Search Results (206)

Accurate ocean wave sensing in polar and other low-temperature marine environments is of great significance for marine environmental observation, climate research, and navigation safety. However, conventional wave sensors rely on external power supplies and suffer from poor stability under low-temperature and high-salinity conditions, making long-term self-powered waves sensing a significant challenge. Herein, a highly stable composite eutectogel electrode is developed by integrating sodium lignosulfonate, Fe³⁺ crosslinking, Zn²⁺-carboxylate coordination interactions, and a choline chloride/urea deep eutectic solvent (DES). The DES effectively suppresses solvent crystallization and endows the gel with excellent low-temperature tolerance, while the synergistic effect of metal coordination and multiple non-covalent interactions constructs a robust ion-conducting network with enhanced structural stability. Furthermore, eutectogel-based composite electrode architecture is designed to improve electrical conductivity and charge collection efficiency, thereby enabling stable electrical output under harsh marine conditions. Based on the as-prepared eutectogel electrode, a self-powered solid–liquid triboelectric nanogenerator is fabricated for ocean wave-motion sensing. The device can detect the wave amplitude, with an accuracy of 0.2 cm, and sense the frequency of waves ranging from 0.2 Hz to 1.6 Hz. More importantly, the SL-TENG exhibits excellent environmental adaptability, operating reliably in 3.5 wt% simulated seawater and at 0 °C. The current retention ratio reaches approximately 91% at 0 °C, which is significantly higher than that of the hydrogel-based device (≈6%). The remarkably low-temperature and salt-tolerant performance originates from the stable ion-transport network and anti-freezing characteristics of the eutectogel electrode. This work provides an effective strategy for constructing environmentally resilient eutectogel-based triboelectric devices and offers a promising route toward self-powered wave sensing systems for long-term deployment in harsh marine environments. Full article

Wireless edge systems increasingly rely on on-device learning for tasks such as user localization and misuser detection, but centralized training is often infeasible because of privacy and bandwidth constraints. In this work, we study communication-efficient federated learning schemes that jointly train lightweight neural models across clients. We compare a multi-task shared-backbone strategy with mid-training backbone freezing against task-separate baselines. Using a wireless graph simulator, we prototype both multilayer perceptrons (MLPs) and an edge-aware graph neural network (GNN) encoder trained with FedAvg, and we evaluate localization error, misuser F1-score, and byte-level communication over training rounds. The results show that the GNN-Shared model achieves a 28.9% lower localization error than the MLP-Shared baseline and a 17.7% lower localization error than the GNN-Loc_only baseline, while improving the best misuser F1-score by 2.7% compared with GNN-Mis_only. In terms of communication, the GNN-Shared model reduces the total communication cost by 40.1% compared with MLP-Shared over 40 federated rounds. Additional experiments with FedProx, GraphSAGE, and GAT baselines show that the proposed federated graph-learning framework is flexible across different graph backbones. Multi-seed experiments over five random seeds further confirm the robustness of graph-based federated learning, with GraphSAGE-FedAvg achieving strong average localization and misuser detection performance. Scalability experiments with up to 20 clients, 150 nodes, and different Dirichlet non-IID parameters show that the framework remains stable under larger graph sizes, while communication cost grows mainly with the number of participating clients. To move the framework closer to edge deployment, this paper also introduces a lightweight hardware-oriented GNN-Lite inference prototype with multi-neighbor accumulation, finite-state-machine-based sequential computation, and read-only-memory-based coefficient storage. The prototype achieved timing closure on an Artix-7 field-programmable gate array with 213 LUTs, 111 FFs, 18 DSPs, 0 BRAMs, and 0.106 W estimated total on-chip power. The dynamic power was only 0.001 W, and the estimated total energy consumption was 10.6 nJ per inference. Overall, the results show that federated graph learning is a promising direction for wireless edge intelligence, and that different graph backbones can be selected depending on the target trade-off among localization accuracy, misuser detection performance, communication cost, robustness, and hardware deployment. Full article

Gel-based materials are emerging as lightweight, mechanically compliant, and electromagnetically tunable platforms for next-generation antennas, electromagnetic interference (EMI) shields, microwave absorbers, and radomes. This review summarizes recent progress in hydrogel-, aerogel-, ionogel-, organohydrogel-, and xerogel-based electromagnetic materials, with emphasis on how network structure, pore architecture, solvent phase, and functional fillers regulate permittivity, conductivity, impedance matching, and attenuation. The device-level roles of gels are discussed in miniaturized and reconfigurable antennas, absorption-dominated shielding systems, broadband microwave absorbers, high-temperature wave-transparent radomes, and metamaterial, energy-harvesting, and bioelectronic systems. Particular attention is paid to the mechanisms of dipolar relaxation, ionic conduction, interfacial polarization, conduction loss, magnetic loss, and multiple scattering. Finally, key challenges are identified, including hydrogel dehydration and freezing, aerogel fragility, ionogel cost and leakage, limited long-term reliability, and the lack of standardized performance metrics. Future directions toward durable, scalable, multifunctional, and device-integrated gel-based electromagnetic materials are proposed. Full article

Transbronchial lung cryobiopsy (TBLC) is increasingly used as a minimally invasive approach for tissue acquisition in the evaluation of interstitial lung disease (ILD), serving as an alternative to surgical lung biopsy (SLB) within multidisciplinary diagnostic pathways. Despite its growing adoption, variability in diagnostic yield and complication rates highlight the importance of procedural technique, probe selection, and freezing parameters. This narrative review summarizes the current landscape of TBLC, with emphasis on factors that influence diagnostic performance and safety, including procedural considerations involving endobronchial balloon blockade (EBB), radial probe endobronchial ultrasound (RP-EBUS), and cone-beam computed tomography (CBCT) for biopsy localization and airway management. Much of the existing experience is based on conventional cryoprobes, including 2.4 mm and 1.9 mm devices, typically used with freezing times of several seconds. While these approaches have defined the current role of TBLC in ILD, outcomes remain variable across centers, prompting continued refinement of procedural strategies to improve consistency. More recently, attention has expanded to include a broader range of smaller cryoprobe sizes—1.7 mm and 1.1 mm. Overall, this review provides a framework for understanding contemporary TBLC practice and highlights key areas where further study is needed to better define optimal technique and improve consistency in clinical outcomes. Full article

Machine learning (ML) is reshaping the design and deployment of conductive hydrogel biosensors for wearable health monitoring by coupling material chemistry with scalable manufacturing and robust signal analytics. Persistent bottlenecks include hydration stability (dehydration and freezing), data scarcity, device variability, and model transfer across users and environments. Recent advances demonstrate ML-enabled gains across electrochemical, mechanical, optical, and multimodal transduction, improving feature extraction, drift compensation, and generalization in applications spanning electrophysiology, sweat chemistry, and soft tactile sensing. On the material side, polymer informatics and graph-based representations are emerging to predict gel properties and guide composition/structure selection. In analytics, physics-informed models are enhancing impedance and voltammetry interpretation and reliability. Building on these trends, this review outlines standards for dataset curation (metadata on ionic milieu, temperature, humidity history, and mechanical loading) and strategies for cross-user and domain generalization. This review closes with actionable design guidelines for standardization, real-time analytics, and the clinical translation of hydrogel wearables. Full article

Polyvinyl alcohol (PVA)-based hydrogels suffer from insufficient mechanical strength, while aramid nanofibers (ANF) have intrinsic insulation that limits their sensing applications, and the synergistic effect of composite fillers remains underexplored. This study aims to develop a multifunctional PVA/ANF/NaCl composite organohydrogel for high-performance flexible sensors. The gel was fabricated via freeze–thaw crosslinking, solvent exchange and NaCl impregnation, with systematic investigations of its microstructure, mechanical, electrical and multifunctional sensing properties, and a corresponding triboelectric nanogenerator (TENG) and self-powered handwriting recognition system were constructed. Results show that 2% ANF significantly enhances the gel’s mechanical performance, 0.5 M NaCl achieves optimal mechanical-electrical balance, the gel-based sensor exhibits excellent distance, pressure and strain sensing with high cyclic stability, the TENG delivers stable electrical output, and the recognition system achieves 95% accuracy on the test set. This work provides a new material and design strategy for advanced flexible electronic devices. Full article

Federated edge learning on microcontrollers (MCUs) enables privacy-preserving adaptation, but on-device training faces a hardware tradeoff: fitting backpropagation into a limited static random-access memory (SRAM) often relies on on-chip flash as auxiliary storage, while repeated parameter persistence rapidly consumes finite program/erase (P/E) endurance. This paper proposes TinySLFL, a flash-endurance-aware federated learning framework for resource-constrained MCUs. On the client, layer-wise training bounds the peak SRAM usage to one layer, and delayed aggregation keeps intermediate updates in SRAM so that each communication round incurs only one flash persistence. On the server, dynamic aggregation combines loss-aware freezing with proxy-accuracy-guided filtering to improve the robustness under non-independently and identically distributed (Non-IID) data while suppressing unnecessary rounds. Experiments on CIFAR-10 and SVHN under a severe Dirichlet label skew and on a naturally heterogeneous FEMNIST showed, in a server-side simulation, that TinySLFL reduces the cumulative protocol-level erase-block operations (EOs) required to reach a common target accuracy by 97.8–98.6% relative to sequential layer training (SLT) and improves the mean Top-1 accuracy by up to 5.24 percentage points over the same ResNet-8 backbone in a five-seed evaluation. The power, latency, SRAM, and deployment feasibility were reported from actual ESP32-S3 measurements. These results demonstrate durable federated learning for extreme-edge MCUs. Full article

Ionic thermoelectric (i-TE) materials have attracted increasing attention for low-grade heat harvesting owing to their high thermovoltage output under small temperature gradients. However, the development of n-type i-TE materials remains challenging. Electrode-enabled polarity regulation provides a promising alternative to material-design strategies for developing n-type i-TE devices. In this work, a poly(vinyl alcohol) (PVA)-based ionic hydrogel was prepared with dimethyl sulfoxide (DMSO) and potassium chloride (KCl) through a freeze–thaw process, and its thermoelectric behavior was regulated by electrodes. While the i-TE hydrogel device with typical Cu electrodes exhibited p-type behavior, replacing the electrodes with graphite paper (GP) electrodes converted the device response from p-type to n-type. Morphological and spectroscopic analyses suggest that the GP surface selectively adsorbed K⁺ ions through cation–π interactions, suppressing cation thermodiffusion and enabling Cl⁻-dominated ion migration under a temperature gradient. As a result, the PVA-GP device achieved a maximum S_i of −4.36 ± 0.26 mV K⁻¹. In addition, the device exhibited favorable thermoelectric output, with a maximum PF_i of 57.668 μW m⁻¹ K⁻², a room-temperature ZT of 0.0864, and a peak transient power density of 2.33 mW m⁻² during short-time discharge. Owing to the large interfacial area of the GP electrodes, the device could also function as an ionic thermoelectric supercapacitor with appreciable energy-storage capability. This work demonstrates an effective electrode-engineering strategy for constructing n-type i-TE devices and provides a feasible route for simultaneous low-grade heat harvesting and transient energy storage. Full article

Conductive hydrogels have emerged as promising candidates for flexible strain sensors owing to their high water content, low elastic modulus, and intrinsic ionic conductivity. However, conventional hydrogel networks often suffer from an inherent trade-off among conductivity, mechanical robustness, and long-term stability, which limits their practical deployment in wearable sensing scenarios. The introduction of metal–ligand coordination bonds into hydrogel networks offers a versatile strategy to address these challenges: dynamic coordination cross-links can dissipate energy under deformation and reform upon unloading, thereby enhancing toughness, enabling self-healing, and contributing to ionic transport. This review focuses on metal-ion-coordinated conductive hydrogels designed for strain-sensing applications. Representative coordination systems based on Fe³⁺, Ca²⁺, Zn²⁺, Al³⁺, Cu²⁺, Ti⁴⁺, and Zr⁴⁺ are surveyed, with emphasis on their characteristic polymer matrices, ligand chemistries, and network-construction strategies. Key sensing-relevant properties—including ionic conductivity, mechanical stretchability, self-healing capability, interfacial adhesion, freezing resistance, and resistance to dehydration—are discussed in relation to coordination network design. Typical application demonstrations in large-deformation motion monitoring and subtle physiological signal detection are reviewed. Unlike existing reviews that survey conductive hydrogels broadly by conductive mechanism or sensor type, this review takes metal-ion coordination as the central organizing principle and systematically traces its influence across the full design chain—from ion–ligand coordination chemistry through network architecture to macroscopic sensing output. By comparatively analyzing seven representative metal-ion systems within a unified framework, this work aims to clarify how the choice of metal ion governs the interplay among conductivity, mechanical robustness, self-healing, and strain sensitivity—a perspective that has not yet been systematically addressed in prior reviews. Finally, current challenges—including the conductivity–mechanics coupling bottleneck, insufficient long-term stability, biosafety concerns for skin-contact deployment, the lack of standardized evaluation protocols, and device-integration barriers—are identified, and future directions for this field are outlined. Full article

Background: Freezing of gait (FoG) is a common symptom of Parkinson’s disease, especially in its later stages of progression. Characterized by involuntary stopping during normal gait patterns, FoG greatly increases fall risk, reducing quality of life. Given the complex presentation and etiology of FoG, current treatments have proven ineffective in managing episodes. In recent years, machine learning algorithms have been leveraged to derive actionable clinical insights from biomedical datasets. As a manifestation of neuromechanical dysfunction, impending FoG episodes may be characterized through data collected by wearable devices and sensors. Objective: This scoping review evaluates the current landscape of machine and deep learning-derived biomarkers to enhance the personalized management of FoG. Methods: This scoping review was conducted using established methodological frameworks for scoping reviews and is reported in accordance using the PRISMA-ScR checklist. Three databases were queried, with screening yielding 60 studies. Results: Thirty-nine papers reported on deep learning techniques, with the most common architectures being convolutional neural networks and long short-term memory models. Conclusions: Inertial measurement units, which can be worn on various locations, may be a promising modality for practical implementation. To generate closed-loop FoG therapies, algorithms can be integrated into real-time systems like robotic exoskeletons or adaptive deep brain stimulation. Future work in generating datasets from ambulatory devices, as well as distributed computing strategies, may lead to real-time FoG management. Full article

Deep eutectic solvent (DES), when used as the continuous phase of eutectogels, can significantly improve their electrical and mechanical properties due to its excellent conductivity, freeze resistance and chemical stability. The development of eutectogels effectively solves the key limitations of traditional hydrogels and organogels, such as low-temperature freezing, high-temperature volatilization, and organic solvent leakage. It also realizes the collaborative optimization of environmental friendliness and comprehensive performance, which makes it show broad application prospects in the field of flexible sensing. This review summarizes the design principles, material selection, sensing mechanisms, and flexible sensing applications of eutectogels. By examining the design of eutectogels, the selection of DES, and the synthesis of the gel network, it provides a theoretical basis for the development of eutectogel-based sensor devices. A detailed description of the sensing mechanism is provided to elucidate the signal generation and transition in eutectogels toward the purpose of the practical applications. Finally, the application prospects of eutectogels for high-performance sensors and detection devices are discussed. Additionally, we provide a theoretical support for their structural design, performance optimization, and practical application. Full article

The emergence of the Internet of Things (IoT) has introduced major security challenges. Deep learning models have shown strong potential for intrusion detection. However, they often require large datasets and high computational resources. In contrast, IoT environments are resource-constrained and lack sufficient labeled data. This paper proposes a lightweight intrusion detection approach based on Long Short-Term Memory (LSTM) networks and homogeneous transfer deep learning. The model is first trained on a subset of the BoT-IoT dataset as a source domain. It is then fine-tuned on a disjoint subset containing a rare attack type. This setup represents adaptation to unseen attack behaviors within the same environment. By freezing earlier layers and fine-tuning only the final layers, the method reduces training overhead while preserving performance. This is important to meet the IoT requirement for frequent, lightweight model updates on resource-constrained devices. The proposed model achieved 99.9% accuracy, a macro F1-score of 0.96, and a 47.8% reduction in training time compared to training from scratch. Extensive experiments confirm that it maintains balanced detection across both common and rare classes. Full article

Condensation of water vapor into discrete droplets on the surface of transparent optical devices-commonly known as fogging-severely degrades their optical performance. To address this issue, a highly transparent, self-healing, and durable polymer-based anti-fogging coating was developed via a facile one-pot copolymerization of 2-acrylamido-2-methylpropanesulfonic acid (AMPS), acrylic acid (AA), and vinyltrimethoxysilane (VTMOS). The chemical structure and composition were thoroughly characterized. The introduction of VTMOS constructs a hydrophilic-hydrophobic microphase structure through in situ formation of a Si–O–Si network, which significantly enhances the mechanical stability and water resistance. The polymer coating can maintain high transparency (>90%) under extreme conditions (85 °C steam and −40 °C freezing), exhibits long-term anti-frosting performance for 180 days, and demonstrates rapid water-assisted self-healing within 30 s. Differential scanning calorimetry (DSC) analysis reveals that each polymer unit binds approximately seven water molecules, elucidating the mechanism behind its exceptional anti-frosting capability. This work presents a practical strategy for designing high-performance, long-lasting anti-fogging coatings suitable for extreme environment applications. Full article

Cryotherapy involves flash freezing of tissue and removing unwanted tissue. Mechanism of injury is causing cell membrane rupture by rapid multiple freeze–thaw cycles, while reserving tissue architecture and the collagen matrix. This promotes favorable wound healing. In recent years, it has gained increasing attention as a treatment option for upper gastrointestinal diseases (Barrett’s Esophagus and early cancer). Currently, two FDA-approved delivery methods are available in the GI tract: Cryoballoon and spray cryotherapy, which will be discussed. In this review, we also propose to examine the expanding role of cryotherapy in gastrointestinal practice, drawing from both clinical studies and illustrative vignettes. In addition, we will highlight its established role in eradicating Barrett’s with low and high-grade dysplasia and compare its outcomes and safety profile with radiofrequency ablation (RFA). We will also discuss the application and safety of spray cryotherapy in the palliation of malignant esophageal strictures when compared with Esophageal stent placement. Cryotherapy may have immunological potential, and it may shrink both primary and metastatic diseases. Ongoing research in this field of Cryo-immunology will be highlighted. Beyond esophageal neoplasia, cryotherapy is increasingly utilized in other upper gastrointestinal precancerous conditions. Through this synthesis, our goal is to provide a timely and comprehensive overview of advancements in cryotherapy and its potential to reshape novel therapeutic approaches in upper gastrointestinal cancers. Finally, we highlight the evolution of a novel platform using nitrous oxide delivered by a handheld device, a contact balloon, and a small replaceable cartridge. This approach may make delivery of cryogen application favorable and a first-line approach in the management of Barrett’s esophagus and early cancer. In addition, Cryoballoon therapy for dysphagia palliation for malignant esophageal strictures may become a preferred approach as more data evolves. Full article

Cryopreservation is a key tool in assisted reproduction, but it often compromises post-thaw sperm quality due to cryodamage. Optimizing the initial cooling phase, specifically from room temperature to 5 °C, is a critical determinant of successful outcomes. This study aimed to evaluate the impact of different pre-freeze cooling rates on stallion sperm quality using a novel, precision cooling device. Semen samples from five healthy stallions were divided into three groups and cooled at distinct rates: Slow (0.3 °C/min), Moderate (1 °C/min), and Fast (approximately 30 °C/min). Sperm motility parameters were assessed using a Computer-Assisted Sperm Analyzer (CASA) before freezing and after thawing. Additionally, sperm integrity and physiological parameters, including viability, acrosomal integrity, Reactive Oxygen Species (ROS) expression, and mitochondrial membrane potential, were assessed by flow cytometry post-thaw. The analysis of post-thaw kinematics revealed a significant interaction between the cooling rate and processing stage (post-cooling vs. post-thaw). The Fast-cooling protocol resulted in higher post-thaw total motility (51.8%) compared to the Slow protocol (45.01%). Crucially, no significant differences were detected among cooling rates for the critical parameter of progressive motility or curvilinear velocity (VCL). Circle motility had higher values in the Fast-cooling group compared to the Slow group. Cell viability demonstrated a tendency (p = 0.08), where the Slow cooling group exhibited higher mean values (65.59%) compared to the Fast group (61.67%). Comprehensive flow cytometry assessments of other cellular integrity markers, including acrosomal integrity, mitochondrial function (MMP), and ROS expression, were statistically equivalent across all cooling rates (p > 0.05). The results confirm that this fast pre-freeze cooling rate, integrated within the highly controlled environment of Directional Freezing technology, successfully preserved essential sperm function and structure. Critically, the demonstrated functional equivalence in progressive motility validates the Fast protocol as an efficacious strategy to increase the efficiency and adaptability of equine semen cryopreservation protocols for commercial utilization. Full article