Search Results (2,692)

Electrocardiogram (ECG) and photoplethysmogram (PPG) signals are widely used noninvasive methods for assessing cardiovascular activity and provide complementary information about the cardiac cycle. ECG records cardiac electrical activity, whereas PPG records optically detected blood-volume changes in peripheral tissue. This paper describes a synchronized ECG-PPG dataset collected from 148 apparently healthy subjects under a controlled seated protocol at rest and during post-exercise recovery after two treadmill-running conditions. Signals were acquired using a custom card-type handheld finger-contact prototype that records single-lead ECG and dual-wavelength PPG at 660 nm and 940 nm concurrently. The dataset contains 444 condition-specific records, with each subject contributing one seated resting record, one seated recovery record after light treadmill running, and one seated recovery record after moderate treadmill running. Both raw ADC-count signals and preprocessed signals are provided, and the accompanying software and example code are publicly available. The dataset is intended for research on synchronized ECG-PPG signal analysis, waveform-quality assessment, controlled post-exercise recovery physiology, and exploratory PPG-to-ECG reconstruction under controlled conditions. It should not be interpreted as a free-living wearable dataset or as clinical diagnostic ECG ground truth without external validation. Full article

The integration of olfactory feedback into Virtual Reality (VR) applications remains significantly underexplored compared with other sensory modalities, particularly within room-scale Cave Automatic Virtual Environments (CAVEs), where related research is even more limited. To address this gap, this paper presents Scentree, a custom olfactory system capable of delivering scents in real time based on user interactions, along with Smelling Ancient Greece, an olfactory-enhanced VR experience developed for integration within our CAVE system. Central to the proposed approach is the concept of the Diegetic Olfactory Feedback Loop, which reframes olfaction from a passive ambient effect into an active, interaction-driven feedback mechanism embedded within the narrative context of the virtual environment. To evaluate the system, we conducted a technical performance assessment and an exploratory user study ($N=51$) examining participant perceptions of immersion, presence, perceived realism, usability, and overall user experience. The findings support the feasibility of interaction-driven olfactory feedback as a multisensory design approach for CAVE environments and provide a foundation for future controlled investigations of olfactory feedback in immersive VR. Full article

The application of functional electrical stimulation (FES) to restore hand motor function remains challenging due to the difficulty of calibrating multi-channel stimulation to produce coordinated finger movements. This study proposes a compositional FES calibration framework to customize the stimulation of isolated finger actions and enable their combination into functional hand gestures. The proposed method was validated through a two-session experimental study involving thirteen participants. In the first session, subject-specific stimulation sites and parameters were identified for eight individual finger movements using a structured spatial grid defined over the forearm. The second session, conducted on a subset of five participants, evaluated the generation of seven hand gestures derived from combinations of the isolated movements. Results showed that ten of the thirteen participants achieved at least six movements, while three participants successfully elicited all targeted motions. Successfully elicited movements were generally well isolated, although thumb and ring/little finger extensions proved more difficult to isolate. The second session demonstrated that individually calibrated finger activations can be combined to produce coordinated multi-finger movement patterns, with average finger excursions matching the expected motions. Overall, these preliminary results support the use of compositional calibration strategies to achieve functional multi-finger control with multi-channel FES. Full article

An electroencephalogram (EEG) signals provide vital insights for stroke rehabilitation, yet analyzing these complex, high-dimensional data to detect gait anomalies remains challenging. Artificial intelligence offers a promising solution to precisely identify abnormal movements, assisting physicians in optimizing personalized treatments. This exploratory pilot study aims to evaluate multi-class deep learning frameworks for classifying eight distinct normal and abnormal motor activities in stroke patients using EEG data. EEG signals from eight stroke patients were utilized to train and evaluate a customized Convolutional Neural Network (CNN), DeepConvNet, and EEGNet. Furthermore, channel reduction configurations (32, 22, and 15 channels) were investigated to determine optimal clinical setups. In the Leave-One-Out Cross-Validation (LOOCV) evaluation involving seven patients, EEGNet attained the highest descriptive average F1-score of 0.810. Moreover, when assessed independently on an unseen patient, it achieved an F1-score of 0.915, indicating its potential in accommodating individual differences within this limited cohort. Moreover, EEGNet exhibited a low false positive rate of 0.175, minimizing false alarms. While the 32-channel setup yielded the highest consistency, reduced configurations served as hypothesis-generating for specific tasks. In conclusion, EEGNet demonstrated superior average performance in differentiating complicated gait patterns in this exploratory pilot study, underscoring its promise for real-time, non-invasive monitoring in stroke neurorehabilitation. Full article

Backfilling the industrial solid waste coal gangue into deep coal mine goafs for CO₂ geological sequestration is a crucial pathway to achieve the synergistic effect of pollution reduction and carbon mitigation. However, in complex deep geological environments, the chemical evolution of multiple mineral phases of coal gangue under gas–water–rock coupling effects and the carbon-controlling mechanism of residual total organic carbon (TOC) remain unclear. In this study, coal gangue from the goaf of the Xiaobaodang Coal Mine was used as the research object. Relying on a customized high-temperature and high-pressure reaction system to simulate the deep in situ environment (45 °C, 10 MPa), and combined with X-ray diffraction (XRD), total organic carbon determination, and isothermal CO₂ adsorption experiments, the geochemical mechanism by which inorganic minerals and organic residual carbon synergistically control the ultimate CO₂ adsorption potential was systematically revealed. The results show that the modification of the CO₂ adsorption potential of coal gangue by gas–water–rock reactions exhibits strong mineral phase differentiation. Systems rich in active silicates generate a large amount of secondary clay minerals through intense carbonation alteration, achieving a significant increase in micro–nano pores and absolute adsorption capacity. Systems rich in carbonates steadily release deep primary adsorption potential by widening mass transfer channels through mineral dissolution. In contrast, systems rich in primary clay minerals face an irreversible attenuation of adsorption space due to physical clogging of pore throats caused by fluid migration. Furthermore, the initial organic carbon content exerts a significant non-linear regulatory effect on the development of the micropore network. The physical adsorption sites provided by the high relative content of layered clay minerals (>41%), coupled with the interfacial enhancement effect exerted by a moderate organic carbon content (0.12~0.16%), constitute an optimal physicochemical synergistic enhancement network, which is the core geological reason for stimulating the ultimate carbon sequestration capacity of coal gangue. The results of this study not only enrich the multiphase interfacial thermodynamic theory of complex heterogeneous geological bodies but also provide solid theoretical support for the precise optimization of target areas and the long-term evaluation of carbon sinks in goaf CO₂ sequestration engineering. Full article

Lithium-ion batteries with high energy density and long cycle life have been widely used as secondary batteries in electric vehicles and energy storage systems. With the growing demand for high energy density in lithium-ion batteries, silicon-based materials, which possess a high theoretical specific capacity (4200 mAh g⁻¹), are regarded as core candidates for anode materials. However, Si-based materials undergo severe volume expansion (up to 300%), which leads to the collapse of the electrode structure, inducing pulverization of the active material and capacity loss, thereby hindering the commercial application of silicon-based materials. To address these issues, scholars from various countries have developed many silicon-based materials with different compositions and three-dimensional structures, and have made some research progress. This review first elaborates on the lithium storage mechanisms and advantages of diverse silicon-based anode materials by taking Si, SiO_x, SiN_x, and SiP_x as representative examples with distinct characteristics. Subsequently, from the two aspects of dimensional design (0D, 1D, 2D and 3D) and architecture design (core–shell, sandwich-like and network structure), the design strategies for various silicon-based anode structures and their enhancement on electrochemical performance are analyzed. Finally, this review elucidated the challenges faced by silicon-based anodes from the perspectives of mechanism elucidation, structural customization, industrialization, and full-cell applications. It also proposed future development directions for silicon anodes by combining actual challenges and focusing on aspects such as structure optimization, machine learning, advanced characterization techniques, and mechanistic analysis. Full article

In the ongoing data-rich era, intelligent cognition is playing an increasingly important role in advancing remote sensing applications. However, traditional intelligent methods for remote sensing processing no longer fully meet the growing demands of this era and still suffer from several limitations, such as passive data-dependent processing, predefined-task execution, and lack of closed-loop optimization. As a customized GeoAI innovation for remote sensing, Remote Sensing Agent has entered an early stage of research explosion. This paper focuses on its paradigm-shifting role in reshaping remote sensing information processing, clarifies the “4+1” core characteristics including multimodal spatial perception, goal-driven spatial mission planning, geoscientific knowledge reference, geospatial workflow execution, and feedback loop. It elaborates the threefold reshaping of remote sensing information processing from initiation mode, execution mode, and evaluation criterion, namely shifting from passive data processing to active task-driven, from predefined-task processing to multi-agent collaboration, and from result-oriented output to full-process closed-loop optimization. Future prospects of Remote Sensing Agent in geoscientific knowledge base optimization, multi-agent collaboration efficiency, and complex-scenario adaptability are discussed. This paper provides targeted and forward-looking perspectives for intelligent innovation research in remote sensing. Full article

This study focuses on the design and development of instructional materials tailored for the subject fundamentals of food processing, with the primary objective of equipping students with foundational knowledge and practical competencies essential to understanding core concepts and principles within the discipline. The instructional content was purposefully crafted to align with established course learning outcomes and the broader curricular framework. Drawing upon contemporary research and pedagogical best practices, the materials were customized to address the specific academic needs, interests, and learning preferences of students. Emphasis was placed on interactivity and inclusivity, with the integration of varied media formats to support diverse learning styles and enhance accessibility. The expert’s evaluation of the instructional materials is based on the three criteria: content, organization and structure, and support for learning. Overall, the instructional material has a mean of 3.71, with a verbal interpretation of high evidence and a standard deviation of 0.11, indicating high reliability. The highest mean score is 3.79 for the content category. This indicates that the instructional material is highly effective in aligning with course requirements, currently accurate, and bias-free. The lowest mean score is 3.67 on organization structure and support for learning. These scores suggest that while these areas are well regarded, they have certain aspects that could be further improved. Moreover, the materials must exhibit flexibility and adaptability to accommodate various teaching methodologies. They should seamlessly integrate with various instructional strategies, including project-based learning, problem-based learning, and hands-on activities. This versatility ensures that educators can employ diverse approaches to cater to their students’ needs and learning preferences. Full article

Background: The relationship between the stomatognathic system and postural control has been widely investigated, suggesting that dental occlusion may influence neuromuscular coordination and athletic performance. However, evidence in figure skating remains limited. This study aimed to evaluate the effects of a customized occlusal splint on neuromuscular activity and postural balance in professional figure skaters. Methods: A prospective single-arm pre–post interventional study was conducted on 52 professional female figure skaters (mean age: 17.1 ± 1.7 years). Electromyographic (EMG) activit of the masseter, anterior temporalis, sternocleidomastoid, and trapezius muscles, along with kinesiographic parameters, were assessed at baseline and after six months of continuous occlusal splint use. Postural control was evaluated using the Flamingo Balance Test under three testing conditions. Statistical analysis included paired tests with a significance level set at p < 0.05. Results: After six months of splint therapy, a significant increase in EMG activity of the masseter and anterior temporalis muscles was observed in most participants, along with a reduction in muscular asymmetries. Improvement in sternocleidomastoid and trapezius activity was noted in a subset of subjects. All participants showed correction of mandibular retrusion. Postural performance significantly improved, with enhanced ability to maintain balance during the Flamingo Balance Test. No major adverse effects were reported. Conclusions: Within the limitations of this uncontrolled prospective single-arm study, customized occlusal splint use was associated with changes in neuromuscular activity and postural balance parameters in professional figure skaters. However, causal relationships cannot be established, and randomized controlled studies are required to confirm the efficacy of this intervention. Full article

Microfinance institutions (MFIs) are vital in promoting financial inclusion for underserved populations. However, these institutions face growing security threats, including sophisticated burglary tactics like underground tunneling. In the Philippines, notable incidents, such as the “Termite Gang” heist in Marikina City and a mall robbery in Ozamiz, highlight the limitations of conventional security systems in addressing subterranean intrusions. This study addresses the gap in existing security technologies by developing a real-time detection system that integrates a vibration sensor, a Global System for Mobile Communications (GSM) module for sending real-time SMS alerts, an audible alarm, and a solar-powered backup system for continuous operation. The system was simulated in the electrical technology laboratory to enhance classroom learning. The system’s core is an Arduino Uno microcontroller that processes inputs from the SW-420 vibration sensor, activating alarms and triggering SMS notifications via the SIM900A module when it detects unusual vibrations. Simulations A, B, and C were conducted to evaluate the system’s response time, with results showing a progressive reduction in detection time from five seconds to one second, indicating improved calibration and system efficiency. These findings also support the existing literature on user interaction with vibration alerts, demonstrating high accuracy in interpreting haptic notifications and the cognitive trade-offs involved. The proposed solution offers a proactive, energy-resilient, and cost-effective security system specifically designed to address underground burglary attempts. It applies to MFIs, pawnshops, and other high-risk financial environments. Future research should explore the application of machine learning for adaptive threat detection, expand the system’s scalability, and integrate mobile applications to enable user customization and enhance alert management. Full article

In hopper-based binder jetting 3D printing, a consistent powder dispensing rate from the hopper to the powder bed is essential for reliable printing. This study investigates the effects of adding a custom-built auto-stirrer to the hopper system on the consistency of powder dispensing rate for the ExOne Innovent+ binder jetting 3D printer (Desktop Metal, Burlington, MA, USA). The auto-stirrer incorporates rotating augers that actively agitate the powder in the hopper. Working together with the ultrasonic vibrator, the auto-stirrer facilitates consistent dispensing of powder through the hopper outlet. Experiments with algae powder demonstrated that adding the auto stirrer reduced fluctuations in dispensing rate by over 30% compared with the standard hopper. Statistical analysis confirmed that these improvements were significant (at a significance level of 0.05). These results indicate that integrating active mechanical agitation into hopper-based powder dispensing systems could help to achieve more consistent powder dispensing rates in hopper-based binder jetting 3D printing that uses cohesive powder feedstocks. Full article

Drawing on the uncanny valley framework, trust theory, and similarity attraction theory, this study examines how customers’ multidimensional perceptions of humanoid service robots shape their approach and avoidance behaviors through two relational states: trust and rapport. Subsequently, structural equation modeling and mediation analysis were employed for testing. The results indicate that customers’ overall perceptions of service robots not only encourage approach behaviors but may simultaneously intensify avoidance tendencies, reflecting the ambivalent nature of human–robot interactions. We interpret this dual activation through the uncanny valley framework, in which humanlike robots simultaneously elicit attraction and aversion. Trust and rapport play critical mediating roles in this process, effectively reducing avoidance responses while strengthening customers’ approaches. Further analyses reveal that different perceptual dimensions operate through distinct mechanisms in the formation of trust and rapport. This study aims to deepen the comprehension of customer response mechanisms to humanoid service robots through a relational perspective, and offers practical insights for hotels seeking to balance operational efficiency with emotional experience in robot design and management. Full article

We report a cross-station replication of endogenous circadian rhythms in plant bioelectric voltage, recorded continuously for 42 days at three independent sensor stations within a public science exhibition (Phänomena, Dietikon, Switzerland; March–April 2026). Three primrose (Primula vulgaris) stations were equipped with custom Biolingo bioelectric sensors (ESP32 + AD8232) and recorded autonomously through approximately 21,000 visitor interactions. We extracted DC-invariant spectral features from 5–10 s voltage windows (n = 78,431 quality-filtered files) and fitted two-stage cosinor models with bootstrap 95% confidence intervals. All three stations show a robust 24 h rhythm in the 1–5 Hz band power (bp_1–5), with peak-to-trough amplitudes between 0.35× and 1.19× of mesor (R²_med 0.72–0.87). Acrophase varies across stations from 05:00 to 11:00 local time. Critically, the rhythm survives an overnight-only restriction (18:00–09:00, no visitors) at all three stations, ruling out visitor presence as the rhythm driver. The most visitor-intensive station (faces of museum visitors triggering an emotion-recognition installation) additionally shows a sharp daytime amplitude collapse coincident with the exhibition opening at 09:00, during the hours of sustained visitor presence. This temporal coincidence is consistent with—though not by itself proof of—the cardiovascular-mechanosensory coupling characterized at single-subject resolution in a companion study. We argue that bp_1–5—the spectral band most directly related to plant action-potential activity—carries an endogenous circadian signal in Primula vulgaris and that this station-level signal co-varies with sustained nearby human presence in a manner consistent with frequency-selective mechanosensory coupling, although the observational design cannot establish this mechanism. From a biomimetic perspective, this suggests that the plant’s evolved bioelectric sensing apparatus might be leveraged as a live ambient biosensor for nearby human activity, complementing the more common biomimetic approach of replicating plant sensing in synthetic devices. Full article

Background/Objectives: Digital technologies are increasingly explored as complementary tools in speech and language therapy for children with neurodevelopmental disorders. However, evidence on virtual reality-based interventions for children with developmental language disorder (DLD) remains limited. This study aimed to evaluate the effects of a Virtual Reality Rehabilitation System (VRRS)-based language intervention combined with standard speech therapy in preschool children with DLD. Secondary objectives included assessing the feasibility, usability, and safety of the VRRS-integrated intervention. Methods: A randomized controlled pilot study was conducted in preschool children diagnosed with DLD. Participants were allocated to an experimental group receiving VRRS-based language intervention integrated with conventional therapy or to a control group receiving standard speech therapy alone. Both groups attended two 60 min sessions per week for six months. Clinical language outcomes were assessed at baseline (T0) and post-intervention (T1). Feasibility was evaluated through adherence and retention rates, usability through a therapist-completed questionnaire, and safety through monitoring of adverse events during sessions. Results: All participants in the experimental group completed the intervention (100% retention). No adverse events were observed. Therapists reported good usability of the VRRS system, highlighting ease of exercise customization, intuitive monitoring of progress, and good integration into routine therapy. Conclusions: VRRS-based activities integrated into conventional speech therapy appear feasible, safe, and well accepted in preschool children with DLD. Further controlled studies with larger samples are needed to confirm these findings. Trial Registration: ClinicalTrials.gov (NCT07438639). Full article

Background: The early identification of individuals with autism spectrum disorder (ASD) is crucial for their proper integration into the educational system and society. AI methods introduce novel approaches for the detection and classification of individuals diagnosed with ASD. Methods: We employed three custom-built Convolutional Neural Networks (CNNs) alongside two pretrained CNNs, specifically YOLO8 and ResNet18. The integrated Convolutional Block Attention Module (CBAM) was utilized to enhance feature representations for classifying individuals with ASD and non-ASD. Results: The results from the binary classification using the YOLO8-CBAM model demonstrated notable performance metrics: an accuracy of 77.6%, an F1-score of 72.6%, a Matthews Correlation Coefficient (MCC) of 60%, and an area under the curve (AUC) of 0.912. Conclusion: The backbone of the pretrained YOLO8-CBAM, enhanced by the integration of the CBAM after selected convolutional blocks, improved the feature refinement utilized in the classification process. Additionally, the gradient-weighted Class Activation Mapping (Grad-CAM) model provides interpretability by highlighting the regions that are most influential in distinguishing between individuals with ASD and those without. Full article