Search Results (100)

Remote tele-rehabilitation requires objective pain assessment, but existing approaches fail in two distinct ways. Self-report scales such as the Visual Analog Scale and the Numeric Pain Rating Scale are easy to falsify, opening a special case of the Oracle problem in blockchain-based insurance. Cloud-based computer vision handles falsification but transmits raw biometric video off the patient’s device, violating privacy requirements. A decentralized Edge AI-Oracle architecture is proposed that combines MediaPipe Face Mesh landmark extraction with a recurrent classifier mapping Action-Unit feature sequences to a learned pain score aligned with the Prkachin and Solomon Pain Intensity scale. The recurrent cell is selected empirically across short-context (T = 2) and long-context (T = 120 frames at 24 fps) regimes, with a two-layer Long Short-Term Memory (LSTM) network adopted for deployment. Inference and Elliptic Curve Digital Signature Algorithm (ECDSA) signing run inside an ARM TrustZone Trusted Execution Environment (TEE). Biometric logs are stored off-chain on the InterPlanetary File System (IPFS). Smart contracts anchor results on-chain and open a 24 h optimistic verification window for an off-chain Watchtower auditor. On SynPAIN the LSTM reaches F1 = 0.683 on T = 120 video (leave-one-stratum-out), with a directional but non-significant advantage over Gated Recurrent Unit (GRU) (Wilcoxon p = 0.167). Cross-dataset validation on BioVid Heat Pain Database Part A (87 subjects, 174 paired observations, leave-one-subject-out) yields F1 = 0.519 for LSTM and 0.499 for GRU (Wilcoxon p = 0.549). A processor-only TEE surrogate benchmark estimates 1.96 ms (FP32) and 0.45 ms (INT8) inference latency at T = 120 with a 0.34 MB footprint and 707 µs ECDSA signing latency, leaving the INT8 inference latency more than an order of magnitude below the 33 ms per-frame budget. The dual-layer storage reduces gas costs by a factor of 23.4 (160,261 vs. 3,744,872 gas), corresponding to an illustrative mainnet cost of approximately 0.53 USD per submission at 1 gwei, rising to roughly 16 USD at a busier 30 gwei, and falling to approximately 0.005 USD on Arbitrum One (April 2026 reference parameters), so that continuous monitoring is economically practical on Layer-2. An adaptive-adversary analysis of the Watchtower shows that gross score tampering is detected at every usable operating threshold, whereas a rational adversary who inflates by less than the dispute threshold, or who shapes the injected score to fall just inside it, evades detection. Because the false-positive rate reaches zero only for $\mathsf{\delta }\gtrsim 0.15$, the protocol bounds rather than eliminates patient-side fraud and motivates a zero-knowledge proof-of-inference successor. The framework is architecturally and economically feasible as a cryptographically verifiable, privacy-preserving tele-rehabilitation substrate aligned with General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) requirements through the Zero-Video Transmission principle, while remaining economically viable under post-Dencun mainnet and Layer-2 conditions. Recognition accuracy on real-world data and robustness to small-magnitude tampering remain limitations that the interchangeable recognition and audit components must improve before clinical deployment. Full article

The uplink/downlink (UL/DL) decoupled access, which allows users to associate with different base stations (BSs), including small BSs (SBSs) and macro BSs (MBSs), has emerged as a network architecture in heterogeneous cellular vehicle-to-everything (C-V2X) communications. It can be tailored to mitigate the signal interference and attenuation impairments that cell-edge vehicles face, while vehicles closer to a BS can opt for coupled access. Therefore, a UL/DL intelligent decoupled access network that integrates decoupled and coupled access approaches is urgently needed for C-V2X communications. In this paper, we present a novel framework for UL/DL intelligent decoupled access in C-V2X networks in the context of fifth-generation mobile communications (5G) and beyond 5G (B5G). We propose a joint optimization approach for radio resource allocation, power control, and user association to enhance the network throughput of UL and DL while meeting the service quality requirements of vehicle users. Specifically, we formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and transform it into a standard convex optimization problem by introducing various auxiliary variables. An efficient iterative algorithm based on successive convex optimization techniques is introduced to obtain a sub-optimal solution. The proposed framework uniquely integrates decoupled and coupled access modes within a unified optimization formulation, enabling dynamic mode selection based on network load. Extensive simulation results demonstrate a significant performance improvement of the proposed UL/DL intelligent decoupled access in C-V2X networks compared with benchmark schemes. Full article

Infestation by boring sponges poses a serious problem for Pacific oyster Magallana gigas (Thunberg, 1793) aquaculture. This study aimed to assess the effect of Pione vastifica sponge infestation on the oysters’ capacity for shell repair, antioxidant defence status, and hemocyte functional state. We analysed the expression of VEGF pathway genes and biomineralisation enzymes, molecular chaperones (Hsp70, Hsp90), growth arrest and DNA damage gene (Gadd45α), antioxidant enzyme activity and lipid peroxidation levels in the hemolymph and various mantle parts (central and outer-edge). Intracellular reactive oxygen species (ROS) levels and mitochondrial membrane potential in hemocytes were evaluated. The results showed that infection significantly increases intracellular ROS levels in hemocytes without changing mitochondrial membrane potential. Oxidative damage was localised primarily in the central mantle contacting the damaged shell. In the outer-edge mantle responsible for shell growth, marked upregulation of SodMn, Cat, and Gadd45α was observed, coupled with suppression of VEGF-R receptor expression and organic matrix genes. Heat shock protein expression decreased in all examined tissues of infected molluscs. Our results demonstrate that shell damage induced by boring sponges triggers a tissue-specific reorganisation of physiological priorities, manifesting as a bioenergetic trade-off where limited energy resources are reallocated from the ATP-demanding process of biomineralisation to sustain antioxidant defence and cell survival. Full article

Corner detection is a fundamental task in computer vision that plays a critical role in applications such as image registration, 3D reconstruction, and object tracking. In biological visual systems, simple cells in the primary visual cortex exhibit high selectivity to edge stimuli of specific orientations, while end-stopped cells can detect geometric singular structures such as line segment endpoints and corners. Existing corner detection methods based on visual neural computation typically employ a strategy of densely distributed end-stopped cells for corner localization, which suffers from significant localization deviation under small angle conditions due to mutual interference between responses of adjacent neurons. To address this problem, this paper proposes a bionic corner detection method based on cooperative processing of simple cells and end-stopped cells. The method constructs a two-stage cooperative processing framework: the edge filtering stage employs a Gabor filter bank to simulate the orientation selectivity of simple cells, extracting edge positions and orientation information; the dynamic construction stage builds unilateral end-stopped cells only at filtered edge positions based on local orientation information, fundamentally avoiding computational redundancy and response interference caused by global dense distribution; the corner localization stage determines precise corner coordinates through hierarchical clustering and dual-cluster centroid fusion strategies. Experimental results demonstrate that, in the 15° acute-angle regime where dense end-stopped schemes are most severely affected by response interference, the proposed method reduces the mean localization error from 8.76 to 2.34 pixels, corresponding to a 73.3% improvement; averaged across the eight tested angle levels from 15° to 165°, the improvement is approximately 40.9%, and all per-angle differences are statistically significant (paired t-test, p < 0.01 or below, N = 10 independent runs). On standard test images, the method attains the lowest mean localization error among the eight compared detectors (1.58 pixels, versus 1.68–3.42 pixels for Harris, FAST, COSFIRE, KAZE, SuperPoint, Deep Corner, and Wei et al.), while maintaining competitive detection rate, false-alarm rate, and runtime. Physiological plausibility validation experiments show that the correlation coefficient between the detection deviation of this method and human perceptual deviation reaches 0.923, indicating that the output of the framework aligns with previously reported human perceptual bias patterns and supporting its biological plausibility as a biologically inspired—rather than mechanistic—model of corner perception. The source code, dataset, and experimental results are publicly available (see Data Availability Statement). Full article

This study proposes an end-to-end mathematical framework to automatically transform warehouse layout images into optimization-ready route matrices. The objective is to convert visual spatial information into a discrete, graph-based representation suitable for combinatorial route optimization. The problem is formulated as a mapping from continuous image space to a structured grid representation, integrating image segmentation, graph construction, and Traveling Salesman Problem (TSP)-based routing. Synthetic warehouse layouts were generated to create labeled training data, and a U-Net convolutional neural network was trained to perform multi-class segmentation of warehouse elements. The predicted grid representation was then converted into a graph structure, where feasible cells define vertices and adjacency defines edges. Shortest path distances were computed using Breadth-First Search, and the resulting distance matrix was used to solve a TSP instance. The segmentation model achieved approximately 98% training accuracy and 95–97% validation accuracy. The generated route matrices enabled successful construction of feasible and optimal round-trip routes in all tested scenarios. The proposed framework demonstrates that warehouse layouts can be automatically transformed into discrete mathematical representations suitable for logistics optimization, reducing manual preprocessing and enabling scalable integration into digital logistics systems. Full article

With the aid of non-orthogonal multiple access (NOMA), this paper investigates simultaneous two-way communications for cooperative cognitive radio networks, where a group of secondary access points (APs) scattered over a primary cell not only serve their own users but also help the primary cell-edge users′ transmissions cooperatively. As a reward for the cooperation, these APs are granted full access to the primary frequency spectrum. To coordinate the two-way transmissions of the primary and secondary networks, we propose a spectrum-efficient cooperative scheme that only involves two transmission phases, and particularly, the two variable-length transmission phases endow the system with the capability of adapting to possible DL and UL traffic asymmetry. For the system design, we formulate a power minimization problem subject to the bidirectional transmission rate constraints of both networks. The formulated problem is shown to be nonlinear and nonconvex, and for the numerically efficient solution, we propose an iterative algorithm facilitated by the successive convex approximation technique. Simulation results show that the proposed design algorithm has fast convergence speed and is superior to the hybrid orthogonal multiple access and NOMA schemes. Full article

Unmanned aerial vehicles (UAVs) are playing increasingly important roles in military operations, disaster relief, agriculture, and communications. However, their performance is limited by energy management problems, especially in hybrid systems such as those combining fuel cells with a lithium battery. The potential of deep learning to significantly improve UAV power management is investigated in this work through adaptive forecasting and real-time optimization. We develop smart algorithms that automatically balance energy efficiency and communication performance for heterogeneous wireless networks. The simulation results demonstrate energy consumption savings, optimized flight altitudes, and spectral efficiency improvements compared to Fixed Weight and Fuzzy Logic Weight schemes. At saturated user densities, the model enables up to 42% lower energy consumption and 54% higher throughput. Moreover, predictive models based on recurrent and transformer-based deep networks allow UAVs to predict energy requirements over a variety of mission and environmental contexts, shifting from reactive approaches to proactive control. The adoption of these methods in UAV-aided beyond-5G (B5G) and future 6G network scenarios can potentially prolong endurance times and enhance mission connectivity and reliability in challenging environments. This work lays the foundation for an all-aspect framework to control and manage UAV energy in the 5G era, which takes advantage of not only deep learning but also edge computing and hybrid power systems. Deep learning is confirmed to be a keystone of sustainable, autonomous, and energy-aware UAVs operation for next-generation networks. Full article

Post-translational modifications (PTMs) are crucial chemical alterations occurring on proteins post-synthesis, impacting various cellular processes. During viral infections, PTMs are shown to play a multitude of roles in viral replication, host interaction, and immune evasion. Thus, these modifications can influence infectivity, with direct impact on the anti-viral host immune responses and potentially viral adaptation across species. This field is still scarcely explored, whilst understanding PTMs is not only important to advance the knowledge of virus pathology but also potentially to provide insights for vaccine development. In this review, we attempt to summarize the latest findings mainly published over the last 10 years, focusing on the roles of PTMs involved in virus infection and anti-viral immune responses, in the context of relevant human respiratory infections: influenza A virus (IAV), respiratory syncytial virus (RSV), and SARS-CoV-2. We decided to concentrate on these three viruses because they currently represent a global health problem due to recurrent outbreaks and pandemic potential. A deeper characterization of the PTMs may help in understanding virus–host interaction with possible implications on curative strategies. Further, we will report on cutting-edge technologies to study in vitro virus infection in different cellular-based systems. In particular, we describe and discuss the application of 2D and 3D lung organoid cell-culture systems as in vitro models to mimic respiratory environments and to study the PTMs in a controlled setting. Finally, we will discuss the importance of PTMs in the context of next-generation vaccine design, especially for their potential role to offer effective protection against respiratory viruses. Full article

Corneal problems, such as delayed and incomplete wound repair, are frequent in diabetes, affecting up to 70% of diabetic patients. In skin, histone deacetylases (HDACs) have been previously found to repress expression of the glycerol channel aquaporin-3 (AQP3), the deficiency of which delays corneal wound healing. We hypothesized that the pan-HDAC inhibitor suberoylanilide hydroxamic acid (SAHA) would improve corneal healing in diabetic mice. Diabetic and normoglycemic C57BL/6J male and female mice were subjected to corneal debridement. Wounds were treated topically with vehicle or SAHA every four hours until they healed. Treatment with SAHA improved wound healing in both normoglycemic and hyperglycemic male mice but, unexpectedly, no changes were detected in female mice. In male mice interleukin-1beta (IL-1β) and tumor necrosis factor (TNF) were significantly increased in diabetic corneas, and SAHA reduced their expression, returning IL-1β and TNF to levels comparable to those in normoglycemic mice regardless of treatment. In normoglycemic male mice, AQP3 levels were not changed in the cornea with SAHA treatment but the expression of AQP3 was increased in the wound’s edge relative to the rest of the cornea. In vitro SAHA treatment of human corneal epithelial cells (HCECs) significantly increased protein expression of AQP3, important for corneal wound healing, but had no effect on ROS production. In conclusion, treatment with SAHA improved corneal wound healing, not only in male mice with diabetes and delayed wound healing but also in normoglycemic male mice; therefore, SAHA could potentially be repurposed as a topical treatment clinically to improve corneal wound healing. Full article

With the rapid development of edge-intelligence systems, multi-UAV platforms have become vital for large-scale 3D reconstruction. However, efficient task scheduling remains a critical challenge due to constraints on UAV energy, communication range, and the need for balanced workload distribution. To address these issues, this paper presents a novel, centralized two-stage task scheduling framework. In the first stage, the framework partitions the target area into communication-feasible subregions by applying cell decomposition that accounts for no-fly zones and workload. It then models the subregion allocation as a Capacitated Vehicle Routing Problem (CVRP) with an added balancing constraint to optimize the traversal sequence for each operational sortie. In the second stage, a time-efficient, scan-based heuristic algorithm allocates viewpoints among UAVs to ensure workload balance, minimizing the mission completion time. Extensive simulations demonstrate that our proposed approach achieves superior performance in workload balance, path efficiency, and reconstruction quality. Overall, this work provides a scalable and energy-aware solution for centralized multi-UAV 3D reconstruction, highlighting an effective approach to ensure cooperation and efficiency in complex multi-agent systems. Full article

Terpenoids, as a class of natural products with extensive biological activities, hold broad application prospects in the fields of medicine, food, materials, and energy, with the global market scale projected to reach USD 10 billion by 2030. Traditional chemical synthesis and plant extraction methods rely on petroleum and plant resources, suffering from problems such as environmental pollution, cumbersome procedures, low yields from plant sources, enantioselectivity, geographical constraints, and competition for resources. Biocatalytic conversion of biomass feedstocks via microbial cell factories serves as an environmentally friendly alternative for the synthesis of terpenoids, but current production mostly depends on starch-based glucose, which triggers issues of food security and competition for arable land and water resources. This review focuses on the biocatalytic conversion of non-food alternative carbon sources (namely lignocellulose, acetate, glycerol, and waste oils) in the microbial synthesis of terpenoids, systematically summarizing the current research status and cutting-edge advances. These carbon sources exhibit potential for sustainable production due to their low cost, wide availability, and ability to reduce resource competition, but they also face significant technical bottlenecks. We systematically analyze the current problems in the biocatalytic conversion process and put forward some available solutions. It is hoped that this study will provide theoretical and technical suggestions for breaking through the bottlenecks in the biocatalytic conversion of non-food carbon sources and promoting the efficient and sustainable production of terpenoids. Full article

Automatic detection of clue cells is crucial for rapid diagnosis of bacterial vaginosis (BV), but existing algorithms suffer from low sensitivity. This is because clue cells are highly similar to normal epithelial cells in terms of macroscopic size and shape. The key difference between clue cells and normal epithelial cells lies in the surface texture and edge morphology. To address this specific problem, we propose an clue cell detection algorithm named CA-YOLO. The contributions of our approach lie in two synergistic and custom-designed feature extraction modules: the context-aware module (CAM) extracts and captures bacterial distribution patterns on the surface of clue cells; and the shuffle global attention mechanism (SGAM) enhances cell edge features and suppresses irrelevant information. In addition, we integrate focal loss into the classification loss to alleviate the severe class imbalance problem inherent in clinical samples. Experimental results show that the proposed CA-YOLO achieves a sensitivity of 0.778, which is 9.2% higher than the baseline model, making the automated BV detection more reliable and feasible. Full article

Cerebral malaria is a life-threatening neurological complication of Plasmodium falciparum infection and a leading cause of pediatric mortality in endemic regions of sub-Saharan Africa. It is defined clinically by coma accompanied by peripheral parasitemia, without alternative causes. Pathogenetically, cytoadherence of parasitized erythrocytes in the cerebral microvasculature, together with a widespread inflammatory response and endothelial activation, causes profound microvascular injury. This injury includes disruption of the blood–brain barrier and the development of multifactorial cerebral oedema (both vasogenic and cytotoxic), resulting in elevated intracranial pressure and often diffuse brain swelling as seen on imaging in fatal cases. Recent high-resolution MRI studies in pediatric cohorts from these endemic regions have identified characteristic neuroimaging findings such as basal ganglia infarcts, brainstem lesions, and corpus callosum abnormalities that strongly predict poor outcomes. Notably, circulating extracellular vesicles—released by parasitized erythrocytes and activated endothelial cells have emerged as potent mediators of microvascular inflammation. Extracellular vesicles contain parasite-derived antigens and host inflammatory signals, implicating them in disease mechanisms. These vesicles are under investigation as novel diagnostic and prognostic biomarkers for severe malaria. Importantly, survivors of cerebral malaria often endure persistent neurocognitive impairments, behavioral problems, and epilepsy, underscoring the need to prevent secondary neuronal injury during the acute phase to reduce long-term disability. Taken together, these insights highlight the interplay between cerebral microvascular pathology and neurological outcome in cerebral malaria. This review synthesizes recent advances in the pathophysiology of cerebral malaria and cutting-edge diagnostic modalities. It highlights novel therapeutic targets and neuroprotective strategies that may enable precision medicine approaches aimed at preventing lasting neurological disability in survivors. Full article

The rapid development of intelligent high-speed railways (HSRs) has significantly improved the transportation efficiency of modern transit systems, while also imposing higher bandwidth demands on mobile communication systems. Free-space optical (FSO) communication technology, as a promising solution, can effectively meet the high-speed data transmission requirements in intelligent HSR scenarios. In this paper, we consider an intelligent HSR system based on beamwidth-adaptive FSO communication and investigate the coverage performance of the system. Different from the circular cells used in traditional radio frequency wireless communication systems, this paper focuses on the coverage problem of narrow-strip-shaped cells in HSR systems based on FSO communication. When the transmitter emits a wide beam, the channel gain includes geometric loss, atmospheric attenuation, and atmospheric turbulence. When the transmitter emits a narrow beam, the channel gain includes pointing error, atmospheric attenuation, and atmospheric turbulence. To adapt the width of the transmitter’s beam, we propose a beamwidth-adaptive HSR system and a beamwidth-adaptive method. Furthermore, we derive closed-form expressions of the edge coverage probability (ECP) and the percentage of cell coverage area (CCA), where the ECP is the probability that the received signal-to-noise ratio at the cell edge is greater than or equal to a given threshold, and the percentage of CCA dictates the percentage of locations within a cell that are not in outage. The accuracy of the derived theoretical expressions is validated through Monte-Carlo simulations. The average relative error of the ECP between theoretical and simulation results is only 0.035%, and the corresponding error of the percentage of CCA is 0.087%. In addition, the impacts of factors such as cell diameter, transmission power, signal-to-noise ratio threshold, and weather visibility on coverage performance are also discussed. Full article

Automated generative design is increasingly used across engineering disciplines to accelerate innovation and reduce costs. Generative design offers the prospect of simplifying manual design tasks by exploring the efficacy of solutions automatically. However, existing generative design frameworks rely heavily on expensive optimisation procedures and often produce customised solutions, lacking reusable generative rules that transfer across different problems. This work presents a bioinspired generative design algorithm utilising the concept of evolutionary development (EvoDevo). This evolves a set of developmental rules that can be applied to different engineering problems to rapidly develop designs without the need to run full optimisation procedures. In this approach, an initial design is decomposed into simple entities called cells, which independently control their local growth over a development cycle. In biology, the growth of cells is governed by a gene regulatory network (GRN), but there is no single widely accepted model for this in artificial systems. The GRN responds to the state of the cell induced by external stimuli in its environment, which, in this application, is the loading regime on a bridge truss structure (but can be generalised to any engineering structure). Two GRN models are investigated: graph neural network (GNN) and graph-based Cartesian genetic programming (CGP) models. Both GRN models are evolved using a novel genetic search algorithm for parameter search, which can be re-used for other design problems. It is revealed that the CGP-based method produces results similar to those obtained using the GNN-based methods while offering more interpretability. In this work, it is shown that this EvoDevo approach is able to produce near-optimal truss structures via growth mechanisms such as moving vertices or changing edge features. The technique can be set up to provide design automation for a range of engineering design tasks. Full article