Search Results (259)

Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030. Full article

When new purchasers or products are added in the supply chain management system, the recommendation system will face severe challenges of data sparsity and cold start. A knowledge graph that can enrich the representations of both procurement managers and products offers a promising pathway to mitigate the challenges. This paper proposes a knowledge-aware recommendation network for supply chain management, called KnoChain. The proposed model refines purchaser representations through outward propagation along knowledge graph links and enhances product representations via inward aggregation of multi-hop neighbourhood information. This dual approach enables the simultaneous discovery of purchasers’ latent preferences and products’ underlying characteristics, facilitating precise and personalised recommendations. Extensive experiments on three real-world datasets demonstrate that the proposed method consistently outperforms several state-of-the-art baselines, achieving average AUC improvements of 9.36%, 5.91%, and 8.81%, and average accuracy gains of 8.56%, 6.27%, and 8.67% on the movie, book, and music datasets, respectively. These results underscore the model’s potential to enhance recommendation robustness in supply chain management. The KnoChain framework proposed in this article combines purchaser-aware attention with knowledge graphs to improve the accuracy of purchaser SKU matching. The method can help enhance supply chain resilience and reduce returns caused by over-ordering, inventory backlog, and incorrect procurement. In addition, the model provides interpretable recommendation paths based on the knowledge graph, which improves trust and auditability for procurement personnel and helps balance environmental and operational costs. Full article

Influence maximization is a crucial research domain in social network analysis, playing a vital role in optimizing information dissemination and managing online public opinion. Traditional IM models focus on network topology, often overlooking user heterogeneity and server-driven propagation dynamics, which often leads to limited model adaptability. To overcome these shortcomings, this study proposes the “Social–Interest Hybrid Influence Maximization” (SIHIM) problem, which explicitly models the joint influence of social topology and user interest in server-mediated propagation, aiming to enhance the effectiveness of information propagation by integrating users’ social relationships and interest preferences. To model this problem, we develop a Server-Based Independent Cascading (SB-IC) model that captures the dynamics of influence propagation. Based on this model, we further propose a novel hybrid centrality algorithm named Pascal Centrality (PaC), which integrates both topological and interest-based attributes to efficiently identify key seed nodes while minimizing influence overlap. Experimental evaluations on ten real-world social network datasets demonstrate that PaC improves influence spread by 5.22% under the standard IC model and by 7.04% under the SB-IC model, outperforming nine state-of-the-art algorithms. These findings underscore the effectiveness and adaptability of the proposed algorithm in complex scenarios. Full article

Course recommendation is a critical service in Intelligent Tutoring Systems (ITS) that helps learners discover relevant courses from massive online educational platforms. Despite substantial progress in this field, two key challenges remain unresolved: (1) existing methods fail to leverage the differences in learners’ interests across different courses during knowledge propagation processes, and (2) while sequential relationships have been considered in course recommendations, there is still significant room for improvement in effectively integrating sequential patterns with knowledge-graph-based approaches. To overcome these limitations, we propose PGDB (Preference-aware Graph Diffusion network and Bi-LSTM), an innovative end-to-end framework for course recommendation. Our model consists of four key components: First, a course knowledge graph diffusion module recursively collects multiple knowledge triples related to learners to construct their knowledge background. Second, a preference-aware diffusion attention mechanism analyzes learners’ preferences for courses and relational paths using multi-head attention, effectively distinguishing semantic diversity across different contexts and capturing varying learner interests during knowledge transmission. Third, a temporal sequence modeling module utilizes bidirectional long short-term memory networks to identify learners’ interest evolution patterns, generating learner-dependent representations that efficiently leverage sequential relationships between courses. Finally, a prediction module combines the final representations of learners and courses to output selection probabilities for candidate courses. Extensive experimental results demonstrate that PGDB significantly outperforms state-of-the-art baseline models across multiple evaluation metrics, validating the effectiveness of our approach in addressing data sparsity and sequential modeling challenges in course recommendation systems. Full article

Background: Interleukin-1 (IL-1) inhibitors are approved for the treatment of various inflammatory diseases associated with immune system abnormalities. However, large-scale real-world studies to assess their security are still limited. Therefore, a pharmacovigilance study was conducted based on the data from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Methods: Adverse events (AEs) linked to IL-1 inhibitors were analyzed using the FAERS database from Q1 2004 to Q3 2024. Risk signals were identified through disproportionality analysis algorithms, including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS). Results: Among 17,670 AE reports where an IL-1 inhibitor was the “primary suspected” drug, 27 significant system organ classes (SOCs) were identified. Notable signals included infections and infestations (ROR: 2.31, 95% CI: 2.25–2.37) and congenital, familial, and genetic disorders (ROR: 2.26, 95% CI: 2.05–2.48). At the preferred term (PT) level, 263 significant AE signals were detected, such as pyrexia (ROR: 5.27, 95% CI: 5.03–5.53), nasopharyngitis (ROR: 2.31, 95% CI: 2.10–2.54), and injection site erythema (ROR: 6.09, 95% CI: 5.67–6.55). Importantly, we also identified less common or previously unreported AEs, including cardiac disorders (e.g., postural orthostatic tachycardia syndrome with anakinra; pulmonary valve incompetence with rilonacept) and endocrine disorders (e.g., secondary adrenocortical insufficiency with canakinumab). Furthermore, 36.33% of cases emerged after more than 360 days of treatment with IL-1 inhibitors. Conclusions: This study revealed real-world safety data on IL-1 inhibitors, providing important insights to enhance the clinical use of IL-1 inhibitors and minimize potential AEs. Full article

Background/Objectives: Human immunodeficiency virus (HIV) continues to be a global public health concern. Several antiretroviral drugs have been approved for the treatment, post-exposure, and pre-exposure prophylaxis of HIV. Darunavir (DRV) is a protease inhibitor (PI) approved for the management of HIV globally. This study aims to generate safety signals for DRV through data mining and analysis of adverse events (AEs) reported to the United Kingdom (UK) Medicines and Healthcare products Regulatory Agency (MHRA) Yellow Card Scheme. Methods: Disproportionality analysis was conducted using reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) approaches to identify potential safety signals. Results: The MHRA database contained n = 779 reports (n = 1791 AEs) attributed to DRV. The majority of AEs were reported for males. Positive safety signals were identified at both the system organ class (SOC, n = 5) and preferred term level (PT, n = 95). At SOC level, endocrine disorders emerged as a signal of interest n = 33 cases (ROR: 8.17, 95% CI: 5.78–11.56; PRR:7.96, 95% CI: 5.68–11.15; and IC: 2.85, IC025: 2.51). Among the results, 40 new potential safety signals are not listed on the product labelling in the UK. These include serious AEs such as cerebrovascular accident, brain injury, thrombosis, and pregnancy, puerperium, and perinatal AEs. Conclusions: This study provides additional real-world safety data for DRV in the UK and paves the way for future observational studies to investigate the identified safety signals. Full article

Background/Objectives: As crucial natural predators, hornets contribute to ecosystem function by preying on agricultural and forest pests and facilitating plant pollination. However, the predatory preference of hornets for honeybees poses a significant threat to honeybee pollination and the development of the beekeeping industry. Foraging and pollination behaviors in hornets are largely governed by a sensitive olfactory system, but their olfactory molecular mechanisms remain poorly understood. Methods: VvelCSP1 and VvelCSP4 were successfully expressed in the prokaryotic expression system and purified by Ni-NTA affinity chromatography column. Fluorescence competitive binding assays were employed to evaluate their binding affinities to volatile compounds derived from the seed elaiosome of Stemona tuberosa and honeybees. Molecular docking was further performed to analyze key residues and interaction patterns within the binding pockets. Results: Fluorescence competitive binding assays showed that both proteins prefer long-chain alkanes yet exhibit significant substrate selectivity and high ligand specificity. VvelCSP1 specifically binds to hexacosane, while VvelCSP4 specifically recognizes docosane. Molecular docking results demonstrated that the binding process between VvelCSP1, VvelCSP4 and their respective ligands is dominated by hydrophobic interactions. Conclusions: This study provides functional evidence for investigating the olfactory molecular regulation mechanisms underlying hornet-mediated seed dispersal. These findings establish a foundation for potential applications of hornets in plant propagation, biological pest control, crop pollination and ecological balance maintenance in agroforestry systems. Full article

Eucalyptus plantations worldwide experience significant productivity losses due to herbivory caused by the weevil Gonipterus platensis (Coleoptera: Curculionidae. Marelli 1927); however, the role of leaf nutritional status in host preference remains poorly understood. In this study, we evaluated the incidence and severity of defoliation on two seed-propagated eucalypts—Eucalyptus globulus Labill. and Eucalyptus nitens Maiden, as well as two clonally propagated E. nitens × E. globulus hybrids—at a trial site in Mulchén, Chile. Sampling occurred after peak weevil activity (December 2022) and during austral autumn (May 2023). We determined foliar concentrations of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), boron (B), carbon (C), and the carbon-to-nitrogen (C/N) ratio, and analyzed their relationships with herbivory using ANOVA, principal component analysis (PCA), and linear regression. Overall defoliation was low (<7%), but significantly higher on E. globulus, with hybrids exhibiting intermediate damage. Seasonally, N and Mg concentrations declined, while K and Ca levels increased, resulting in an elevated C/N ratio in autumn. A positive correlation was observed between leaf Ca concentration and both the incidence and severity of herbivory during peak activity in the susceptible E. globulus genotype (R² = 0.96, p < 0.05). These findings suggest that calcium accumulation may influence weevil feeding preferences. Further research should explore nutrient-mediated resistance to guide selection and fertilization strategies for developing more resilient eucalyptus varieties. Full article

Diet is a key factor modulating the trade-off between fecundity and longevity, a cornerstone of life-history theory. While laboratory studies have demonstrated that high-protein-to-carbohydrate (P:C) ratio diets increase reproductive output at the cost of lifespan, it remains unclear how this trade-off operates in species exposed to natural dietary variability and prey choice. We tested whether diet-mediated trade-offs between fecundity and longevity are modulated by prey-insect type in the endangered tiger beetle, Cicindela anchoralis, a species with a short adult lifespan. Tiger beetles were offered a choice between a high-P:C diet (cricket) and low-P:C diet (ant). Tiger beetles consuming the high-P:C diet exhibited increased fecundity and reduced longevity, while those feeding on the low-P:C diet showed the opposite pattern. Despite these consequences, both sexes showed a consistent preference for the high-P:C diet, suggesting that beetles prioritize reproductive output over lifespan. These results suggest that prey-insect selection might be an adaptive way to boost reproductive success within a limited adult lifespan, which may raise tiger beetles’ intrinsic rate of natural increase. Our findings highlight the ecological relevance of diet-driven life-history trade-offs and offer practical guidance for mass propagation strategies to support endangered tiger beetle recovery. Full article

In the shale oil reservoirs, sandstone and shale often overlie each other. This significantly affects the vertical propagation of hydraulic fractures (HFs); however, the underlying mechanisms still remain unclear. This study employs Xsite software to investigate the influence of rock fracture toughness, tensile strength, elastic modulus, Poisson’s ratio, interlayer stress contrast, and the flow rate and viscosity of fracturing fluid on the propagation behaviour of HFs in sandstone–shale interbeds. As the type-I fracture toughness of the shale layer increases, the area of the vertical HF decreases and the average HF width becomes smaller. As the tensile strength of the sandstone layer increases, the distribution range of fluid pressure at the interface expands. The HF prefers to propagate in the softer rock rather than the harder one. A relatively narrower HF width is created in the layer with a higher elastic modulus resulting in a higher flow resistance to fracturing fluid. A shale layer with a high Poisson’s ratio is more likely to undergo a lateral expansion, causing stress at the fracture tip to be dispersed. When the effect of lithological interfaces is considered, an increasing interlayer stress contrast causes HFs to gradually transition from penetrating the interfaces to becoming confined between the two interfaces. When the influence of the lithological interface is not considered, an increasing interlayer stress contrast causes the HF to gradually transition from a penny-shaped fracture to a blade-shaped fracture. The HF penetrates the interfaces more easily at a higher injection rate and fluid viscosity, because most of the injected energy is used to create new fractures rather than leakoff into the interfaces. Understanding the influence of these factors on the HF propagation behaviour is of great significance for optimising hydraulic fracturing design. Full article

Long Range Wide Area Network (LoRaWAN) has become an attractive option for maritime communication because it is low-cost, long-range, and energy-efficient. Yet its performance at sea is often limited by fading, interference, and the strict energy budgets of maritime Internet of Things (IoT) devices. This review, prepared in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, examines 23 peer-reviewed studies published between 2019 and 2025 that explore adaptive antenna solutions for LoRaWAN in marine environments. The work covered four main categories: switched-beam, phased array, reconfigurable, and Artificial Intelligence or Machine Learning (AI/ML)-enabled antennas. Results across studies show that adaptive approaches improve gain, beam agility, and signal reliability even under unstable conditions. Switched-beam antennas dominate the literature (45%), followed by phased arrays (30%), reconfigurable designs (20%), and AI/ML-enabled systems (5%). Unlike previous reviews, this study emphasizes maritime propagation, environmental resilience, and energy use. Despite encouraging results in signal-to-noise ratio (SNR), packet delivery, and coverage range, clear gaps remain in protocol-level integration, lightweight AI for constrained nodes, and large-scale trials at sea. Research on reconfigurable intelligent surfaces (RIS) in maritime environments remains limited. However, these technologies could play an important role in enhancing spectral efficiency, coverage, and the scalability of maritime IoT networks. Full article

In the V-cycle of distributed electric wheel loaders (DEWLs), transport accounts for about 70% of the cycle, making energy saving urgent, while shovel-stage slip limits traction stability. This paper proposes a two-module control framework: (i) a preferred-motor transport strategy that reduces parasitic losses and concentrates operation in high-efficiency regions; and (ii) a load-ratio-based front–rear torque distribution for shoveling that allocates tractive effort according to instantaneous axle vertical loads so that each axle’s torque respects its available adhesion. For observability, we deploy a pre-calibrated lookup-table (LUT) mapping from bucket cylinder pressure to the front-axle load ratio, derived offline from a back-propagation neural network (BP-NN) fit. Tests on a newly developed DEWL show that, compared with dual-motor fixed-ratio control, transport-stage mechanical and electrical power drop by 18–37%, and drive-system efficiency rises by 6–13%. During shoveling, the strategy reduces the peak inter-axle slip from 22–35% to 13–15% and lowers the mean slip to 2.6–5.9%, suppressing sawtooth-like wheel-speed oscillations without sacrificing peak capacity. The method reduces parasitic energy flow, improves traction utilization, and is readily deployable. Full article

In developing a mathematical model of a railway track, the question of determining the dimensions of the modeling domain inevitably arises. If the modeling area is too small, boundary effects may significantly influence the results, reducing their accuracy. Conversely, excessively large areas can increase computational complexity without substantial improvements in accuracy. An optimal choice of dimensions enables the balancing of computational costs and accuracy. Solving this problem is non-trivial, as it depends on numerous factors, primarily the type of mathematical model and the problem being addressed. In most cases, preference is given to minimal domain sizes that ensure the approach’s adequacy. The aim of this study is to justify the dimensions of the modeling domain by addressing such tasks as load scaling, introducing additional boundary conditions, and making relevant assumptions. The main object of the study is the minimum adequate longitudinal length of the track for the spatial model. The research is based on the analytical application of modern approaches in the theory of elasticity. The results are analyzed using mathematical methods, such as modeling the railway track through the propagation of elastic waves and finite element modeling. These findings can be applied to a wide range of problems related to the mathematical modeling of the stress–strain state of railway tracks. Full article

In large-scale recommendation scenarios, achieving high-precision ranking requires simultaneously modeling user interest dynamics and content propagation potential. In this work, we propose a unified framework that integrates a temporal interest modeling stream with a multimodal virality encoder. The temporal stream captures sequential user behavior through the self-attention-based modeling of long-term and short-term interests, while the virality encoder learns latent virality factors from heterogeneous modalities, including text, images, audio, and user comments. The two streams are fused in the ranking layer to form a joint representation that balances personalized preference with content dissemination potential. To further enhance efficiency, we design hierarchical cascade heads with gating recursion for progressive refinement, along with a multi-level pruning and cache management strategy that reduces redundancy during inference. Experiments on three real-world datasets (Douyin, Bilibili, and MIND) demonstrate that our method achieves significant improvements over state-of-the-art baselines across multiple metrics. Additional analyses confirm the interpretability of the virality factors and highlight their positive correlation with real-world popularity indicators. These results validate the effectiveness and practicality of our approach for high-precision recommendation in big data environments. Full article

Addressing the challenges of insufficient incentives, weak constraints, and superficial adoption in promoting human–robot collaboration (HRC) in the construction industry, this study develops a tripartite evolutionary game model among government, contractors, and on-site teams under bounded rationality. Lyapunov stability analysis and numerical simulation are employed to conduct parameter sensitivity analyses. The results show that a strategy profile characterized by flexible regulation, deep adoption, and high-effort collaboration constitutes a stable evolutionary outcome. Moderately increasing government incentives helps accelerate convergence but exhibits diminishing returns under fiscal constraints, indicating that subsidies alone cannot sustain genuine engagement. Reducing penalties for contractors and on-site teams, respectively, induces superficial adoption and low effort, whereas strengthening penalties for bilateral violations simultaneously compresses the space for opportunistic behavior. When the payoff advantage of deep adoption narrows or the payoff from perfunctory adoption rises, convergence toward the preferred steady state slows markedly. Based on the discussion and simulation evidence, we recommend dynamically matching incentives, sanctions, and performance feedback: prioritizing flexible regulation to reduce institutional frictions, configuring differentiated sanctions to maintain a positive payoff differential, reinforcing observable performance to stabilize frontline effort, and adjusting policy weights by project stage and actor characteristics. The study delineates how parameter changes propagate through behavioral choices to shape collaborative performance, providing actionable guidance for policy design and project governance in advancing HRC. Full article