Search Results (1,949)

Vehicular Ad Hoc Networks (VANETs) support safety-critical and traffic-optimization applications through low-latency, reliable V2X communication. However, securing integrity and auditability with blockchain is challenging because conventional BFT-style consensus incurs high message overhead and latency. We introduce CoCoChain , a concept-aware consensus mechanism tailored to VANETs. Instead of exchanging full payloads, CoCoChain trains a sparse autoencoder (SAE) offline on raw message payloads and encodes each message into a low-dimensional concept vector; only the top-k activations are broadcast during consensus. These compact semantic digests are integrated into a practical BFT workflow with per-phase semantic checks using a cosine-similarity threshold $\theta =0.85$ (calibrated on validation data to balance detection and false positives). We evaluate CoCoChain in OMNeT++/SUMO across urban, highway, and multi-hop broadcast under congestion scenarios, measuring latency, throughput, packet delivery ratio, and Age of Information (AoI), and including adversaries that inject semantically corrupted concepts as well as cross-layer stress (RF jamming and timing jitter). Results show CoCoChain reduces consensus message overhead by up to 25% and confirmation latency by 20% while maintaining integrity with up to 20% Byzantine participants and improving information freshness (AoI) under high channel load. This work focuses on OBU/RSU semantic-aware consensus (not 6G joint sensing or multi-base-station fusion). The code, configs, and an anonymized synthetic replica of the dataset will be released upon acceptance. Full article

Background/Objectives: Enhancing endothelial nitric oxide (NO) bioavailability through natural products may provide a promising strategy for the prevention and management of hypertension. This study investigated the phytochemical composition of ethanolic lotus (Nelumbo nucifera) seed extract (LSE), its vasorelaxant mechanisms, effects on endothelial NO production, and antihypertensive activity. Methods: LSE was characterized via LC-ESI-QTOF-MS using accurate mass data and fragmentation patterns. Vasorelaxant effects were evaluated in isolated rat aortas, and the underlying mechanisms were explored using pharmacological inhibitors. NO production was assessed in human endothelial EA.hy926 cells. Hypotensive activity was examined in normotensive rats following intravenous administration of LSE (10, 30, and 100 mg/kg). Molecular docking was performed to analyze interactions between LSE bioactive compounds and endothelial nitric oxide synthase (eNOS). Results: LC-ESI-QTOF-MS analysis identified 114 compounds, including primary and secondary metabolites. LSE induced vasorelaxation in endothelium-intact aortas, which was reduced by endothelium removal (p < 0.001) and by L-NAME (p < 0.001). LSE also inhibited receptor-operated, Ca²⁺ channel-mediated vasoconstriction (p < 0.05). In vivo, LSE decreased blood pressure in a dose-dependent manner. In EA.hy926 cells, LSE (750 and 1000 µg/mL) increased NO production, an effect attenuated by L-NAME. Molecular docking showed that LSE alkaloids, including nelumborine, nelumboferine, neferine, and isoliensinine had strong affinities for binding with eNOS at the tetrahydrobiopterin (BH4) binding site. Nelumborine exhibited the highest affinity, suggesting its potential as an eNOS modulator. Conclusions: LSE promotes vasorelaxation through the stimulation of endothelium-derived NO release and Ca²⁺ influx inhibition, contributing to blood pressure reduction. These findings support LSE as a potential natural antihypertensive supplement. Full article

Autonomous social robots aim to reduce human supervision by performing various tasks. To achieve this, they are equipped with multiple perceptual channels to interpret and respond to environmental cues in real time. However, multimodal perception often leads to sensory overload, as robots may receive numerous simultaneous stimuli with varying durations or persistent activations across different sensory modalities. Sensor overstimulation and false positives can compromise a robot’s ability to prioritise relevant inputs, sometimes resulting in repeated or inaccurate behavioural responses that reduce the quality and coherence of the interaction. This paper presents a Bioinspired Attentional System that uses Reinforcement Learning to manage stimulus prioritisation in real time. The system draws inspiration from the following two neurocognitive mechanisms: Inhibition of Return, which progressively reduces the importance of previously attended stimuli that remain active over time, and Attentional Fatigue, which penalises stimuli of the same perception modality when they appear repeatedly or simultaneously. These mechanisms define the algorithm’s reward function to dynamically adjust the weights assigned to each stimulus, enabling the system to select the most relevant one at each moment. The system has been integrated into a social robot and tested in three representative case studies that show how it modulates sensory signals, reduces the impact of redundant inputs, and improves stimulus selection in overstimulating scenarios. Additionally, we compare the proposed method with a baseline where the robot executes expressions as soon as it receives them using a queue. The results show the system’s significant improvement in expression management, reducing the number of expressions in the queue and the delay in performing them. Full article

Climate risk, as a significant factor affecting human sustainable development, has emerged as a focal topic of concern for governments and all sectors of society. Using a dataset from China’s Shanghai and Shenzhen A-share markets spanning 2007 to 2019, this study empirically examines how climate risk influences corporate financialization. The empirical results show that heightened climate risk significantly reduces the level of corporate financialization, a finding that remains robust across multiple tests. Further heterogeneity analyses indicate that the suppressive effect of climate risk is particularly evident among state-owned enterprises, firms operating in intensely competitive industries, and those located in regions subject to more stringent environmental policies. Mechanism analysis suggests that climate risk inhibits corporate financialization primarily by intensifying firms’ financing constraints while simultaneously stimulating their innovation capacity. These findings imply that corporate financialization in China is largely driven by profit-maximizing behaviors rooted in “investment substitution” and “real-sector intermediation” motives. Collectively, this research enhances understanding of the channels through which climate risk impacts corporate financial behavior and offers valuable empirical insights for policymakers aiming to optimize climate regulations and redirect financial resources toward productive real-sector activities. Full article

This study presents the design and evaluation of a dexterous prosthetic hand featuring five fingers, ten independently actuated joints, and four passively driven joints. The hand’s dexterity is enabled by a novel rigid–flexible coupled finger mechanism that incorporates a 1-active–1-passive joint configuration, which can enhance the dexterity of traditional rigid actuators while achieving a human-like workspace. Each finger is designed with a specific degree of rotational freedom to mimic natural opening and closing motions. This study also elaborates on the mapping of eight-channel electromyography to finger grasping force through improved TCN, as well as the control algorithm for grasping flexible objects. A functional prototype of the prosthetic hand was fabricated, and a series of experiments involving adaptive grasping and handheld manipulation tasks were conducted to validate the effectiveness of the proposed mechanical structure and control strategy. The results demonstrate that the hand can stably grasp flexible objects of various shapes and sizes. This work provides a practical solution for prosthetic hand design, offering promising potential for developing lightweight, dexterous, and highly anthropomorphic robotic hands suitable for real-world applications. Full article

The voltage-gated proton channel (H_v1) selectively transports protons (H⁺) across biological membranes in response to membrane potential changes. H_v1 is assembled as a dimer, and unlike most voltage-gated ion channels, it lacks a traditional central pore domain; instead, the voltage-sensing domain (VSD) of each monomer facilitates proton conduction via a hydrogen-bond network. H_v1 is widely expressed in various human cell types (e.g., immune cells, sperm, etc.) including tumor cells. In tumor cells, the accumulation of acidic intermediates generated by glycolysis under hypoxic conditions or ROS production leads to significant cytosolic acidification. H_v1 can remove protons from the cytosol rapidly, contributing to the adaptation of the cells to the tumor microenvironment, which may have significant consequences in tumor cell survival, proliferation, and progression. Therefore, H_v1 may be very promising not only as a tumor marker but also as a potential therapeutic target in oncology. Molecules that modulate the proton flux through H_v1 can be divided into two broad groups: inhibitors and activators. H_v1 inhibitors can be simple ions, small molecules, lipids, and peptides. In contrast, fewer H_v1 activators are known, including albumin, NH29, quercetin, and arachidonic acid. The mechanism of action of some inhibitors is well described, but not all. H_v1 modulation has profound effects on cellular physiology, especially under stress or pathological conditions, like cancer and inflammation. The therapeutic application of selective H_v1 inhibitors or activators could be a very promising strategy in the treatment of several serious diseases. Full article

Background: Epilepsy is a high-burden neurological disorder worldwide, and several sedative drugs are used as therapy. Topiramate is among the more recent drugs shown to be effective in some patients, although its benefits are limited. Two carbohydrate derivatives, FB1 (from D-fructose) and AB1 (from D-arabinose), as well as phenylboronic acid, were recently reported as sedative and safe agents in mice. Their sedative properties and structural similarity to topiramate suggest potential antiseizure activity. Objective: The objective of this study was to evaluate the antiseizure potential of FB1 and AB1. Methods: Boron-containing compounds were administered to mice with seizures induced by pentylenetetrazol (a GABA-A receptor antagonist), 4-aminopyridine (a non-selective K⁺ channel blocker), or pilocarpine (a muscarinic agonist) to assess efficacy across models and explore potential mechanisms of action. Neuronal and glial toxicity was evaluated both in vitro and in vivo. Results: AB1 reduced seizure activity after intraperitoneal administration, whereas FB1 did not exhibit anticonvulsant effects, although it modified motor performance and limited neuronal loss. The effect of AB1 was comparable to that of topiramate across all three seizure models. Docking studies suggested that these compounds can interact with GABA-A (chloride), NMDA (glutamate), calcium, and potassium channels. Toxicity assays indicated that the concentrations required to affect neurons or glial cells were ≥300 µM, supporting the safety of these compounds. Conclusions: This preliminary evaluation demonstrates the antiseizure potential of AB1. Further experimental studies are needed to clearly establish its mechanism(s) of action. Full article

Driven by China’s “dual carbon” strategy, concerns about channel fairness and green investment have become key frontier issues in supply chain management. This study focuses on a two-tier supply chain under a low-carbon background and innovatively incorporates both fairness concerns and green investment perspectives. It systematically explores the impact mechanisms of fairness concern coefficients and green investment levels on channel pricing and profit distribution across four scenarios: information symmetry vs. asymmetry and the presence vs. absence of channel encroachment. The simulation results reveal the following: (1) Under information symmetry and without channel encroachment, an increase in the retailer’s fairness concern significantly enhances its bargaining power and profit margin, while the supplier actively adjusts the wholesale price to maintain cooperation stability. (2) Channel encroachment and changes in information structure intensify the nonlinearity and complexity of profit distribution. The marginal benefit of green investment for supply chain members shows a diminishing return, indicating the existence of an optimal investment range. (3) The green premium is predominantly captured by the supplier, while the retailer’s profit margin tends to be compressed, and order quantity exhibits rigidity in response to green investment. (4) The synergy between fairness concerns and green investment drives dynamic adjustments in channel strategies and the overall profit structure of the supply chain. This study not only reveals new equilibrium patterns under the interaction of multidimensional behavioral factors but also provides theoretical support for achieving both economic efficiency and sustainable development goals in supply chains. Based on these findings, it is recommended that managers optimize fairness incentives and green benefit-sharing mechanisms, improve information-sharing platforms, and promote collaborative upgrading of green supply chains to better integrate social responsibility with business performance. Full article

This study examines the mechanical, thermal, and electrical properties of copper tool electrodes processed via Equal Channel Angular Pressing (ECAP), with a specific focus on their performance in Electrical Discharge Machining (EDM) applications. A novel Crystal Plasticity Finite Element Method (CPFEM) framework is employed to model anisotropic slip behavior and microscale deformation mechanisms. The primary objective is to elucidate how initial crystallographic orientation influences hardness, thermal conductivity, and electrical conductivity. Simulations are performed on single-crystal copper for three representative Face Centered Cubic (FCC) orientations. Using an explicit CPFEM model, the study examines texture evolution and deformation heterogeneity during the ECAP process of single-crystal copper. The results indicate that the <100> single-crystal orientation exhibits the highest Taylor factor and the most homogeneous distribution of plastic equivalent strain (PEEQ), suggesting enhanced resistance to plastic flow. In contrast, the <111> single-crystal orientation displays localized deformation and reduced hardening. A decreasing Taylor factor correlates with more uniform slip, which improves both electrical and thermal conductivity, as well as machinability, by minimizing dislocation-related resistance. These findings make a novel contribution to the field by highlighting the critical role of crystallographic orientation in governing slip activity and deformation pathways, which directly impact thermal wear resistance and the fabrication efficiency of ECAP-processed copper electrodes in EDM. Full article

Early prediction of weaning outcomes in mechanically ventilated patients has significant potential to influence the duration of treatment as well as associated morbidity and mortality. This study aimed to investigate the utility of signal analysis using electromyographic diaphragm (EMG) and electrocardiography (ECG) signals to classify the success or failure of weaning in mechanically ventilated patients. Electromyographic signals of 40 subjects were recorded using 5-channel surface electrodes placed around the diaphragm muscle, along with an ECG recording through a 3-lead Holter system during extubation. EMG and ECG signals were recorded from mechanically ventilated patients undergoing weaning trials. Linear and nonlinear signal analysis techniques were used to assess the interaction between diaphragm muscle activity and cardiac activity. Supervised machine learning algorithms were then used to classify the weaning outcomes. The study revealed clear differences in diaphragmatic and cardiac patterns between patients who succeeded and failed in the weaning trials. Successful weaning was characterised by a higher ECG-derived respiration amplitude, whereas failed weaning was characterised by an elevated EMG amplitude. Furthermore, successful weaning exhibited greater oscillations in diaphragmatic muscle activity. Spectral analysis and parameter extraction identified 320 parameters, of which 43 were significant predictors of weaning outcomes. Using seven of these parameters, the Naive Bayes classifier demonstrated high accuracy in classifying weaning outcomes. Surface electromyographic and electrocardiographic signal analyses can predict weaning outcomes in mechanically ventilated patients. This approach could facilitate the early identification of patients at risk of weaning failure, allowing for improved clinical management. Full article

Deploying deep learning (DL) models in real-world environments remains a major challenge, particularly under resource-constrained conditions where achieving both high accuracy and compact architectures is essential. While effective, Conventional pruning methods often suffer from high computational overhead, accuracy degradation, or disruption of the end-to-end training process, limiting their practicality for embedded and real-time applications. We present Dynamic Attention-Guided Pruning (DAGP), a Dynamic Attention-Guided Soft Channel Pruning framework that overcomes these limitations by embedding learnable, differentiable pruning masks directly within convolutional neural networks (CNNs). These masks act as implicit attention mechanisms, adaptively suppressing non-informative channels during training. A progressively scheduled L1 regularization, activated after a warm-up phase, enables gradual sparsity while preserving early learning capacity. Unlike prior methods, DAGP is retraining-free, introduces minimal architectural overhead, and supports optional hard pruning for deployment efficiency. Joint optimization of classification and sparsity objectives ensures stable convergence and task-adaptive channel selection. Experiments on CIFAR-10 (VGG16, ResNet56) and PlantVillage (custom CNN) achieve up to 98.82% FLOPs reduction with accuracy gains over baselines. Real-world validation on an enhanced PlantDoc dataset for agricultural monitoring achieves 60 ms inference with only 2.00 MB RAM on a Raspberry Pi 4, confirming efficiency under field conditions. These results illustrate DAGP’s potential to scale beyond agriculture to diverse edge-intelligent systems requiring lightweight, accurate, and deployable models. Full article

Mitochondria are crucial for a wide range of cellular processes. One of the most important is innate immunity regulation. Apart from functioning as a signaling hub in immune reactions, mitochondrial nucleic acids can themselves act as damage-associated molecular patterns (DAMPs) to participate in immune processes directly. This review synthesizes the current understanding of mitochondrial RNA (mtRNA) biology and its link to immune activation through aberrant accumulation. We focus on its origin through bidirectional mitochondrial transcription and metabolism, encompassing maturation (cleavage, polyadenylation, modification) and degradation. Dysregulation of mtRNA metabolism leads to mt-dsRNA (mitochondrial double-stranded RNA) accumulation, which escapes mitochondria via specific channels into the cytosol and serves as DAMPs to trigger an immune response. We discuss the critical roles of key regulatory factors, including PNPT1 (PNPase, Polyribonucleotide Nucleotidyltrans ferase 1), in controlling mt-dsRNA levels and preventing inappropriate immune activation. Finally, we review the implications of mt-dsRNA-driven inflammation in human diseases, including autoimmune disorders, cellular senescence, and viral infection pathologies, highlighting unresolved questions regarding mt-dsRNA release mechanisms. Full article

P2X receptors are a family of ATP-gated ion channels widely distributed in various tissues, especially in neuronal cells and hematopoietic cells. ATP activates P2X receptors, causing the opening of an ionic channel with preferential permeability to the passage of mono- and divalent cations. High concentrations of ATP stimulate the P2X7 subtype through prolonged activation, which opens pores and causes inflammation, proalgesic effects, and cell death. Peptides, including antimicrobials (antimicrobial peptides), are present in several organisms, such as amphibians, mammals, fish, arachnids, and plants, where they act as the first line of defense. Thus, these peptides have the capacity to eliminate a wide spectrum of microorganisms, such as bacteria, fungi, and some viruses. In general, the mechanism of action of antimicrobial peptides involves interactions with the lipid bilayer of the cell membrane, which can lead to an increase in the internal liquid content of liposomes. However, many peptides can act on ion channels, such as those of the P2X family, especially the P2X7 receptor. We investigated the action of peptides that directly modulate P2X7 receptors, such as beta-amyloid, LL-37/hCap18, Pep19-2.5, rCRAMP, ADESG, and polymyxin B. Additionally, we evaluated peptides that modulate the activity of P2X family receptor subtypes. In this review, we intend to describe the relationships between peptides with distinct characteristics and how they modulate the functionality of P2X receptors. Full article

Sonochlorination (US/chlorine) is an emerging sonohybrid advanced oxidation process whose performance reportedly surpasses that of its individual components. However, the underlying oxidant budget is still being debated. We mapped the mechanism by systematically probing the US/chlorine system with selective scavengers (ascorbic acid, nitrobenzene, tert-butanol, 2-propanol, and phenol), competing anions (nitrite), and natural organic matter (humic acid). The kinetic hierarchy US/chlorine > US > chlorine remained consistent across all conditions, though its magnitude depended heavily on the matrix composition. Efficient ^•OH traps, such as alcohols and nitrobenzene, only partially suppressed the US/chlorine system. However, they greatly slowed sonolysis. This reveals a substantial non-^•OH channel in the hybrid process. Ascorbic acid eliminated synergy by stoichiometrically removing free chlorine. Phenol quenched HOCl and chlorine-centered radicals. Nitrite imposed a dual penalty by scavenging ^•OH and consuming HOCl via the nitryl chloride (ClNO₂) pathway. Humic acid acted as a three-way sink for ^•OH, HOCl, and chlorine radicals. These patterns suggest that reactivity is co-controlled by Cl^•, Cl₂^•−, and ClO^•. The results obtained are mechanistically consistent with cavitation-assisted activation of HOCl/OCl⁻ at pH 5–6, where HOCl concentration is maximal. This yields a mixed oxidant suite in which Cl₂^•− is the dominant bulk oxidant, Cl^• provides fast interfacial initiation, and ClO^• offers selective support. Full article

Cardiac arrhythmia is a leading cause of sudden cardiac death. Its early detection and continuous monitoring hold significant clinical value. Photoplethysmography (PPG) signals, owing to their non-invasive nature, low cost, and convenience, have become a vital information source for monitoring cardiac activity and vascular health. However, the inherent non-stationarity of PPG signals and significant inter-individual variations pose a major challenge in developing highly accurate and efficient arrhythmia classification methods. To address this challenge, we propose a Fusion Deep Multi-domain Attention Network (Fusion-DMA-Net). Within this framework, we innovatively introduce a cross-scale residual attention structure to comprehensively capture discriminative features in both the time and frequency domains. Additionally, to exploit complementary information embedded in PPG signals across these domains, we develop a fusion strategy integrating interactive attention, self-attention, and gating mechanisms. The proposed Fusion-DMA-Net model is evaluated for classifying four major types of cardiac arrhythmias. Experimental results demonstrate its outstanding classification performance, achieving an overall accuracy of 99.05%, precision of 99.06%, and an F1-score of 99.04%. These results demonstrate the feasibility of the Fusion-DMA-Net model in classifying four types of cardiac arrhythmias using single-channel PPG signals, thereby contributing to the early diagnosis and treatment of cardiovascular diseases and supporting the development of future wearable health technologies. Full article