Search Results (1,782)

The increasing convergence of enterprise architecture and IT governance frameworks, such as TOGAF 9.2, COBIT 5, and NIST CSF 1.1, with LegalTech regulations including GDPR 2016/679, eIDAS 910/2014, and NIS2 2022/2555, has created a growing need for rigorous and automated governance-validation mechanisms. However, to date, no formal approach exists to assess semantic conformance between a normative ontological model and its operational implementation in microservice-based systems, leaving critical governance gaps difficult to detect in legally sensitive environments. This paper proposes MALTG (Multidimensional Architecture for LegalTech Governance), a configurable and reusable formal framework that combines OWL 2 ontology engineering, a JSON-LD-based SDT (Structural Digital Twin), semantic conformance mapping, a hierarchical coverage function, and a conformance gap metric to support automated governance assessment and prioritised remediation. The framework accepts any organisational architecture as input, enabling application to arbitrary LegalTech case studies by replacing the reference SDT. The proposed framework models nine governance dimensions through an ontology of 59 classes and 15 properties and validates them against a semi-real, public-source SDT composed of 39 microservice components and 54 directed connections. The reference SDT was populated through an ontology-driven data-collectionprocess: a scraping campaign guided by the MALTG ontology (data/MALTG_Ontology.owl) harvested public information from the official portal of Ecuador’s Council of the Judiciary (Consejo de la Judicatura, CJ)—probing its technological-maturity level and digital-governance compliance—which was condensed into a single JSON-LD artefact, enforcing domain rigour and traceability on the search for LegalTech governance evidence. Experimental results demonstrated an overall ontological score of 82.6 and an SDT score of 73.7, with a mean conformance gap of 8.9. Five dimensions achieved full conformance, while the LegalTech dimension presented the largest gap. Graph-theoretic validation further confirmed monotonic improvement throughout the remediation sequence. Overall, the findings suggest that MALTG provides a formally grounded and reproducible approach for automating multi-framework LegalTech governance conformance assessment while maintaining semantic and structural traceability between normative models and operational architectures. As a proof of concept, this validation relies on a configurable, ontology-driven public-source (semi-real) SDT; external validity in real production LegalTech organisations remains untested and is left for future work. Full article

Robust object detection for autonomous driving requires perception models that remain reliable when visible imagery is degraded by darkness, glare, rain, fog, motion blur, or long-range small targets. Visible and thermal infrared cameras provide complementary evidence, yet many RGB–thermal detectors fuse modalities, mainly as aligned tensors, and may underuse relational structure in channel responses, spatial layouts, semantic scales, and modality-specific uncertainty. This paper presents TopoGraph-Fusion, a hierarchical graph-guided dual-modal object detector that formulates fusion as topology-aware reasoning rather than direct feature concatenation. The proposed framework builds a dual-stream backbone for RGB and thermal images, constructs channel-wise topology through a channel-topology graph aggregation module, derives relation-aware spatial and channel global attention from affinity graphs, and replaces fixed feature-pyramid communication with a Graph-Guided Feature-Pyramid Network. A topology-regularized detection objective further encourages stable cross-modal correspondence while suppressing noisy all-to-all connections. Experiments on M3FD, FLIR, RGBTDronePerson, and VEDAI512 cover road scenes, adverse illumination, drone–person perception, and aerial vehicle detection. Within this validation scope, the results and visual analyses indicate that topology-guided fusion improves small-object recall, cross-modal consistency, and robustness under modality imbalance. Full article

This work examines the implementation and evaluation of shortest-path algorithm in a Geographic Information System environment integrating QGIS with a PostgreSQL/PostGIS spatial database. Spatial edge and junction layers stored in the PostgreSQL/PostGIS define a directed weighted network in which edge costs are derived from spatial distance and modified through a gradient-based cost function. The routing algorithm is implemented in Python using the PyQGIS library and a binary heap priority queue. Network data are loaded from the geodatabase and processed in memory to compute the optimal route between selected nodes. The resulting path is reconstructed as a dissolved polyline feature stored in the geodatabase and visualized within the GIS environment. The study also includes a theoretical comparison of several classical shortest-path algorithms—Dijkstra, $A^{*}$, Bellman–Ford, and Floyd–Warshall—with respect to their applicability to sparse spatial graphs typical of transportation networks. The analysis confirms the suitability of Dijkstra’s algorithm for such networks and demonstrates that routing outcomes depend directly on the definition of the edge cost function. The proposed workflow relies exclusively on open-source GISs and database technologies. Full article

Agricultural outward foreign direct investment (OFDI) is an important mechanism for strengthening food security, enhancing agricultural capacity, and advancing sustainable development through greater supply-chain resilience and resource-use efficiency. This study applies social network analysis to characterize the global agricultural OFDI network and uses a temporal exponential random graph model (TERGM) to examine its drivers. The results show that the network remains sparse but has become more efficiently connected, reciprocal, and clustered, while retaining a pronounced core–periphery structure. Developed economies, led by the United States, occupy the network core, whereas the positions of emerging Asian economies, including China, have strengthened over time. Both endogenous network dependencies and exogenous attributes jointly shape tie formation. The post-2018 estimates indicate weaker network persistence, stronger resource-endowment effects, and greater investment attraction in host economies with less restrictive institutional environments. Across development groups, agricultural capital from high-income economies follows a predominantly unidirectional core–periphery pattern, whereas developing economies exhibit more frequent reciprocal and cyclical investment ties. Full article

Build systems become an indispensable part of the software implementation and deployment process. New programming languages are released with the build system integrated into the language tools, for example, Go, Rust, or Zig. However, in the hardware description domain, no official build systems have been released with the predominant Hardware Description Languages (HDL) such as VHDL or SystemVerilog. Moreover, hardware design projects are often multilingual. This paper characterizes and compares two common approaches for hardware build system implementations. The first one is the direct-Tcl approach, in which the build system code is executed directly by the EDA tool during the design build flow. The second one is the indirect-abstract approach, in which the build system produces a Tcl script, which is later run by a proper EDA tool. As none of the existing direct-Tcl build systems were close to the indirect-abstract build systems in terms of supported functionalities, this paper also presents a new direct-Tcl hardware build system called HBS. The implemented build system was used as a representative of direct-Tcl build systems in comparison with indirect-abstract build systems. The main goals of the system include simplicity, readability, a minimal number of dependencies, ease of integration with the existing EDA tools, and independence from version control systems. The system provides a broad design automation, including support for multiple synthesis EDA tools, listing automatically detected cores and targets, automatic testbench targets detection, parallel testbench running, and dependency graph generation. Full article

Task decomposition is a fundamental challenge in multi-agent collaborative maintenance systems, where unstructured natural language instructions must be precisely translated into logically coherent, executable sub-task sequences. This paper formulates task decomposition as a constrained optimal path search problem on a heterogeneous knowledge graph that encodes coal mine equipment topology, fault causality, and maintenance procedures. We construct a composite cost function that systematically integrates semantic similarity from graph neural network embeddings, relation-type weights, and structural path length, transforming instruction parsing into a mathematically tractable combinatorial optimization. The cost function is derived from the principles of shortest-path reasoning in knowledge graphs: the relational weights capture domain-specific association strengths, the semantic similarity term promotes contextually coherent chains, and the path-length penalty prevents unnecessarily long derivations. A multi-hop reasoning algorithm coupling heterogeneous graph convolution with beam search is developed to solve this problem efficiently, achieving high-quality approximate solutions while ensuring computational tractability. The reasoning process is inherently interpretable, as the optimal path directly maps to a traceable atomic task sequence with explicit dependency relations. A formal complexity analysis shows the algorithm scales as $O$($b$·$K$·$d_{max}$), where b is the beam width and d_max is the maximum node degree. Several theoretical properties of the proposed framework are further derived: the cost function is non-negative and strictly monotonic, optimal paths satisfy the optimal substructure property, cycle-free optimal paths always exist, and beam search can yield globally optimal solutions given a sufficiently large beam width. These theoretical conclusions establish mathematical guarantees for the presented decomposition framework. Experiments on 200 composite maintenance instructions with gold-standard annotations (inter-annotator agreement Cohen’s $\kappa =0.88$) demonstrate that the proposed method achieves 94.3% task sequence accuracy (95% CI: 91.2–96.8%) and 96.4% dependency accuracy (95% CI: 93.5–98.1%), substantially outperforming both a rule-augmented baseline (58.6%, 62.1%) and a GPT-4o few-shot chain-of-thought baseline (73.2%, 70.5%); McNemar’s test yields p < 0.001 for both comparisons. The average inference time is 29.7 ms (SD 2.1 ms), meeting stringent industrial real-time constraints. Ablation studies quantify the contribution of each cost function component and confirm the robustness of the chosen beam width and hyperparameters. When integrated into a full multi-agent system, the framework delivers end-to-end response time within 3 s (P50: 1.87 s, P95: 2.83 s) and maintains an 86.7% task success rate even under dual agent failures, validating the robustness of the proposed mathematical formulation. This work establishes a rigorous graph-theoretic foundation for instruction decomposition in multi-agent systems, with direct applicability to safety-critical industrial environments. Full article

The high rate of growth of misinformation on decentralized platforms causes a risk to public confidence and the integrity of decisions and requires a system of verification that is not only accurate but can be causally informed and interpreted via proxy causal metrics, reliable, and privacy-safe as well. The state-of-the-art federated learning (FL)-based fact-verification models mainly use correlation-driven patterns and do not provide ways to deal with causal reasoning, measuring formal reliability, or being resilient to Byzantine adversaries. This paper proposes a unified framework called CORE-FACT (causal optimization and reliability-enhanced fact-tracking) that can be used to conduct interpretable and robust misinformation detection in a distributed environment by combining causally informed optimization with reliability-weighted federated optimization. The proposed three-tier architecture includes: (1) a causal graph construction module, where variational attention is utilized to learn directed relationships of claims and evidence; (2) a reliability-weighted federated optimization module, where Byzantine-resilient aggregation (adaptive trust scoring) is achieved; and (3) an adaptive fact-tracking module, which is used to achieve fusion of multi-source evidence by combining hierarchical consistency verification with knowledge-graph embeddings. Empirical testing on the LIAR and FEVER datasets shows that CORE-FACT achieves 94.7% and 96.3% accuracy, respectively, outperforming state-of-the-art baselines by 3.5 to 4.8 percentage points in accuracy, with 17% lower latency than GEAR and 31% lower latency than DAGNN, and 98.2% robustness against 30% Byzantine attacks. Under differential-privacy guarantees verified at ε = 10.96, δ = 10⁻⁵ (Renyi DP composition, empirical membership inference validation committed for revision), CORE-FACT achieves a 31% reduction in false positives through explicit causally informed reasoning. These findings make CORE-FACT a scalable and interpretable framework that consolidates causal optimization, trustworthiness evaluation, and secure aggregation for next-generation federated misinformation detection. Full article

Short-term traffic flow forecasting becomes especially difficult when incomplete observations, within-window frequency variation, and state-dependent sensor relations occur together. Missing readings can affect both node features and the spatial dependencies inferred from them, yet these issues are commonly modeled separately. We therefore propose RIFT-STGNN, a Robust Interleaved Frequency–Trend Spatio-Temporal Graph Neural Network for multi-step traffic flow forecasting. RIFT-STGNN follows a coordinated information flow: observation status is retained during temporal–frequency encoding, the frequency representation supports both node features and graph construction, and four graph sources are fused before each Graph-GRU update. Trend-aware temporal attention then produces direct multi-step forecasts. Experiments on PEMS03, PEMS04, PEMS07, and PEMS08 show competitive numerical performance relative to selected literature-reported baselines under the 12-step setting. On PEMS04 and PEMS08, the three-run mean MAE values are 17.73 and 13.14, respectively. These values are numerically 4.63% and 9.00% lower than the corresponding literature-reported sAMDGCN values. Component ablations, state-dependent graph analysis, and controlled missing-rate experiments support the roles of dynamic graph learning, within-window frequency encoding, and mask-aware input handling under the evaluated settings. Full article

The growing demand for accurate and computationally efficient combustion mechanisms for simulations has driven significant advances in chemical kinetic mechanism reduction for multicomponent fuels. This review systematically examines the evolution of reduction methodologies, from early size- and stiffness-reduction techniques to modern construction-based and optimisation-driven strategies. Key approaches including directed relation graph (DRG) methods, sensitivity analysis, path flux analysis, computational singular perturbation (CSP), and genetic algorithms are critically assessed. The review further explores the foundational kinetic mechanisms of hydrocarbons, alcohols, ethers, and esters, highlighting the hierarchical role of C₀–C₄ core chemistry in multicomponent fuel oxidation. It provides a consolidated understanding of reduction strategies and workflows; highlights access to detailed kinetic mechanisms for practical fuels. A central finding is that co-oxidation interactions between fuel components significantly influence combustion behavior and must be explicitly retained in reduced mechanisms. Despite substantial progress, challenges remain in developing universal reduction frameworks, establishing standardised validation datasets, and achieving fully automated mechanism construction. This review provides a roadmap for researchers seeking to implement skeletal/reduced chemical kinetic models in multidimensional engine simulations while retaining chemical fidelity across wide operating conditions. Full article

In smart cities, crowding in transportation hubs, large event venues, and commercial/tourist districts can rapidly escalate from service congestion to public-safety incidents. Real-world operations are constrained by heterogeneous sensing coverage, delayed statistics, privacy requirements, and the need for accountable multi-agency decisions. Following a rigorous PRISMA protocol, we synthesized 107 primary empirical studies (2020–2026) to systematically review context-aware crowd technologies. Moving beyond isolated algorithmic benchmarks, we organized these advances into a mathematically formalized closed-loop framework (Sensing–Prediction–Intervention–Feedback). Crowd sensing has evolved toward edge-based computer vision, passive mobile signaling, and multimodal fusion to balance operational trade-offs among density applicability, environmental robustness, privacy burdens, and end-to-end latency. Prediction architectures—converging on Spatiotemporal Graph Neural Networks (ST-GNNs) and simulation-augmented digital twins—are critically evaluated against constraints in predictive horizon, computational overhead, and explainability. To bridge theory and practical deployment, we deduce a multidimensional evaluation taxonomy and a hierarchical trigger-and-escalation matrix, tailoring control philosophies (e.g., spatiotemporal capacity synchronization and dynamic demand reshaping) to the three heterogeneous scenarios. Finally, we propose four strategic directions to chart a deployment-oriented roadmap for the integrated Urban Brain: edge-based privacy-preserving fusion, cross-scenario generalization, accountable Explainable Artificial Intelligence (XAI) with human-in-the-loop synergy, and end-to-end outcome-oriented empirical validation. Full article

Satellites are critical components of modern space information systems. During long-term on-orbit operation, satellite telemetry often exhibits multi-scale temporal dynamics, heterogeneous channel behavior, and time-varying inter-variable dependencies, which pose substantial challenges to anomaly detection. Existing methods remain limited in adaptively representing anomaly patterns across temporal scales, jointly modeling temporal evolution and dynamic asymmetric channel dependencies, and preventing over-generalized reconstruction of anomalous inputs. To address these limitations, this paper proposes MSGMamba, a multi-scale graph state space model for satellite telemetry anomaly detection. First, a multi-scale temporal patch decomposition and gated fusion mechanism partitions telemetry sequences into patches of different granularities and adaptively integrates their representations at each temporal position, enabling the joint modeling of short-term transients and relatively slow-varying patterns. Second, a graph–sequence alternating propagation mechanism couples selective state space updates with dynamic graph interaction. At each temporal patch, a directed and asymmetric dependency graph with self-connection priors is generated from the temporally encoded features, allowing temporal evolution and time-varying cross-channel dependencies to be modeled within a unified framework. Third, an orthogonal memory-augmented anomaly discrimination mechanism introduces an orthogonality-constrained memory bank to reduce redundancy among nominal prototypes and constrain the reconstruction space. A dual-pathway anomaly score further combines signal-space reconstruction error with encoder–memory discrepancy to improve the separability of nominal and anomalous samples. Experiments on the SMAP, MSL, and EIRSAT-1 datasets show that MSGMamba outperforms representative baseline methods in terms of average PA-F1 and AFF-F1. Full article

This paper develops a stability-certified distributed residual neural network (ResNN) controller for leaderless consensus of continuous-time first-order multi-agent systems over undirected connected graphs. Unlikeconventional linear protocols and post-training verification methods, the proposed framework combines nonlinear transient shaping with certificate feasibility throughout learning. Each agent uses relative state differences with neighboring agents and applies a shared ResNN policy composed of a linear diffusive backbone and a bias-free neural residual branch. A computable residual-gain bound yields a sufficient condition for global exponential consensus, with the certified rate determined by the backbone–residual gain margin and the algebraic connectivity of the graph. Spectral-norm projection and gain reparameterization preserve this condition after every training update. A complementary forward-Euler certificate provides an explicit step-size condition for exponentially convergent digital implementation. The controller is trained through differentiable closed-loop simulation using an objective that balances disagreement and control effort. Ournumerical results verify the certified decay bound, demonstrate competitive transient trade-offs against three baselines, and evaluate direct deployment across different graph topologies and network sizes. Full article

Industrial-style 3D point clouds frequently exhibit intricate geometric structures such as abrupt curvature changes, multi-surface junctions, and fine surface seams. Extracting fine boundary cues from these data remains challenging due to three intertwined factors: cross-boundary neighborhood contamination caused by purely spatial neighbor selection, orientation-sensitive boundary contrast in arbitrary poses, and over-smoothing from uniform feature aggregation. To address these issues, we present GeoRefine-Net, a geometry-consistent boundary refinement framework that progressively refines boundary predictions through three dedicated mechanisms. The geometry-consistent graph construction (GCG) module incorporates multi-order geometric statistics and a learnable boundary prior to actively discourage erroneous cross-surface neighbor associations during graph construction. The Rotation-aware Boundary Contrast Encoding (RBC) module captures local directional disparities using a composite descriptor incorporating normal cross-product interaction, angular divergence, curvature discrepancy, and tangent-plane projection, thereby providing rotation-consistency-enhanced boundary cues across varying object orientations. The Confidence-Guided Boundary Refinement (CBR) module introduces a gating mechanism that adaptively amplifies or suppresses neighbor contributions based on learned consistency scores, producing sharper boundary activation while suppressing noisy responses. The main novelty of GeoRefine-Net lies in the coupled design of geometry-consistent graph construction, rotation-consistency-enhanced boundary contrast encoding, and confidence-guided aggregation, rather than in any single handcrafted descriptor or isolated network component. Additional evaluations using SymBLE, PR-AUC, directed-versus-symmetric GCG, normal and curvature perturbations, normal-free variants, coefficient sensitivity analysis, and inference-time profiling further characterize the robustness and limitations of the proposed framework. Comprehensive evaluations on the ABC CAD dataset demonstrate that GeoRefine-Net outperforms the strongest directly compared baseline by 2.8 percentage points in the F1 score. Full article

Accurate wind vector prediction is essential for renewable energy utilization and power system stability, yet existing methods struggle to jointly model local dynamics, global structures, and temporal robustness. To address this limitation, an Adaptive Multi-Branch Heterogeneous Fusion Wind Prediction Network (AMBHFN) is proposed. Local dynamic, global structural, and temporal robustness modeling are assigned to dedicated heterogeneous branches, whose outputs are coordinated through the Adaptive Multi-Branch Prediction Collaboration Mechanism (AMBPC). Multi-source meteorological variables and terrain information are used for local dynamic modeling, while global spatiotemporal structures are captured by a 3D U-shaped fully convolutional branch and temporal robustness is enhanced by an iTransformer-based multi-agent branch with graph convolution. Experiments on ERA5 data show that AMBHFN outperforms eight retrained baselines over the 0–23 h forecast horizon, with an average error reduction of more than 12%. At the first forecast step, the root mean square error (RMSE) and mean absolute error (MAE) are 0.33 m/s and 0.25 m/s, respectively. Under the strict 22.5° threshold, wind direction forecast accuracy (WDFA) reaches 97.72% at 0 h and 78.06% at 6 h. Fine-tuning in two target regions reduces the 13–23 h RMSE to 1.54 and 1.96. Statistical tests confirm significant improvements over MFWPN, and ablation studies verify the complementarity of the three branches. With 128 giga floating-point operations (GFLOPs) and a 22 ms per-sample forward inference time, AMBHFN achieves a competitive balance among accuracy, stability, and efficiency. Full article

Material screening, including oxide catalyst discovery, remains constrained by costly synthesis-and-test cycles, making sample-efficient experiment selection a central challenge. Active learning can accelerate this process, but standard acquisition rules mainly optimize predictive utility and offer limited support for clarifying the causal structure behind material-screening targets. CIAL is a causal-structure-aware acquisition framework. It combines an intervention-inspired graph-structure term with expected improvement, using an adaptive schedule that begins with structure clarification and shifts toward prediction-oriented exploitation. On synthetic benchmarks with known ground-truth causal graphs, CIAL reduces final-iteration structural Hamming distance by 76.2% relative to the standard EI baseline; significance is assessed using two-sided Welch’s t-tests on final-iteration metrics across five random seeds, with $p=0.0036$ for SHD and $p<0.001$ for F1. For real-data screening, CIAL is evaluated on a Materials Project transition metal oxide benchmark built from filtered oxide entries with formation-energy labels and generic bulk/compositional descriptors. Formation energy is an upstream stability-relevant property, not a direct catalytic activity label. On the main MP benchmark, CIAL is competitive with EI; its clearest advantage over EI appears under NOTEARS-based cross-split validation, but random sampling achieves the highest inferred-reference graph agreement in several real-data settings. An exploratory OC20 catalyst dataset check follows the same ordering but remains statistically inconclusive. Ablation results show that neither the causal nor the predictive component alone recovers the full benefit of the combined policy. CIAL improves graph recovery on controlled synthetic benchmarks; the real-data evidence is mixed and protocol-dependent, establishing CIAL as a proof-of-concept structure-aware acquisition method rather than a validated catalyst-discovery framework. Full article