Search Results (2,535)

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

Tire deformation monitoring is a critical requirement for improving vehicle safety, performance, and intelligent transportation systems. However, most existing flexible strain sensors either lack directional sensitivity or have not been validated in real-world driving environments, limiting their practical application in smart tires. In this work, we report the fabrication of a flexible piezoresistive strain sensor based on a porous laser-induced graphene (LIG) network embedded in an Ecoflex elastomer matrix, with integrated directional force recognition. The LIG–Ecoflex sensor exhibits a high gauge factor of 9.7, fast response and recovery times, and stable performance over 10,000 cycles. More importantly, the anisotropic structure of the LIG enables accurate multi-directional stress recognition when combined with a convolutional neural network (CNN), achieving an overall classification accuracy exceeding 98%. To further validate real-world applicability, the sensor was mounted inside passenger car tires and tested under different loads and speeds. The results demonstrate reliable monitoring of tire deformation with clear correlations to load and velocity, confirming robustness under dynamic driving conditions. This study provides a new pathway for the integration of direction-aware, high-performance strain sensors into intelligent tire systems, with broader potential for wearable electronics, vehicle health monitoring, and next-generation Internet of Vehicles applications. Full article

Smart-beehive technologies represent a paradigm shift in beekeeping, transitioning from traditional, reactive methods toward proactive, data-driven management. This systematic literature review investigates the current landscape of intelligent systems applied to beehives, focusing on the integration of IoT-based monitoring, sensor modalities, machine learning techniques, and their applications in precision apiculture. The review adheres to PRISMA guidelines and analyzes 135 peer-reviewed publications identified through searches of Web of Science, IEEE Xplore, and Scopus between 1990 and 2025. It addresses key research questions related to the role of intelligent systems in early problem detection, hive condition monitoring, and predictive intervention. Common sensor types include environmental, acoustic, visual, and structural modalities, each supporting diverse functional goals such as health assessment, behavior analysis, and forecasting. A notable trend toward deep learning, computer vision, and multimodal sensor fusion is evident, particularly in applications involving disease detection and colony behavior modeling. Furthermore, the review highlights a growing corpus of publicly available datasets critical for the training and evaluation of machine learning models. Despite the promising developments, challenges remain in system integration, dataset standardization, and large-scale deployment. This review offers a comprehensive foundation for the advancement of smart apiculture technologies, aiming to improve colony health, productivity, and resilience in increasingly complex environmental conditions. Full article

Fiber-reinforced composites (FRCs) have emerged as transformative alternatives to traditional marine construction materials, owing to their superior corrosion resistance, design flexibility, and strength-to-weight ratio. This review comprehensively examines the current state of FRC technologies in marine deck and underwater applications, with a focus on manufacturing methods, durability challenges, and future innovations. Thermoset polymer composites, particularly those with epoxy and vinyl ester matrices, continue to dominate marine applications due to their mechanical robustness and processing maturity. In contrast, thermoplastic composites such as Polyether Ether Ketone (PEEK) and Polyether Ketone Ketone (PEKK) offer advantages in recyclability and hydrothermal performance but are hindered by higher processing costs. The review evaluates the performance of various fiber types, including glass, carbon, basalt, and aramid, highlighting the trade-offs between cost, mechanical properties, and environmental resistance. Manufacturing processes such as vacuum-assisted resin transfer molding (VARTM) and automated fiber placement (AFP) enable efficient production but face limitations in scalability and in-field repair. Key durability concerns include seawater-induced degradation, moisture absorption, interfacial debonding, galvanic corrosion in FRP–metal hybrids, and biofouling. The paper also explores emerging strategies such as self-healing polymers, nano-enhanced coatings, and hybrid fiber architectures that aim to improve long-term reliability. Finally, it outlines future research directions, including the development of smart composites with embedded structural health monitoring (SHM), bio-based resin systems, and standardized certification protocols to support broader industry adoption. This review aims to guide ongoing research and development efforts toward more sustainable, high-performance marine composite systems. Full article

Wearable computing is evolving from a passive data collection paradigm into an active, precision-guided health orchestration system. This survey synthesizes developments across sensing modalities, wireless protocols, computational frameworks, and AI-driven analytics that collectively define the state of the art in mental and physical health monitoring. A narrative review methodology is used to map the landscape of hardware innovations—including microfluidic sweat sensing, smart textiles, and textile-embedded biosensing ecosystems—alongside advances in on-device AI acceleration, context-aware multimodal fusion, and privacy-preserving learning frameworks. The analysis highlights a shift toward multiplexed biochemical sensing for real-time metabolic profiling, neuromorphic and analog AI processors for ultra–low-power analytics, and closed-loop therapeutic systems capable of adapting interventions dynamically to both physiological and psychological states. These trends are examined in the context of emerging clinical and consumer use cases, with a focus on scalability, personalization, and data security. By grounding these insights in current research trajectories, this work positions wearable computing as a cornerstone of preventive, personalized, and participatory healthcare. Addressing identified technical and ethical challenges will be essential for the next generation of systems to become trusted, equitable, and clinically indispensable tools. Full article

Ensuring the safety, reliability, and longevity of Lithium-ion (Li-ion) batteries is crucial for sustainable integration of Electric Vehicles (EVs) within Intelligent Transportation Systems (ITSs). However, thermal stress and degradation-induced anomalies can cause sudden performance failures, posing critical operational and safety risks. Capturing complex, non-linear, and high-dimensional patterns remains challenging for traditional Machine Learning (ML) models. We propose a hybrid anomaly detection method that incorporates a Variational Quantum Neural Network (VQNN), which uses the principles of quantum mechanics, such as superposition, entanglement, and parallelism, to learn complex non-linear patterns. The VQNN is integrated with Isolation Forest (IF) and a Median Absolute Deviation (MAD)-based spike characterisation method to form a Quantum Anomaly Detector (QAD). This method distinguishes between normal and anomalous spikes in battery behaviour. Using an Arrhenius-based model, we simulate how the State of Health (SoH) and voltage of a Li-ion battery reduce as temperatures increase. We perform experiments on NASA battery datasets and detect abnormal spikes in 14 out of 168 cycles, corresponding to $8.3$% of the cycles. The QAD achieves the highest Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of $0.9820$, outperforming the baseline IF model by $7.78\%$. We use ML to predict the SoH and voltage changes when the temperature varies. Gradient Boosting (GB) achieves a voltage Mean Squared Error (MSE) of $0.001425$, while Support Vector Regression (SVR) achieves the highest $R^2$ score of $0.9343$. These results demonstrate that Quantum Machine Learning (QML) can be applied for anomaly detection in Battery Management Systems (BMSs) within intelligent transportation ecosystems and could enable EVs to autonomously adapt their routing and schedule preventative maintenance. With these capabilities, safety will be improved, downtime minimised, and public confidence in sustainable transport technologies increased. Full article

Background/Objectives: Adequate adolescent nutrition is vital for lifelong health, yet traditional school meal programs often emphasize processed foods. Digital nudges, subtle digital changes, may help promote healthier food choices. This study aimed to evaluate the impact of a digital nudge intervention in a smart school canteen on students’ food choices and nutrient intake over three months. Methods: A pre-post single-arm (quasi-experimental) study was conducted among 502 high school students (aged 15–17) in Shenyang, China. In August 2023, the school implemented a smart canteen with a mobile mini-program for meal pre-ordering. Embedded digital nudges included improved visibility of healthy options, nutritional information, and default settings favoring nutritious choices. Dietary intake was assessed using a 3-day 24 h dietary record and a food frequency questionnaire. Paired t-tests, Wilcoxon signed-rank, and chi-square tests were used for analysis. Results: Post-intervention, the weekly consumption frequency of coarse grains (p = 0.017), fruits (p < 0.001), seafood (p < 0.001), and soy products (p < 0.001) significantly increased, while sweets (p = 0.033), sugary drinks (p = 0.015), fast food, and eating out (both p < 0.001) decreased. Daily calcium intake rose from 683.00 mg to 804.11 mg (p < 0.1), and the proportion meeting recommendations increased from 39.3% to 50.9%. No significant change was observed in vitamin C intake (p = 0.192). Conclusions: The digital nudge intervention in the smart school canteen effectively improved students’ dietary choices, particularly increasing the consumption frequency of healthy foods and dietary calcium intake. Full article

Technological advancements, shifting consumer preferences, and societal changes drive the cosmetics industry to evolve continuously. The cosmetics industry is experiencing a renaissance, with new ingredients that are more environmentally friendly, natural, and transparent in terms of sourcing and manufacturing and, last but not least, which are also multifunctional. The use of technology in cosmetics has been rising, including AI (artificial intelligence) and AR (augmented reality) for virtual try-ons, skin analysis tools, and smart beauty devices that provide at-home skincare treatments. Meanwhile, fermented cosmetic ingredients are becoming increasingly popular in the beauty industry due to their improved efficacy and skin benefits. The benefits include enhanced absorption, improved stability (due to the self-produced preservatives), microbiome-friendliness (supporting the skin’s microbiome), and anti-inflammatory and soothing effects. The most common cosmetic ingredients produced by microorganisms are fermented rice, soy, green tea, fruits, and vegetables. Our laboratory investigates a mineral rock called alginite, which has shown many benefits in other fields, such as agriculture and cosmetics (e.g., as a facemask). It has been proven that alginite combined with LAB (lactic acid-producing bacteria) probiotics is beneficial for health and can increase biomass production. However, cell lysates with alginite have never been investigated for cosmetic purposes. This study aimed to investigate the potential of alginite, a mineral rock, in enhancing the cosmetic properties of LAB lysates, specifically focusing on antioxidant effects, skin-whitening properties, and, in preliminary tests, skin-moisturising effects. LAB strains were cultured with and without alginite, and the resulting cell lysates were analysed for these cosmetic applications. The preliminary results suggested that alginite may boost the hydrating effect of LAB lysate, increasing it tenfold compared to LAB lysate alone. The antioxidant effect was enhanced fivefold in the case of Lactobacillus acidophilus when cultured with alginite. However, no significant effect was observed on mushroom tyrosinase inhibition, suggesting no impact on pigment formation. Further research is needed to fully understand the mechanisms underlying these effects and to explore potential applications in cosmetic formulations. Limitations of this study include the focus on specific LAB strains and the need for in vivo studies to confirm the observed effects on human skin. Full article

Designing fair and sustainable pricing mechanisms for health insurance requires accurate risk assessment and the formulation of incentive-compatible strategies among stakeholders. This study proposes a hybrid framework that integrates machine learning with game theory to determine optimal, risk-based premium rates. Using a comprehensive dataset of insured individuals, the XGBoost algorithm is employed to predict medical claim costs and calculate corresponding premiums. To enhance transparency and explainability, SHAP analysis is conducted across four risk-based groups, revealing key drivers, including healthcare utilization and demographic features. The strategic interactions among the insurer, insured, and employer are modeled as a repeated game. Using the Folk Theorem, the conditions under which long-term cooperation becomes a sustainable Nash equilibrium are explored. The results demonstrate that XGBoost achieves high predictive accuracy (R² ≈ 0.787) along with strong performance in error measures (RMSE ≈ 1.64 × 10⁷ IRR, MAE ≈ 1.08 × 10⁶ IRR), while SHAP analysis offers interpretable insights into the most influential predictors. Game-theoretic analysis further reveals that under appropriate discount rates, stable cooperation between stakeholders is achievable. These findings support the development of equitable, transparent, and data-driven health insurance systems that effectively align the incentives of all stakeholders. Full article

This study conducts a simulation-based assessment of a recently commissioned office building in the Republic of Korea, representing a typical public office facility. The building was modeled using EnergyPlus 23.1.0 after construction, although no validation was performed due to the absence of metered consumption data. Previous approaches relying on simplified methods such as the Radiant Time Series (RTS), which neglect dynamic building behavior, have often led to overestimated cooling and heating loads. This has emerged as a major obstacle in designing energy-efficient buildings within the context of compact and smart cities pursuing carbon neutrality. Consequently, the trend in building performance analysis is shifting toward dynamic simulations and digital twin-based design methodologies. Furthermore, electrification of buildings without adequate thermal load assessment may also contribute to overdesign, irrespective of urban environmental characteristics. From an urban planning standpoint, there is a growing need for performance criteria that reflect occupant behavior and actual usage patterns. However, dynamics-based building studies remain scarce in the Republic of Korea. In this context, the present study demonstrates that passive design strategies, implemented through systematic changes in envelope materials, HVAC operational standards, and compliance with ASHRAE 90.1 criteria, can significantly enhance thermal comfort and indoor air quality. The simulation results show that energy consumption can be reduced by over 36.21% without compromising occupant health or comfort. These findings underscore the importance of thermal load understanding prior to electrification and highlight the potential of LEED-aligned passive strategies for achieving high-performance, low-energy buildings. Full article

Background/Objectives: Pharmacists’ roles are shifting from dispensing medications to managing chronic diseases and prevention. Diabetes is a growing public health issue requiring early detection and management, where pharmacists can play a key role. The SMART Pharmacist Program promotes continuing education and expanded care, and a nationwide blood sugar screening campaign in North Macedonia was conducted to evaluate the impact of pharmacists in detecting undiagnosed diabetes and supporting glycemic control. Methods: This descriptive observational cross-sectional study was conducted mainly on 14 November 2024, in 98 community pharmacies across 14 cities. Participants over 18 years old were recruited via voluntary sampling. A total of 998 measurements were performed on the campaign day, with additional screening extending to 24 January 2025, totaling 1085 participants. Blood glucose was measured by finger prick testing and classified according to national and NICE guidelines. A structured questionnaire collected demographic, medical, and lifestyle data. Results: Among 1085 participants (65.1% female, mean age 57.6 ± 14.5 years), 258 (23.8%) had diagnosed diabetes, mostly Type 2 (226; 20.8%), while 827 (76.2%) were undiagnosed. Males had 1.7 times higher odds of diabetes. Diabetes prevalence correlated with physical inactivity, higher BMI, smoking, and chronic diseases. Among the undiagnosed, 17.8% were prediabetic and 4.3% diabetic. Of diagnosed patients, 57% had well-controlled and 42% poorly controlled diabetes. Metformin-based therapies were most common for Type 2 diabetes. Conclusions: Community pharmacists can effectively support early detection of diabetes and identify patients with suboptimal glycemic control, enhancing diabetes management in the community. Full article

Pesticides are essential for modern agriculture but leave harmful residues that threaten human health and ecosystems. This paper reviews key pesticide detection technologies, including chromatography and mass spectrometry, spectroscopic methods, biosensing (aptamer/enzyme sensors), and emerging technologies (nanomaterials, AI). Chromatography-mass spectrometry remains the gold standard for lab-based precision, while spectroscopic techniques enable non-destructive, multi-component analysis. Biosensors offer portable, real-time field detection with high specificity. Emerging innovations, such as nano-enhanced sensors and AI-driven data analysis, are improving sensitivity and efficiency. Despite progress, challenges persist in sensitivity, cost, and operational complexity. Future research should focus on biomimetic materials for specificity, femtogram-level nano-enhanced detection, microfluidic “sample-to-result” systems, and cost-effective smart manufacturing. Addressing these gaps will strengthen food safety from farm to table while protecting ecological balance. This overview aids researchers in method selection, supports regulatory optimization, and evaluates sustainable pest control strategies. Full article

Background/Objectives: Age-related macular degeneration (AMD) is a leading cause of visual impairment in older adults and often coexists with cataracts. The indication of presbyopia-correcting intraocular lenses (IOLs) in these patients remains controversial. This study aimed to evaluate the clinical performance of a non-diffractive extended depth-of-focus (EDOF) IOL (LuxSmart™) compared to a monofocal plus IOL (Tecnis Eyhance™) in cataract patients with early-stage dry AMD. Methods: In this prospective observational study, 41 patients with early-stage AMD underwent bilateral cataract surgery with either LuxSmart™ or Tecnis Eyhance™ IOL implantation, targeting postoperative emmetropia. The eye selected for analysis was the first eye scheduled for surgery. Preoperative and postoperative evaluations included high and low-contrast distance visual acuity, intermediate and near visual acuity, defocus curves, ocular light scatter (halometry), and quality of life assessment (NEI VFQ-25). Postoperative biometric accuracy and refractive outcomes were also analyzed. Results: Both IOLs showed high refractive accuracy, with 100% of eyes within ±0.50 D of target. Postoperative uncorrected distance visual acuity was 0.10 ± 0.06 LogMAR for Eyhance and 0.07 ± 0.02 for LuxSmart (p = 0.06). Low contrast VA at 20% was 0.22 ± 0.11 (Eyhance) and 0.26 ± 0.16 (LuxSmart) (p = 0.49). Depth of focus was approximately 1.75 D for both lenses. Light scatter (LDI) improved postoperatively in both groups with no significant differences (p = 0.54). VFQ-25 scores showed improvement in daily activities, though no changes were observed in driving or mental health domains. Conclusions: Both lenses are safe and effective options for early AMD patients undergoing cataract surgery, providing good functional vision at multiple distances Full article

Background: The skin microbiome plays a key role in maintaining skin health, and its composition can be influenced by cosmetic treatments. This study aimed to investigate the effects of SMART DNA Therapy treatment on facial skin microbiome composition, with specific focus on changes in commensal and pathogenic bacterial populations following multi-component anti-aging intervention. Methods: This clinical study included 10 Caucasian female participants aged 28–50 years (Clinical trial registration number: 0406/2023). Each participant received three Retix.C SMART DNA THERAPY treatments at 14-day intervals over 6 weeks. The protocol included three phases: chemical peeling with ferulic acid, peptide microinjections for DNA repair, and home-care products with antioxidants. Bacterial samples were collected from forehead and cheek skin before treatment and 2 weeks after the final treatment. Samples were analyzed using bacterial culture and PCR methods. Results: After treatment, the skin microbiome showed beneficial changes with increased numbers of helpful bacteria and elimination of harmful bacteria: complete removal of Cutibacterium acnes and Staphylococcus aureus was observed, Staphylococcus epidermidis and other beneficial bacteria increased on both forehead and cheek areas. Overall bacterial diversity decreased, and participants exhibited more similar microbiome patterns after treatment. Conclusions: SMART DNA Therapy treatment successfully modified the skin microbiome by increasing protective bacteria and eliminating pathogenic species. The treatment may support skin health through microbiome modulation and the potential antioxidant effects of its active ingredients, although these were not directly assessed in this study. Full article

As mental health issues such as stress, anxiety, and depression become increasingly prevalent in urban populations, there is a critical need to embed restorative functions into the built environment. Urban parks, as integral components of ecological infrastructure, play a vital role in promoting psychological well-being. This study explores how diverse park environments facilitate mental health recovery through multi-sensory engagement, using integrated psychophysiological assessments in a wetland park in Zhengzhou, China. Electroencephalography (EEG) and perceived restoration scores were employed to evaluate recovery outcomes across four environmental types: waterfront, wetland, forest, and plaza. Key perceptual factors—including landscape design, spatial configuration, biodiversity, and facility quality—were validated and analyzed for their roles in shaping restorative experiences. Results reveal significant variation in recovery effectiveness across environments. Waterfront areas elicited the strongest physiological responses, while plazas demonstrated lower restorative benefits. Two recovery pathways were identified: a direct, sensory-driven process and a cognitively mediated route. Biodiversity promoted physiological restoration only when mediated by perceived restorative qualities, whereas landscape and spatial attributes produced more immediate effects. Facilities supported psychological recovery mainly through cognitive appraisal. The study proposes a smart park framework that incorporates environmental sensors, adaptive lighting, real-time biofeedback systems, and interactive interfaces to enhance user engagement and monitor well-being. These technologies enable urban parks to function as intelligent, health-supportive infrastructures within the broader built environment. The findings offer evidence-based guidance for designing responsive green spaces that contribute to mental resilience, aligning with the goals of smart city development and healthy life-building environments. Full article