Search Results (6,526)

This article formulates and evaluates historical hypotheses on the origin and formation of the Villafañe lineage in Santiago del Molinillo (León) within the broader dynamics that connected the urban patriciate and the rural hidalguía (minor nobility) of late medieval and early modern Castile. Through an integrated examination of population registers, parish records, hidalguía lawsuits, and notarial protocols, the study reconstructs the family’s trajectory and its institutional anchoring in the concejo and parish. The evidence suggests an urban origin on León’s Rúa through Doña Elena de Villafañe y Flórez, whose marriage to Ares García—an hidalgo from the Ordás area—established the local house and the compound surname “García de Villafañe” as both an identity marker and a patrimonial device. The consolidation of the lineage resulted from deliberate family strategies, including selective alliances with neighboring lineages (Quiñones, Gavilanes, Rebolledo), participation in municipal and ecclesiastical offices, and the symbolic use of heraldry and memory. The migration of Lázaro de Villafañe to colonial La Rioja and Cordova in the seventeenth century extended this social status across the Atlantic while maintaining Leonese continuity. Although the surviving evidence is fragmentary, convergent archival, onomastic, and heraldic indicators support interpreting the Molinillo branch as a legitimate and adaptive extension of the urban lineage. By combining genealogical and microhistorical analysis with interdisciplinary perspectives—particularly gender and genetics—this article proposes a transferable framework for testing historical hypotheses on lineage continuity, social mobility, and identity formation across early modern Castile and its transatlantic domains. Full article

Foot drop, a form of paralysis affecting ankle and foot control, impairs walking and increases the risk of falls. Effective rehabilitation requires monitoring gait to guide personalized interventions. This study presents a proof-of-concept smart foot–ankle brace integrating low-cost sensors, including gyroscopes, accelerometers, and a Fiber Bragg Grating (FBG) array, with an Arduino-based processing platform. The system captures, in real time, the key locomotion parameters, namely, angular rotation, acceleration, and sole deformation. Experiments using a 3D-printed insole demonstrated that the device detects foot-drop-related gait deviations, with toe acceleration approximately twice that of normal walking. It also precisely detects foot deformation through FBG sensing. These results demonstrate the feasibility of the proposed system for monitoring gait abnormalities. Unlike commercial gait analysis devices, this work focuses on proof-of-concept development, providing a foundation for future improvements, including wireless integration, AI-based gait classification, and mobile application support for home-based or tele-rehabilitation applications. Full article

With the rapid growth of wireless devices, security has become a key research concern in beyond-5G (B5G) and sixth-generation (6G) networks. Non-orthogonal multiple access (NOMA), one of the supporting technologies, is a strong contender to enable massive connectivity, increase spectrum efficiency, and guarantee high-quality access for a sizable user base. Furthermore, the scientific community has recently paid close attention to the effects of hardware impairments (HIs). The safe transmission of NOMA in a two-user uplink relay network is examined in this paper, taking into account both hardware limitations and the existence of listening devices. Each time frame in a mobile network environment comprises two phases in which users use a relay (R) to interact with the base station ($BS$). The research focuses on scenarios where a malicious device attempts to intercept the uplink signals transmitted by users through the R. Using important performance and security metrics, such as connection outage probability (COP), secrecy outage probability (SOP), and intercept probability (IP), system behavior is evaluated. To assess the system’s security and reliability under the proposed framework, closed-form analytical expressions are derived for SOP, IP, and COP. The simulation results provide the following insights: (i) they validate the accuracy of the derived analytical expressions; (ii) the study significantly deepens the understanding of secure NOMA uplink transmission under the influence of HIs across all the network entities, paving the way for future practical implementations; and (iii) the results highlight the superior performance of secure and reliable NOMA uplink systems compared to benchmark orthogonal multiple access (OMA) counterparts when both operate under the same HI conditions. Furthermore, an extended model without a relay is considered for comparison with the proposed relay-assisted scheme. Moreover, the numerical results indicate that the proposed communication model achieves over 90% reliability (with a COP below 0.1) and provides approximately a 30% improvement in SOP compared to conventional OMA-based systems under the same HI conditions. Full article

Rice, a dietary staple for over half of the global population, is highly susceptible to bacterial and fungal diseases such as bacterial blight, brown spot, and leaf smut, which can severely reduce yields. Traditional manual detection is labor-intensive and often results in delayed intervention and excessive chemical use. Although deep learning models like convolutional neural networks (CNNs) achieve high accuracy, their computational demands hinder deployment in resource-limited agricultural settings. We propose MCP-X, an ultra-compact CNN with only 0.21 million parameters for real-time, on-device rice disease classification. MCP-X integrates a shallow encoder, multi-branch expert routing, a bi-level recurrent simulation encoder–decoder (BRSE), an efficient channel attention (ECA) module, and a lightweight classifier. Trained from scratch, MCP-X achieves 98.93% accuracy on PlantVillage and 96.59% on the Rice Disease Detection Dataset, without external pretraining. Mechanistically, expert routing diversifies feature branches, ECA enhances channel-wise signal relevance, and BRSE captures lesion-scale and texture cues—yielding complementary, stage-wise gains confirmed through ablation studies. Despite slightly higher FLOPs than MobileNetV2, MCP-X prioritizes a minimal memory footprint (~1.01 MB) and deployability over raw speed, running at 53.83 FPS (2.42 GFLOPs) on an RTX A5000. It achieves 16.7×, 287×, 420×, and 659× fewer parameters than MobileNetV2, ResNet152V2, ViT-Base, and VGG-16, respectively. When integrated into a multi-resolution ensemble, MCP-X attains 99.85% accuracy, demonstrating exceptional robustness across controlled and field datasets while maintaining efficiency for real-world agricultural applications. Full article

Maternal health care during labor requires the continuous and reliable monitoring of analgesic procedures, yet conventional systems are often subjective, indirect, and operator-dependent. Infrared thermography (IRT) offers a promising non-invasive approach for labor epidural analgesia (LEA) monitoring, but its practical implementation is hindered by clinical and hardware limitations. This work presents a novel artificial intelligence-driven mobile platform to overcome these hurdles. The proposed solution integrates a lightweight deep learning model for semantic segmentation, a B-spline-based free-form deformation (FFD) approach for non-rigid dermatome registration, and efficient on-device inference. Our analysis identified a U-Net with a MobileNetV3 backbone as the optimal architecture, achieving a high Dice score of 0.97 and a 4.5% intersection over union (IoU) gain over heavier backbones while being 73% more parameter-efficient. The entire AI pipeline is deployed on a commercial smartphone via TensorFlow Lite, achieving an on-device inference time of approximately two seconds per image. Deployed within a user-friendly interface, our approach provides straightforward feedback to support decision making in labor management. By integrating thermal imaging with deep learning and mobile deployment, the proposed system provides a practical solution to enhance maternal care. By offering a quantitative, automated tool, this work demonstrates a viable pathway to augment or replace subjective clinical assessments with objective, data-driven monitoring, bridging the gap between advanced AI research and point-of-care practice in obstetric anesthesia. Full article

This study explores the potential connections between the digital economy and water conservation technologies in the context of China’s water resource consumption from 2008 to 2021. The research employs a state-of-the-art M-MQR technique, including the PCA index, and yields several significant findings. Empirical results reveal that digital technologies play a crucial role in reducing water consumption: Mobile technology decreases water use by −0.00001 to −0.00002 across quantiles, while internet access cuts consumption by −0.0000306 at lower quantiles and −0.0000167 at higher quantiles. The digital economy index shows an overall reduction in water consumption of −0.0537 at lower quantiles and −0.0292 at higher quantiles. Water conservation technologies, such as sprinkler irrigation, also contribute significantly, with reductions of −0.005 at the 10th quantile. Furthermore, water-saving investments show a positive effect on reducing water consumption, with reductions of −0.0105 at the 95th quantile. The study emphasizes that digitalization moderates the impact of water-saving technologies, reducing consumption by −0.0124 to −0.0118 at lower quantiles and −0.00812 to −0.00761 at middle quantiles. These results highlight the potential of digital infrastructure and water-saving investments to improve water use efficiency and address China’s water resource challenges. This study proposes that digital water supply and distribution system devices can help develop smart water infrastructure, reduce waste, and improve efficiency. Full article

Monitoring electrocardiogram (ECG) and respiration continuously and non-invasively is essential for managing cardiopulmonary health. An effective wearable device can be used to regularly monitor key vitals, reducing the need for clinical visits. In this work, we propose a custom device for real-time continuous ECG by inkjet printed (IJP) dry electrodes and respiration monitoring by using a novel single 6-axis inertial measurement unit (IMU). The proposed system can extract the heart rate (HR) and respiration rate (RR) during static and dynamic postures. The respiration process implements a quaternion-based update and multiple filtering stages to estimate the signal. The custom device uses Bluetooth protocol to send the raw and processed data to a mobile application. The RR is investigated in stationary, i.e., sitting and standing, and dynamic, i.e., walking, running, and cycling, postures. The proposed device is evaluated with commercial Go Direct^® respiration belt from Vernier^® for RR and offers an overall accuracy of 99.3% and 98.6% for static and dynamic conditions, respectively. The wearable also offers 98.9% and 97.9% accuracy for HR measurements, respectively, in static and active postures when compared with the Kardia^® device. Furthermore, the device is assessed in an ambulatory monitoring setup in both indoor and outdoor environments. The low-power wearable consumes an average of only 7.4 mA of current during data processing. The device performs effectively and efficiently in both stationary and active states, offering a low complexity, portable solution for real-time monitoring. The proposed system can benefit from the continuous monitoring and early detection of pulmonary and cardio-respiratory health issues. Full article

This paper investigates and evaluates a compact, high-Q glass transformer with a 3D spiral structure that offers low loss and high area efficiency. Furthermore, we designed a CMOS power amplifier (PA) with an output-matching network implemented using an off-chip high-Q glass transformer to validate its operation. Two transformer types were developed: a five-port transformer with a center-tap and a four-port transformer without a center tap. The high-Q property of the transformer leads to low loss and tight coupling, as evidenced by an increase in maximum available gain (MAG). Compared with an integrated CMOS transformer, the high-Q transformer exhibits significantly lower loss while maintaining similar area and inductance, despite being an external component. A test PA comprising the CMOS PA and the off-chip transformer was evaluated with simulations and measurements, and it was also compared with a fully integrated PA at the simulation level to verify performance improvements. The proposed PA achieved a saturation power of 29.8 dBm, which was 1.7 dB higher than that of the fully integrated PA. The PAE also improved by 11 percentage points, from 32.1% to 43.1% in simulation. The results show substantial performance gains in simulation, while the total area increases only slightly. Measurements show the same trend as the simulations; with shorter bond-wire lengths, the measured results are expected to approach the simulated performance. These findings demonstrate the feasibility of an ultra-compact CMOS–off-chip hybrid PA that delivers high performance while maintaining a footprint comparable to that of a fully integrated PA, enabling applications in compact devices including mobile products. Full article

Biometric authentication systems, including fingerprint readers, are widely used in mobile devices but remain vulnerable to spoofing attacks. This paper evaluates the properties of carbon nanotube (CNT)-modified silicone fingerprint replicas for use in security testing. Microscopic analyses, roughness measurements, and electrical conductivity measurements showed that the effectiveness of the replicas depends on the type of silicone matrix and the concentration of CNTs. Replicas made with Double 32 at 3% CNT exceeded the percolation threshold, achieving significantly higher conductivity. In practical tests, capacitive scanners proved susceptible to recording artificial prints, while ultrasonic readers were more resistant. The results indicate that although CNTs improve the properties of replicas, their ability to reproduce higher-order features remains limited. Full article

Diabetes is a significant global health issue that demands accurate, accessible, and non-invasive diagnostic methods for effective prevention and treatment. Conventional diagnostic systems are often expensive, painful, time-consuming, and not universally available. In this study, we present a smart system for acetone estimation using simulated breath and a recurrent neural network (RNN) model. The detection system employs a new sensor fabricated from a composite of 1D nanostructured KWO (K₂W₇O₂₂) and 2D nanosheet MXene (Ti₃C₂), designed to measure the chemiresistive response to acetone by mimicking human breath. Resistance data collected by the sensor are used to compute sensitivity values for each acetone concentration (in parts per million, PPM). These values serve as input features for the RNN model, which learns to evaluate health as healthy, high-risk, or diabetic. Trained on acetone concentrations ranging from 0.4 to 2 PPM, the RNN achieves an R² of 99.41% in predicting potential for accurate breath acetone prediction. In future work, we aim to develop a smart device and mobile application based on this model to facilitate real-time diabetes monitoring and prediction. Full article

The rapid proliferation of devices on the Internet of Things (IoT) in smart city environments enables autonomous decision-making, but introduces challenges of scalability, coordination, and privacy. Existing reinforcement learning (RL) methods, such as Multi-Agent Actor–Critic (MAAC), depend on centralized critics and recurrent structures, which limit scalability and create single points of failure. This paper proposes a Federated Decision Transformer (FDT) framework that integrates transformer-based sequence modeling with federated learning. By replacing centralized critics with self-attention-driven trajectory modeling, the FDT preserves data locality, enhances privacy, and supports decentralized policy learning across distributed IoT nodes. We benchmarked the FDT against MAAC in a mobile edge computing (MEC) environment with identical hyperparameter configurations. The results demonstrate that the FDT achieves superior reward efficiency, scalability, and adaptability in dynamic IoT networks, although with slightly higher variance during early training. These findings highlight transformer-based federated RL as a robust and privacy-preserving alternative to critic-based methods for large-scale IoT systems. Full article

The integration of neural interfaces with assistive robotics has transformed the field of prosthetics, rehabilitation, and brain–computer interfaces (BCIs). From brain-controlled wheelchairs to Artificial Intelligence (AI)-synchronized robotic arms, the innovations offer autonomy and improved quality of life for people with mobility disorders. This article discusses recent trends in brain–computer interfaces and their application in robotic assistive devices, such as wheelchair-mounted arms, drone control systems, and robotic limbs for activities of daily living (ADLs). It also discusses the incorporation of AI systems, including ChatGPT-4, into BCIs, with an emphasis on new innovations in shared autonomy, cognitive assistance, and ethical considerations. Full article

This paper investigates Electrocardiogram (ECG) rhythm classification using a progressive deep learning framework that combines time–frequency representations with complementary hand-crafted features. In the first stage, ECG signals from the PhysioNet Challenge 2017 dataset are transformed into scalograms and input to diverse architectures, including Simple Convolutional Neural Network (SimpleCNN), Residual Network with 18 Layers (ResNet-18), Convolutional Neural Network-Transformer (CNNTransformer), and Vision Transformer (ViT). ViT achieved the highest accuracy (0.8590) and F1-score (0.8524), demonstrating the feasibility of pure image-based ECG analysis, although scalograms alone showed variability across folds. In the second stage, scalograms were fused with scattering and statistical features, enhancing robustness and interpretability. FusionViT without dimensionality reduction achieved the best performance (accuracy = 0.8623, F1-score = 0.8528), while Fusion ResNet-18 offered a favorable trade-off between accuracy (0.8321) and inference efficiency (0.016 s per sample). The application of Principal Component Analysis (PCA) reduced the dimensionality of the feature from 509 to 27, reducing the computational cost while maintaining competitive performance (FusionViT precision = 0.8590). The results highlight a trade-off between efficiency and fine-grained temporal resolution. Training-time augmentations mitigated class imbalance, enabling lightweight inference (0.006–0.043 s per sample). For real-world use, the framework can run on wearable ECG devices or mobile health apps. Scalogram transformation and feature extraction occur on-device or at the edge, with efficient models like ResNet-18 enabling near real-time monitoring. Abnormal rhythm alerts can be sent instantly to users or clinicians. By combining visual and statistical signal features, optionally reduced with PCA, the framework achieves high accuracy, robustness, and efficiency for practical deployment. Full article

Multi-sensor fusion can improve daily stress monitoring. Methods: A wrist-worn device includes a system of the galvanic skin response (GSR), PPG-derived heart rate variability (HRV), skin temperature, and SpO₂, paired with self-reported questionnaires. The device streams data to a mobile app over Bluetooth Low Energy and updates the UI within 1–2 s. The physiological features are captured within a fixed window around each questionnaire time and undergo a mid-level fusion; late fusion is also evaluated using self-reports. Results: Against a commercial reference device, the proposed system achieved a mean absolute error of 0.23 for SpO₂ and 4.94 for BPM in a one-day benchmark session. The system was validated through a technical evaluation using representative inputs and simulated survey labels. The fusion model was evaluated using simulated physiological and survey data. Using a support vector machine algorithm, a mean squared error of 0.08 was achieved when predicting simulated stress labels. Temperature was shown to have the strongest correlation with simulated stress levels at −0.43, followed by heart rate variability (HRV) at 0.36, while SpO₂ had a negligible correlation at 0.09 in the current dataset. Conclusion: The system integrates multi-sensing, on-device preprocessing, BLE transmission, and a clear fusion workflow that creates a useful predictive performance of daily stress monitoring. Full article

Hand Gesture Recognition (HGR) is a vital technology that enables intuitive human–computer interaction in various domains, including augmented reality, smart environments, and assistive systems. Achieving both high accuracy and real-time performance remains challenging due to the complexity of hand dynamics, individual morphological variations, and computational limitations. This paper presents a lightweight and efficient skeleton-based HGR framework that addresses these challenges through an optimized multi-stream Convolutional Neural Network (CNN) architecture and a trainable ensemble tuner. Dynamic 3D gestures are transformed into structured, noise-minimized 2D spatiotemporal representations via enhanced data-level fusion, supporting robust classification across diverse spatial perspectives. The ensemble tuner strengthens semantic relationships between streams and improves recognition accuracy. Unlike existing solutions that rely on high-end hardware, the proposed framework achieves real-time inference on consumer-grade devices without compromising accuracy. Experimental validation across five benchmark datasets (SHREC2017, DHG1428, FPHA, LMDHG, and CNR) confirms consistent or superior performance with reduced computational overhead. Additional validation on the SBU Kinect Interaction Dataset highlights generalization potential for broader Human Action Recognition (HAR) tasks. This advancement bridges the gap between efficiency and accuracy, supporting scalable deployment in AR/VR, mobile computing, interactive gaming, and resource-constrained environments. Full article