Search Results (567)

In the digital transformation of the petroleum industry, massive multi-source heterogeneous tables are distributed across databases, Word documents, PDF reports, and engineering systems. Their unstructured format and weakly expressed inter-table relationships make it difficult for conventional keyword-based or single-table retrieval methods to locate the complete set of tables needed for complex queries. To address this problem, this paper proposes Relatab, a multi-table retrieval framework based on implicit association reasoning. Relatab first estimates query–table relevance through a dual-level semantic matching mechanism that combines table-level signals, including captions and column names, with value-level signals weighted by entropy and CRITIC criteria. It then constructs an implicit table graph using column-name and column-content similarity, and applies a max-product multi-hop propagation rule with decay and pruning to identify complementary tables that are not directly matched by the query. Finally, direct relevance and inter-table complementarity are fused to produce the retrieved table set. Experiments on Spider, Bird, and CementingTables show that Relatab achieves Top-2 recall rates of 79.21%, 61.26%, and 77.88%, respectively, outperforming DTR by 1.74, 2.33, and 1.85 percentage points. The results indicate that explicit modeling of implicit inter-table associations improves retrieval coverage in complex multi-table scenarios while remaining applicable to petroleum-domain documents. Full article

Tungsten trioxide (WO₃) is the most widely studied cathodic electrochromic (EC) material, serving as the core component of energy-efficient smart windows, displays, and optical modulation devices. Post-deposition annealing, as a critical post-processing technique, precisely regulates the microstructure, crystallinity, oxygen vacancy concentration, and electronic structure of WO₃ thin films, thereby directly determining their EC performance. This review summarizes the research progress of annealing effects on WO₃ films, focusing on the synergistic regulation of annealing temperature, atmosphere, and dwell time. It elaborates on the fundamental EC mechanisms of amorphous and crystalline WO₃, including polaron hopping and free-electron Drude behavior, and analyzes the influence of different deposition methods (magnetron sputtering, sol–gel, electrodeposition, etc.) on the annealing response of films. The optimal annealing windows for balancing optical modulation, coloration efficiency, switching speed, and cycling stability are clarified: moderate temperatures (200–350 °C) and inert/air atmospheres yield mixed amorphous–nanocrystalline structures with optimal oxygen vacancy content. Current challenges such as the inherent contrast–stability trade-off and thermal budget limitations of flexible substrates are discussed, and future directions including spatially resolved annealing, interface co-design, and machine learning-assisted optimization are prospected. This work provides a theoretical reference and process guidance for the development of high-performance WO₃-based EC devices. Full article

Graphene oxide (GO) membranes present a compelling alternative to the permeability-selectivity trade-off inherent in conventional polymer membranes. However, the incomplete mechanistic understanding and the absence of scalable, defect-controlled fabrication processes continue to hinder their practical deployment. This review synthesizes and integrates transport mechanisms, computational modeling, fabrication, and translational constraints across graphene-based membrane architectures into a comprehensive design-oriented framework. Five key aspects of this synthesis are highlighted. Firstly, the available evidence supports a three-regime transport model, which unifies viscous near-frictionless flow, activated molecular hopping, and solution–diffusion. This reframes selectivity as a tunable function of the C/O ratio and interlayer chemistry. Secondly, a quantitative parity analysis of literature data reveals that classical molecular dynamics tends to overestimate GO laminate water permeance by a representative factor of approximately 3–8× across the matched comparisons examined. This discrepancy can be corrected using a tortuosity–porosity factor derived from wet-state XRD. Machine-learning force fields (GAP, MACE), while still in an early stage of development with limited reported applications, narrow the residual discrepancy to within 1.5–2× in the studies reviewed. Thirdly, a tiered computational roadmap identifies nuclear quantum effects as critical for proton-transport applications but unresolved for water permeance in GO laminate geometry. Fourthly, performance across water nanofiltration, gas separation, ion recovery, and osmotic energy harvesting is benchmarked against commercial references, with explicit caveats regarding the heterogeneity of testing conditions across cited studies, alongside a technology readiness assessment. Lastly, a standardized 500-h hydraulic stability protocol is proposed to facilitate cross-laboratory comparison. Collectively, this synthesis provides a structured, albeit not exhaustively validated, basis for the discussion of next-generation membrane design. Full article

Grade IV lateral patellar luxation is an advanced orthopedic condition that may be associated with severe skeletal deformities, chronic soft-tissue contracture, and irreversible loss of normal stifle function. Although reconstructive procedures are considered the treatment of choice in most dogs, some end-stage cases may not be amenable to conventional correction. This report describes the clinical presentation, surgical management, and long-term outcome of a young dog with severe bilateral grade IV lateral patellar luxation treated with single-stage bilateral stifle arthrodesis. A 1-year-old, 42-kg intact female Romanian Shepherd Dog was presented with chronic severe hindlimb dysfunction, crouched posture, and permanent bilateral patellar luxation. Orthopedic and radiographic examination revealed severe femoral valgus, proximal tibial varus, external tibial torsion, absence of a functional trochlear groove, marked caudal thigh muscle contracture, and severely restricted stifle extension. Computed tomography was considered but not performed because of financial limitations. Based on the severity of the deformities and the limited preoperative range of motion, conventional reconstructive procedures were considered unlikely to restore functional joint motion. Bilateral stifle arthrodesis was therefore performed during a single anesthetic event using tibial tuberosity osteotomy, temporary Kirschner-wire fixation, and bilateral 3.5-mm locking compression plates. Due to severe chronic soft-tissue contracture, final arthrodesis angles of 95° and 99° were achieved. Progressive clinical improvement and radiographic bone union were observed throughout follow-up. At 12 months, complete bilateral arthrodesis and implant stability were confirmed radiographically. At 24 months, the dog remained independently ambulatory with a markedly improved quality of life despite persistent mechanical gait abnormalities, including circumduction and intermittent bunny-hopping. This case suggests that bilateral stifle arthrodesis may represent a salvage option in carefully selected dogs with end-stage bilateral grade IV lateral patellar luxation when conventional reconstructive procedures are unlikely to restore useful limb function. Full article

Aiming at the problems of the traditional Statistical Priority-Based Multiple Access (SPMA) protocol in multi-hop Flying Ad Hoc Networks (FANETs), such as single-dimensional queueing only according to priority, unreasonable First-In-First-Out (FIFO) scheduling, high timeout dropping probability of multi-hop forwarding packets, and insufficient utilization of channel opportunities, this paper proposes a multi-hop adaptive SPMA protocol (MA-SPMA) suitable for dynamic multi-hop scenarios. The protocol adopts the Neighbor-Priority Two-Dimensional Queueing (NPTQ) mechanism to store packets jointly according to the next-hop neighbor and priority. A Priority-Utility Dual-round Decision (PUDD) mechanism is designed: in the first round, candidate queues that meet channel load conditions are selected in parallel; in the second round, a utility function constructed by normalized delay, priority, and the end-to-end transmission success rate is used to select the optimal packet for transmission. Theoretical analysis shows that the time and space complexity of MA-SPMA are linearly related to the number of neighbor nodes, with controllable overhead, which is suitable for resource-constrained Unmanned Aerial Vehicle (UAV) platforms. In the MATLAB simulation environment, the Reference Point Group Mobility (RPGM) model is used to construct a multi-hop topology, and comparisons are conducted with two typical improved protocols for multi-hop networks: DCLS-SPMA and BiLSTM-SPMA. The results show that the proposed protocol can significantly improve the end-to-end transmission success rate and network throughput, with more obvious advantages in scenarios with a high proportion of multi-hop services. This paper provides an effective solution for Medium Access Control (MAC) protocol design in FANETs. Full article

In response to the increasing needs for environmentally friendly products, lignocellulosic natural fibers have been of interest as potential replacements for synthetic reinforcement materials in textiles, composites, and related applications. Among these resources, bast fibers derived from plant stems (flax, hemp, nettle, jute, hop), which contain a high cellulose content, have good mechanical properties, low density, and are renewable, are highly promising. Steam explosion has emerged as a green fiber extraction, defibrillation, and surface modification pretreatment technology. Despite the growing number of studies on steam-exploded natural fibers, a comprehensive understanding of the relationships between processing conditions, fiber modifications, mechanisms, and end-use performance remains limited. This review investigates the structural, chemical, and morphological influences of steam explosion on bast fibers. Specifically, it focuses on the mechanism of steam explosion including the solubilization of hemicellulose, partial lignin redistribution or removal, fiber individualization, and cellulose enrichment. The literature indicates that steam explosion can improve fiber separation, fineness, surface morphology, and interfacial adhesion of the composite materials and reduce the use of hazardous chemicals compared with conventional extraction methods. Nonetheless, conflicting results have also been documented, where the same steam explosion conditions can yield distinct fiber characteristics according to biomass type, composition of biomass, moisture concentration, and the amount of processing involved. Excessive treatment severity may lead to fiber shortening, cellulose degradation, and deterioration of fiber quality, particularly for textile applications requiring long fibers. This review highlights current knowledge gaps regarding the optimization of processing conditions, the understanding of steam explosion mechanisms, and the scale-up of the technology for industrial applications. Full article

Background/Objectives: This study investigated kinetic performance, leg stiffness and gastrocnemius muscle (GM) activity during shod and barefoot two-legged hopping in female court athletes, while also assessing potential sport specialization-by-footwear interactions. Methods: Forty-two elite female Volleyball, Basketball, and Handball athletes (n = 14 per group) performed two-legged hopping at 130 bpm under both shod and barefoot conditions. Vertical ground reaction force (Fz) (Kistler forceplate sampling at 1000 Hz) was recorded in synchronization with GM vibromyographic intensity (TSD250, Biopac Systems, sampling at 2000 Hz). Kinetic metrics, kleg and GM activation were analyzed via repeated-measures ANOVA (alpha = 0.05, PSS 30.0). For all metrics, results indicated no significant sport-by-footwear interaction (p > 0.05). Results: Footwear significantly altered hopping kinematics; while absolute contact durations remained constant, relative total and effective contact durations were elongated and shortened, respectively. In the shod condition, kleg increased (+6.1%, p < 0.05) alongside a reduction in GM activation (−7.7%, p < 0.05). Additionally, Fz peak increased (+4.3%, p < 0.05) and occurred relatively earlier in the contact phase (−0.7%, p < 0.05). Conclusions: These findings indicate that footwear acts as a mechanical buffer, enabling a stiffer leg spring and reduced neuromuscular demand. The earlier timing of Fz peak suggests a facilitated eccentric-to-concentric transition, most likely allowing athletes to maintain efficient energy return despite the compliance of the footwear interface. Full article

Wireless sensor networks (WSNs) are recognized as a promising enabling technology for health monitoring of elongated infrastructures such as bridges, tunnels and railways. However, the significant distribution span of WSN nodes within narrow spaces requires monitoring data to be transmitted to the base station via multi-hop routing, which poses higher demands on network energy efficiency and lifespan. This paper proposes a Multi-hop Cluster Routing Algorithm based on an Improved Sparrow Search Algorithm (ISSAMC) aimed at optimizing the optimal multi-hop path from cluster heads (CHs) to the base station, thereby extending the stability period and overall lifespan of WSNs in narrow spaces. The ISSAMC first employs a non-uniform clustering mechanism, taking into account the residual energy of nodes and the distance to the base station, to generate a CH distribution that aligns with the topological characteristics of the narrow structure. Next, a multi-objective fitness function is constructed to simultaneously minimize the total energy consumption of the CHs and the variance in energy consumption, along with a dynamic weight adjustment strategy to adapt to the time-varying characteristics of the network state. Finally, multi-hop path optimization is performed using an improved SSA that incorporates strategies such as population initialization based on Sobol sequences, discrete encoding and decoding mechanisms, and crossover techniques, resulting in high-quality multi-hop paths. Simulation results show that under the unified ideal simulation benchmark, compared with MH-LEACH, GAECH, BEBMCR and EBPSO algorithms, ISSAMC improves the network stability period by 231%, 94%, 60% and 49.5%, respectively, and extends the overall network lifetime by 55%, 31%, 25.5% and 24%, respectively. Full article

In Space–Air–Ground Integrated Networks (SAGINs), the inherent high bit error rate (BER) and prolonged propagation latency of satellite links, compounded by the highly dynamic topologies and multi-hop nature of Unmanned Aerial Vehicle (UAV) networks, present severe bottlenecks to end-to-end transport performance. To mitigate performance degradation within these heterogeneously converged SAGIN-UAV architectures, this paper proposes a SAGIN-enabled Adaptive End-to-End Congestion Control scheme. By exploiting the distinct transmission characteristics of long-delay, high-BER satellite links alongside terrestrial mobile multi-hop UAV networks, the Proposed Scheme optimizes data injection during the slow-start phase and introduces a high-precision loss differentiation mechanism during the congestion avoidance phase. This framework accurately distinguishes non-congestive losses (e.g., channel errors or topology switching induced by UAV mobility) from genuine buffer overflows. The simulation results demonstrate that the proposed adaptive scheme significantly reduces queuing delays at UAV nodes, accelerates transmission efficiency across multi-hop terminals, and enhances data throughput in high-latency environments. Ultimately, this scheme offers a resilient solution for optimizing end-to-end transport control and maximizing the overall transmission capability of SAGIN-enabled UAV networks. Full article

With the continuous expansion and increasing topological complexity of modern power grids, achieving high-precision fault localization under sparse measurement conditions has become a core challenge in the operation and maintenance of smart grids. Existing methods based on deep graph networks generally face complex spatiotemporal coupling between fault types and fault localization. To address this, this paper proposes a recognition method for fault localization based on sparse measurements and spatial configuration. A reinforcement learning algorithm with a Checking-Action mechanism, termed DQN-CA, is adopted to identify optimal PMU installation buses. In parallel, a dual-path spatio-temporal multi-task graph convolutional network, termed ST-MTGCN, is developed to decouple fault-type-related features from topology-sensitive fault-Localization features through a global feature dimensionality-reduction path and a K-hop spatial graph convolution path, thereby accomplishing the fault localization task. Experimental results on the IEEE 39-bus system show that ST-MTGCN achieves 99.68% fault type accuracy, 89.94% fault localization accuracy, and 88.62% accuracy for 185 joint fault type-Localization classes under the OPT13 configuration. Comparative experiments, PMU configuration sensitivity analysis, and ablation studies further demonstrate the effectiveness of the proposed framework under sparse measurement conditions. Full article

The Constrained Application Protocol (CoAP) is widely adopted to ensure end-to-end reliability in resource-constrained Artificial Intelligence of Things (AIoT) and Wireless Sensor Networks (WSNs). However, CoAP’s default retransmission timeout (RTO) mechanism lacks algorithmic responsiveness under volatile channel conditions, and state-of-the-art benchmarks like CoCoA+ and FASOR often suffer from over-conservative backoff states or destabilizing retransmission storms. To overcome these operational bottlenecks, this paper proposes a novel dual-adaptive Dynamic RTO algorithm specifically engineered for heterogeneous IoT deployment scales. The proposed framework dynamically adjusts its parameter inspection cycle (N) based on instantaneous round-trip time (RTT) variance while simultaneously scaling its tuning coefficient (α) in response to real-time packet loss indicators. To rigorously validate the algorithmic resilience, performance evaluations were conducted within a highly volatile network environment governed by the Gilbert–Elliott dynamic loss model across multi-hop linear (1 × 6) and grid (3 × 6, 5 × 6) topologies. Experimental results demonstrate that the proposed Dynamic RTO consistently optimizes the throughput–latency trade-off, achieving a total communication time of 25.92 s in complex grids—outperforming CoCoA+ and FASOR by 14.28% and 8.89%, respectively. Furthermore, the proposed mechanism significantly curtails transmission overhead, restricting the cumulative retransmission footprint to just 59 counts under severe localized impairments, thereby establishing a scalable, resource-efficient, and empirically robust transport-layer solution for next-generation edge-computing infrastructures. Full article

Skeleton-based human action recognition (HAR) requires models that preserve the local kinematic structure of the human body while capturing long-range spatiotemporal dependencies under noisy or incomplete joint observations. Traditional Graph Convolutional Networks (GCNs) provide topology-aligned inductive bias but are often limited by local information aggregation from neighboring joints. In contrast, attention-based mechanisms capture global interactions, yet they may attend to spurious correlations when skeletal constraints are weakly enforced. This paper proposes Differential Hyperedge Attention-enhanced GCN (DHA-eGCN), a hybrid architecture that couples structure-aware Differential Hyperedge Attention with multi-scale temporal convolution for spatiotemporal skeleton sequence processing. DHA injects skeletal structure into attention via hop-distance relative positional encoding and hyperedge context tokens generated via joint-to-part pooling. It further employs differential attention to suppress shared noisy correlations and enhance interaction selectivity. To strengthen spatial grounding, an explicit GCN branch is added under partial- or full-depth configurations, where the first four or all ten layers are applied with graph convolutions. The model further employs an ensemble strategy that combines predictions from multiple complementary model instances. Our experiments on NTU RGB+D 60 under the X-Sub and X-View protocols, NTU RGB+D 120 under the X-Sub and X-Set protocols, and Northwestern-UCLA demonstrate that DHA-eGCN consistently outperforms or remains competitive with strong graph-based, transformer-based, and hybrid state-of-the-art methods based on the same four-stream architecture. The best configuration achieves 93.7% and 97.0% on NTU RGB+D 60 X-Sub and X-View, respectively; 90.9% and 91.9% on NTU RGB+D 120 X-Sub and X-Set, respectively; and 97.6% on Northwestern-UCLA. Full article

Biosensing performance in graphene-derived field-effect transistors (BioFETs) is widely attributed to surface chemistry, yet the role of the underlying charge transport mechanism remains poorly understood. This work establishes a direct correlation between disorder-driven transport and biosensing transduction in vertical graphene (VG) and nanocrystalline graphite (NCG) FET devices. Temperature-dependent electrical characterization (15–500 K) reveals a hybrid transport regime: three-dimensional Mott variable-range hopping below 240 K, transitioning to thermally activated Arrhenius-type conduction above 240 K. The extracted VRH parameters characteristic temperature T₀, localization length ξ, and density of states N(E_F) quantify fundamentally distinct disorder landscapes: VG operates in a strongly localized, edge-dominated regime, while NCG forms a continuous percolative network with greater transport stability. Surface functionalization via PASE and amine-terminated ssDNA probes, followed by DNA hybridization across four nucleobase systems, demonstrates that the sequence-dependent electrical response is mechanistically interpretable within the VRH–transconductance framework. NCG transduces biomolecular binding through direct charge transfer and hopping pathway perturbation, whereas VG responds through interfacial electrostatic reorganization. These results introduce a unified VRH–transconductance–sensing framework, providing a rational physical basis for next-generation graphene BioFET design. Full article

Multi-hop question answering (MQA) requires models to perform multi-step reasoning and integrate multiple knowledge sources. However, existing methods combining pre-trained language models (PLMs) and graph neural networks (GNNs) often suffer from low computational efficiency, insufficient deep semantic fusion, and imbalanced modeling of heterogeneous relations. To solve these problems, we propose a Dynamic Hierarchical Adaptive Graph Convolution Network (DHACNet). First, to deal with the issues of insufficient computational efficiency and feature interpretability, we introduce Dynamic Sparse Activation (DSA). A trainable gate unit is used to generate importance masks for the encoder outputs, keeping only the task-relevant neurons. This greatly decreases the computational burden and enhances the interpretability of the model’s decisions. Second, to alleviate insufficient deep semantic fusion, we design a Hierarchical Feature Fusion (HFF) mechanism. It adaptively weights and fuses hidden states from different layers, enhancing the extraction and representation of deep textual semantics. Furthermore, for graph structure modeling, we present Adaptive Graph Convolution (AGC), which assigns learnable weights to different edge types in the graph, thereby improving heterogeneous relation modeling. Finally, hierarchical graph pooling is introduced, which integrates attention mechanism and Top-$K$ selection to achieve efficient and robust graph-level representation. The experimental results show that our proposed model maintains the symmetry between the text representation and graph representation through adaptive layered fusion and relational perceptual graph propagation. This symmetry-aware reasoning process encourages semantic consistency during multi-hop inference and makes knowledge integration more robust. Full article

Unmanned Surface Vehicle (USV) clusters operating in maritime environments face dynamic communication conditions, including varying sea states, electromagnetic interference, and satellite denial, that render static communication topologies suboptimal. Existing approaches assess link quality through single indicators, typically the SNR, and lack mechanisms for automatic topology adaptation. This paper presents a multi-layer adaptive communication framework that achieves a mean communication quality score of 0.72 (vs. 0.51–0.66 for baselines), a message delivery rate of 94.1% under benign conditions, and a failure recovery time of 3.2 s (vs. 5.8–8.4 s for baselines) across five communication failure scenarios. The framework integrates three layers: a weighted multi-indicator communication quality metric fusing the SNR, packet loss rate, latency, and link stability into a unified score; a topology utility function that selects among centralized, distributed, and hierarchical topologies by optimizing a quality–threat–overhead objective; and a multi-modal backup communication manager with physics-based underwater acoustic, optical line-of-sight, and multi-hop relay fallback modes. Simulation results demonstrate consistent improvements over single-indicator and static-topology baselines, with particularly strong performance under satellite denial and jamming scenarios where multi-modal backup communication sustains delivery rates above 85% under simulated conditions. In summary, the framework demonstrates consistent improvements across all metrics (communication quality, delivery rate, recovery time) relative to four baselines, with the largest gains observed under the most challenging conditions (satellite denial and jamming). We emphasize that the framework adaptively selects among pre-defined canonical topologies (star, mesh, tree) based on real-time conditions rather than synthesizing optimal topologies de novo—a distinction between topology management and topology optimization. Full article