Search Results (3,432)

This article presents an integrated framework for robust control and cybersecurity of an industrial robot, combining Quantitative Feedback Theory (QFT), digital twin (DT) technology, and a programmable logic controller–based architecture aligned with the requirements of the NIS2 Directive. The study considers a five-axis industrial manipulator modeled as a set of decoupled linear single-input single-output systems subject to parametric uncertainty and external disturbances. For position control of each axis, closed-loop robust systems with QFT-based controllers and prefilters are designed, and the dynamic behavior of the system is evaluated using predefined key performance indicators (KPIs), including tracking errors in joint space and tool space, maximum error, root-mean-square error, and three-dimensional positional deviation. The proposed architecture executes robust control algorithms in the MATLAB/Simulink environment, while a programmable logic controller provides deterministic communication, time synchronization, and secure data exchange. The synchronized digital twin, implemented in the FANUC ROBOGUIDE environment, reproduces the robot’s kinematics and dynamics in real time, enabling realistic hardware-in-the-loop validation with a real programmable logic controller. This work represents one of the first architectures that simultaneously integrates robust control, real programmable logic controller-based execution, a synchronized digital twin, and NIS2-oriented mechanisms for observability and traceability. The conducted simulation and digital twin-based experimental studies under nominal and worst-case dynamic models, as well as scenarios with externally applied single-axis disturbances, demonstrate that the system maintains robustness and tracking accuracy within the prescribed performance criteria. In addition, the study analyzes how the proposed architecture supports the implementation of key NIS2 principles, including command traceability, disturbance resilience, access control, and capabilities for incident analysis and event traceability in robotic manufacturing systems. Full article

Reliable and low-latency seizure detection from electroencephalography (EEG) is critical for continuous clinical monitoring and emerging wearable health technologies. Spiking neural networks (SNNs) provide an event-driven computational paradigm that is well suited to real-time signal processing, yet achieving competitive seizure detection performance with constrained model complexity remains challenging. This work introduces a hybrid spike encoding scheme that combines Delta–Sigma (change-based) and stochastic rate representations, together with two spiking architectures designed for real-time EEG analysis: a compact feed-forward HybridSNN and a convolution-enhanced ConvSNN incorporating depthwise-separable convolutions and temporal self-attention. The architectures are intentionally designed to operate on short EEG segments and to balance detection performance with computational practicality for continuous inference. Experiments on the CHB–MIT dataset show that the HybridSNN attains 91.8% accuracy with an F1-score of 0.834 for seizure detection, while the ConvSNN further improves detection performance to 94.7% accuracy and an F1-score of 0.893. Event-level evaluation on continuous EEG recordings yields false-alarm rates of 0.82 and 0.62 per day for the HybridSNN and ConvSNN, respectively. Both models exhibit inference latencies of approximately 1.2 ms per 0.5 s window on standard CPU hardware, supporting continuous real-time operation. These results demonstrate that hybrid spike encoding enables spiking architectures with controlled complexity to achieve seizure detection performance comparable to larger deep learning models reported in the literature, while maintaining low latency and suitability for real-time clinical and wearable EEG monitoring. Full article

Cryotherapy and radiofrequency (RF) treatments modulate tissue temperature to induce therapeutic effects; however, improper application can result in thermal injury. Traditional temperature-based monitoring methods rely on multiple thermal sensors whose accuracy strongly depends on their number and spatial positioning, often failing to detect early tissue crystallization. This study introduces a fractional order bioimpedance modelling framework for the early detection of tissue freezing during cryogenic and thermal medical treatments, with the feasibility and effectiveness of this approach having been reported in our prior publications. While bioimpedance spectroscopy itself is a well-est. The corresponablished technique in biomedical engineering, its novel application to predict and identify premature freezing events provides a new pathway for safe and efficient energy-based therapies. Fractional-order models derived from the Cole family accurately reproduce the complex electrical behavior of biological tissues using fewer parameters than classical integer-order models, thus reducing both hardware requirements and computational cost. Experimental impedance data from human abdominal, gluteal, and femoral regions were modelled to extract fractional parameters that serve as sensitive indicators of phase-transition onset. The results demonstrate that the proposed approach enables real-time identification of freezing-induced electrical transitions, offering a physiologically grounded alternative to conventional temperature-based monitoring. Furthermore, the fractional order bioimpedance method exhibits high reproducibility and selectivity, and its analytical figures of merit, including the limits of detection and quantification, support its use for reliable real-time tissue monitoring and early injury detection. Overall, the proposed fractional order bioimpedance framework enhances both safety and control precision in cryogenic and thermal medical applications. Full article

Acoustic-based anomaly detection in elevators is crucial for predictive maintenance and operational safety, yet it faces significant challenges in real-world settings, including pervasive multi-source acoustic interference within confined spaces and severe class imbalance in collected data, which critically degrades the detection performance for minority yet critical acoustic events. To address these issues, this study proposes a novel hierarchical self-distillation framework. The method embeds auxiliary classifiers into the intermediate layers of a backbone network, creating a deep teacher–shallow student knowledge transfer paradigm optimized jointly via Kullback–Leibler divergence and feature alignment losses. A self-attentive temporal pooling layer is introduced to adaptively weigh discriminative time-frequency features, thereby mitigating temporal overlap interference, while a focal loss function is employed specifically in the teacher model to recalibrate the learning focus towards hard-to-classify minority samples. Extensive evaluations on the public UrbanSound8K dataset and a proprietary industrial elevator audio dataset demonstrate that the proposed model achieves superior performance, exceeding 90% in both accuracy and F1-score. Notably, it yields substantial improvements in recognizing rare events, validating its robustness for elevator acoustic monitoring. Full article

Probiotics and microbiome-based therapeutics are increasingly used to prevent antibiotic-associated diarrhea (AAD) and support gut microbiota health across children, adults, and elderly populations. Evidence synthesized in this narrative review from randomized controlled trials and meta-analyses (>20,000 participants) suggests that early probiotic administration, particularly Lactobacillus rhamnosus GG, Bifidobacterium species, multistrain formulations, and Saccharomyces boulardii, is associated with a 30–40% relative reduction in AAD incidence across heterogeneous studies, with absolute risk reductions of approximately 5–12% depending on baseline risk, strain, dose, and timing. Probiotics are generally well tolerated, with mild gastrointestinal adverse effects reported in 3–5% of users and rare serious events mainly in immunocompromised individuals. However, heterogeneity in formulations, populations, and limited long-term real-world data underscores the need for further pharmacoepidemiological studies, microbiome surveillance, and evaluation of antimicrobial resistance implications. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

Background/Objectives: Functional impairment of the complex motility system in the upper gastrointestinal tract is high in patients suffering from obesity and even higher after metabolic bariatric surgery (MBS). Sleeve gastrectomy (SG) and gastric bypass (GB) represent the most common MBS procedures worldwide. Despite procedural standardization, no diagnostic method is able to depict the functional consequences resulting from intraoperative anatomical changes during MBS. This pilot study was conducted to reveal immediate intraoperative functional effects of MBS on the anti-reflux barrier in SG and GB. Methods: A prospective analysis was performed on consecutive patients with informed consent for MBS. A standard protocol for each procedure was established prior to study onset to analyze functional parameters at the lower esophageal sphincter (LES). Measurements were conducted intraoperatively during minimally invasive SG and GB. Distensibility index (DI), intra-balloon pressure, diameter (Dmin), and minimal cross-sectional area (CSA) at the LES served as points of interest for analyzation. Results: Intraoperative evaluation was performed successfully in 40 patients and no directly related adverse events were reported. DI and Dmin intraoperatively significantly increased immediately in SG (2.1 mm²/mmHg (±0.5) vs. 2.9 mm²/mmHg (±1.3), 95% CI: −1.6 to −0.14, p = 0.023 and 12.0 mm (±1.2) vs. 13.9 mmH (±2.8), 95% CI: −3.6 to −0.2, p = 0.028, respectively) whereas GB did not affect functional measurements. Conclusions: Sleeve gastrectomy immediately and significantly influences the LES and increases the opening area whereas gastric bypass surgery appears not to influence LES distensibility or opening diameters. Intraoperative standardized EndoFLIP^TM measurements are feasible and safe and add additional real-time information during MBS. Full article

Industrial control systems (ICSs) are increasingly interconnected with enterprise IT networks and remote services, which expands the attack surface of operational technology (OT) environments. However, collecting sufficient attack traffic from real OT/ICS networks is difficult, and the resulting scarcity and class imbalance of malicious data hinder the development of intrusion detection systems (IDSs). At the same time, large language models (LLMs) have shown promise for security analytics when system events are expressed in natural language. This study investigates an LLM-based network IDS for a smart-factory OT/ICS environment and proposes a synthetic data generation method that injects domain knowledge into attack samples. Using the ICSSIM simulator, we construct a bottle-filling smart factory, implement six MITRE ATT&CK for ICS-based attack scenarios, capture Modbus/TCP traffic, and convert each request–response pair into a natural-language description of network behavior. We then generate synthetic attack descriptions with GPT by combining (1) statistical properties of normal traffic, (2) MITRE ATT&CK for ICS tactics and techniques, and (3) expert knowledge obtained from executing the attacks in ICSSIM. The Llama 3.1 8B Instruct model is fine-tuned with QLoRA on a seven-class classification task (Benign vs. six attack types) and evaluated on a test set composed exclusively of real ICSSIM traffic. Experimental results show that synthetic data generated only from statistical information, or from statistics plus MITRE descriptions, yield limited performance, whereas incorporating environment-specific expert knowledge is associated with substantially higher performance on our ICSSIM-based expanded test set (100% accuracy in binary detection and 96.49% accuracy with a macro F1-score of 0.958 in attack-type classification). Overall, these findings suggest that domain-knowledge-infused synthetic data and natural-language traffic representations can support LLM-based IDSs in OT/ICS smart-factory settings; however, further validation on larger and more diverse datasets is needed to confirm generality. Full article

Background and Objectives: Corpus atrophic gastritis (CAG) is associated with vitamin B₁₂ deficiency due to impaired gastric acid and intrinsic factor secretion. Untreated vitamin B₁₂ deficiency can lead to pernicious anemia, severe neurological consequences, and acute cardiocerebral-vascular events. Timely vitamin B₁₂ supplementation is relevant; however, the dosage of intramuscular (IM) vitamin B₁₂ supplementation has not been standardized to date. The objective was to assess the efficacy of a 1st and 2nd treatment schedule of IM-cyanocobalamin treatment in CAG patients with vitamin B₁₂ deficiency at long-term follow-up and to identify the predictors of increased cyanocobalamin requirement. Methods: This monocentric real-life cohort study included 213 CAG patients with vitamin B₁₂ deficiency. Inclusion criteria were adult age, histological diagnosis of CAG with vitamin B₁₂ deficiency (<220 pg/mL), and follow-up of more than 12 months. The 1st-treatment-schedule (TxA) was 5000 µg IM cyanocobalamin every 5 days for 3 times, followed by 5000 µg IM cyanocobalamin every 3 mos (20,000 µg/yr); the 2nd-treatment-schedule (TxB) was 5000 µg IM cyanocobalamin every 5 days for 3 times, followed by 5000 µg IM cyanocobalamin every 2 mos (30,000 µg/yr). The treatment endpoint was serum vitamin B₁₂ normalization. Clinical-biochemical follow-up was scheduled every 12 ± 6 mos: patients who satisfied the endpoint maintained the TxA, otherwise, TxB was prescribed. Results: Of the 213 CAG patients with vitamin B₁₂ deficiency, 48.3% had anemia, and 26.3% macrocytosis without anemia. TxA efficaciously corrected vitamin B₁₂ deficiency in 146 (68.5%) patients, maintaining efficacy until the longest available follow-up (42.2 ± 2.6 months). The remaining 67 patients (31.5%) were switched to TxB due to persistent vitamin B₁₂ deficiency observed at 12 (6–36) months and were maintained until the longest available follow-up (50.2 ± 4.1 months). At the longest available follow-up, a significant increase in Hb (TxA: 11.9 ± 0.2 to 13.1 ± 0.1 g/dL, p < 0.001; TxB: 12.2 ± 0.3 to 13.6 ± 0.2 g/dL, p = 0.003) and serum vitamin B₁₂ (TxA: 168 ± 7 to 402 ± 19 pg/mL, p < 0.0001; TxB: 157 ± 12 to 340 ± 24 pg/mL, p < 0.0001) was shown in both schedules. A significant decrease in MCV was shown in TxB only (p = 0.0003). In logistic regression, switching to TxB was significantly associated with severe corpus intestinal metaplasia (OR 11.0, 95% CI 2.8–43.7), macrocytosis at CAG diagnosis (OR 2.7, 95% CI 1.2–6.3), and male sex (OR 2.4, 95% CI 1.1–5.2). Conclusions: In this real-world setting, at long-term follow-up, nearly 70% of CAG patients with vitamin B₁₂ deficiency restored their vitamin B₁₂ levels with 20,000 µg/yr of cyanocobalamin, while the remaining 30% required 30,000 µg/yr. Male vitamin B₁₂-deficient CAG patients with advanced gastric damage and severe macrocytosis required higher dosages of cyanocobalamin. They should be carefully monitored to avoid suboptimal supplementation and potentially dangerous consequences of vitamin B₁₂ deficiency. Full article

Hylocereus undatus growth is limited by long-term heat stress, and heat shock protein 70 (Hsp70) is crucial in the plant’s heat stress (HS) response. In a previous study, transcriptomic data revealed that Hsp70 family members in pitaya seedlings respond to temperature changes. This study identified 27 HuHsp70 genes in pitaya, analyzed their physicochemical properties (such as molecular weight and isoelectric point), and divided them into five subfamilies with conserved gene structures, motifs (short conserved sequence patterns), and cis-acting elements (regulatory DNA sequences). The Ks value (synonymous substitution rate) ranged from 0.93~3.54, and gene duplication events occurred between 71.17 and 272.19 million years ago (Mya). Under HS, eight and nine differentially expressed genes (DEGs) were detected at 24 h and 48 h, respectively. Quantitative real-time PCR (qRT-PCR, a method for measuring gene expression) verified the expression trends, with HuHsp70-11 expression increasing with heat shock duration, indicating that HuHsp70-11 is a key candidate. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that HuHsp70s, especially HuHsp70-11, play key roles in responding to high temperatures (HT) in H. undatus seedlings. A potential model by which HuHsp70-11 removes excess reactive oxygen species (ROS) and enhances cell membrane permeability was constructed. These results provide new perspectives for exploring the HS response mechanisms and adaptability of H. undatus plants to heat stress. Full article

Hand exoskeletons are increasingly used to support post-stroke reach-to-grasp, yet most intention-detection strategies trigger assistance from local hand events without considering the synergy between proximal arm transport and distal hand shaping. We evaluated whether proximal arm kinematics, alone or fused with EMG, can predict flexor and extensor digitorum activity for synergy-aligned hand assistance. We trained nine models per participant: linear regression (LINEAR), feedforward neural network (NONLINEAR), and LSTM, each under EMG-only, kinematics-only (KIN), and EMG+KIN inputs. Performance was assessed by RMSE on test trials and by a synergy-retention analysis, comparing synergy weights from original EMG versus a hybrid EMG in which extensor and flexor digitorum measure signals were replaced by model predictions. Results have shown that kinematic information can predict muscle activity even with a simple linear model (average RMSE around 30% of signal amplitude peak during go-to-grasp contractions), and synergy analysis indicated high cosine similarity between original and hybrid synergy weights (on average 0.87 for the LINEAR model). Furthermore, the LINEAR model with kinematics input has been tested in a real-time go-to-grasp motion, developing a high-level control strategy for a hand exoskeleton, to better simulate post-stroke rehabilitation scenarios. These results suggest the intrinsic synergistic motion of go-to-grasp actions, offering a practical path, in hand rehabilitation contexts, for timing hand assistance in synergy with arm transport and with minimal setup burden. Full article

This study develops a statistical and technological framework to analyze extreme rainfall in Colima, Mexico, by integrating historical precipitation records, probabilistic modeling, and spatial visualization. Using data from CONAGUA meteorological stations, we identify high-intensity rainfall events and model their recurrence using the Generalized Extreme Value (GEV) distribution to estimate key return periods. The results support flood-risk assessment and territorial planning in Colima. Spatial interpolation was performed in Python (version 3.13), and QGIS (version 3.38) produces exceedance maps that illustrate geographic variations in rainfall intensity across the state. These exceedance maps reveal a consistent spatial pattern, with the northern and western areas of Colima experiencing the highest frequencies of extreme events. Based on these results, the integration of real-time sensor technologies and satellite observations may improve flood monitoring and risk management frameworks. Full article

To ensure the safety of vehicles and occupants under failures or functional limitations of ego vehicles, a minimum risk maneuver (MRM) has been proposed as a key automated driving system (ADS) function. However, executing an MRM may pose certain potential risks when sensor failures occur. This study proposed an MRM strategy designed to enhance highway-driving safety during MRM execution under multiple sensor-failure conditions. A hazard and operability study analysis, based on an ADS behavior model, is conducted to systematically identify hazards, determine potential hazardous events, and categorize the associated safety risks arising from sensor failures. Within the proposed strategy, virtual objects are generated to account for potential hazards and support risk assessments. Adaptive MRM behavior is determined in real time by analyzing surrounding objects and evaluating time-to-collision and time headway. The strategy is verified by using a MATLAB–CARLA co-simulation environment across three representative highway scenarios with combined sensor failures. The result demonstrates that the proposed MRM strategy can mitigate collision risk in hazardous scenarios while effectively leveraging the remaining functional sensors to guide the ego vehicle toward an appropriate minimum risk condition during MRM execution. Full article

Automotive dashboard cameras are widely used to record driving events and often serve as critical evidence in accident investigations and insurance claims. However, the availability of free and low-cost editing tools has increased the risk of video tampering, underscoring the need for reliable methods to verify video authenticity. Temporal tampering typically involves manipulating frame order through insertion, deletion, or duplication. This paper proposes a computationally efficient framework that transforms high-dimensional video into compact one-dimensional temporal signals and learns tampering patterns using a shallow one-dimensional convolutional neural network (1D-CNN). Five complementary features are extracted between consecutive frames: frame-difference magnitude, structural similarity drift (SSIM drift), optical-flow mean, forward–backward optical-flow consistency error, and compression-aware temporal prediction error. Per-video robust normalization is applied to emphasize intra-video anomalies. Experiments on a custom dataset derived from D²-City demonstrate strong detection performance in single-attack settings: 95.0% accuracy for frame deletion, 100.0% for frame insertion, and 95.0% for frame duplication. In a four-class setting (non-tampered, insertion, deletion, duplication), the model achieves 96.3% accuracy, with AUCs of 0.994, 1.000, 0.997, and 0.988, respectively. Efficiency analysis confirms near real-time CPU inference (≈12.7–12.9 FPS) with minimal memory overhead. Cross-dataset tests on BDDA and VIRAT reveal domain-shift sensitivity, particularly for deletion and duplication, highlighting the need for domain adaptation and augmentation. Overall, the proposed multi-feature 1D-CNN provides a practical, interpretable, and resource-aware solution for temporal tampering detection in dashcam videos, supporting trustworthy video forensics in IoT-enabled transportation systems. Full article

Global Navigation Satellite Systems (GNSS) have become essential tools in geomatics engineering for precise positioning, cadastral surveys, topographic mapping, and deformation monitoring. Recent advances integrate GNSS with emerging technologies such as artificial intelligence (AI), machine learning (ML), cloud computing, and unmanned aerial systems (UAS), which have greatly improved accuracy, efficiency, and analytical capabilities in managing geospatial big data. In this study, we propose a hybrid Convolutional Neural Network–Long Short Term Memory (CNN-LSTM) architecture for seismic detection using high-rate (5 Hz) GNSS velocity time series. The model is trained on a large synthetic dataset generated by and real high-rate GNSS non-event data. Model performance was evaluated using real event and non-event data through an event-based approach. The results demonstrate that a hybrid deep-learning architecture can provide a reliable framework for seismic detection with high-rate GNSS velocity time series. Full article