Real Case Study Research Articles

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

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

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

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

Modular Microgrid Technology with a Single Development Environment Per Life Cycle

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

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

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

Amplified squeezed states: analyzing loss and phase noise

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

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

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

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.

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

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

Multi-objective intermodal transportation planning with real-life application

ABSTRACT This paper explores the optimal route selection problem within an intermodal transportation network that comprises road, rail, sea, and inland waterways. Initially, we propose a multi-objective integer linear programming model that aims to minimize total cost, transport time, and carbon emissions. Subsequently, we apply two multi-objective programming approaches: Fuzzy Goal Programming and Compromise Programming. These approaches are implemented in a real-life case study, aiming to determine the optimal intermodal transportation route from Türkiye (Denizli) to Germany (Duisburg). Four out of the six methods suggest the same road and maritime pathway. Furthermore, this model has the potential to aid decision-makers in selecting routes based on customer requirements and serve as a valuable tool for policymakers in establishing an efficient intermodal transport network.

Designing a sustainable delivery network with parcel locker systems as collection and transfer points

Collection and delivery points, both attended and fully automated, have emerged as a popular solution in last-mile delivery. They offer not only operational advantages for the delivery company but also present a pathway to mitigating the negative environmental impact of parcel delivery operations. In this study, we explore a new approach, where parcel locker systems have a dual role: as collection points for customers and as transfer points for the delivery company. We introduce a variant of the Location-Routing Problem, optimizing the strategic location and capacity size of parcel locker systems as well as the delivery routes from those points. We develop an efficient branch-and-price algorithm and apply it to a real-life case study that focuses on the design of a delivery network with parcel lockers in a residential neighborhood in the city of Groningen, the Netherlands. Our case study findings underscore that the dual role of parcel locker systems considerably reduces the travel distance of delivery vehicles, is well-suited to deal with potential future increase in demand for parcel collection, and reduces the likelihood of customers traveling to parcel locker by car.

A time-driven simulation–optimization framework for the dynamic heterogeneous order-courier assignment problem for instant deliveries

In recent years, instant delivery services have become very popular for transporting meals to urban areas, with an extensive range of products now available to order. The platforms that offer these services rely on crowdsourced couriers who utilize their personal vehicles, resulting in heterogeneous fleets. Furthermore, the competition among companies to retain both customers and couriers is very intense, which underscores the importance of developing superior decision support systems. These systems must generate real-time assignments that meet the expectations of service providers, customers, and couriers. In this study, we designed a time-driven simulation–optimization framework that addresses the dynamic heterogeneous order-courier assignment problem and incorporates order-vehicle restrictions. The framework efficiently manages real-time order arrivals, courier movements, and positional updates while considering dynamic factors such as traffic congestion and regional speed limits for various vehicle types. Extensive testing using literature instances demonstrated the framework’s ability to satisfactorily address the defined problem. Additionally, the time-driven simulation–optimization framework was applied to a realistic case study, resulting in an approximately 4.5% reduction in the total delivery times (from the submission of the order until the delivery to the client) for all orders when compared to the original assignment.

Exploring the significance and priority of digital product passports implementation in building renovation projects targeting circular economy

PurposeThis study explores the significance and implementation priorities for Digital Product Passports (DPP) in the context of building renovation projects. It aims to reveal bottlenecks and how a data-driven workflow bridges the DPP understanding/implementation gap, facilitating the transition towards practices aligned with the EU Green Deal goals.Design/methodology/approachA mixed-methods embedded design was employed for a real-case study exploration. Desk research and field observations ground the two-level analysis combining project documentation, namely the Bill of Quantities (BoQ), with different criteria in digitalisation and sustainability, such as economic ratio, 3D modelling, waste management, hazards, energy performance and facility management. All results were interpreted from the DPP lens.FindingsThe analysis revealed a system for identifying building products representing a significant part of the renovation budget. About 11 priority DPPs were found. Some are crucial for both the deconstruction and construction phases, highlighting the need for an incremental and strategic approach to DPP implementation.Research limitations/implicationsThe study is limited to a single case study. Constraints are minimised given the sample's archetype representativeness. The outcomes introduce the need for strategic thinking for incremental DPP implementation. Future research will explore additional criteria and cases.Originality/valueThe research has resulted in a classification framework for DPPs' significance and priority, which is provided with case results. The outcome of the framework provides views on concept alignment to make the implementation in construction more straightforward. Its practical use can be replicated in other projects, emphasizing the importance of data structure and management for the circular economy.

Process mining-enhanced quality management in food processing industries

PurposeThe purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. The aim is to derive roots and patterns of quality deviations and non-conforming finished products as well as best practice facilitating employee training in the food processing industry. Thereby, a key focus is on recognising tacit knowledge hidden in event logs to improve quality processes.Design/methodology/approachThis study applied process mining to detect root causes of quality deviations in operational process of food production. In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.FindingsThe findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.Originality/valueProcess mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.

Nondestructive fatigue life prediction for additively manufactured metal parts through a multimodal transfer learning framework

Understanding the fatigue behavior and accurately predicting the fatigue life of laser powder bed fusion (L-PBF) parts remain a pressing challenge due to complex failure mechanisms, time-consuming tests, and limited fatigue data. This study proposes a physics-informed data-driven framework, a multimodal transfer learning (MMTL) framework, to understand process-defect-fatigue relationships in L-PBF by integrating various modalities of fatigue performance, including process parameters, XCT-inspected defects, and fatigue test conditions. It aims to leverage a pre-trained model with abundant process and defect data in the source task to predict fatigue life nondestructively with limited fatigue test data in the target task. MMTL employs a hierarchical graph convolutional network (HGCN) to classify defects in the source task by representing process parameters and defect features in graphs, thereby enhancing its interpretability. The feature embedding learned from HGCN is then transferred to fatigue life modeling in neural network layers, enabling fatigue life prediction for L-PBF parts with limited data. MMTL validation through a numerical simulation and real-case study demonstrates its effectiveness, achieving an F1-score of 0.9593 in defect classification and a mean absolute percentage log error of 0.0425 in fatigue life prediction. MMTL can be extended to other applications with multiple modalities and limited data.

Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data

ABSTRACT Federated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility because data is not independent and identically distributed (non-IID). Recent work has proposed to cluster clients according to dataset fingerprints to improve model utility in such situations. These fingerprints aim to capture the key characteristics of clients’ local data distributions. Recently, a mechanism was proposed to calculate dataset fingerprints from raw client data. We find that this fingerprinting mechanism comes with substantial time and memory consumption, limiting its practical use to small datasets. Additionally, shared raw data fingerprints can directly leak sensitive visual information, in certain cases even resembling the original client training data. To alleviate these problems, we propose a Feature-based dataset FingerPrinting mechanism (FFP). We use the MedMNIST database to develop a highly realistic case study for FL on medical image data. Compared to existing methods, our proposed FFP reduces the computational overhead of fingerprint calculation while achieving similar model utility. Furthermore, FFP mitigates the risk of raw data leakage from fingerprints by design.

Održiva sveobuhvatna obnova zgrada: studija slučaja iz Švicarske

Renovation is a pivotal element in realising sustainability objectives in the construction industry, as established by the EU Commission. By analysing a real-life case study in Switzerland, this study provides insights into strategies and measures crucial not only for attaining an energy-efficient building state but also for achieving broader sustainability goals encompassing social and economic aspects. In addition, a general decision-making framework for residential building renovations is provided. The presented case study may serve as a role model for the successful sustainable renovation of other similar residential buildings and stimulate further research that facilitates effective holistic sustainable renovation.

A pragmatic approach to teaching physician assistant students basic pain management.

Chronic pain is increasing in prevalence, with new cases now outnumbering those of diabetes, depression, or hypertension. Advanced practice providers have reported that their training in pain management inadequately prepared them to care for patients suffering from painful conditions. In response, the authors of this work developed a basic pain management conceptual framework to provide physician assistant (PA) students with the foundational knowledge necessary to manage and treat patients suffering from a wide variety of painful conditions. The devised framework activity includes categories of pain management such as conservative therapies, medications, injections, minimally invasive procedures, and moderately to highly invasive procedures. This framework can be incorporated into the existing PA educational curriculum and presented alongside a realistic pain patient case study. Furthermore, other health science educational programs, such as nurse practitioner or pharmacy programs, could adopt this framework to increase student knowledge in pain management.

Optimal variable acceptance sampling plan for exponential distribution using Bayesian estimate under Type-I hybrid censoring

Acceptance sampling plans with censoring schemes are crucial for improving quality control by efficiently managing incomplete information. This approach improves cost and time effectiveness compared to traditional methods, providing a more accurate assessment of product quality. In this study, a variable acceptance sampling plan under Type-I hybrid censoring is designed for a lot of independent and identical units with exponential lifetimes using Bayesian estimation of the mean life. This novel approach diverges from conventional methods in acceptance sampling plans, which rely on maximum likelihood estimation and the minimization of Bayes risk. Bayesian estimation is obtained using both squared error loss and Linex loss functions. Under each method, a nonlinear optimization problem is solved to minimize the testing cost, and the optimal values of the plan parameters are determined. The proposed plans are illustrated using various numerical examples, with each plan presented in tables. The acceptance sampling plan using the squared error loss function proves to be more cost-effective than the plan using the Linex loss function. A comparative analysis of the proposed plans with existing work in the literature demonstrates that our cost is much lower than the cost of existing plans using maximum likelihood estimation. Additionally, a real-life case study is conducted to validate the approach.

Leveraging convolutional neural networks for enhanced efficiency and safety in commercial applications

This essay describes the ways in which Convolutional Neural Networks (CNNs) can revolutionise the use of AI in many commercial applications, such as product quality inspection, automated monitoring and analysis of customer behaviours. These applications provide several important improvements in operational efficiency, especially for companies that used to perform human-based visual work. Moreover, through the ability of CNNs to process and provide real-time data, companies can analyse defects, predict equipment failures and understand customer preferences, which then can help them to make decisions and allocate resources effectively. Additionally, this essay discusses some difficulties in implementing CNNs, like data requirements and computing demands, as well as present some real-life case studies of the implementation of CNNs within different types of industries. Furthermore, the essay concludes that incorporating CNN with other new technologies such as Internet of Things (IoT) and edge computing can help businesses make progress in achieving their goals of having agile and responsive operations. The essay mentions several advantages in using CNNs, such as accuracy, cost minimisation and better customer experiences, but also some ethical concerns about privacy and data security.

Environment Texture Optimization for Augmented Reality

Augmented reality (AR) platforms now support persistent, markerless experiences, in which virtual content appears in the same place relative to the real world, across multiple devices and sessions. However, optimizing environments for these experiences remains challenging; virtual content stability is determined by the performance of device pose tracking, which depends on recognizable environment features, but environment texture can impair human perception of virtual content. Low-contrast 'invisible textures' have recently been proposed as a solution, but may result in poor tracking performance when combined with dynamic device motion. Here, we examine the use of invisible textures in detail, starting with the first evaluation in a realistic AR scenario. We then consider scenarios with more dynamic device motion, and conduct extensive game engine-based experiments to develop a method for optimizing invisible textures. For texture optimization in real environments, we introduce MoMAR, the first system to analyze motion data from multiple AR users, which generates guidance using situated visualizations. We show that MoMAR can be deployed while maintaining an average frame rate > 59fps, for five different devices. We demonstrate the use of MoMAR in a realistic case study; our optimized environment texture allowed users to complete a task significantly faster (p=0.003) than a complex texture.