Search Results (97)

Background: Ostomy care consultations are essential for promoting patient autonomy and quality-of-life. The integration of innovative technologies may enhance health education and support effective self-care among ostomized patients. Objective: To evaluate the impact of a nursing-led health education intervention supported by smart-glasses on the quality of life of ostomized patients. Methods: A pre–post quasi-experimental design was employed with 14 patients who had undergone digestive surgery resulting in an ostomy. The intervention consisted of a single 60-min session comprising three phases: (1) assessment of baseline knowledge on ostomy management, (2) personalized feedback, and (3) a hands-on workshop using Vuzix© smart-glasses to demonstrate ostomy care techniques. Quality of life was assessed using the SF-36 questionnaire before and after the intervention. Results: The intervention significantly improved overall SF-36 scores, with notable advancements in emotional role (78.57 ± 36.06 to 97.44 ± 9.25, d = 10.54), mental health (79.14 ± 20.10 to 87.38 ± 13.94, d = 6.27), and vitality (69.29 ± 20.56 to 71.15 ± 16.98, d = 4.19). Social function remained high throughout the study, while bodily pain showed a slight decline. A strong correlation (ρ = 0.923, p = 0.001) was observed between pre- and post-intervention quality of life scores. Conclusions: The findings suggest that integrating smart-glasses into nursing-led health education may enhance the quality of life and self-care capabilities of ostomized patients. However, the small sample size, lack of a control group, and exploratory nature of the study limit the generalizability of the results. Further research is needed to validate these findings in larger, controlled trials. Full article

In smart manufacturing environments, production scheduling is highly susceptible to multi-source disruptions. However, traditional methods often struggle to accurately characterize the complex interdependencies between control points and disruptions, along with their systemic propagation effects, thereby constraining the proactivity and precision of scheduling optimization. This paper proposes a novel data-driven approach that integrates Weighted Association Rule Mining (WARM) with a two-layer directed weighted complex network to achieve precise identification of critical control points in production scheduling. First, a production loss function integrating delay duration and resource idle cost is constructed, and the max-pooling method is applied to map control point weights, thereby quantifying their intrinsic importance. Subsequently, under the constraint that association rule antecedents are restricted to control points, an improved Apriori algorithm is employed to mine directed “Control Point-Disruption” association rules. These rules are then used to construct a two-layer directed weighted complex network. Furthermore, by combining weighted PageRank and edge betweenness centrality analyses, critical control points and high-risk propagation paths are identified from the dual dimensions of node influence and path propagation capability. A case study conducted in a crankshaft production workshop demonstrates that the proposed method effectively identifies low-frequency yet high-impact hidden nodes often overlooked by traditional rules. The resulting scheduling optimization scheme reduces the occurrence rate of high-impact disruptions by 53% and significantly improves key performance indicators such as on-time delivery rate and equipment utilization. This research provides new theoretical support and a technical pathway for manufacturing enterprises to suppress system disturbances through flexible interventions targeting high-betweenness paths. Full article

With the rapid development of Industry 5.0, smart manufacturing has become a key focus in production systems. Hence, achieving efficient planning and scheduling on the shop floor is important, especially in job shop environments, which are widely encountered in manufacturing. However, traditional job shop scheduling problems (JSP) assume fixed operation sequences, whereas in modern production, some operations exhibit sequence flexibility, referred to as sequence-free operations. To mitigate this gap, this paper studies the JSP with discrete operation sequence flexibility (JSPDS), aiming to minimize the makespan. To effectively solve the JSPDS, a mixed-integer linear programming model is formulated to solve small-scale instances, verifying multiple optimal solutions. To enhance solution quality for larger instances, a digital twin (DT)–enhanced initialization method is proposed, which captures expert knowledge from a high-fidelity virtual workshop to generate high-quality initial population. In addition, a statistical learning-assisted local search method is developed, employing six tailored search operators and Thompson sampling to adaptively select promising operators during the evolutionary algorithm (EA) process. Extensive experiments demonstrate that the proposed DT-statistical learning EA (DT-SLEA) significantly improves scheduling performance compared with state-of-the-art algorithms, highlighting the effectiveness of integrating digital twin and statistical learning techniques for shop scheduling problems. Specifically, in the Wilcoxon test, pairwise comparisons with the other algorithms show that DT-SLEA has p-values below 0.05. Meanwhile, the proposed framework provides guidance on utilizing symmetry to improve optimization in complex manufacturing systems. Full article

Establishing secure vehicular communication is essential for the development of autonomous driving capabilities and to make new functionalities possible, such as smart traffic management, or systems aimed at avoiding or mitigating traffic accidents. In this scenario, where the deployment of a Public Key Infrastructure (PKI) may be difficult, the use of identity-based cryptography is proposed as a good alternative, because this approach simplifies encryption by enabling dynamic key generation and secure communication, without requiring prior key exchanges, making it highly scalable and efficient. In this way, this paper proposes a communication protocol applicable to V2V (vehicle-to-vehicle) and V2X (vehicle-to-all) communications, replacing schemes based on PKI with identity-based cryptographic schemes using the VIN (Vehicle Identification Number) as an unequivocal vehicle identifier. This paper also describes a prototype implementation in a conventional vehicle and the performance metrics of the system. Through a defined proof of concept, we obtained various quantitative results, demonstrating the importance of the processors used for the encryption and decryption operations required. The proposed system provides secure and flexible vehicle identification, with multiple practical applications. It can enables digital authentication, support toll payments without extra hardware, and facilitate V2X communication via the VIN for improved traffic management and safety. Additionally, it can streamline processes in repair workshops and optimize fuel payment and tracking at service stations. Full article

Digital transformation embeds smart cities, e-health, and Industry 4.0 into critical infrastructures, thereby increasing reliance on digital systems and exposure to cyber threats and boosting complexity and dependency. Research involving over 200 executives reveals that under rising complexity, only 15% of cyber risk investments are effective, leaving most organizations misaligned or vulnerable. In this context, the role of artificial intelligence (AI) in cybersecurity requires systemic scrutiny. This study analyzes how AI reshapes systemic structures in cyber risk management through a multi-method approach: literature review, expert workshops with practitioners and policymakers, and a structured kill chain analysis of the Colonial Pipeline attack. The findings reveal three new feedback loops: (1) deceptive defense structures that misdirect adversaries while protecting assets, (2) two-step success-to-success attacks that disable defenses before targeting infrastructure, and (3) autonomous proliferation when AI applications go rogue. These dynamics shift cyber risk from linear patterns to adaptive, compounding interactions. The principal conclusion is that AI both amplifies and mitigates systemic risk. The core recommendation is to institutionalize deception in security standards and address drifting AI-powered systems. Deliverables include validated systemic structures, policy options, and a foundation for creating future simulation models to support strategic cyber risk management investment. Full article

In the context of carbon neutrality and smart manufacturing, balancing the challenge of carbon and operational efficiency has become a hotspot issue. However, within the specific stage of multi-equipment collaborative manufacturing operational coupling in the production process, multi-state characteristics of equipment operation, multidependencies among operational states, the multi-source of carbon emissions, and spatiotemporal sequence coupling raise the dynamics and complexity of carbon emission modeling and carbon efficiency evaluation. Therefore, a novel methodology to integrate carbon efficiency into a multi-equipment collaboration manufacturing service cell (MECMfg-SC) is proposed in this paper. The stage of multi-equipment collaboration manufacturing operational coupling (MECMfg-OC) in the process of multi-equipment collaboration manufacturing is presented and explained. Then, the operational coupling energy consumption model is constructed based on the MECMfg-OC. The environmental cost performance indicators for smart manufacturing systems, including energy efficiency evaluation (EEe) indicators and carbon efficiency evaluation (CEe) indicators, are proposed. At last, a ball screw smart workshop in a leading Chinese NEV enterprise is introduced to verify the proposed approach. Empirical results confirm the approach’s effectiveness and practical viability. Full article

In response to mounting regulatory and environmental pressures, the maritime sector must urgently improve energy efficiency and reduce greenhouse gas emissions. However, conventional operational interfaces often fail to deliver real-time, actionable insights needed for informed decision-making onboard. This work presents an innovative Augmented Reality (AR) interface integrated with an established shipboard data collection system to enhance real-time monitoring and operational decision-making on commercial vessels. The baseline data acquisition infrastructure is currently installed on over 800 vessels across various ship types, providing a robust foundation for this development. To validate the AR interface’s feasibility and performance, a field trial was conducted on a representative dry bulk carrier. Through hands-free AR smart glasses, crew members access real-time overlays of key performance indicators, such as fuel consumption, engine status, emissions levels, and energy load balancing, directly within their field of view. Field evaluations and scenario-based workshops demonstrate significant gains in energy efficiency (up to 28% faster decision-making), predictive maintenance accuracy, and emissions awareness. The system addresses human–machine interaction challenges in high-pressure maritime settings, bridging the gap between complex sensor data and crew responsiveness. By contextualizing IoT data within the physical environment, the AR-IoT platform transforms traditional workflows into proactive, data-driven practices. This study contributes to the emerging paradigm of digitally enabled sustainable operations and offers practical insights for scaling AR-IoT solutions across global fleets. Findings suggest that such convergence of AR and IoT not only enhances vessel performance but also accelerates compliance with decarbonization targets set by the International Maritime Organization (IMO). Full article

Background/Objectives: Dementia is a leading cause of cognitive decline, with significant challenges for early detection and timely intervention. The lack of effective, user-centred technologies further limits clinical response, particularly in underserved areas. This study aimed to develop and describe a co-design process for creating a Diagnostic and Statistical Manual of Mental Disorders (DSM-5)-compliant, AI-powered Smart Assistant (SmartApp) to monitor neurocognitive decline, while ensuring accessibility, clinical relevance, and responsible AI integration. Methods: A co-design framework was applied using a novel combination of Agile principles and the Double Diamond Model (DDM). More than twenty iterative Scrum sprints were conducted, involving key stakeholders such as clinicians (psychiatrist, psychologist, physician), designers, students, and academic researchers. Prototype testing and design workshops were organised to gather structured feedback. Feedback was systematically incorporated into subsequent iterations to refine functionality, usability, and clinical applicability. Results: The iterative process resulted in a SmartApp that integrates a DSM-5-based screening tool with 24 items across key cognitive domains. Key features include longitudinal tracking of cognitive performance, comparative visual graphs, predictive analytics using a regression-based machine learning module, and adaptive user interfaces. Workshop participants reported high satisfaction with features such as simplified navigation, notification reminders, and clinician-focused reporting modules. Conclusions: The findings suggest that combining co-design methods with Agile/DDM frameworks provides an effective pathway for developing AI-powered clinical tools as per responsible AI standards. The SmartApp offers a clinically relevant, user-friendly platform for dementia screening and monitoring, with potential to support vulnerable populations through scalable, responsible digital health solutions. Full article

Intelligent manufacturing research has focused on digital twins (DTs) due to the growing integration of physical and cyber systems. This study thoroughly explores the Internet of Things (IoT) as a cornerstone of DTs, showing its promise and limitations in intelligent shipbuilding digital transformation workshops. We analyze the progress of IoT protocols, digital twin frameworks, and intelligent ship manufacturing. A unique bidirectional digital twin system for shipbuilding workshops uses the Internet of Things to communicate data between real and virtual workshops. This research uses a steel-cutting workshop to demonstrate the digital transformation of the production line, including data collection, transmission, storage, and simulation analysis. Then, major hurdles to digital technology application in shipbuilding are comprehensively examined. Critical barriers to DT deployment in shipbuilding environments are systematically analyzed, including technical standard unification, communication security, real-time performance guarantees, cross-workshop collaboration mechanisms, and the deep integration of artificial intelligence. Adaptive solutions include hybrid edge-cloud computing architectures for latency-sensitive tasks and reinforcement learning-based smart scheduling algorithms. The findings suggest that IoT-driven digital transformation may modernize shipbuilding workshops in new ways. Full article

The rapid increase in the global population of older adults presents a significant challenge, but also a unique opportunity to leverage technological advancements for promoting independent living and well-being. This study introduces the CIREI framework, which is a comprehensive model designed to enhance the integration of smart home and assistive technologies specifically for pre-frail older adults. Developed through a systematic literature review and innovative and comprehensive co-design activities, the CIREI framework captures the nuanced needs, preferences, and challenges faced by older adults, caregivers, and experts. Key findings from the co-design workshop highlight critical factors such as usability, privacy, and personalised learning preferences, which directly influence technology adoption. These insights informed the creation of an intelligent middleware prototype named WISE-WARE, which seamlessly integrates commercial off-the-shelf (COTS) devices to support health management and improve the quality of life for older adults. The CIREI framework’s adaptability ensures it can be extended and refined to meet the ever-changing needs of the ageing population, providing a robust foundation for future research and development in user-centred technology design. All workshop materials, including tools and methodologies, are made available to encourage the further exploration and adaptation of the CIREI framework, ensuring its relevance and effectiveness in the dynamic landscape of ageing and technology. This research contributes significantly to the discourse on ageing in place, digital inclusion, and the role of technology in empowering older adults to maintain independence. Full article

Application development for the cyber-physical systems (CPS) domain is considered a quite complex procedure, since it not only requires a high level of expertise but also deep knowledge of heterogeneous domains. On the other hand, modern low-code solutions and DSLs are developed to offload domain complexity by developing models at a higher level of abstraction. In this work we propose an approach based on multiple high-level domain-specific languages (DSLs) as the vehicle to alleviate the developers from the intricacies of the CPS domain, enabling them to easily design and develop different layers (e.g., device, system or application layers) and aspects (e.g., automation processes, observation or monitoring dashboards) of a CPS. The materialized outcome of our approach is the LocSys platform, which allows the integration of DSLs, the development and management of models, and the development of pipelines of transformations between DSL models in a uniform platform, covering different aspects of complex domains. The efficacy of this approach was evaluated during a workshop that included more than 80 participants, with varying levels of expertise and experience in the field. The workshop documented the usability and acceptance of the study using SUS measurements. Preliminary findings suggest that the multi-DSL approach is highly usable (average SUS score 80.65, A− grade) and has been well received by non-domain experts. These results are promising, as they indicate that the LocSys platform can be successfully implemented to build smart environments with embedded automation processes and monitoring dashboards. Full article

With the advent of Industry 4.0, the manufacturing industry is facing unprecedented data challenges. Sensors, PLCs, and various types of automation equipment in smart factories continue to generate massive amounts of heterogeneous data, but existing systems generally have bottlenecks in data collection standardization, real-time processing capabilities, and system scalability, which make it difficult to meet the needs of efficient collaboration and dynamic decision making. This study proposes a multi-level industrial data processing framework based on edge computing that aims to improve the response speed and processing ability of manufacturing sites to data and to realize real-time decision making and lean management of intelligent manufacturing. At the edge layer, the OPC UA (OPC Unified Architecture) protocol is used to realize the standardized collection of heterogeneous equipment data, and a lightweight edge-computing algorithm is designed to complete the analysis and processing of data so as to realize a visualization of the manufacturing process and the inventory in a production workshop. In the storage layer, Apache Kafka is used to implement efficient data stream processing and improve the throughput and scalability of the system. The test results show that compared with the traditional workshop, the framework has excellent performance in improving the system throughput capacity and real-time response speed, can effectively support production process judgment and status analysis on the edge side, and can realize the real-time monitoring and management of the entire manufacturing workshop. This research provides a practical solution for the industrial data management system, not only helping enterprises improve the transparency level of manufacturing sites and the efficiency of resource scheduling but also providing a practical basis for further research on industrial data processing under the “edge-cloud collaboration” architecture in the academic community. Full article

Urban decarbonisation requires governance models that overcome the fragmentation and rigidity of traditional urban planning. This article presents a systemic and digital framework for managing urban decarbonisation portfolios aligned with the EU Mission for Climate-Neutral and Smart Cities. Grounded in systems thinking and portfolio theory, this study develops an analytical taxonomy and an interactive digital tool to support strategic coordination, multistakeholder collaboration, and adaptive decision-making. The framework is empirically validated through the case of Madrid’s Climate City Contract, demonstrating its functionality and transferability. Using a mixed-method approach—combining co-creation workshops, interviews, document analysis, and iterative prototyping—this research maps interdependencies among projects, actors, and levers of change. The digital tool enables real-time visualisation of collaboration patterns, gaps, and synergies, enhancing strategic foresight and coordination capacity. Findings reveal that 75% of initiatives in Madrid’s CCC address climate adaptation, 80.36% are linked to knowledge generation, and key anchor projects serve as integrative hubs within the portfolio. This study concludes that the portfolio approach strengthens systemic innovation and reflexive governance by integrating digital infrastructures with collaborative planning processes. While challenges persist—including data integration, institutional capacity, and political dynamics—this research offers a replicable methodology for embedding mission-oriented strategies into urban governance. The digital portfolio emerges as a complementary governance tool that enhances transparency, organisational learning, and alignment across governance levels. Full article

This study proposes a design method for the evaluation and redesign of product–service systems (PSSs) from the perspective of social sustainability, one that applies Max-Neef’s framework of fundamental human needs. The proposed method systematically connects PSS functions and requirements—identified through service blueprints and value graphs—to “satisfiers” and “barriers” extracted via needs-based workshops. This connection enables the identification of functions that either contribute to or hinder the fulfillment of fundamental human needs and guide the generation of redesign proposals aimed at sufficiency-oriented outcomes. A case study involving a smart-cart system in Osaka, Japan, was conducted to demonstrate the applicability of the method. Through an online workshop, satisfiers and barriers related to both physical and online shopping experiences were identified. The analysis revealed that existing functions such as promotional information and automated checkout processes negatively impacted needs such as understanding and affection due to information overload and reduced human interaction. In response, redesign concepts were developed, including filtering options for information, product background storytelling, and optional slower checkout lanes with human assistants. The redesigned functions contribute to the fulfillment of fundamental human needs, indicating that the proposed method can enhance social sustainability in PSS design. This study offers a novel framework that extends beyond traditional customer requirement-based approaches by explicitly incorporating human needs into function-level redesign. Full article

Many cities are developing semantic 3D models as digital representation of their environment to assess the outcomes of the implementation of new mobility services before their real-life piloting. So far, digital twins have reinforced evidence-based approaches for policy-making. The purpose of this paper is to present how digital twins can be used as a tool for vulnerable citizen engagement, improving efficiency of co-creation in mobility planning. An innovative methodological approach of integrating digital twins in engagement processes, by transforming them from real-time data-driven replicas, targeted to the facilitation of the decision-making process for the public authorities to a tool for enhancing citizen engagement in the mobility context has been developed and tested in Thessaloniki Smart Mobility Living Lab. Through a 3-steps workshops’ approach, the mobility needs set by vulnerable users are collected, prioritized and matched with policy plans. By combining citizens feedback with real-time data incorporated in digital twins, citizens receive back visualized scenarios of the impact of specific mobility measures on their neighborhoods. By lifting the citizen engagement in mobility planning to the next participatory level, the results of the study create a bridge between participatory processes and digital twins. Full article