Search Results (9,853)

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

The increasing elderly population presents major challenges to traditional healthcare due to the need for continuous care, a shortage of skilled professionals, and increasing medical costs. To address this, smart elderly care homes where multiple residents live with the support of caregivers and IoT-based assistive technologies have emerged as a promising solution. For their effective operation, a reliable high speed network like 5G is essential, along with intelligent resource allocation to ensure efficient service delivery. This study proposes a deep reinforcement learning (DRL)-based resource management framework for smart elderly homes, formulated as a Markov decision process. The framework dynamically allocates computing and network resources in response to real-time application demands and system constraints. We implement and compare two DRL algorithms, emphasizing their strengths in optimizing edge utilization and throughput. System performance is evaluated across balanced, high-demand, and resource-constrained scenarios. The results demonstrate that the proposed DRL approach effectively learns adaptive resource management policies, making it a promising solution for next-generation intelligent elderly care environments. Full article

Alzheimer’s disease is a growing global health concern, demanding innovative solutions for early detection, continuous monitoring, and patient support. This article reviews recent advances in Smart Wearable Medical Devices (SWMDs), Internet of Things (IoT) systems, and mobile applications used to monitor physiological, behavioral, and cognitive changes in Alzheimer’s patients. We highlight the role of wearable sensors in detecting vital signs, falls, and geolocation data, alongside IoT architectures that enable real-time alerts and remote caregiver access. Building on these technologies, we present LumiCare, a conceptual, context-aware mobile system that integrates multimodal sensor data, chatbot-based interaction, and emerging 6G network capabilities. LumiCare uses machine learning for behavioral analysis, delivers personalized cognitive prompts, and enables emergency response through adaptive alerts and caregiver notifications. The system includes the LumiCare Companion, an interactive mobile app designed to support daily routines, cognitive engagement, and safety monitoring. By combining local AI processing with scalable edge-cloud architectures, LumiCare balances latency, privacy, and computational load. While promising, this work remains at the design stage and has not yet undergone clinical validation. Our analysis underscores the potential of wearable, IoT, and mobile technologies to improve the quality of life for Alzheimer’s patients, support caregivers, and reduce healthcare burdens. Full article

The significant rise of electric vehicles in urban areas calls for research on smart charging to promote electric mobility. Existing research is fragmented, with inconsistent findings, focusing on single aspects of smart charging, such as challenges, charging technologies, and sustainability concerns. Thus, a bibliometric analysis was conducted to identify the key themes and propose future research agendas on smart charging for electric mobility in urban areas, to guide policy formulation and promote widespread uptake of electric vehicles. A total of 201 publications covering the period 2005 to 2025 were extracted from the Scopus database; the first was published in 2011 and numbers peaked in 2024, with 39 publications. The topic is young, with an average age per publication of 4.17 years, with China as the top-ranked country, with 97 publications. Research on smart charging for e-mobility in urban areas focuses on four key themes: smart charging technologies and optimisation strategies, grid integration and vehicle-to-grid systems, renewable energy and environmental sustainability, and urban mobility systems and infrastructure development. Despite their importance, real-world testing and smarter integration with cities and grids remain largely underexplored, especially in developing countries. Future research should focus on large-scale vehicle-to-grid integration, user behaviour analysis, and coordinated planning of smart charging with urban transport and policy frameworks. Full article

Food packaging systems play a critical role in reducing resource wastage, extending shelf-life, and enhancing supply chain sustainability. Carbon dots (CDs) have emerged as promising nanofillers for sustainable active and smart packaging due to their exceptional optical properties, biocompatibility, and antimicrobial activity. This review synthesizes recent advances in CD-based food packaging technologies, focusing on their multifunctional applications and performance enhancements. We systematically analyze how CDs improve packaging materials’ mechanical strength, gas barrier properties, and functional performance (antioxidant, antimicrobial, and smart sensing capabilities). Current research demonstrates CDs’ ability to enable intelligent functions such as pH responsiveness and freshness monitoring while maintaining excellent biocompatibility. However, challenges remain in scaling up production, long-term toxicological evaluation, and matrix compatibility. Future research directions should address these limitations while exploring the full potential of CD-based multifunctional films as sustainable alternatives for next-generation food packaging systems. Full article

In this study, the effects of plasma assistance on the electron beam physical vapour deposition (EB-PVD) process were investigated using an industrial coater (Smart Coater ALD Vacuum Technologies GmbH) equipped with a dual hollow cathode system. This configuration enabled the generation of a plasma environment during the deposition of the ceramic top coat onto a metallic substrate. The objective was to assess how plasma assistance influences the microstructure and thickness distribution of 7% wt. yttria-stabilised zirconia (YSZ) thermal barrier coatings (TBCs). Coatings were deposited with and without plasma assistance to enable a direct comparison. The thickness uniformity and columnar morphology of the 7YSZ top coats were evaluated by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The mechanical properties of the deposited coatings were verified by the scratch test method. The results demonstrate that, in the presence of plasma, columnar grains become more uniformly spaced and exhibit sharper, well-defined boundaries even at reduced substrate temperatures. XRD analysis confirmed that plasma-assisted EB-PVD processes allow for maintaining the desired tetragonal phase of YSZ without inducing secondary phases or unwanted texture changes. These findings indicate that plasma-assisted EB-PVD can achieve desirable coating characteristics (uniform thickness and optimised columnar structure) more efficiently, offering potential advantages for high-temperature applications in aerospace and power-generation industries. Continued development of the EB-PVD process with the assistance of plasma generation could further improve deposition rates and TBC performance, underscoring the promising future of HC-assisted EB-PVD technology. Full article

Smart grids (SGs) are becoming increasingly complex with the integration of communication, protection, and automation technologies. However, this digital transformation has introduced new vulnerabilities, especially false data injection attacks (FDIAs) and Denial of Service (DoS) attacks. FDIAs can subtly corrupt measurement data, misleading operators without triggering traditional bad data detection (BDD) methods in state estimation (SE), while DoS attacks disrupt the availability of sensor data, affecting grid observability. This paper presents a deep learning-based framework for detecting and localizing FDIAs, including under DoS conditions. A hybrid CNN, Transformer, and BiLSTM model captures spatial, global, and temporal correlations to forecast measurements and detect anomalies using a threshold-based approach. For further detection and localization, a Multi-layer Perceptron (MLP) model maps forecast errors to the compromised sensor locations, effectively complementing or replacing BDD methods. Unlike conventional SE, the approach is fully data-driven and does not require knowledge of grid topology. Experimental evaluation on IEEE 14–bus and 118–bus systems demonstrates strong performance for the FDIA condition, including precision of 0.9985, recall of 0.9980, and row-wise accuracy (RACC) of 0.9670 under simultaneous FDIA and DoS conditions. Furthermore, the proposed method outperforms existing machine learning models, showcasing its potential for real-time cybersecurity and situational awareness in modern SGs. Full article

This paper introduces a novel approach to tree modeling architecture integrated with blockchain technology, aimed at enhancing landscape spatial planning and forest monitoring systems. The primary objective is to develop a low-cost, automated tree CAD modeling methodology combined with blockchain functionalities to support smart forest projects and collaborative design processes. The proposed method utilizes a parametric tree CAD model consisting of four 2D tree-frames with a 45° division angle, enriched with recorded tree-leaves’ texture and color. An “AI Text-by-Voice CAD Programming” technique is employed to create tangible tree-model NFT tokens, forming the basis of a thematic “Internet-of-Trees” blockchain. The main results demonstrate the effectiveness of the blockchain/Merkle hash tree in tracking tree geometry growth and texture changes through parametric transactions, enabling decentralized design, data validation, and planning intelligence. Comparative analysis highlights the advantages in cost, time efficiency, and flexibility over traditional 3D modeling techniques, while providing acceptable accuracy for metaverse projects in smart forests and landscape architecture. Core contributions include the integration of AI-based user voice interaction with blockchain and behavioral data for distributed and collaborative tree modeling, the introduction of a scalable and secure “Merkle hash tree” for smart forest monitoring, and the facilitation of fintech adoption in environmental projects. This framework offers significant potential for advancing metaverse-based landscape architecture, smart forest surveillance, sustainable urban planning, and the improvement of citizen involvement in sustainable forestry paving the way for a greener future. Full article

The increasing frequency of extreme weather events and rapid urbanization has exacerbated pluvial flood risks, underscoring the urgent need to strengthen the assessment of pluvial flood resilience in China’s southwestern mountainous regions. Kunming—a plateau basin city—was selected as a case study, and an urban pluvial flood resilience assessment system was developed based on the DPSIR model. The analytic hierarchy process (AHP), entropy method, and game theory-informed combination weighting were applied to determine indicator weights, while the extension cloud model was utilized to quantitatively assess resilience evolution from 2013 to 2022. The results reveal that: (1) Kunming’s pluvial flood resilience experienced a clear three-stage evolution—initial construction (Level II), resilience enhancement (Level III), and resilience reinforcement (Level IV)—reflecting a transition from rudimentary resilience to advanced adaptive capacity; (2) the ranking of primary indicator weights is as follows: Driving Forces > Pressure > State > Response > Impact, with Flood Disaster Risk (P6), Flood Disaster Early Warning Capability (R1), and Topographic and Geomorphological Characteristics (P7) identified as key influencing factors; (3) marked disparities exist across the five dimensions: the Driving Forces dimension demonstrates increasing economic support; the Pressure dimension reflects structural vulnerabilities and climate variability; the State and Impact dimensions advance incrementally through policy implementation; and the Response dimension has substantially improved due to smart city technologies, although persistent gaps in inter-agency emergency coordination remain. This research offers a scientific basis for enhancing pluvial flood resilience in southwestern mountainous cities. Full article

This study presents a comprehensive analysis aimed at validating the use of an innovative nanosensor based on graphitic nanomembranes for the smart monitoring of industrial wastewater. The validation of the potential of the nanosensor was carried out through the development of advanced analytical methodologies, a direct experimental comparison with commercially available electrode sensors commonly used for the detection of chemical species, and the evaluation of performance under conditions very similar to real-world field applications. The investigation involved a series of controlled experiments using an organic pollutant—benzoquinone—at varying concentrations. Initially, data analysis was performed using classical linear regression models, representing a conventional approach in chemical analysis. Subsequently, a more advanced methodology was implemented, incorporating machine-learning techniques to train a classifier capable of detecting the presence of pollutants in water samples. The study builds upon an experimental protocol previously developed by the authors for the nanomembranes, based on electrochemical impedance spectroscopy. The results clearly demonstrate that integrating the nanosensor with machine-learning algorithms yields significant performance. The intrinsic properties of the nanosensor make it well-suited for potential integration into field-deployable platforms, offering a real-time, cost-effective, and high-performance solution for the detection and quantification of contaminants in wastewater. These features position the nanomembrane-based sensor as a promising alternative to overcome current technological limitations in this domain. Full article

This study addresses the challenge of accurately detecting agricultural land-use changes in bi-temporal remote sensing imagery, which is hindered by cross-temporal interference, multi-scale feature modeling limitations, and poor large-area scalability. The study proposes the Semantic Change Detection (SCD) with Single-Head Dual-Attention Vision Transformer (SHDAViT) and Multidimensional Collaborative Attention (MCA) Block-Based Network (SMBNet). The SHDAViT module enhances local-global feature aggregation through a single-head self-attention mechanism combined with channel–spatial dual attention. The MCA module mitigates cross-temporal style discrepancies by modeling cross-dimensional feature interactions, fusing bi-temporal information to accentuate true change regions. SHDAViT extracts discriminative features from each phase image, MCA aligns and fuses these features to suppress noise and amplify effective change signals. Evaluated on the newly developed AgriCD dataset and the JL1 benchmark, SMBNet outperforms five mainstream methods (BiSRNet, Bi-SRUNet++, HRSCD.str3, HRSCD.str4, and CDSC), achieving state-of-the-art performance, with F1 scores of 91.18% (AgriCD) and 86.44% (JL1), demonstrating superior accuracy in detecting subtle farmland transitions. Experimental results confirm the framework’s robustness against label imbalance and environmental variations, offering a practical solution for agricultural monitoring. Full article

Limb amputation causes significant challenges for patients in achieving effective mobility and functionality through prosthetic limbs. The prosthetic socket plays a pivotal role in the success of rehabilitation. This review explores the current advancements in prosthetic socket design and fabrication, focusing on traditional techniques like casting and lamination, and emerging technologies such as 3D printing and computer-aided design (CAD). By comparing these methods, this review highlights the advantages, limitations, and suitability for different clinical needs. This article discusses the importance of pressure distribution in socket design, emphasizing the need to relieve pressure in sensitive areas to prevent skin complications. It also examines the materials used in socket fabrication, from high-density polymers to advanced composites, assessing their impact on patient comfort and prosthetic performance. Additionally, we discuss the challenges practitioners face in prosthetic care, particularly in low-resource settings, and propose potential solutions through innovative techniques and materials. Advancements in computational modeling improved socket design and validation, enhancing patient comfort and improving the overall biomechanical interaction between the prosthesis and the user. The manuscript concludes by identifying future research opportunities, particularly in personalized prosthetic design and the integration of smart materials, to further enhance the comfort, functionality, and accessibility of prosthetic sockets. Full article

As manufacturing processes evolve towards greater automation and efficiency, the integration of augmented reality (AR) and virtual reality (VR) technologies has emerged as a transformative approach that offers innovative solutions to various challenges in manufacturing applications. This comprehensive review explores the recent technological advancements and applications of AR and VR within the context of manufacturing. This review also encompasses the utilization of AR and VR technologies across different stages of the manufacturing process, including design, prototyping, assembly, training, maintenance, and quality control. Furthermore, this review highlights the recent developments in hardware and software components that have facilitated the adoption of AR and VR in manufacturing environments. This comprehensive literature review identifies the emerging technologies that are driving AR and VR technology toward technological maturity for implementation in manufacturing applications. Finally, this review discusses the major difficulties in implementing AR and VR technologies in the manufacturing sectors. Full article

Compared to non-smart communities, smart communities expand the boundaries of community management and provide a platform for the deep integration of fitness and health. However, a single-entity governance model reduces the management efficiency of smart communities and hinders the realization of fitness-health integration within them. A collaborative governance model involving governments, businesses, social organizations, and residents replaces the traditional linear governance model that relies on a single entity through resource integration. This study, based on collaborative governance theory, employs three scenario-based experimental designs and quantitative analysis, with Xiamen’s smart city community and non-smart urban village community as research subjects. It explores the multistakeholder collaborative governance model for the deep integration of fitness and health, compares the differences in fitness-health integration between smart communities and non-smart communities, and measures the effectiveness differences between multistakeholder collaborative governance and single-entity governance models. The findings indicate: (1) Residents in smart communities have higher satisfaction with comprehensive fitness-health services; (2) Residents in smart communities perceive shorter psychological distances when engaging in fitness-health activities compared to non-smart environments; (3) The governance model moderates the impact of psychological distance on service satisfaction. Compared to the single-actor model, multiactor collaborative governance more effectively enhances perceived psychological proximity and improves satisfaction. The research findings contribute theoretically to advancing understanding of collaborative governance theory while expanding the application of the technology acceptance model (TAM) and the construal level theory in the context of community governance. Practically, they offer insights for public policymakers to optimize resource allocation and for community managers to strengthen digital governance. Full article

The increased importance of sustainability imperatives has required a profound reconsideration of the interaction between materials, manufacturing, and design fields. Biomimetic smart materials such as shape-memory polymers, hydrogels, and electro-active composites represent an opportunity to combine adaptability, responsiveness, and ecological intelligence in systems and products. This work reviews the confluence of such materials with leading-edge manufacturing technologies, notably additive and 4D printing, and how their combining opens the door to the realization of time-responsive, low-waste, and user-adaptive design solutions. Through computational modeling and mathematical simulations, the adaptive performance of these materials can be predicted and optimized, supporting functional integration with high precision. On the basis of case studies in regenerative medicine, architecture, wearables, and sustainable product design, this work formulates the possibility of biomimetic strategies in shifting design paradigms away from static towards dynamic, from fixed products to evolvable systems. Major material categories of stimuli-responsive materials are systematically reviewed, existing 4D printing workflows are outlined, and the way temporal design principles are revolutionizing production, interaction, and lifecycle management is discussed. Quantitative advances such as actuation efficiencies exceeding 85%, printing resolution improvements of up to 50 μm, and lifecycle material savings of over 30% are presented where available, to underscore measurable impact. Challenges such as material scalability, process integration, and design education shortages are critically debated. Ethical and cultural implications such as material autonomy, transparency, and cross-cultural design paradigms are also addressed. By identifying existing limitations and proposing a future-proof framework, this work positions itself within the ongoing discussion on regenerative, interdisciplinary design. Ultimately, it contributes to the advancement of sustainable innovation by equipping researchers and practitioners with a set of adaptable tools grounded in biomimicry, computational intelligence, and temporal design thinking. Full article