Search Results (5,516)

Flexible pressure sensors serve as core sensing components for wearable health monitoring systems, electronic skins for soft robots, and flexible human–machine interaction devices. Benefiting from the interfacial electric double-layer polarization effect, iontronic sensing delivers far higher pressure response sensitivity than conventional parallel-plate capacitive sensors, endowing it with distinctive advantages in the detection of weak physiological signals. Nevertheless, current dense ionic thin-film dielectric layers suffer from limited deformation space under compression and poor low-pressure sensing capability. Mainstream high-precision micropillar arrays are fabricated via photolithography, 3D printing, and metal etching molds, which require costly equipment and complicated fabrication procedures, making large-area mass production unfeasible. Random frosted concave-convex microstructures feature disordered dimensions, leading to severe device hysteresis and narrow linear ranges, which fail to achieve ultrahigh sensitivity alongside a wide pressure detection range simultaneously. To address the aforementioned multiple bottlenecks, this paper proposes a low-cost resin template replication process to fabricate TPU-based ionic thin-film dielectric layers with ordered micropillar array microstructures. Combined with inkjet-printed silver conductive PI flexible electrodes, an iontronic flexible pressure sensor with a sandwich layered structure is constructed. Multi-dimensional investigations including microscopic morphology characterization, electromechanical sensing performance calibration, and human wearable application tests are systematically implemented to thoroughly elucidate the synergistic enhancement mechanism of the arrayed micropillars. Test results demonstrate that the effective pressure detection range of the sensor spans 0–1038 kPa, accommodating ultra-low pressures such as pulse signals as well as medium-to-high-pressure loads including joint bending. The sensitivity reaches 23.27 kPa⁻¹ within the low-pressure range of 0–200 kPa and remains stable at 3.52 kPa⁻¹ in the high-pressure range of 200–1038 kPa, with piecewise linear fitting correlation coefficients of 0.93 and 0.96 respectively. Both the response time and recovery time of the device are 40 ms, and the hysteresis error throughout the loading-unloading cycle is merely 2.62%. After 20,000 consecutive cyclic loading-unloading tests, the peak capacitance output only decays by 5.1%, verifying outstanding mechanical fatigue resistance and electrical stability. Validations in multi-scenario applications prove that the sensor can accurately capture human physiological and motion signals including radial artery pulses, laryngeal deformation induced by multi-syllable vocalization, and multi-angle bending of fingers and elbow joints, suitable for home-based health monitoring, quantitative rehabilitation training, flexible tactile interaction and other scenarios. The entire fabrication process eliminates high-precision micro-nano processing equipment such as photolithography systems, plasma etchers and 3D printers; only general chemical raw materials and conventional laboratory instruments are adopted. The reusable templates enable low manufacturing costs and large-area coating forming, offering a novel low-cost technical solution for the engineering implementation and industrialization of high-performance iontronic flexible pressure sensors. Full article

Convective drying of Zingiberaceous roots, particularly ginger (Zingiber officinale), requires a uniform distribution of airflow and temperature to ensure energy efficiency and product quality. However, many drying systems exhibit aerothermal limitations that produce temperature gradients and non-uniform drying conditions. To address this issue, this study proposes a dual-swirl dryer featuring two air inlets: an upper helical inlet and a lower tangential inlet. Both inlet configurations generate swirling airflow patterns that enhance thermal uniformity and increase the residence time of hot air within the drying chamber. The internal flow behavior was investigated using Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent2025 R1 version. A three-dimensional polyhedral mesh was generated to improve computational efficiency and numerical accuracy. Turbulence and recirculation phenomena were modeled using the Realizable k–$ϵ$ turbulence model, while temperature distribution was analyzed through the energy conservation equation. Numerical predictions were experimentally validated using temperature sensors integrated into an automatic control system. The comparison between numerical and experimental results demonstrated that the dual-swirl configuration improves airflow redistribution, reduces thermal stagnation zones, and promotes a more homogeneous temperature field throughout the drying chamber. These findings confirm that the proposed system is an efficient alternative for agro-industrial drying applications. Full article

With the rapid development of Industrial Internet of Things (IIoT), large amounts of sensor data generated by industrial devices and edge nodes have become the basis of intelligent manufacturing applications. Collaborative modeling on these distributed data is important for tasks such as anomaly detection, equipment monitoring, and predictive maintenance. Federated learning offers a practical way to train models without exposing raw sensor data, but it still faces privacy leakage and malicious poisoning attacks. To address these issues, this paper proposes a hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning. Starting from the sensitivity differences among parameters at different model layers, the proposed method designs a hierarchical privacy-budget allocation strategy that enhances protection for sensitive information while minimizing the performance impact of perturbation. Meanwhile, a multi-layer, multi-feature anomaly-detection mechanism is adopted to identify malicious updates by jointly exploiting directional consistency, scale stability, and inter-layer similarity, and majority voting together with update clipping is used to further improve system robustness. Experiments on Fashion-MNIST, MVTec AD, and C-MAPSS demonstrate that the proposed method can effectively suppress global-model degradation under multiple poisoning attacks and achieves a favorable balance among privacy protection strength, robustness, and training efficiency. Full article

Miniaturized spatial electric field sensors often exhibit insufficient front-end charge coupling because of their limited sensing area. To address this material-level bottleneck, KH550-functionalized graphene composite films were developed as candidate electrode materials for spatial electric-field sensing. Single-layer and multilayer graphene powders were modified with the silane coupling agent KH550 and dispersed in a waterborne polyurethane/PVP matrix to fabricate composite films. The sensing mechanism was analyzed from the Maxwell–Wagner–Sillars interfacial polarization and electrode-equivalent capacitance perspectives. The modified materials were characterized by SEM, EDS, Raman spectroscopy, FTIR spectroscopy, low-frequency dielectric measurements, and broadband high-frequency impedance measurements. KH550 functionalization introduced Si- and N-containing surface species and increased disorder or sp³-related structural features while retaining the layered graphene structure. The film formulation selected through qualitative visual screening contained 0.16 g of graphene, 10 mL of waterborne polyurethane, and 0.05 g of PVP. Under AC excitation, the relative permittivity of the composite film containing KH550-functionalized multilayer graphene was approximately 18% higher than that of its unmodified counterpart. Broadband measurements showed material-dependent changes in the reflection and impedance responses of the electrode–fixture configurations. The modified multilayer-graphene electrode exhibited a different distribution of reflection minima, resistance maxima, and capacitive–inductive transitions from the unmodified and copper electrodes. Because the measured response includes contributions from the coating, substrate, fixture, and parasitic elements, these results are interpreted as comparative system-level responses. These results indicate that interfacial engineering of graphene composite films can enhance electrode-level dielectric response and charge-coupling capability, providing a material basis for non-contact electric field sensing electrodes. Full article

Advancements in pulmonary drug delivery have enabled effective treatment approaches for more severe disease conditions, such as chronic obstructive pulmonary diseases, asthma, cystic fibrosis, and other pulmonary disorders, via targeted, sustained, and immediate drug delivery routes with minimal systemic side effects. However, conventional delivery systems have many limitations, such as poor drug targeting, adherence, and deposition, which ultimately cause variations in drug profiles and therapeutic efficacy. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the development of smart inhaler drug–device combination systems for personalized therapy using predictive formulation parameters, design variables, device performance, and inhalation pattern monitoring. Advanced AI techniques, such as artificial neural networks, deep learning, random forests, support vector machines, deep learning algorithms, and computational modeling, predict the mass median aerodynamic diameter (MMAD), fine-particle fraction (FPF), emitted dose, and regional lung deposition. Smart inhalation devices coupled with digital sensors and computing systems enable the real-time monitoring of inhalation profiles and adherence. Moreover, AI- and ML-enabled Quality by Design (QbD) and digital twin framework technologies enhance the optimization of manufacturing process parameters, consistency, robustness, and scale-up performance. Although several developments have been reported, there is still room for improvement in terms of data heterogeneity, algorithm transparency, interpretability, cybersecurity, regulations, and long-term clinical standardization. This review emphasizes the use of AI to improve the performance of pulmonary drug delivery through smart inhaler drug–device combination therapies, focusing on technological advancements, formulation optimizations, smart inhalers, regulatory issues, current limitations, and future perspectives of AI-based pulmonary drug delivery. Full article

Micromachined ultrasonic transducers (MUTs) represent a notable advance in miniaturized sensing, enabling compact, low-power, and complementary metal-oxide-semiconductor (CMOS)-integrated platforms that extend ultrasonic capabilities into wearable, implantable, and edge-computing domains. The integration of artificial intelligence (AI) has introduced new approaches for signal interpretation, adaptive control, and data-driven optimization, enhancing performance in specific areas such as compressed sensing, neural beamforming, and learned image enhancement that complement conventional signal processing. Meanwhile, sensor fusion strategies that combine ultrasonic data with complementary modalities have improved robustness, contextual awareness, and diagnostic accuracy across applications ranging from industrial monitoring to clinical diagnostics. This review provides a comprehensive analysis of this active research area, systematically covering transducer hardware platforms, design methodologies, and intelligent signal processing frameworks. While traditional bulk piezoelectric transducers remain the benchmark for high-power applications, capacitive and piezoelectric micromachined variants offer superior acoustic impedance matching and monolithic CMOS compatibility essential for portable systems. We examine the evolution from deterministic analytical and numerical modeling toward AI-powered inverse design, which enables the discovery of non-intuitive, high-performance geometries beyond human intuition. Furthermore, the integration of machine learning (ML) for signal recovery, image enhancement, and multi-modal sensor fusion is discussed as a pathway to compensate for hardware constraints such as limited aperture, sparse sampling, and low signal-to-noise ratio (SNR), while pointing out that AI technology cannot overcome fundamental physical limits including acoustic attenuation, thermal noise floors, and transduction efficiency boundaries. By synthesizing recent advancements, this review demonstrates how the convergence of classical acoustic physics and data-driven intelligence is guiding the development of of intelligent ultrasonic systems. Full article

Optimal sensor placement (OSP) is critical to building a full-lifecycle health monitoring network for high-aspect-ratio unmanned aerial vehicle wings, as it directly affects the deformation sensing of the wing shape. To address this challenge, this paper proposes a novel sensor placement method for wings based on a chaotic strengthened aquila optimizer (CSAO) that integrates chaotic mapping and a nonlinear search strategy. Specifically, the proposed method introduces a uniform initialization strategy based on the piecewise chaotic map and a nonlinear criterion for switching between exploration and exploitation in the basic aquila optimizer (AO). These enhancements increase the diversity of the initial population and raise the probability of global search in later iterations, thereby accelerating convergence and strengthening global optimization capability. First, the performance of the CSAO is compared with that of other popular intelligent algorithms on 10 benchmark functions. The results show that the proposed method exhibits superior convergence speed, higher-quality solutions, stronger global search ability, and better robustness, making it suitable for OSP problems involving tens of thousands of candidate points. Next, the CSAO is applied to sensor placement on a wing-shaped plate. Compared with other OSP methods, the proposed method offers significant advantages in terms of sensor distribution, computational time, and hardware cost. Finally, experimental validation is conducted using a wing test platform equipped with fiber Bragg grating (FBG) strain sensors. The measurement results demonstrate that the reconstructed shape is in excellent agreement with the measured shape. Therefore, the proposed CSAO-based OSP method, combined with the FBG-based structural monitoring system, offers a promising solution for health monitoring of deformable structures in extreme environments. Full article

Electrochromic devices (ECDs) in smart buildings have been advanced as a potential solution for improving energy savings and visual and thermal comfort. The current paper is a review of advanced control strategies for ECDs with respect to predictive, environmental, and adaptive strategies for improving building performance. One of the most frequently employed methods is rule-based control (RBC). RBC is being complemented by more sophisticated model predictive control (MPC) and machine learning (ML) procedures. By adjusting ECD behaviour in dynamic response to changing external circumstances, like daylight, glare, temperature, and solar radiation, these improved techniques ensure a major improvement in real-time adaptivity, energy savings, and occupant comfort. The paper systematically examines ECD control techniques available in the literature, detailing performance indicators, energy conservation, and comfort enhancement for various climatic conditions. It also examines hybrid techniques based on MPC and ML models that tackle the obstacles faced by conventional control systems. Furthermore, their compatibility with renewable energy sources such as PV and thermochromic systems is outlined in relation to net-zero energy buildings. The paper ends with a review of future directions that could lead towards standardization in the form of models, sensor networks and AI-based adaptive frameworks to increase the scale as well as the real-world relevance of ECDs in varying building contexts. Full article

Lower-limb rehabilitation exoskeletons have the potential to improve gait recovery after stroke by providing intensive and repetitive training. However, conventional control strategies often rely on fixed control parameters and exhibit limited adaptability to patient-specific characteristics, sensor noise, and dynamic uncertainties. This paper proposes an adaptive control framework that combines deep reinforcement learning (RL) with model-based impedance control for personalised lower-limb exoskeleton assistance. Patient-specific biological parameters are incorporated into the simulation environment and reward formulation to improve adaptability and robustness. Three state-of-the-art deep RL algorithms, Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC), are evaluated in a continuous control environment under varying signal-to-noise ratio (SNR) conditions ranging from 5 dB to noise-free conditions. Results demonstrate that TD3 achieves the most stable learning performance, obtaining a mean reward of −354.24 under noise-free conditions, while DDPG provides the highest joint-angle tracking accuracy with an RMSE of 0.0369 rad. SAC exhibits superior robustness in noisy environments, achieving the highest learning ratio of 0.51 at 5 dB SNR. Furthermore, the proposed personalised framework reduces tracking errors by up to 27% compared with non-personalised baseline approaches. The findings indicate that integrating patient-specific information with RL-based adaptive control can significantly enhance robustness, tracking performance, and personalisation in exoskeleton-assisted gait rehabilitation, providing a promising direction for future intelligent rehabilitation systems. Full article

Indirect observation systems, such as hyperspectral remote sensing and passive geophysical measurements, retrieve useful information from redundant observations of the same scene. However, the resulting data are often contaminated by structured coherent artifacts caused by sensor nonuniformity, calibration residuals, or incomplete illumination. These artifacts are difficult to suppress because they are spatially organized components with directional continuity and non-negligible correlation. Their signal-like coherence allows them to mimic image textures or physical events, making conventional denoising methods prone to residual artifacts or signal leakage. To address this problem, we propose a consistency-guided collaborative filtering framework for suppressing structured coherent artifacts while preserving useful signals. The proposed framework extends paired-observation similarity analysis into a consistency-guided strategy for redundant observations. Paired observations of the same target are constructed to distinguish useful signals from physically inconsistent artifacts. This consistency contrast is incorporated into collaborative filtering to guide block matching and aggregation, while a coherent noise power spectral density model characterizes the directional and spatial correlation of the artifacts for targeted noise shrinkage. The proposed framework is evaluated primarily on hyperspectral remote-sensing images contaminated by simulated stripe artifacts, with additional validation on synthetic and field geophysical paired-observation data containing nonphysical coherent events. The results demonstrate that the proposed method can suppress structured coherent artifacts while preserving useful signals and maintaining high signal fidelity. This work provides a unified way to exploit observational redundancy for enhancing imaging reliability. Full article

Long-term functional impairments, including deficits in upper limb mobility, cancer-related fatigue, and reduced physical performance, are often reported by breast cancer survivors, underlining the need for sustainable and easily accessible rehabilitation techniques. Telemonitoring technologies have shown promising results in extending rehabilitation outside of clinical settings. However, the increasing number of technological solutions make it difficult to outline the optimal approach in the comprehensive management of breast cancer patients. Therefore, this narrative review aimed to provide an overview of contemporary digital and sensor-based telemonitoring solutions for breast cancer rehabilitation. To find pertinent research published in recent years, major scientific databases were searched, with emphasis on clinical applications and technology advancements in oncology rehabilitation. The results identified different digital solutions, including wearable sensors, surface electromyography systems, computer vision techniques, virtual reality environments, three-dimensional motion analysis, and mobile health platforms. These technologies have shown promising results in remote delivery of rehabilitation programs, enhancing patient involvement and adherence, and providing objective and ongoing evaluation of functional performance. However, a lack of standardization and a lack of clinical validation in cancer populations are major limitations. Altogether, telemonitoring is a potential strategy for improving rehabilitation pathways in breast cancer survivors, but further studies are required to develop solid standards and enable widespread adoption in cancer rehabilitation clinical practice. Full article

This study presents the development and validation of an intelligent error compensator based on Long Short-Term Memory (LSTM) recurrent neural networks for dynamic weighing systems in copper concentrate belt conveyors. Conventional weighing systems fail to capture nonlinear temporal dynamics, leading to measurement inaccuracies during container filling operations. The methodology comprised data acquisition from load cells, speed sensors, and inclinometers; systematic hyperparameter optimization; and evaluation using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Hyperparameter optimization identified an optimal configuration with one LSTM layer (20 units, learning rate 0.001, window size 20 steps). Evaluation on an independent test set showed that the compensator reduced MAPE from 8.5% (uncompensated system) to 3.01%, representing a 64.6% improvement, and reduced RMSE from 12.3 to 4.2 tons (65.9% improvement), with an R² of 0.95. Feature importance analysis confirmed physical consistency, with load cell voltage as the dominant predictor (42%). These results demonstrate that LSTM-based compensation significantly enhances weighing accuracy. The study provides a replicable framework for industrial metrology modernization, contributing to sustainable mining operations through material loss reduction and logistics optimization. While the proposed model has been validated offline using historical data, its deployment in the live production environment remains pending. Full article

Smart chemical sensors have emerged as promising tools for real-time monitoring of food spoilage in both fermented and non-fermented products. By detecting key spoilage indicators—including biogenic amines, ammonia, hydrogen sulfide, methane, pH variations, and microbial volatile organic compounds (MVOCs)—these systems enable rapid, on-site assessment of food quality, offering a viable alternative to conventional, time-consuming laboratory analyses. Recent advances encompass diverse sensing mechanisms, including chemiresistive platforms based on conducting polymers and MEMS (Microelectromechanical Systems); optical/colorimetric systems using dyes, metal–organic frameworks, and porphyrins; and electrochemical and biosensing approaches employing enzymes, antibodies, aptamers, and whole-cell recognition elements. These sensors demonstrate high sensitivity (ppb–ppm range), enabling early detection of spoilage before sensory perception or microbiological threshold exceedance. Their applicability has been validated across a wide range of food matrices, including meat, fish, dairy products, vegetables, beverages, and fermented foods. Despite significant progress, key challenges persist, including signal drift, limited specificity, susceptibility to environmental factors such as humidity and temperature, and interference from complex food matrices. Furthermore, integration into intelligent packaging requires the development of flexible, food-safe, and regulatory-compliant materials. Emerging approaches that combine sensor arrays with machine learning and MVOC pattern recognition are enhancing predictive accuracy and enabling food classification across commodity types. Overall, smart chemical sensing technologies are rapidly transitioning from laboratory prototypes to practical applications in intelligent packaging and wireless monitoring systems, with ongoing research focused on improving robustness, standardization, and scalability for commercial deployment. This article provides an overview of the topic, drawing on the available bibliography from the last five years and the most-cited scientific databases. Full article

Data-driven models are transforming structural health monitoring (SHM) for composites. However, excessive sensor costs and scarce, noise-contaminated data hinder model accuracy and generalizability. In this study, an electromechanical impedance (EMI)-based physical features and data-driven framework for high-precision damage assessment under conditions with noise and limited data is proposed. An experimental system that incorporates random noise to simulate operational environment noise was used to simulate seven progressive simulated damage states in CFRP laminates. Adaptive low-pass parabolic filtering via a fast Fourier transform smoothing filter (FFT-SF) denoised conductance signals in the frequency domain, increased efficiency over the Hinkley criterion, and significantly suppressed false alarms from sensor drift. Three input variables were selected: the resonant frequency F (reflecting structural stiffness), the resonant amplitude A (reflecting structural damping), and the root mean square deviation (RMSD) index (a statistical measure of spectral deviation). These three variables, two physics-based features and one statistical index, formed the inputs to a three-input artificial neural network (ANN). The fusion model achieved an RMSE of 0.0752 and an R² of 0.9807 on 105 small samples, significantly outperforming a purely data-driven single-input RMSD-ANN (RMSE of 0.1066; R² of 0.9612). Critically, Shapley Additive Explanation (SHAP) analysis revealed that the physical features significantly enhance model interpretability and predictive reliability, maintaining >98% simulated damage identification accuracy with extremely limited real data. This provides a cost-effective, high-precision, and scalable paradigm for aerospace composite SHM. Full article

Acrylamide (AA) is a common contaminant in foods processed at high temperatures and has attracted significant attention due to its potential neurotoxicity and carcinogenicity. Therefore, the development of a highly sensitive, highly selective sensing technology suitable for on-site detection is of great importance for ensuring food safety. In this study, an aptamer (Apt)-based capacitive AA sensor was developed based on the alternating current electrokinetics (ACEK) effect. The sensor utilizes an aptamer as the biomimetic recognition element, which can specifically recognize AA, thereby enabling quantitative detection. Additionally, a detachable detection fixture and data acquisition system were designed to enhance the detection stability and convenience of sensor. Within the linear range of 1 nmol/L to 10 µmol/L, the sensor response (dC/dt) exhibited a good linear relationship with AA concentration, with a detection limit as low as 0.4235 nmol/L. The sensor exhibits good selectivity toward structural analogs of AA, with a recovery relative standard deviations (RSDs) of less than 5.42% in spiked coffee samples. This portable detection system provides a sensitive and user-friendly tool for the analysis of acrylamide in food and holds great potential for application in food safety. Full article