Search Results (144)

Although compression therapy is widely used to improve wound healing, selecting the appropriate pressure remains a challenge in clinical practice. This work proposes an intelligent patch integrated into a bandage that allows for the simultaneous monitoring of the applied pressure and wound condition using Near-Field Communication (NFC). The proposed patch integrates a force-sensitive resistive sensor to measure pressure and a capacitive sensor to detect wound exudate through capacitance variations. Capacitance is obtained by analyzing the delay in the stepwise response of the sensor, while resistance is measured from the voltage drop across a resistive divider, which is read by a microcontroller’s analog-to-digital converter. The system is powered wirelessly through NFC energy harvesting, triggered by a mobile device that acts as a reader. The NFC module can be moved away after measurement to improve patient comfort or remain integrated into the dressing for periodic monitoring. Experimental results demonstrate pressure measurements up to 140 mmHg and exudate detection up to 200 $\mathsf{\mu }$L, confirming the feasibility of battery-free NFC smart bandages for therapeutic monitoring based on wound compression. Full article

Accurate residential load forecasting is crucial for enhancing the efficiency and reliability of energy systems in smart grid and demand response applications. However, residential load data are characterized by strong stochasticity, high volatility, and pronounced multi-scale temporal dynamics while being highly susceptible to noise and outliers. These challenges hinder existing methods from effectively capturing complex temporal patterns and learning reliable inter-variable dependencies, thereby limiting forecasting accuracy and stability. To address these issues, this paper proposes a Prior-Guided Multi-Scale Neural Network (PG-MSNN) for multi-step residential load forecasting. The proposed framework integrates prior-guided dependency modeling with multi-scale temporal representation learning in an end-to-end trainable architecture. Specifically, a learnable periodic prior space is constructed, within which a Prior-Guided Module (PGM) is designed to learn cross-variable dependencies and provide structured global periodic guidance. In parallel, a Multi-Scale Patch-LSTM Encoder (MS-PLE) is developed to model temporal dynamics across multiple scales through patch-based sequence representation and adaptive cross-scale fusion. Extensive experiments on three real-world datasets, including IHEPC, REC, and CN-OBEE, demonstrate that, under within-household temporal forecasting settings, the proposed method achieves consistent and competitive performance across various forecasting horizons. Full article

In this study the electromechanical response of a cantilever composite beam with surface-bonded piezoelectric patches is examined, focusing on interface stresses that may initiate delamination. A thermodynamically consistent electroelastic framework was specialized to the linear piezoelectric law used in finite element software, and a two-dimensional (2D) finite element model was developed and validated under static actuation. The predicted tip displacement was compared against the analytical Euler–Bernoulli solution across all seven mesh levels of the convergence study; findings indicated that the converged ANSYS 17.1 result (h = 5 × 10⁻⁵ m) differed from the analytical value by 5.8%, a discrepancy attributed to the plane-strain assumption and the neglect of shear deformation in the Euler–Bernoulli formulation. To resolve the delamination-critical behavior, three-dimensional (3D) models were built using SOLID185/SOLID5 and SOLID186/SOLID226 elements. Interfacial peel σ_y and shear τ_xy stresses were evaluated along lengthwise (PATH1) and transverse (PATH2) paths at the patch–core interface, with maximum interface stresses occurring along the transverse PATH2 near the free end, where strong three-dimensional edge effects developed. Both element sets predicted a similar tip displacement, but the SOLID186/SOLID226 elements yielded peak interface stresses approximately 19% higher in peel and 87% higher in shear along the critical transverse PATH2. These findings demonstrate that element choice minimally affects global stiffness but significantly influences local interface stress prediction, providing practical guidance for the selection of appropriate models when assessing the delamination risk in piezoelectric-actuated composite beams. Full article

Planar microwave (MW) sensors offer high-resolution, non-invasive technology for monitoring critical soil properties, serving as a support for modern precision agriculture. While laboratory studies confirm their exceptional sensitivity, the widespread adoption of these sensors is severely impeded by critical translational challenges that constitute a defining “lab-to-field gap”. These barriers include high sensor-to-sensor variability, debilitating thermal cross-sensitivity, soil heterogeneity necessitating unique site-specific calibration, and the enduring tension between high-performance and cost-effective scaling. This review systematically synthesizes the current state of planar permittivity MW technology, moving beyond technical mechanisms to critically assess these operational limitations. We detail advanced architectural strategies designed to bridge this gap, focusing particularly on the transition toward more robust solutions. The key strategies analyzed include the adoption of differential sensor designs using microstrip patch antennas to mitigate common-mode environmental errors, the integration of ultra-compact metamaterial structures such as split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) for enhanced field robustness and deep soil sensing, and the necessity of multi-parameter sensing capabilities (moisture, pH, and salinity). By establishing a comprehensive roadmap that prioritizes field stability, cost efficiency, and seamless IoT integration, this review demonstrates that planar MW sensors are poised to become reliable and scalable tools. Addressing these critical translational hurdles will ensure optimal resource management, significantly enhance crop productivity, and enable sustainable practices within smart farming ecosystems. Full article

Personalized transdermal drug delivery systems (TDDS) represent a transformative approach in precision medicine by enabling patient-specific, non-invasive, and controlled therapeutic administration. Conventional transdermal patches are limited by fixed dosing, passive diffusion, and interindividual variability in skin permeability and metabolism, often leading to suboptimal therapeutic outcomes. Recent advances in materials science, nanotechnology, microneedle engineering, and digital health have enabled the development of next-generation personalized TDDS capable of programmable, adaptive, and feedback-controlled drug release. Smart wearable patches integrating biosensors, microfluidics, microneedles, and wireless connectivity allow real-time monitoring of physiological and biochemical parameters, enabling closed-loop drug delivery tailored to individual metabolic profiles. Nanocarriers such as lipid nanoparticles, polymeric nanoparticles, and stimuli-responsive hydrogels further enhance drug stability, penetration, and controlled release, while 3D-printing technologies facilitate patient-specific customization of patch geometry, drug loading, and release kinetics. Artificial intelligence (AI) and machine learning tools are increasingly being employed to predict drug permeation behavior, optimize enhancer combinations, and personalize dosing regimens based on pharmacogenomic and pharmacokinetic data. Despite these advances, regulatory complexity, manufacturing standardization, long-term biocompatibility, and cybersecurity considerations remain critical challenges for clinical translation. This review highlights recent innovations in personalized TDDS, discusses their clinical potential, and examines regulatory and technological barriers. Collectively, these emerging smart transdermal platforms offer a promising pathway toward adaptive, patient-centered therapeutics that can significantly improve treatment efficacy, safety, and compliance. Future research should focus on integrating multimodal biosensing, advanced biomaterials, scalable manufacturing strategies, and robust regulatory frameworks to enable clinically validated, fully autonomous transdermal systems that can dynamically adapt to real-time patient needs in diverse therapeutic settings. Full article

Myocardial infarction is a major cardiovascular event that leads to heart failure and death. Although current vascular regeneration and pharmacological therapies can salvage some myocardial tissue, they cannot effectively reverse established necrosis, fibrosis, or adverse ventricular remodeling, thus necessitating novel repair strategies. Silk fibroin (SF), a natural biomaterial, has emerged as an ideal substrate for cardiac tissue engineering owing to its excellent biocompatibility, tunable mechanical properties, and controllable biodegradability. This paper systematically reviews SF-based myocardial repair strategies: SF cardiac patches can be directly applied to infarct areas, providing mechanical support and delivering bioactive substances, while injectable SF hydrogels can be formed in situ via minimally invasive methods, serving as three-dimensional delivery vehicles for cells or drugs. These approaches synergistically promote cardiac repair through multiple mechanisms, including active regulation of inflammation, promotion of angiogenesis, and inhibition of fibrosis. Future development of SF-based therapies will focus on creating smart responsive materials, constructing biomimetic structures via advanced biomanufacturing techniques, and accelerating clinical translation, thereby providing comprehensive solutions for myocardial infarction repair. Full article

This work introduces a new mathematical model designed to describe the glucose–insulin dynamics associated with a glucose-responsive smart microneedle patch reported in the literature. The model captures the complete sequence of the patch behavior, from detecting glucose changes to controlled transdermal insulin delivery and gradually restoring blood glucose levels to the normal range. Our simulations show that the patch can effectively manage glucose not only during fasting conditions but also after single and multiple meals, restoring glucose levels to healthy levels within a short period. The model predictions are consistent with experimentally reported trends in previously published studies, which strengthens confidence in the biological realism of the proposed mechanism. Because some parameters in such systems are difficult to measure directly, we also performed a comprehensive sensitivity analysis to understand how variations in key parameters influence system stability. The results highlight the central role of the insulin release rate and the five glucose–regulation parameters examined in the sensitivity analysis, providing clear guidance on the most critical aspects of patch design for reliable performance. Overall, this study provides a simplified yet robust mathematical framework that makes the behavior of a glucose-responsive microneedle patch easy to understand and analyze. It lays the groundwork for future refinement of control strategies and optimization of patch design, improving control strategies, and developing more advanced systems that can maintain healthy glucose levels naturally and intuitively. Full article

Controlled drug delivery systems (CDDSs) are increasingly attracting interest from the scientific community in order to achieve highly precise, customized, and efficient therapeutic treatment of various diseases. The challenge is to develop highly innovative devices and appropriate administration methods in order to reduce side effects and further improve patient compliance. In this context, transdermal drug delivery systems (TDDSs) represent smart tools that permit supplying therapeutically effective amounts of drugs at a fixed time using the skin as the administration route. They are non-invasive and allow for avoiding gastric side effects and first-pass metabolism occurring in the liver. TDDSs have been produced using numerous therapeutic agents and, more recently, also biological molecules. However, it must be highlighted that they are complex systems, and their formulation requires a multidisciplinary approach and expertise in polymer chemistry and materials science. A contribution in this direction is given from the integration of membrane technology with biological and pharmaceutical sciences. The present review deals with a general overview of controlled drug delivery systems. Particular attention is devoted to TDDSs and to the materials used for producing polymeric membrane-based TDDSs with a membrane engineering perspective. It also describes the passive and the most advanced active strategies for transdermal delivery. Finally, different transdermal membrane-based release systems, like patches, mixed-matrix membranes, and imprinted membranes are discussed. Full article

This paper presents a novel broadband circularly polarized optically transparent monopole antenna using indium tin oxide (ITO) and PMMA. The proposed design successfully integrates ultra-wideband circular polarization characteristics with exceptional optical transparency. The antenna, constructed with a three-layer configuration utilizing ITO films as both the radiating patch and ground plane, along with transparent PMMA serving as the substrate, features compact dimensions of 40 × 40 × 1 mm³. By leveraging a co-optimized design incorporating a slotted hexagonal-ring radiating patch, triangular perturbation ground plane, and stepped-impedance feeding structure, the antenna achieves a circularly polarized operating bandwidth of 2.8–6.6 GHz (fractional bandwidth of 77.9%), with an axial ratio < 3 dB and return loss < −15 dB. The experimental findings exhibit strong consistency with the simulations, illustrating a high level of visible-light transmittance and radiation patterns characterized by right-hand circular polarization in the positive z-axis direction (+z) and left-hand circular polarization in the negative z-axis direction (−z). This innovative antenna shows great potential for applications in smart windows, display integration, and 5G communication systems. Full article

This paper presents an experimental framework that allows damage identification and retrofitting assessment in reinforced concrete (RC) beam with implemented piezoelectric lead zirconate titanate (PZT) sensors embedded into the concrete matrix. The study was conducted with concrete prepared from 30% refuse-derived fuel (RDF) fly ash and 70% cement as part of research on sustainable materials for structural health monitoring (SHM). Electromechanical impedance (EMI) was employed for detecting structural degradation, with progressive damage and evaluation of recovery effects made using root-mean-square deviation (RMSD) and conductance changes. Concrete beam specimens with dimensions of 700 mm × 150 mm × 150 mm and embedded with 10 mm × 10 mm × 0.2 mm PZT sensors were cast and later subjected to three damage stages: concrete chipping (Damage I), 50% steel bar cutting (Damage II), and 100% steel bar cutting (Damage III). Three retrofitting stages were adopted: reinforcement welding (Retrofitting I and II), and concrete patching (Retrofitting III). The results demonstrated that the embedded PZT sensors with EMI and RMSD analytics represent a powerful technique for early damage diagnosis, reserved retrofitting assessment, and proactive infrastructure maintenance. The combination of SHM systems and sustainable retrofitting strategies can be a promising path toward resilient and smart civil infrastructure. Full article

Symplocos is an ecologically important genus that plays vital roles in subtropical evergreen broad-leaved mountain forests, including contributing to nutrient cycling, providing shelter and habitats for various organisms, and supporting overall plant diversity across East and Southeast Asia. Many species exhibit high levels of endemism and sensitivity to environmental change. China, with its wide range of ecosystems and climatic zones, is home to 18 endemic Symplocos species. Studies revealed that global warming is driving shifts in species diversity, particularly in mountains. Our study explores the current and projected richness patterns of endemic Symplocos species in China under climate change scenarios, emphasizing the implications for conservation planning. We applied stacked species distribution models (SSDMs), using key bioclimatic and environmental variables to predict current and future habitat suitability for endemic Symplocos species, evaluated model performance through multiple accuracy metrics, and generated ensemble projections to assess richness patterns under climate change scenarios. To assess the spatial configuration and fragmentation patterns of the endemic species richness under current and future climate scenarios, landscape metrics were calculated based on classified richness maps. The produced models demonstrated high accuracy with AUC > 0.9 and TSS > 0.75, highlighting the critical role of bioclimatic variables, particularly precipitation and temperature, in shaping endemic Symplocos distribution. Our analysis identifies the current hotspots of Symplocos endemism along southeastern China, particularly in Zhejiang, Fujian, Jiangxi, Hunan, southern Anhui, and northern Guangdong and Guangxi. These areas are at high risk, with up to 35% of endemic Symplocos species richness predicted to be lost over the next 60 years due to climate change. The study predicts a high decrease in endemic Symplocos species richness, especially in South China (e.g., Fujian, Guangdong, Guizhou, Yunnan, southern Shaanxi), and mid-level decreases in East China (e.g., Heilongjiang, Jilin, eastern Inner Mongolia, Liaoning). Conversely, potential increases in endemic Symplocos species richness are projected in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui, suggesting these regions may serve as future refugia for endemic Symplocos species. The analysis of the landscape structure and configuration revealed relatively minor but notable variations in the spatial structure of endemic Symplocos richness patterns under current and future climate scenarios. However, under the SSP585 scenario by 2080, the medium richness class showed a more pronounced decrease in aggregation index and increase in number of patches relative to other richness classes, suggesting that higher emissions may drive fragmentation of moderately rich areas, potentially isolating populations of Symplocos. These structural changes suggest a potential reduction in habitat quality and connectivity, posing significant risks to the persistence of endemic Symplocos populations, which underscores the urgent need for targeted smart-climate conservation strategies that prioritize both current hotspots and potential future refugia to enhance the resilience of endemic Symplocos forests and their ecosystems in the face of climate change. Full article

Fast and accurate indoor occupancy detection is critical for energy efficiency and emergency rescue in the fields of smart building and indoor positioning. However, existing image-based indoor occupancy detection models often neglect small human targets and suffer from large parameters, compromising detection accuracy, real-time performance, and deployment on resource-constrained devices. To address these issues, this study proposes a modified lightweight indoor occupancy detection model based on YOLO v8. Firstly, a patch expanding layer is added to the neck of the YOLO v8 model for reshaping the feature maps of adjacent dimensions into higher-resolution feature maps. Secondly, the standard convolution in the original neck is replaced with the GSConv, boosting the non-linear representation by adding DSC layers and a shuffle operation, efficiently preserving hidden connections between channels. Additionally, the VoV-GSCSP in the neck is designed to adopt one-shot aggregation with GS bottlenecks based on GSConv, followed by a cross-stage partial network module. Experiments on the SCUT-HEAD dataset show the modified lightweight YOLO v8 reduces parameters by 9.3% and computational complexity by 8.6%, while increasing mAP50 by 1.4% compared to the baseline. The proposed model can detect indoor occupancy in a fast and precise manner. Full article

Deep neural networks have outperformed conventional methods in various fields such as image recognition, natural language processing, and speech recognition. In particular, vision models are widely applied to real-world domains including medical image analysis, autonomous driving, smart factories, and security surveillance. However, these models are vulnerable to adversarial attacks, which pose serious threats to safety and reliability. Among different attack types, this study focuses on evasion attacks that perturb the inputs of deployed models, with an emphasis on black-box settings. The zeroth order optimization (ZOO) attack can approximate gradients and execute attacks without access to internal model information, but it becomes inefficient and exhibits low success rates on high-resolution images due to its dependence on image resizing and its high memory complexity. To address these limitations, this study proposes a patch-based fast zeroth order optimization attack, FP-ZOO. FP-ZOO partitions images into patches and generates perturbations effectively by employing probability-based sampling and an $ϵ$-greedy scheduling strategy. We conducted a large-scale evaluation of the FP-ZOO attack on the CIFAR-10, CIFAR-100, and ImageNet datasets. In this evaluation, we adopted attack success rate, ${\mathcal{L}}_2$ distance, and adversarial example generation time as performance metrics. The evaluation results showed that the FP-ZOO attack not only achieved an attack success rate of 97–100% against ImageNet in untargeted attacks, but also demonstrated performance up to 10 s faster compared to the ZOO attack. However, in targeted attacks, it showed relatively lower performance compared to baseline attacks, leaving it as a future research topic. Full article

Silk fibroin (SF) has evolved from a traditional biopolymer to a leading regenerative medicine material. Its combination of mechanical strength, biocompatibility, tunable degradation, and molecular adaptability makes SF a unique matrix that is both bioactive and intelligent. Advances in hydrogel engineering have transformed SF from a passive scaffold into a smart, living hydrogel. These systems can instruct cell fate, sense microenvironmental signals, and deliver therapeutic signals as needed. By incorporating stem cells, progenitors, or engineered immune and microbial populations, SF hydrogels now serve as synthetic niches for organoid maturation and as adaptive implants for tissue regeneration. These platforms replicate extracellular matrix complexity and evolve with tissue, showing self-healing, shape-memory, and stimuli-responsive properties. Such features are redefining biomaterial–cell interactions. SF hydrogels are used for wound healing, musculoskeletal repair, neural and cardiac patches, and developing scalable organoid models for disease and drug research. Challenges remain in maintaining long-term cell viability, achieving clinical scalability, and meeting regulatory standards. This review explores how advances in SF engineering, synthetic biology, and organoid science are enabling SF-based smart living hydrogels in bridging the gap between research and clinical use. Full article

Urban tree detection is fundamental to effective forestry management, biodiversity preservation, and environmental monitoring—key components of sustainable smart city development. This study introduces a deep learning framework for urban tree crown segmentation that exclusively leverages high-resolution satellite imagery from GeoEye-1, WorldView-2, and WorldView-3, thereby eliminating the need for additional data sources such as LiDAR or UAV imagery. The proposed framework employs a Residual U-Net architecture augmented with Attention Gates (AGs) to address major challenges, including class imbalance, overlapping crowns, and spectral interference from complex urban structures, using a custom composite loss function. The main contribution of this work is to integrate data from three distinct satellite sensors with varying spatial and spectral characteristics into a single processing pipeline, demonstrating that such well-established architectures can yield reliable, high-accuracy results across heterogeneous resolutions and imaging conditions. A further advancement of this study is the development of a hybrid ground-truth generation strategy that integrates NDVI-based watershed segmentation, manual annotation, and the Segment Anything Model (SAM), thereby reducing annotation effort while enhancing mask fidelity. In addition, by training on 4-band RGBN imagery from multiple satellite sensors, the model exhibits generalization capabilities across diverse urban environments. Despite being trained on a relatively small dataset comprising only 1200 image patches, the framework achieves state-of-the-art performance (F1-score: 0.9121; IoU: 0.8384; precision: 0.9321; recall: 0.8930). These results stem from the integration of the Residual U-Net with Attention Gates, which enhance feature representation and suppress noise from urban backgrounds, as well as from hybrid ground-truth generation and the combined BCE–Dice loss function, which effectively mitigates class imbalance. Collectively, these design choices enable robust model generalization and clear performance superiority over baseline networks such as DeepLab v3 and U-Net with VGG19. Fully automated and computationally efficient, the proposed approach delivers cost-effective, accurate segmentation using satellite data alone, rendering it particularly suitable for scalable, operational smart city applications and environmental monitoring initiatives. Full article