Search Results (5,466)

Non-invasive, real-time monitoring of plant water status is critical for precision agriculture and plant physiology. However, existing methods often lack continuous in situ measurement capability or are limited by temporal resolution. This paper proposes a novel non-invasive method based on xylem electrical conductivity, inspired by industrial non-contact fluid measurement. As a ground-based complement to remote sensing, this approach demonstrates the feasibility of online, in situ, and non-invasive monitoring of water stress in grapevine stems under controlled laboratory conditions. The industrial C⁴D sensing system is adaptively modified into a specialized Plant-C⁴D sensor with an array-based design for batch signal acquisition. To validate the electrical response to water loss, a gravimetric natural dehydration experiment was conducted, demonstrating a clear correlation between electrical signals and water content changes in detached stem samples. Full-day dynamic experiments are conducted under three conditions: normal water supply, varying water stress, and plant inactivation. Sensitive characteristic parameters are extracted through signal analysis, and a pattern recognition framework is established to eliminate environmental interference and suppress individual differences. Experimental results on 24 plant samples (Shine Muscat) show that the method accurately discriminates viable from inactivated plants with an accuracy of 91.67% (22/24 correct). Furthermore, the Fuzzy C-Means (FCM) clustering algorithm successfully quantifies the severity of water stress in viable plants, yielding results consistent with actual water supply conditions. While these findings demonstrate the capability of Plant-C⁴D sensor to capture stem water status-related information, the current results do not establish full physiological validation, warranting further exploration with in vivo experiments. Full article

Diabetes management depends on regular glucose monitoring, yet conventional blood-based methods are invasive and can reduce user comfort. This review presents an overview of wearable glucose monitoring technologies, with emphasis on non-invasive approaches. It first distinguishes invasive, minimally invasive, and non-invasive monitoring and discusses the use of interstitial fluid, sweat, saliva, tears, urine, and breath as alternative sensing media. The review then summarizes optical, electrochemical, electrical/electromagnetic, and nanotechnology-enabled sensing methods, together with representative wearable and commercially reported devices. At the end, textile-based systems are compared with non-textile platforms in terms of comfort, flexibility, signal reliability, durability, and practical integration. Across these approaches, major limitations include variable relationships between alternative biofluids and blood glucose, interference from physiological and environmental factors, calibration requirements, motion artifacts, limited durability, and insufficient clinical validation. Future development requires more reliable sensing, improved wearable integration, standardized testing, and validation under real-world conditions. Full article

Green low-carbon data centers operate as coupled cyber-energy systems whose dispatch must coordinate renewable generation, grid exchange, battery storage, cooling load, flexible computing workload, carbon-intensity signals, and reliability constraints. This study develops and evaluates a safety-constrained deep reinforcement learning framework for source–load–storage coordinated operation of a grid-connected green data center. The operating problem is formulated as a constrained Markov decision process with state variables describing the IT load, deferrable workload backlog, renewable availability, electricity price, marginal carbon intensity, battery state of charge, server-room temperature, reserve margin, and calendar context. The action space covers grid import and export, renewable utilization, storage charge and discharge, workload shifting, and cooling control. The learning architecture combines a constrained actor–critic policy, adaptive Lagrangian safety critics, and a control barrier function (CBF)-based action shield that projects unsafe actions onto an explicitly defined operating set before plant execution. The shield is specified as a low-dimensional quadratic projection over state-dependent SOC, thermal, reserve, SLA, and grid-interface constraints, while cumulative risks are priced through Lagrangian safety budgets during policy training. The evaluation uses a controlled and auditable benchmark simulation with normalized public-data-compatible profiles, declared scenarios, random seeds, neural-network settings, and mechanism-matched baselines; it is not a telemetry-based verification or hardware certification of a deployed data center. Within this declared benchmark, the proposed safe DRL controller produces a simulated 13.1% emission reduction relative to the Rule-based controller, 95.8% renewable utilization, a normalized annual cost of 0.91, and fewer boundary contacts than the tested unconstrained, Lagrangian-only, and shield-only PPO variants. These percentages are simulator outputs relative to the stated benchmark and must not be interpreted as measured field savings. The results show how separating reward learning, cumulative safety pricing, and one-step engineering projection changes low-carbon dispatch within the specified model. Full article

As China advances price reform and modernizes its governance system, the optimization of residential increasing-block tariffs (IBT) has received increasing policy attention. Using data from the China Family Panel Studies (CFPS), this paper employs a multi-period difference-in-differences (DID) model to examine the impact of changing the implementation cycle of increasing-block tariffs from monthly to annual on residential electricity consumption. The results show that extending the billing cycle significantly increases residential electricity consumption. Mechanism analysis indicates that annual implementation smooths monthly consumption fluctuations, reduces the probability of entering higher tariff tiers, and lowers the average effective electricity price, thereby weakening households’ sensitivity to marginal electricity prices and increasing electricity use. Heterogeneity analysis further shows that this effect is mainly concentrated among low-income and less-educated households. These findings suggest adopting a hybrid policy combining monthly implementation with seasonal tier design to improve price signal clarity and enhance both efficiency and equity. Full article

In recent years, nanopore technology has enhanced analyte detection, enabled higher resolution and achieved single-molecule sensing capability. Aptamer-conjugated nanopore sensing technology combines the high specificity of aptamers with the single-molecule resolution of nanopores. By anchoring aptamers to biological, solid-state or hybrid nanopores, target binding events produce distinct electrical signatures that allow sensitive and label-free detection. This approach enables real-time monitoring of small molecules, proteins, and even pathogens, with promising applications in diagnostics, drug screening, environmental monitoring, etc. Hybrid biological/solid state devices produce robust signals and are suitable for PoC applications. The aptamers “magic bullets” have also been exploited to develop single-molecule antigen detection using nanopores, which offers a promising alternative for accurate virus testing to contain their transmission. Chemical conjugation of aptamers to nanopore interfaces improves selectivity for peptides/amino acids and expands robustness for practical samples. Aptamer-based nanopipettes offer high analytical precision by enabling label-free, real-time detection of target molecules in ultra-small sample volumes. This review maps aptamer–nanopore integration across biological, solid-state, and hybrid platforms. It also explores various types of aptamers integrated into nanopore platforms that cater to precise, single-molecule recognition, paving the way for highly sensitive, portable diagnostics and next-generation therapeutic monitoring tools. Full article

Modern power transmission grids are increasingly operated under volatile load, variable generation, and near-limit line-flow conditions. In such environments, deterministic line-flow forecasting is insufficient for operational decision support because operators require calibrated risk probabilities, uncertainty intervals, and reliable early warning signals. This article proposes B-TGPRF, a Bayesian Temporal Graph Probabilistic Risk Forecaster for calibrated overload-risk forecasting in power transmission grids. The proposed framework is positioned as a hybrid probabilistic graph-temporal forecasting system rather than as a new end-to-end graph neural network; its novelty lies in the leakage-controlled sequential combination of temporal forecasting, electrical graph descriptors, calibrated Bayesian risk estimation, residual correction, and compact interval uncertainty assessment. The model integrates temporal line-flow, load, and generation features with graph-topological descriptors, operating-regime indicators, residual correction, conformal interval estimation, probability calibration, and a Bayesian risk layer. A leakage-controlled data preparation pipeline was built using an open large-scale benchmark for machine learning applications in transmission grids. The final modeling dataset contains more than 3.48 million observations, 100 selected critical lines, train-only risk thresholds, and chronological train, validation, test, and external-like scenario splits. B-TGPRF was compared with persistence baselines, linear models, Bayesian baselines, tree ensembles, boosting models, neural temporal models, and compact state-of-the-art-style temporal and graph-temporal architectures. On the strict external-like test, B-TGPRF achieved MAE = 0.3650, RMSE = 0.5478, R² = 0.9962, Brier Score = 0.0147, and ECE = 0.0064. The results show that the proposed model provides a strong overall balance between line-flow forecasting accuracy, calibrated risk estimation, compact interval prediction, and low false-positive risk-signaling, while boosting models remain highly competitive for pure risk-class detection. Full article

Electricity spot markets with time-of-use pricing create hour-by-hour variations in the economic value of thermal adjustments, requiring coal-fired units to adapt cold-end operation to real-time price signals. However, the nonlinear coupling between circulating water flow and condenser backpressure remains insufficiently characterized across the full operating envelope, and existing optimization strategies target steady-state heat consumption without accounting for the time-varying economic value of identical thermal adjustments under spot pricing. This study develops a quasi-steady-state thermodynamic–economic model that links real-time electricity prices with the nonlinear heat-transfer response of the circulating water system. The model enables the adaptive selection of pump combinations and blade-opening angles by balancing marginal pump power savings against marginal turbine output losses under time-of-use price signals. Using actual electricity spot market data from Zhejiang Province, simulations under different seasonal conditions show clear economic gains. The maximum hourly saving reaches 2190.79 CNY during summer negative-price periods, which is about 5.3 times higher than that in winter, while backpressure deviations remain within 12.5% of the design value. The seasonal disparity is governed by the initial heat exchange driving force, a fundamental thermodynamic property amplified by the negative-price superposition effect. The framework establishes a physical basis for market-responsive cold-end regulation across seasonal and load conditions, supporting the economic dispatch of coal-fired units in spot market environments. Full article

Geoelectric field observations play a vital role in geophysical exploration, geological disaster early warning, and underground resource detection. Traditional contact non-polarisable electrodes, which require burial and electrolyte coupling, are hindered by several issues, such as limited adaptability to challenging terrain, significant electrode potential drift, and high susceptibility to environmental interference. Existing non-contact electric field sensors often exhibit insufficient coupling capacitance, poor impedance matching for ultra-weak high-impedance signals, and inadequate low-frequency noise suppression, rendering them unsuitable for the precise acquisition of natural microvolt-level geoelectric field signals. To address these challenges, this study introduces an innovative non-contact geoelectric field sensor with a three-layer composite structure. The sensor operates based on the principle of a parallel-plate capacitor, with a conductive silver paste layer at the top acting as the signal acquisition electrode plate, which forms an equivalent parallel-plate capacitance model with the ground to achieve non-contact capacitive coupling for geoelectric field detection. The intermediate layer uses lead zirconate titanate (PZT) piezoelectric ceramics as a support medium with a high dielectric constant. At the bottom is a silicon-based, flexible, sensitive ground-contacting layer with high elasticity, which allows it to adapt to micro-level surface irregularities, eliminating air gaps between the electrode plate and the ground, increasing plate-to-ground coupling capacitance, and ensuring the stability of the capacitance. The three-layer structure was created using a dry-press sintering integration approach, which eliminates interlayer bonding materials while ensuring consistent dielectric performance and efficient charge transfer. Additionally, a specialised signal-conditioning circuit was designed to match the ultra-high-impedance sensitive unit, utilising the ADA4528-2 ultra-low-noise precision operational amplifier, which achieved low-loss conversion and strong noise suppression for ultra-weak high-impedance charge signals. The circuit simulation results demonstrate that the designed circuit achieves an input impedance of no less than 10 TΩ, an effective operating bandwidth from 0.02 Hz to 20 kHz, and a voltage noise density lower than 1.5 μV/√Hz at 10 Hz, fully covering the ultra-low-frequency effective band of natural geoelectric fields. Field experiments comparing artificial and natural field signals revealed that the proposed sensor could be quickly deployed by simply attaching it to the ground without burial. Its time-domain waveform consistency and frequency-domain component matching were nearly identical to those of commercial standard solid non-polarisable electrodes, with a cross-correlation coefficient greater than 0.98, indicating no significant potential drift or power-frequency interference. By structurally eliminating the inherent electrode potential difference, the sensor offers advantages such as ease of deployment, strong environmental adaptability, high precision for weak signal acquisition, and excellent engineering substitutability. It is well suited for long-term geoelectric field observations in complex field scenarios, including deserts, Gobi areas, and frozen soil regions, and provides a high-performance, novel sensing solution for geoelectric field detection in extreme environments. Full article

This study explores the energy-harvesting performance of aluminum (Al)- and iron (Fe)-filled styrene–butadiene rubber (SBR) composites, with a focus on their mechanical durability and triboelectric properties. Comprehensive mechanical characterization—including tensile strength, elongation at break, fracture toughness, and elasticity—reveals that Fe-filled composites exhibit significantly enhanced reinforcement compared to Al-filled systems at equivalent filler loadings. Raman spectroscopy indicates that Fe atoms can coordinate with the benzene rings of SBR chains through stronger physicochemical bonding, a feature less present in Al-based composites. In addition to improved mechanical properties, Fe-filled composites demonstrate higher electrical conductivity and superior triboelectric energy-harvesting performance. Notably, the composite containing 15 vol% Fe under 1% cyclic compressive strain achieves a peak current density of 127.05 µA/m², a total generated charge of 5.01 nC, and a peak power density of 48.22 µW/m². These values represent substantial enhancements of 246%, 236%, and 2398%, respectively, compared to Al-filled counterparts. Cyclic energy-harvesting tests confirm stable performance with negligible degradation in output or mechanical integrity over repeated cycles. Rubber composite shows good humidity resistance in current and voltage outputs. Furthermore, a layer-by-layer triboelectric nanogenerator (TENG) based on the Fe-filled composite produces output signals of approximately ±1.0 µA and ±5 V under biomechanical hand patting. The superior performance of Fe-based composites is attributed to stronger filler–rubber interactions, likely facilitated by electrostatic interactions, which enhances interfacial charge transfer during mechanical deformation. Overall, Fe-filled SBR composites demonstrate strong potential for cost-effective, environmentally friendly, and durable self-powered energy-harvesting applications. Full article

Reliable monitoring of uterine contractility underpins the assessment of labor, the diagnosis of preterm labor, and the timing of obstetric intervention, yet routine methods measure only the mechanical consequences of contraction. External tocodynamometry and cardiotocography (CTG) are operator- and position-dependent and perform poorly with maternal obesity, detecting as few as ~54% of the contractions confirmed by an intrauterine pressure catheter, whereas surface electrohysterography (EHG) detects upward of ~94% by recording the myometrial electrical activity that drives contraction. This review examines EHG and uterine electromyography (EMG) as measurement modalities, covering their physiological origin, acquisition hardware, signal characteristics, feature extraction, and machine-learning analysis, and compares them with CTG against the intrauterine pressure catheter reference standard. Electrical approaches additionally yield predictive parameters, notably spectral peak frequency and propagation velocity, that mechanical methods cannot provide. The principal barrier to translation is methodological heterogeneity rather than physiology: differences in electrodes, filtering, feature definitions, and outcome measures preclude cross-study comparison and meta-analysis. As its central contribution, this review consolidates prior calls for standardization into a minimum reporting set for EHG studies and appraises translational readiness, identifying prospective external validation, shared datasets, explainable models, and outcome-linked trials as priorities. Full article

Transcutaneous electrical stimulation (TES) is limited by cutaneous discomfort caused by unavoidable activation of superficial sensory nerves during current delivery to deep targets. While psychophysical studies have empirically identified waveforms that reduce skin sensation, existing computational models typically employ quasi-static approximations that neglect the pronounced dielectric dispersion of biological tissues, leaving the biophysical mechanisms poorly understood. We developed a finite-element model of the human forearm incorporating the frequency-dependent dielectric properties of skin, fat, muscle, bone, and nerve tissues (DC to 1 MHz), coupled with a linear time-invariant signal-processing framework based on the system transfer function H(f). The model quantitatively reproduced the waveform-dependent sensation trends reported by Hsu et al. We introduced a penetration ratio to quantify deep-to-superficial nerve activation and found that all time-varying waveforms exhibit lower penetration than direct current (DC), revealing a skin-effect-like behavior of electrical current in biological tissues. Moreover, deep and superficial nerve activations co-varied under waveform parameter changes, indicating that waveform optimization alone cannot improve depth selectivity. Electrode spatial configuration was shown to offer a complementary strategy: positioning the active electrode close to the target muscle nerve while avoiding superficial cutaneous nerves, combined with sufficient transverse spacing, enhances depth selectivity. This work bridges psychophysical observations with tissue electrophysiology and provides a computational tool for waveform and electrode optimization in TES. Full article

Mid-infrared spectroscopy provides molecular fingerprint information for bacterial biofilm analysis, but the absorption signal of a thin biofilm layer is usually weak. In this work, a metasurface-enhanced mid-infrared biosensor was designed to enhance the fingerprint response of Staphylococcus aureus biofilms. The biofilm transmission spectrum was measured by Fourier-transform infrared spectroscopy, and the film thickness was obtained by atomic force microscopy using an edge step-height method. Based on these measurements, an effective extinction coefficient was extracted and used in finite-difference time-domain simulations. A metal–insulator–metal metasurface was then optimized to cover the main biofilm absorption bands in the mid-infrared region. Two resonator designs were studied: a polarization-dependent structure and a polarization-insensitive structure. The polarization-dependent design showed a strong response under x-polarized incidence and weak coupling under y-polarized incidence. The polarization-insensitive design provided a more balanced response for orthogonal polarizations. At the selected biofilm fingerprint wavelengths, the highest enhancement factors reached 8.57 and 7.24 for the polarization-dependent and polarization-insensitive structures, respectively. Near-field distributions confirmed that the enhancement mainly originated from localized electric fields at the metal resonator edges. These results provide a proof-of-concept design strategy for enhancing weak mid-infrared fingerprint signals from S. aureus biofilms. Full article

This paper is a first step toward providing answers to the question of whether fractal pattern formation gives rise to power-law (fractional) kinetics and how such kinetics relate to geometric properties such as fractal dimension. The study focuses on Lichtenberg figures produced by high-voltage discharges on wood, a heterogeneous dielectric medium with anisotropic conductivity and variable moisture content. During breakdown, the discharge propagates through branching streamers and carbonization fronts, exhibiting scale-free growth, long-tailed waiting times, and memory effects. The associated current signals are analyzed using the theory of fractal elements developed by Nigmatullin and Chen. This framework allows complex self-similar waveforms to be decomposed into elementary fractal modes characterized by power-law exponents and amplitudes. The results show that the electrical response is governed by fractional dynamics encoded in these modes. However, no direct one-to-one relationship is found between the fractal dimension of the discharge patterns and the kinetic power-law exponents. This decoupling is attributed to the influence of the heterogeneous medium and the percolation pathways through which the discharge propagates. Full article

Reliable hand gesture recognition (HGR) using wearable sensors remains challenging due to variability in arm posture, motion, and individual muscle activation patterns. To facilitate systematic investigation of multi-modal sensing strategies under realistic operating conditions, this paper presents a comprehensive dataset of surface electromyography (sEMG) and pressure-based force myography (pFMG) signals. The dataset includes three complementary subsets acquired under controlled static arm posture, multiple static arm postures, and combined static and dynamic arm postures. Signals were recorded using a custom-designed co-located sEMG-pFMG armband, enabling the simultaneous capture of electrical muscle activation and mechanical muscle deformation. System validation is conducted from three perspectives. First, hardware-level signal quality is assessed through signal-to-noise ratio (SNR) analysis across all gestures and sensing channels, demonstrating stable and reliable signal acquisition. Second, representative raw waveform examples are provided to qualitatively illustrate modality-specific and condition-dependent signal characteristics under static and dynamic arm-posture scenarios. Third, reproducible baseline gesture recognition experiments are performed using conventional machine learning classifiers. By providing multi-modal data acquired under both static and dynamic arm-posture conditions, along with clearly de-fined experimental protocols and baseline benchmarks, this dataset serves as a valuable resource for developing, evaluating, and comparing gesture recognition algorithms and arm-wearable human–machine interface (HMI) systems. Full article

This study investigates predictive modeling of failure states in manufacturing systems using artificial intelligence in the context of sustainable maintenance and Industry 4.0. The proposed methodological framework is based on historical operational and maintenance data from a single manufacturing device, encompassing multiple process and operational signals such as vibrations, temperature, electric current, and operational logs. The aim is to predict failure within a short-term horizon to support maintenance planning. The article compares Random Forest and XGBoost algorithms at different prediction horizons (8 h and 16 h) to identify the trade-off between classification accuracy and lead time for maintenance planning. Model outputs are analyzed using explainable artificial intelligence and transformed into a risk index compatible with the FMEA methodology. The practical contribution of the proposed approach is illustrated through a scenario-based what-if assessment of potential sustainability impacts, particularly in terms of estimated reductions in unplanned downtime, material waste, and energy consumption. The results point to the potential of integrating AI-supported maintenance as a tool for increasing the reliability and sustainability of manufacturing systems. The novelty of the proposed framework lies in the integration of predictive maintenance, explainable artificial intelligence, replay-based maintenance assessment, dynamic FMEA risk assessment, and sustainability impact quantification into a unified decision-support framework. Full article