Search Results (557)

Sulfamethoxazole (SMX), a widely used antibiotic, poses potential threats to ecosystems and human health due to its persistence and residues in aquatic environments. This study developed a novel intelligent water treatment system, namely Intelligent Pulsed Electrochemical Activation of NaClO₂ (IPEA_NaClO2), which integrates a FeCuC-Ti₄O₇ composite electrode with machine learning (ML) to achieve efficient SMX removal and energy consumption optimization. Six key operational parameters—initial SMX concentration, NaClO₂ dosage, reaction temperature, reaction time, pulsed potential, and pulsed frequency—were systematically investigated to evaluate their effects on removal efficiency and electrical specific energy consumption (E-SEC). Under optimized conditions (SMX 10 mg L⁻¹, NaClO₂ 60~90 mM, pulsed frequency 10 Hz, temperature 313 K) for 60 min, the IPEA_NaClO2 system achieved an SMX removal efficiency of 89.9% with a low E-SEC of 0.66 kWh m⁻³. Among the ML models compared (back-propagation neural network, BPNN; gradient boosting decision tree, GBDT; random forest, RF), BPNN exhibited the best predictive performance for both SMX removal efficiency and E-SEC, with a coefficient of determination (R²) approaching 1 on the test set. Practical application tests demonstrated that the system maintained excellent stability across different water matrices, achieved a bacterial inactivation rate of 98.99%, and significantly reduced SMX residues in a simulated agricultural irrigation system. This study provides a novel strategy for the intelligent control and efficient removal of refractory organic pollutants in complex water bodies. Full article

As the core component in intelligent manufacturing systems, the precise control of the welding clamp’s electrode pressure plays a decisive role in ensuring the quality of spot welding. This paper proposes a novel pressure control strategy for robotic welding clamp based on partitioned adaptive RBF neural networks: (1) Deformation of the clamp body can lead to deviations in workpiece positioning. To address this issue, a deflection compensation method for robot welding clamp based on the PSO-RBF neural network is proposed. By leveraging pre-calibrated empirical data, the intrinsic mapping relationships are identified, and the derived deflection compensation value is integrated into the real-time position command of the robot end-effector. (2) During electrode motion, the system is subjected to external disturbances such as friction and gravitational forces. So, a sliding mode control strategy incorporating adaptive RBF disturbance compensation is proposed to achieve robust speed regulation. Furthermore, the electrode’s reference velocity is dynamically adjusted based on the welding force error and improved admittance control algorithm, enabling indirect regulation of the welding force to reach the desired set value. The results demonstrate that the proposed composite control strategy reduces electrode pressure overshoot to less than 5% and enhances steady-state control accuracy to ±1.5%. Full article

Flexible microscale neural interfaces are advancing current strategies for recording and modulating electrical activity in the brain and spinal cord. The aim of this review is to colligate recent progress in thin-film micro-electrocorticography (μECoG) systems and establish a framework for their translation toward spinal bioelectronic implants. We first outline substrate and electrode material design, ranging from polymeric and hydrogel-based materials to nanostructured conductive materials that enable high-fidelity recording on mechanically compliant platforms. We then summarize structural design rules for μECoG arrays, including electrode size, pitch, and channel scaling, and relate these to data-driven μECoG applications in brain–computer interfaces and closed-loop neuromodulation. Bidirectional μECoG architectures for simultaneous stimulation and recording are examined, with emphasis on safe charge injection, electrochemical and thermal limits, and state-of-the-art hardware and algorithmic strategies for stimulation-artifact suppression. Building upon these cortical technologies, we briefly describe adaptation to spinal interfaces, where anatomical constraints demand optimized mechanical properties. Finally, we discuss the convergence of flexible bioelectronics, wireless power and telemetry, and embedded AI decoding as a path toward autonomous, clinically translatable μECoG and spinal neuroprosthetic systems. Ultimately, by synthesizing these multidisciplinary advances, this review provides a strategic roadmap for overcoming current translational barriers and realizing the full clinical potential of soft bioelectronics. Full article

This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles. Full article

The development of implantable neural interfaces is essential for enabling bidirectional communication between the nervous system and prosthetic devices, yet their evaluation still relies primarily on in vivo models which are costly, variable, and ethically constrained. Here, we report a bio-inspired artificial nerve simulator engineered as a reproducible ex vivo platform for pre-implantation testing of plug-type electrodes. The simulator is fabricated from a conductive hydrogel composite based on reduced graphene oxide (rGO), polyaniline (PANI), agarose, sucrose, and sodium chloride, with embedded conductive channels that replicate the fascicular organization and conductivity of peripheral nerves. The resulting construct exhibits impedance values of ~2.4–2.9 kΩ between electrode needles at 1 kHz, closely matching in vivo measurements (~2 kΩ) obtained in Sus scrofa domesticus nerve tissue. Its structural and electrical fidelity enables systematic evaluation of electrode–nerve contact properties, signal transmission, and insertion behavior under controlled conditions, while reducing reliance on animal experiments. This bio-inspired simulator offers a scalable and physiologically relevant testbed that bridges materials engineering and translational neuroprosthetics, accelerating the development of next-generation implantable neural interfaces. Full article

Pronoun resolution is essential for language comprehension, yet the neural mechanisms underlying this process remain poorly characterized. Here, we investigate the neural dynamics of impersonal pronoun processing using electroencephalography combined with machine learning approaches. We developed a novel experimental paradigm that contrasts impersonal pronoun resolution with direct nominal reference processing. Using electroencephalography (EEG) recordings and machine learning techniques, including local learning-based clustering feature selection (LLCFS), recursive feature elimination (RFE), and logistic regression (LR), we analyzed neural responses from twenty participants. Our approach revealed differential EEG feature patterns across frontal, temporal, and parietal electrodes within multiple frequency bands during pronoun resolution compared to nominal reference tasks, achieving classification accuracies of 78.52% for subject-dependent and 60.10% for cross-subject validation. Behavioral data revealed longer reaction times and lower accuracy for pronoun resolution compared to nominal reference tasks. Combined with differential EEG patterns, these findings demonstrate that pronoun resolution engages more complex mechanisms of referent selection and verification compared to nominal reference tasks. The results establish potential EEG-based indicators for language processing assessment, offering new directions for cross-linguistic investigations. Full article

Background/Objectives: This article brings forward a novel methodology for the intra-op approach of forearm amputation stumps to facilitate their subsequent wireless connection to a neural prosthesis. A neural prosthesis offers the amputee more motor functions compared to myoelectric prostheses, but the neural prosthesis must be connected to the patient’s stump nerves. Methods: An experimental animal study was conducted on 15 Wistar rats. Under anesthesia, the sciatic nerve was carefully dissected and preserved using a folding technique to maintain maximum length without tension. Nerves were repositioned with consideration for future use with biocompatible conduits. Morphometric measurements (nerve length, external diameter, fascicle count) were performed, followed by statistical analysis of length–diameter correlations. Results: The techniques show that the length of the nerves in the amputation stump can be preserved and integrated into the muscle masses with appropriate methods and biomaterials, which ensures the transmission of motor impulses to control the movements of a prosthesis. Fibrosis and mechanical injury have a lower risk of occurring with the nerves protected in the muscle mass. Through statistical analysis we find that sciatic nerve length and diameter have a positive correlation (r = 0.71, p = 0.003), supporting anatomic plausibility for human extrapolation of results. Conclusions: The amputation technique preserves much of the nerve length and viability and is simple to perform. Neural electrode implantation can be facilitated by folding the nerve within a large muscle mass and using biomaterial conduits. Better rehabilitation of the patient may occur with the use of a prosthesis equipped with more functions and superior control. Full article

Accurate identification of liquor vintage is crucial for ensuring product authenticity and optimizing market value, as the price and sensory quality of liquor increase with age. Traditional sensory evaluation by sommeliers is inherently limited by subjectivity, physiological fatigue, and inconsistency, posing challenges for reliable large-scale quality assessment. To address these limitations, this study introduces an innovative homemade electronic tongue (ET) system integrated with machine learning and deep learning algorithms for rapid and precise vintage identification. The ET system, consisting of six metallic electrodes and a MEMS-based temperature sensor, successfully discriminated five consecutive liquor vintages produced at one-year intervals. Using Support Vector Machine (SVM) and Random Forest (RF) algorithms, classification accuracies of 91.0% and 78.0% were achieved, respectively. Remarkably, the proposed one-dimensional convolutional neural network (1D-CNN) model further improved the recognition accuracy to 94.0%, representing the highest reported performance for ET-based vintage prediction to date. The findings demonstrate that the integration of multi-electrode electrochemical sensing with artificial intelligence enables objective, reproducible, and high-throughput evaluation of liquor aging characteristics. This approach provides a scientifically robust alternative to human sensory analysis, offering significant potential for counterfeit detection, liquor authentication, and the broader assessment of food and beverage quality within molecular sensing frameworks. Full article

Graph neural networks (GNNs) have rapidly matured into a unifying, end-to-end framework for energy-materials discovery. By operating directly on atomistic graphs, modern angle-aware and equivariant architectures achieve formation-energy errors near 10 meV atom⁻¹, sub-0.1 V voltage predictions, and quantum-level force fidelity—enabling nanosecond molecular dynamics at classical cost. In this review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, including multi-GPU training, calibrated ensembles, and multimodal fusion with large language models, followed by a discussion of a wide range of recent applications of GNNs in the rapid screening of battery electrodes, solid electrolytes, perovskites, thermoelectrics, and heterogeneous catalysts. Full article

Developing flexible, cost-effective, and durable sensors is a key challenge for integrating Cyber–Physical–Human Systems (CPHSs) into smart homes. This paper introduces a flexible pressure sensor array designed for CPHS applications, addressing the need for cost-effective and durable sensors in smart homes. Our approach combines flexible piezoelectric materials with Swept Frequency Capacitive Sensing (SFCS). Unlike previous pressure sensors made of flexible piezoelectric materials, which can only measure dynamic pressure due to charge leakage, by using SFCS, the piezoelectric material is not directly in the circuit, and our sensor can effectively measure static pressure. While traditional arrays require multiple I/O ports or a matrix configuration, our design measures four distinct locations using only a single I/O port. The sensor is also mechanically flexible and exhibits high durability, capable of functioning even after being cut or torn, provided the electrode contact area remains largely intact. To decode the complex, multiplexed signal from this single channel, we developed a two-stage deep learning pipeline. We utilized data from thin-film resistive pressure sensors as ground truth. A classification model determines which of the four sensors are being touched. Then a regression model uses this touch-state information to estimate the corresponding pressure values. This pipeline employs a hybrid architecture that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The results show that the system can estimate pressure values at each location. To demonstrate its application, the sensor system was integrated into a power recliner, thereby transforming the chair into an interactive tool for daily exercise designed to improve the well-being of older adults. This successful implementation establishes a viable pathway for the development of intelligent, interactive furniture for in-home exercise and rehabilitation within the CPHS paradigm. Full article

Background: Theta-band cordance is a quantitative EEG (qEEG) metric that integrates absolute and relative spectral power and correlates with regional cerebral perfusion. Although widely applied in psychiatric and neurophysiological research, its longitudinal trajectory in healthy adults remains largely unknown. This study aimed to characterize multi-year changes in theta cordance across cortical regions, determine which areas show stability versus decline, and evaluate whether individuals maintain a trait-like cordance profile over time. Methods: Nineteen cognitively healthy, medication-free adults underwent resting-state EEG recordings at two time points, separated by an average of 6.4 years (range: 1.9–14.8). Theta cordance (4–8 Hz) was computed at 19 scalp electrodes using the Leuchter algorithm and aggregated into eight lobar regions (left/right frontal, temporal, parietal, occipital). Paired-samples t-tests assessed longitudinal changes. Inter-regional Pearson correlations examined evolving connectivity patterns. Canonical correlation analysis (CCA), validated via LOOCV and bootstrap confidence intervals, evaluated multivariate stability between baseline and follow-up cordance profiles. Results: Theta cordance remained normally distributed at both time points. Significant longitudinal decreases emerged in the right temporal (t(18) = 5.34, p < 0.001, d = 1.23) and right frontal (t(18) = 2.65, p = 0.016, d = 0.61) regions, while other lobes showed no significant change. Midline Cz demonstrated a robust increase over time (p < 0.001). CCA revealed a strong cross-time association (Rc = 0.999, p = 0.029), indicating preservation of a stable, frontally anchored cordance profile despite regional right-hemisphere decline. Inter-regional correlation matrices showed both preserved posterior synchrony and emerging inverse anterior–posterior and cross-hemispheric relationships, suggesting age-related reorganization of cortical connectivity. Conclusions: Theta cordance exhibits a mixed pattern of trait-like stability and region-specific aging effects. A dominant, stable fronto-central profile persists across years, yet the right frontal and right temporal cortices show significant decline, consistent with lateralized vulnerability in normative aging. Evolving inter-regional correlation patterns further indicate network-level reorganization. Longitudinal cordance assessment may provide a noninvasive marker of functional brain aging and help differentiate normal aging trajectories from early pathological change. This longitudinal quantitative EEG (qEEG) study examined theta-band cordance dynamics across cortical regions in healthy adults over an average follow-up of 6.4 years (range: 1.9–14.8). Resting-state EEGs were recorded at two time points from 19 participants and analyzed using Leuchter’s cordance algorithm across 19 scalp electrodes. Regional cordance values were computed for frontal, temporal, parietal, and occipital lobes. Paired-samples t-tests revealed significant longitudinal decreases in theta cordance in the right frontal (p = 0.016, d = 0.61) and right temporal lobes (p < 0.001, d = 1.23), while other regions remained stable. Inter-regional Pearson correlations showed strong bilateral synchrony in posterior regions and emergent inverse anterior–posterior relationships over time. Canonical correlation analysis revealed a robust multivariate association (Rc = 0.999, p = 0.029) between baseline and follow-up patterns. Partial correlations (controlling for follow-up interval) identified region-specific trait stability, highest in left occipital and right frontal cortices. These findings suggest that theta cordance reflects both longitudinally stable neural traits and regionally specific aging effects in cortical physiology. Full article

Machine learning has emerged as a promising tool to accelerate the screening of lithium–ion battery electrode materials. Gravimetric capacity, a critical performance indicator governing electrode energy density, is intrinsically related to lithium insertion and extraction mechanisms, requiring sophisticated embedding approaches that capture the structural characteristics of cathode materials. The cathode material dataset from the Materials Project database comprises heterogeneous data modalities: numerical features representing chemical properties and categorical features encoding structural characteristics. Naive integration of these disparate data types may introduce semantic gaps from statistical distributional discrepancies, potentially degrading predictive performance and limiting model generalization. To address these limitations, this study proposes a Classified Branch–CapNet model that individually embeds four distinct types of categorical structural data into separate classified branches along with numerical data for independent learning, subsequently integrating them through a late fusion strategy. This approach minimizes interference between heterogeneous data modalities while capturing structure–property relationships with enhanced precision. The proposed model achieved superior performance with a mean absolute error of 2.441 mAh/g, demonstrating substantial improvements of 56.2%, 71.2%, 73.9%, and 51.1% over conventional deep neural networks, recurrent neural networks, long short-term memory architectures, and the encoder-only Transformer, respectively. Furthermore, it achieved the lowest root mean square error of 15.236 mAh/g and the highest coefficient of determination of 0.961, confirming its superior predictive accuracy and generalization capability compared with all benchmark models. Our model therefore demonstrates significant potential to accelerate the efficient screening and discovery of high-performance battery electrode materials. Full article

Transcranial direct current stimulation (tDCS) is a noninvasive form of brain stimulation that involves passing a weak electrical current between electrodes on the scalp to modulate underlying neural tissue. TDCS has been shown to modulate cognition in a variety of domains, including memory, attention, and visual processing. Prior work from our laboratory has shown positive effects of tDCS on learning to detect target objects hidden in complex naturalistic visual scenes and learn rules for categorizing images, though the mechanism for these benefits remains unknown. One possibility is that tDCS optimizes visual search by modulating visual attention or via the reduction in search errors. One method of quantifying visual attention is to use eye tracking to record search patterns to determine if and how visual search is adjusted under verum stimulation conditions. Eye tracking data allows classification of errors into error types, including sampling errors (failing to look in the relevant region), recognition errors (looking at the critical portion of a scene, but failing to recognize it as such as evidenced by visual fixation), and decision-making errors (fixating on the relevant portion of a scene, but making the wrong determination). Our results indicate that the benefit tDCS confers on visual search for targets stems from the reduction in decision-making errors when targets are present (Cohen’s d = 0.86). Also reported is a replication of previous findings showing a tDCS-dependent improvement in learning this task, learning score (Cohen’s d = 0.88); d’ (Cohen’s d = 1.00). This provides support for moving tDCS into the application space by pairing it with analysts who are concerned with the type of search error that is corrected via stimulation. Full article

Background: Quantitatively detecting whether plants exhibit measurable bioelectric differences in the presence of nearby human movement remains challenging, in part because plant signals are low-amplitude, slow, and easily confounded by environmental factors. Methods: We recorded bioelectric activity from 2978 plant samples across three species (basil, salad, tomato) using differential electrode pairs (leaf and soil electrodes) sampling at 142 Hz. Two trained performers executed three specific eurythmic gestures near experimental plants while control plants remained isolated. Random Forest and Convolutional Neural Network classifiers were applied to distinguish the control from treatment conditions using engineered features including spectral, temporal, wavelet, and frequency domain characteristics. Results: Random Forest classification achieved 62.7% accuracy (AUC = 0.67) distinguishing differences in recordings collected near a moving human from control conditions, representing a statistically significant 12.7 percentage point improvement over chance. Individual performer signatures were detectable with 68.2% accuracy, while plant species classification achieved only 44.5% accuracy, indicating minimal species-specific artifacts. Temporal analysis revealed that the plants with repeated exposure exhibited consistently less negative bioelectric amplitudes compared to single-exposure plants. Innovation: We introduce a data-driven approach that pairs standardized, short-window bioelectric recordings with machine-learning classifiers (Random Forest, CNN) to test, in an exploratory manner, whether plant signals differ between human-moving-nearby and isolation conditions. Conclusions: Plants exhibit modest but statistically detectable bioelectric differences in the presence of nearby human movement. Rather than attributing these differences to eurythmic movement itself, the present design can only demonstrate that plant recordings collected within ~1 m of a moving human differ, modestly but statistically, from recordings taken ≥3 m away. The underlying biophysical pathways and specific contributing factors (airflow, VOCs, thermal plumes, vibration, electromagnetic fields) remain unknown. These results should therefore be interpreted as exploratory correlations, not mechanistic evidence of gesture-specific plant sensing. Full article

(1) Background: Understanding neural dynamics during human movement is a core neuroscience objective, yet there are fundamental challenges to the collection of high-fidelity neuroelectric signals during motion. We investigated the effects of electroencephalography (EEG) electrode design for cleaning high-density EEG, using an electrical testbed that mimicked the human head. (2) Methods: We used a 60-channel high-density array of tripolar concentric ring electrodes and conventional disk electrodes to compare the recovery of simulated brainwave activity in the presence of electrical neck muscle artifacts during walking. Simulated brainwave activity consisted of randomly occurring sinusoidal bursts with unique frequency content within human EEG spectral bands (5–37 Hz). Electrical neck muscle activity was recorded from a human subject during walking and broadcast into the head phantom device at scaled surface recording amplitudes (0× 0.5× 0.67×, 1×, 1.5×, 2×). We compared the number and spatial distribution of detected neural sources among electrode channels based on spectral power. (3) Results: At low muscle activation amplitudes, conventional electrodes identified more spectral power peaks (p ≤ 0.01) among more electrodes (p < 0.05) compared to tripolar concentric ring electrodes, indicating poorer spatial selectivity. At greater muscle artifact amplitudes, conventional electrodes identified fewer neural spectral power peaks (p < 0.05) with lesser localization accuracy (p < 0.05) compared to tripolar concentric ring electrodes. (4) Conclusions: We identified improved myoelectric artifact removal from tripolar concentric ring electrode recordings compared to conventional electrodes, offering a promising approach for recovering high-fidelity electrocortical activity from human subjects during locomotion. Full article