuscitation measures was not delayed or accelerated. With eGENA, however, differential diagnoses were discussed significantly more frequently and expanded treatment and diagnostic measures were implemented. During simulation cases eGENA was mainly used by physicians. The initially very positive responses of the evaluations toned down over time. Planned eGENA use for self-study was less frequent (p = 0.006) and there was less approval of the quality of treatment by eGENA than in the first survey (p = 0.002). The cooperation between doctors and nurses in emergency treatment showed an improvement (p < 0.001). The questions about self-assessed safety in emergency treatment on ascale from 0to 10showed higher values in all categories at the second survey (except only circulatory emergencies and 'other' emergencies). The respondents were more likely to be involved in emergency treatment at the second time of the survey (p = 0.03) after a median of 20months. The sense of security and emergency involvement increased significantly over the observation period. The initial need to use eGENA in everyday life has diminished over time; however, these results cannot be causally associated with eGENA. The use of eGENA does not improve resuscitation efforts but does not delay them either. With the use of eGENA better case-associated results are achieved and advanced diagnostics and treatment are implemented more frequently in complex emergency situations. This increases the quality of treatment. Further studies should be carried out with alarger number of cases to confirm the shown effects.

Does education increase ethical sensitivity a semi-experimental study

Ethics education plays a crucial role in enhancing the ethical sensitivity of nursing students. This study aimed to assess the impact of an ethics course on the ethical sensitivity levels of nursing students. In this study, one-group pretest-posttest semi-experimental design method is used. The mean total score of the Ethics Sensitivity Scale for Nursing Students before education was determined as 4.93 (neutral), while the mean score increased to 5.62 (significant) after education. There was a statistically significant difference between the pre-education and post-education mean scores of students who reported encountering ethical problems during clinical practice. The findings underscore the importance of ethics education in fostering the ethical sensitivity and awareness of nursing students. They also highlight the need for high-quality ethics education that integrates theoretical knowledge with engaging content, real-life case studies, and practical applications to enhance students’ learning experiences.

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.

Ensemble-based assisted history matching of DTS monitoring data in HT-ATES systems: application to a real-life case study

Underground heat storage is an important element in accelerating the energy transition. It can significantly contribute to CO2 emission reduction and cost savings since it is one of the cheapest forms of energy storage and it enables the seasonal storage of large energy surpluses from sustainable sources, e.g. wind, sun, geothermal. Numerical models are used for the prediction of thermal behavior important in establishing the high efficiency of the high temperature aquifer thermal energy storage (HT-ATES) systems. However, the lack of exact knowledge of the subsurface conditions introduces modeling uncertainty. It is therefore important to employ approaches that reduce subsurface uncertainty. History matching is a methodology where the numerical models are updated to match historical observations that will in turn not only increase understanding of the subsurface but also improve accuracy of the model predictability of future behavior. In this research, models of the first large-scale operational HT-ATES system in Middenmeer, the Netherlands, were used to evaluate the thermal evolution in the storage aquifer and the over- and underburden clay layers. The HT-ATES system, consisting of a hot and warm well, with a monitoring well inbetween, became operational in the summer of 2021. The extensive monitoring program implemented for the first few operational years provided an opportunity to study the performance of such a system from an environmental and operational point of view. A state-of-the-art assisted history matching approach was applied to the first storage cycle, using a coupling between history matching software and the thermal flow simulator. This approach was compared to a more traditional single-model manual history matching method. Rock properties of the aquifer and over- and underburden layers were updated in the randomly generated prior ensemble of models to fit the simulated temperature evolution measured down the monitoring well with the distributed temperature sensing (DTS) data. The observations gathered during the second year of operations were used to validate the accuracy of the prediction capabilities of the updated models. The obtained results indicate the value of history matching to improve understanding of the subsurface conditions for HT-ATES systems and obtain models with better predictability of the future behavior of heat in the storage reservoir and overburden formations. Such improved models are instrumental in providing engineers with a better quantitative grip on the environmentally responsible storage potential and heat deliverability of the target storage site, which is important to achieve cost-effective site-specific design (e.g. number of wells, well placement) and performing operational strategies (e.g. injection/production rates and temperatures) for new HT-ATES systems. Moreover, the benefits of the assisted history matching approach over manual method are highlighted and both approaches are validated where the assisted history matching method produced more accurate predictions than the manual approach.

Analysing matched continuous longitudinal data: A review.

Longitudinal data are frequently encountered in medical research, where participants are followed throughout time. Additional structure and hence complexity occurs when there is pairing between the participants (e.g. matched case-control studies) or within the participants (e.g. analysis of participants' both eyes). Various modelling approaches, identified through a systematic review, are discussed, including (un)paired -tests, multivariate analysis of variance, difference scores, linear mixed models (LMMs), and new or more recent statistical methods. Next, highlighting the importance of selecting appropriate models based on the data's characteristics, the methods are applied to both a real-life case study in ophthalmology and a simulated case-control study. Key findings include the superiority of the conditional LMM and multilevel models in handling paired longitudinal data in terms of precision. Moreover, the article underscores the impact of accounting for intra-pair correlations and missing data mechanisms. Focus will be on discussing the advantages and disadvantages of the approaches, rather than on the mathematical or computational details.

Achieving Class Crossing through Education: Pathways and Challenges in Chinese Modern Society

In today's Chinese society, education serves as a crucial driving force for individual growth and development, playing an increasingly prominent role in facilitating socio-economic status advancement, occupational hierarchy ascension, and cultural capital accumulation. This paper delves into how education emerges as a pivotal pathway for class crossing in modern Chinese society, analyzing its specific functions in knowledge accumulation, skill enhancement, perspective broadening, and social network construction. Furthermore, it explores the challenges encountered in this process and proposes corresponding strategies. Class crossing, as a significant manifestation of Chinese social mobility, has always been a hot topic in sociology, education, and other fields. With the deepening of globalization and the knowledge economy, education's impact on individual class crossing has become increasingly prominent as a means to enhance human capital and promote social equity and development. This paper, from a theoretical perspective and with real-life case studies, comprehensively examines the multifaceted roles of education in achieving class crossing and the challenges faced along the way in China.

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

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.

An operational discontinuous Galerkin shallow water model for coastal flood assessment

Hydrodynamic modeling for coastal flooding risk assessment is a highly relevant topic. Many operational tools available for this purpose use numerical techniques and implementation paradigms that reach their limits when confronted with modern requirements in terms of resolution and performances. In this work, we present a novel operational tool for coastal hazards predictions, currently employed by the BRGM agency (the French Geological Survey) to carry out its flooding hazard exposure studies and coastal risk prevention plans on International and French territories. The model, called UHAINA (wave in the Basque language), is based on an arbitrary high-order discontinuous Galerkin discretization of the nonlinear shallow water equations with SSP Runge–Kutta time stepping on unstructured triangular grids. It is built upon the finite element library AeroSol, which provides a modern C++ software architecture and high scalability, making it suitable for HPC applications. The paper provides a detailed development of the mathematical and numerical framework of the model, focusing on two key-ingredients : (i) a pragmatic P0 treatment of the solution in partially dry cells which garantees efficiently well-balancedness, positivity and mass conservation at any polynomial order; (ii) an artificial viscosity method based on the physical dissipation of the system of equations providing nonlinear stability for non-smooth solutions. A set of numerical validations on accademic benchmarks is performed to highlight the efficiency of these approaches. Finally, UHAINA is applied on a real operational case of study, demonstrating very satisfactory results.

Improving Critical Thinking Skills of Physics Teacher Prospectives Through Case Based Learning Models

In the complex information era, critical thinking skills become very important for educators to be able to analyze and solve problems effectively. This research aims to improve the critical thinking skills of prospective physics teachers through the application of the Case Based Learning (CBL) model. The CBL model is implemented by exposing students to realistic and relevant case studies, which encourage them to carry out in-depth analysis of limited information. This research is Action Research. The research subjects consisted of 29 prospective physics teacher students who were taking professional education courses. The results of the research show that there has been an increase in students' critical thinking skills, with the average score in cycle 1 being 80.04 to 88.792 in cycle 2. This increase reflects students' ability to provide basic explanations, and further explanations, make conclusions, and plan appropriate actions. The more effective it is based on the problem analysis that has been carried out the better. These findings confirm that the CBL model is empirically proven to be used to improve students' critical thinking skills.

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.

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.

Implementation of Electrolytic Phosphating for Steel Wire Drawing as an Alternative to Conversion Coatings

Wire drawing is a cold deformation process in which a steel wire is pulled through several dies of decreasing size, in order to reduce its diameter. To ensure smooth operations and to maximize the lifetime of the expensive dies, lubrication of the wire is mandatory prior to the drawing step. More importantly, the lubricating layer is required to remain adherent to the steel surface for multiple deformation steps, while also maintaining its performance throughout the entire process. For this application, zinc phosphate conversion coatings are widely utilized in combination with an aqueous lubricant based on stearate salts. In fact, phosphate coatings can easily deform under the compressive load of the drawing step, while their porous nature maximizes the amount of lubricant that can be applied to the wire surface. [1]Despite the good performance offered by conversion phosphating, there is an active interest in replacing this technology as it generates a significant amount of waste byproduct, the so called “phosphate sludge”. At the same time, finding an alternative conversion formulation that ensures the same performance is not a trivial task, especially when price is also factored in. In this context, electrolytic phosphating could offer an elegant, yet effective solution: the process allows the formation of coatings with the same chemical composition and morphology as conversion phosphating but prevents the formation of the sludge byproduct. With the application of an external voltage, it is in fact possible to trigger the local increase in pH responsible for phosphate formation without having to rely on the dissolution of the substrate. [2]In this talk we will present the implementation of electrolytic phosphating in what is arguably its most relevant industrial application. In particular, by designing the phosphating tank around the symmetry of the steel wire, it is possible to ensure uniform distribution of the current density on the substrate, effectively avoiding the main drawback of the electrolytic process. Furthermore, the higher deposition rate obtained with the electrolytic setup allows the implementation of a continuous operation process, with a higher throughput than traditional batch conversion phosphating. The presentation will showcase results coming from laboratory-scale experiments as well as data resulting from real case-studies at the industrial level.

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.