Real Case Study Research Articles

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.

Seismic fragility and risk assessment of Reinforced Concrete bridges undergoing elastomeric bearing deformations induced by landslide

The paper presents a study on the seismic fragility of Reinforced Concrete (RC) bridges isolated by elastomeric bearing devices subjected to differential displacements induced by slow-moving landslides. The seismic behaviour of isolated bridges is ruled by the performance of elastomeric bearings to reduce and dissipate earthquake actions. These bridges are subjected to service loads that usually are accounted for in design, but possible additional actions from the surrounding environment, such as landslides affecting substructure components, can seriously undermine the seismic response. The paper describes a practical approach to investigate the seismic fragility and risk of RC bridges isolated by elastomeric bearings, which may undergo early deformations induced by the differential displacements of substructure components. On the basis of previous existing studies, the proposed methodology is based on numerical modelling accounting for landslide-induced substructure displacements and proper modifications on the constitutive law and hysteretic response of the elastomeric bearings. Subsequently, after establishing specific limit-states, nonlinear time history analyses of the seismic response are used to estimate fragility curves and risk indicators. The study points out that it is possible to quantify the influence of landslide-induced effects on seismic fragility and risk by using only two numerical models, in order to provide decision support to transportation authority responsible for ensuring the safety of bridges and road networks. The proposed approach has been tested on a real-life case study, the Santo Stefano Viaduct in Italy, which was subjected in the past to relevant deformations of the elastomeric bearings due to an active landslide phenomenon.

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.

A Design Flow Based on Docker and Kubernetes for ROS-based Robotic Software Applications

Human-centered robotic applications are becoming pervasive in the context of robotics and smart manufacturing, and such a pervasiveness is even more expected with the shift to Industry 5.0. The always increasing level of autonomy of modern robotic platforms requires the integration of software applications from different domains to implement artificial intelligence, cognition, and human-robot/robot-robot interaction. Developing and (re)configuring such a multi-domain software to meet functional constraints is a challenging task. Even more challenging is customizing the software to satisfy non-functional requirements such as real-time, reliability, and energy efficiency. In this context, the concept of Edge-Cloud continuum is gaining consensus as a solution to address functional and non-functional constraints in a seamless way. Containerization and orchestration are becoming a standard practice, as they allow for better information flow among different network levels as well as increased modularity in the use of multi-domain software components. Nevertheless, the adoption of such a practice along the design flow, from simulation to the deployment of complex robotic applications by addressing the de facto development standards (e.g., ROS - Robotic Operating System) is still an open problem. We present a design methodology based on Docker and Kubernetes that enables containerization and orchestration of ROS-based robotic SW applications for heterogeneous and hierarchical HW architectures. The methodology aims at (i) integrating and verifying multi-domain components since early in the design flow, (ii) mapping software tasks to containers to minimize the performance and memory footprint overhead, (iii) clustering containers to efficiently distribute load across the edge-cloud architecture by minimizing resource utilization, and (iv) enabling multi-domain verification of functional and non-functional constraints before deployment. The article presents the results obtained with a real case of study, in which the design methodology has been applied to program the mission of a Robotnik RB-Kairos mobile robot in an industrial agile production chain. We have obtained reduced load on the robot’s HW with minimal performance and network overhead, thanks to the optimized distributed system.

A conceptual framework-based architecture strategy for implementing industry 4.0 enabling technologies in Indian commercial vehicle manufacturing plant

ABSTRACT From the first industrial revolution to the era of Industry 4.0 (I4.0), Indian manufacturing sectors have undergone significant transformations. The global market’s competitiveness has surged due to the rising demand for customized consumer goods, prompting industries to adopt advanced manufacturing models to maintain their edge. This paper introduces a novel Conceptual Framework-Based Architecture (CFA) using a real-life case study of an Indian commercial vehicle manufacturing plant. The proposed CFA aims to minimize barriers to implementing I4.0 technologies by developing a standardized architecture customized to the Indian manufacturing context. It addresses existing technological requirements and implementation challenges in three phases, considering existing production systems, current technologies, and system needs. The CFA enhances productivity, and transparency through real-time data monitoring, automated system communication, and responsiveness. It also tackles I4.0 implementation challenges between physical and cyber systems, employee adaptability to new technologies, the absence of global standards, and the need for uniform standards in tools, equipment, systems, and data services. The CFA elements were validated for system readiness in an existing manufacturing plant. This framework serves as a reference tool for industrial strategists, decision-makers, policymakers, and researchers, aiding in the integration and configuration of I4.0 technologies in the Indian manufacturing sector.

A home healthcare routing-scheduling optimization model considering time-balancing and outsourcing

Home care services have a significant role in lowering healthcare expenditure. Supply chain management in home healthcare (HHC) ensures efficient delivery of medical supplies and equipment to patients' homes, improving overall quality of care and patient outcomes. This study proposes a routing and scheduling optimization model for HHC by prioritizing patients, developing an effective delivery strategy, and considering home care logistics and services. The model primarily concerns reducing logistics activities’ overall expenses while considering patients’ priorities. A bi-objective optimization model for a multi-period HHC problem is developed by prioritizing patients with urgent critical needs. The best-worst method (BWM) and technique for order of preference by similarity to ideal solution (TOPSIS) are used to prioritize patients using a linear programming metric (Lp-metric). The BWM and TOPSIS have been uniquely used in this study for routing and scheduling in HHC. Eventually, the applicability of the proposed method is demonstrated through a real-life case study with a series of numerical examples and sensitivity analysis. For instance, by analyzing privilege, we see patients are carefully matched with caregivers possessing advanced skills, leading to increased patient satisfaction. Based on assigned routes, caregivers prioritize patients with higher weight and emergency conditions at the start of each path, followed by patients with less urgent conditions. This ensures that patients with more severe conditions are serviced first.

On an efficient global/local stochastic methodology for accurate stress analysis, failure prediction and damage tolerance of laminated composites

The quantification of uncertainties in the mechanical response of composite structures can be a computationally demanding task. This is due both to the number of uncertain parameters in a real study case and the complexity of the model to be analysed. In this paper, an efficient global/local approach to estimate the uncertainties of the quantities of interest in specific regions of interest with limited computational effort is proposed. This is achieved by refining only locally the model taking advantage of Refined Structural Theories. At the same time, since the variance of the uncertain parameters is usually relatively small, the stochastic analysis is dealt with a sensitivity study carried out both in the global and in the local model. In this way, it is possible to assess the influence of global and local uncertain parameters in the same submodeling analysis. The methodology presented is applied to several study cases of interest. The results focus on obtaining probabilistic distributions of the stress field that can be later used in failure criteria to evaluate the subsequent distribution of the failure index. Furthermore, a damage tolerance study case is investigated, showing good correlation with the reference Monte Carlo simulations.

SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems

SecuriDN v. 0.1 is a tool for the representation of the assets composing the IT and the OT subsystems of Distributed Energy Resources (DERs) control networks and the possible cyberattacks that can threaten them. It is part of a platform that allows the evaluation of the security risks of DER control systems. SecuriDN is a multi-formalism tool, meaning that it manages several types of models: architecture graph, attack graphs and Dynamic Bayesian Networks (DBNs). In particular, each asset in the architecture is characterized by an attack graph showing the combinations of attack techniques that may affect the asset. By merging the attack graphs according to the asset associations in the architecture, a DBN is generated. Then, the evidence-based and time-driven probabilistic analysis of the DBN permits the quantification of the system security level. Indeed, the DBN probabilistic graphical model can be analyzed through inference algorithms, suitable for forward and backward assessment of the system’s belief state. In this paper, the features and the main goals of SecuriDN are described and illustrated through a simplified but realistic case study.

SDG Innovations & Impact: Relational Approach in Enabling Innovative Solutions and Impact Creation at the Grassroots

SDG localisation is a primary goal of APPGM-SDG, and solution projects are the key component of this effort. Formed in 2019, mirroring the UK Parliament setup, the APPGM-SDG Malaysia has been the forefront organisation in localising SDGs in parliamentary constituencies. Despite Covid-19 and other practical challenges. The grounded research approach undertaken by APPGM-SDG paved the way for innovative grassroots solutions designed and undertaken by local organisations targeting vulnerable communities in their own localities. From 2020 to November 2023, APPGM-SDG has undertaken targeted solution projects at an unprecedented level, with 664 SDGs solution projects in 4 years and 364 solution providers around the country. These innovative solutions are developed through a unique relational approach between multiple stakeholders and engagements in multiple processes in the making. This approach has been the key framework in designing solution projects that have proven to have a much greater impact towards the community as a whole and personally on the livelihoods of the participants involved. This is also evident in our recent publication, SDG Solution Projects: Best Practices Impacting the Grassroots, which carries 35 best practices for solution projects and innovative stories from the ground that can be examples of model solutions for vulnerable communities. This paper will discuss the relational approach that brings out SDG innovations in finding solutions between the partners involved, the empowerment and participatory models in place, and the processes involved in curating suitable solutions. The lessons learnt from 2020 until now in crafting fine-tuned solutions will also be included in this paper, together with real-life case studies of SDG innovative solutions that have a greater relational approach between stakeholders.

Research on Refined Management and Project Risk Control Strategies in Whole Process Engineering Consultation

Whole Process Engineering Consultation (WPEC) plays an increasingly vital role in modern engineering project management. It not only encompasses the entire project lifecycle from planning and design to implementation and operation but also significantly enhances project efficiency, reduces costs, and mitigates risks. As the complexity of engineering projects grows, refined management emerges as a crucial method for achieving efficient project management. This paper explores the application of refined management in WPEC and analyzes its role in project risk control through real-case studies. By delving into the principles of refined management, management tools, and risk control strategies, this study proposes an integrated approach to optimize project management processes and improve project success rates. Additionally, it discusses the impact of emerging technologies on refined management and risk control, providing insights into future development trends. The aim is to offer theoretical support and practical guidance for engineering project management, ensuring efficient management and risk control.

Enhancing building robustness through a fuse-based segmentation framework

The most widely used design approaches today for improving the robustness of buildings rely on improving continuity within the structural system to ensure that loads supported by failed components can be redistributed to the rest of the system. Although this is effective for small initial failures, it can increase the risk of disproportionate collapse after larger initial failures due to collapsing elements pulling down parts of the structure that would otherwise be unaffected. This form of continuity-enabled collapse propagation can be avoided by dividing a structure into different segments. However, completely separating parts of a building results in lower performance under operational conditions, against lateral loads, and after small initial failures. In fact, the advantages of both continuity and segmentation can be combined through a fuse-based segmentation approach in which predefined segment borders ensure connectivity after small initial failures but separate to isolate collapse after larger initial failures. To ensure that this approach is used effectively to improve the robustness of building structures, a design framework is proposed in this article to systematically consider relevant structural and geometric criteria in order to define suitable segmentation configurations for reinforced concrete and steel framed building structures. An application to a realistic case study is also presented to demonstrate the effectiveness of the proposed framework in enhancing structural robustness.

An ML‐Based P3‐Like Multimodal Two‐Moment Ice Microphysics in the ICON Model

AbstractMachine learning (ML) is used to build a bulk microphysical parameterization including ice processes. Simulations of the Lagrangian super‐particle model McSnow are used as training data. The ML performs a coarse‐graining of the particle‐resolved microphysics to multi‐category two‐moment bulk equations. Besides mass and number, prognostic particle properties (P3) like melt water, rime mass, and rime volume are predicted by the ML‐based bulk model. The ML‐based scheme is tested with simulations of increasing complexity. As a box model, the ML‐based bulk scheme can reproduce the simulations of McSnow quite accurately. In 3d idealized squall line simulations, the ML‐based P3‐like scheme provides a more realistic extended stratiform region when compared to the standard two‐moment bulk scheme in ICON. In a realistic case study, the ML‐based scheme runs stably, but can not significantly improve the results. This shows that ML can be used to coarse‐grain super‐particle simulations to a bulk scheme of arbitrary complexity.

Analyzing the Successful Reasons for Microsoft Cloud Computing Service Business Transformation

With the continuous development of high-tech due to the emergence of artificial intelligence services, resulting in a growing trend in enterprise transformation, Microsoft began to officially transition to cloud computing services in 2010. The purpose of this study, based on the above content, was to investigate the reasons for Microsoft's transformation into a stable period. This paper used a real-life case study to analyze the business value of Microsoft's cloud computing services and artificial intelligence in light of Microsoft's transformation. The study's results indicated that three factorstalent reserve, technological innovation and accumulation, and corporate strategic layoutare responsible for transforming Microsoft's cloud computing service into a stable period. Therefore, Microsoft needs to attract more high-quality and innovative technical talents; vigorously develop technological innovation; increase investment in research and development; and constantly promote technological innovation; strengthen the core business; increase investment in emerging areas; deepen partnerships; strengthen the global layout; and focus on corporate social responsibility and sustainable development in order to maintain its competitive advantage and create more business value. This study's value and significance lie in its analysis of Microsoft's forward-looking cloud computing transformation while still maintaining its business value, providing new ideas for Microsoft's subsequent development, and providing reference value for enterprises in the same industry in the transformation.

A two-stage framework for a resilient medical tourism supply chain considering social aspects and supplier evaluation under uncertainty: a real-case study

A two-stage framework for a resilient medical tourism supply chain considering social aspects and supplier evaluation under uncertainty: a real-case study

An optimization model for determining cost-efficient maintenance policies for multi-component systems with economic and structural dependencies

In most multi-component systems, the cost-efficiency of maintenance policies depends on technical structural dependencies. Motivated by the recognition that these dependencies must be accounted for in the development of optimal maintenance policies, we develop an optimization model to determine cost-efficient maintenance schedules for multi-component systems. Our main contribution is twofold. First, we introduce directed graphs as an expressive tool to represent the economic and structural dependencies of the system, including situations in which the maintenance of a given component may require other components to be disassembled or maintained. Second, we formulate a Markov Decision Process model, which is solved through the modified policy-iteration algorithm to determine the most cost-efficient policy. This policy indicates which maintenance actions consisting of disassembly and component replacement decisions are optimal when mandatory replacements must be made whenever the system fails, or the reliability of the system falls below a predefined reliability threshold. To our knowledge, this is the first model that provides optimal maintenance policies that comply with reliability requirements in the presence of constraints arising from technical structural dependencies. We illustrate the model with a realistic case study on the development of cost-efficient maintenance policies and show that its results compare favorably with heuristic maintenance policies.

Beyond profit margins: Orchestrating social, economic, and environmental sustainability within the Norwegian Salmon Food Supply Chain

Food Supply Chains (FSCs) have become increasingly complex with the average distance between producers and consumers rising considerably in the past two decades. Consequently, FSCs are a major source of carbon emissions and reducing transportation costs a major challenge for businesses. To address this, we present a mathematical model to promote the three core dimensions of sustainability (economic, environmental, and social), based on the Mixed-Integer Linear Programming (MILP) method. The model addresses the environmental dimension by intending to decrease the carbon emissions of different transport modes involved in the logistics network. Several supply chain network characteristics are incorporated and evaluated, with a consideration of social sustainability (job generation from operating various facilities). The mathematical model's robustness is demonstrated by testing and deploying it to a variety of problem instances. A real-life case study (Norwegian salmon supply chain) helps to comprehend the model's applicability. To understand the importance of optimizing food supply networks holistically, the paper investigates the impact of multiple supply chain permutations on total cost, demand fluctuations and carbon emissions. To address fluctuations in retail demand, we undertook sensitivity analysis for variations in demand, enabling the proposed model to revamp Norway's salmon supply chain network. Subsequently, the results are thoroughly examined to identify managerial implications.