Search Results (750)

Geometry-processing and computational-imaging workflows combine heterogeneous data structures, topology-changing edits, dense numerical fields, visualization, and repeated experimental variation. These workflows are often clear as algorithms but obscured in software by traversal boilerplate, representation conversions, build-system boundaries, and ad hoc scripting conventions. This paper presents tgLang, a domain-specific language with explicit, runtime-enforced representation types that makes meshes, point clouds, curve networks, grids, two-dimensional images, and image stacks first-class executable values. The language combines manifest types, typed arrays, modules, deterministic parallel constructs, flow-oriented queries, and runtime-provided domain operations. Its current implementation uses a stack-based bytecode virtual machine for reference semantics and dispatches representation-heavy operations to optimized C++ kernels. The evaluation is organized around complete workflows: topological hole detection, distance-field-based mean camber line extraction, voxel downsampling of point clouds, curve-network generation, surface-mesh smoothing and remeshing, image-stack edge detection, morphological image processing, and image-stack surface extraction. These examples show that a domain-aware source language can express multi-representation geometry and imaging algorithms as compact, reproducible programs while preserving explicit representation choices and a path toward deployable implementations. Full article

Sustainable urban disaster response requires Earth observation (EO) resources to be selected according to event demands, environmental conditions, sensing capabilities, and urban targets. This study proposes an Observation Linked Open Data (O-LOD)-based Urban Disaster Observation Task (UDOT) enhancement framework for semantic EO resource configuration. O-LOD organizes heterogeneous data through four dimensions, Event, Context, Subject, and Object, which an instantiation algorithm populates as task graphs. Layered GeoSPARQL queries then match thematic and analytical capabilities, qualify orbit-derived observation opportunities against context and object constraints, and rank feasible alternatives using the Observation Capability Evaluation Model (OCEM). Evaluation on the 2020 Khartoum flood and Bobcat wildfire narrowed 202 satellite–sensor pairs to three flood-capable and four wildfire-capable pairs and returned two Khartoum and eight Bobcat ranked opportunities, with complete queries executing in 3.0 s and 0.07 s. Constraint ablation and resolution sensitivity analyses identified the conditions governing the feasible set. Publicly accessible Sentinel, Landsat, and MODIS products corroborated both retained and excluded results, supporting water-extent and burn-severity mapping where coverage, spectral bands, and image quality met the task requirements. O-LOD therefore provides a traceable semantic link from disaster observation demand to qualified and ranked EO opportunities and subsequent image assessment, supplying task-oriented inputs for downstream scheduling and supporting context-aware sensing for sustainable urban disaster response. Full article

Approach channels are affected by sedimentation and scour, resulting in continuous changes in underwater topography. Such processes tend to generate shallow spots and inadequate navigable dimensions, posing safety hazards that undermine both waterway resilience and navigation capacity. To address these issues, this paper proposes a multi-level grid-based spatiotemporal indexing method for bathymetric data, aiming to support resilience-oriented management by improving the effectiveness of bathymetric data management. First, a channel-segment-section partitioning strategy is designed to construct hierarchical progressive grids for the efficient organization of massive bathymetric data. Second, a multi-dimensional spatiotemporal integrated query method is developed to meet diverse analytical and retrieval requirements. Third, a digital depth model (DDM) construction method is introduced that integrates boundary-constrained terrain reconstruction with efficient mesh optimization, enabling underwater terrain representation that adapts to the elongated and irregular morphology of approach channels. The contribution of this work lies not in proposing new individual algorithms but in the tailored integration of these techniques to address the specific challenges of approach-channel bathymetric data. Experimental results demonstrate that the proposed method achieves high construction efficiency across different storage and query schemes. The method enhances the retrieval and analytical capabilities of bathymetric data in representative application scenarios, such as shallow spot identification, critical section analysis, dredging analysis, and erosion–deposition evolution. Consequently, these improvements provide technical support for resilience-oriented channel management and ensure navigational safety. Full article

Machine learning is increasingly used to relate molecular descriptors, formulation variables, and biophysical measurements to aggregation, viscosity, solubility, and shelf-life outcomes. The evidence is promising but uneven. Most published datasets contain tens to a few hundred antibodies, use different assays and endpoint definitions, and rely mainly on internal validation. Direct evidence for bispecific antibodies, antibody–drug conjugates, mRNA–lipid nanoparticles, and viral vectors remains limited. This structured critical review evaluates what current models can support, how data and validation choices shape reported performance, and where claims exceed the available evidence. We searched PubMed through 30 June 2026 using predefined queries for machine learning, biopharmaceutical formulation, colloidal stability, advanced modalities, and shelf-life modelling. Studies were assessed by molecular diversity, formulation coverage, endpoint quality, split strategy, external validation, and decision relevance. The strongest current use cases are early antibody developability screening, high-concentration viscosity classification, formulation ranking within a defined experimental domain, and image-based particle classification. Long-term shelf-life prediction may benefit from hybrid kinetic and machine learning models, but real-time confirmation remains necessary. Progress will depend less on larger algorithms than on better labels, molecule-level validation, shared reference datasets, and clear uncertainty reporting. Full article

Planning HVDC-connected renewable energy bases is constrained by the cost of full 8760 h multi-scenario replay and the risk that temporal reduction obscures coupled renewable scarcity, ramping stress, storage depletion, and delivery recovery. This study develops an auditable and extensible workflow in which a seven-dimensional coupled state generates continuous preparation–shock–recovery risk windows that feed replay-based metric diagnosis, surrogate-guided query ordering, and replay-authoritative decision release. Decision-LCB prioritizes a finite 600-vector pool using exact investment costs, predicted variable costs, and heuristic tree dispersion; an information firewall, a separate 120-vector calibration pool, and a replay-all fallback prevent unreplayed predictions from certifying feasibility. Decision-LCB recovered the finite-pool oracle in 60/60 original cases and 59/60 pre-label-frozen follow-up cases; it recorded 0/60 empirical false stops in each layer and achieved 60/60 within-tolerance outcomes and 60/60 gate passes in the follow-up. Ridge cost-greedy recovered 60/60 follow-up oracles with the same pass and false-stop counts, supporting cost-aware acquisition but not a unique LCB advantage. A passing gate requires 360 total vector replays, 40% fewer than full enumeration, whereas failure requires 720, 20% more. Simultaneous lower-bound coverage of the 360 sealed candidates was 0/60, and the measured local implementation increased online and end-to-end wall-clock times by 346.75% and 408.53%; therefore, neither simultaneous regret protection nor runtime improvement is claimed. The principal contribution is therefore architectural rather than algorithm-specific: alternative acquisition policies and higher-fidelity network-constrained or project-specific replay engines can be integrated while the information firewall, chronological replay authority, and replay-all safeguard remain fixed. This modularity provides a route to larger finite design spaces, richer technology portfolios, and broader scenario sets when the simulator cost dominates workflow overheads, while measured-data and deployment-specific validation remain necessary. Full article

Efficient longest prefix matching (LPM) is a fundamental operation in networking applications such as IP lookup, where minimizing unnecessary memory accesses is critical for achieving high performance. Bloom filter (BF)-based approaches have been widely studied to reduce redundant off-chip memory accesses by filtering non-existent prefixes using compact on-chip structures. Recently, longest-first search (LFS) using a single BF has been proposed to further improve lookup efficiency. However, false positives (FPs) in the BF still incur unnecessary off-chip memory accesses, thereby limiting overall lookup performance. In this paper, we propose three enhanced BF-based LFS schemes, specifically length partition-based LFS (LP-LFS), stacked filter-based LFS (SF-LFS), and parent query-based LFS (PQ-LFS), to reduce the false positive rate (FPR) while improving on-chip memory utilization. LP-LFS, SF-LFS, and PQ-LFS reduce unnecessary accesses by lowering the FPR through BF partitioning based on query distributions, frequent FP filtering, and parent-node-based storage, respectively. Experimental results validate the theoretical FPR analysis for the proposed algorithms and demonstrate that the proposed schemes achieve lower FPRs than single-BF-LFS, reducing the FPR by up to 84.26% and the average number of unnecessary off-chip accesses per lookup by up to 91.30%, thereby improving the overall LPM performance. These results confirm the effectiveness of the proposed BF-based designs for efficient prefix lookup. Full article

Flexural strength (FS) governs the structural performance of 3D-printed concrete (3DPC) under bending loads yet remains difficult to predict owing to the coupled influence of binder composition, supplementary cementitious materials, water-to-binder ratio, and fiber reinforcement geometry on interlayer fracture behavior. Much of this compositional diversity stems from supplementary cementitious materials, industrial by-products whose reuse as partial cement replacement lowers the embodied carbon of printable mixes. A machine learning framework was trained on 209 FS records covering OPC- and SAC-based systems (FS: 3.65–45.0 MPa; W/B: 0.15–0.65). Six composite features were constructed from physical principles, two of them specific to bending: Fiber_Pullout_Index encoding post-crack pullout energy and Binder_Efficiency capturing cement quality per unit water content at the fiber–matrix interface; Lasso regularization with the one-standard-error rule reduced the 19-variable space to 14 active predictors. Twenty regression algorithms spanning eight families were benchmarked under 30 independent partitions; the Friedman test rejected equal performance (${\chi }^2=337.02$, $p=4.88\times {10}^{-60}$) and all 19 pairwise Wilcoxon comparisons against CatBoost were Holm-significant. CatBoost ranked first (mean rank of 18.17/20; 30-run $R^2=0.9302\pm 0.0722$; seed-42 partition: $R^2=0.9557$; RMSE = 1.761 MPa; MAPE = 11.20%). n(W/B) emerged as the primary driver across SHAP, ALE and LIME, with a monotonic ALE profile spanning 8.99 MPa and no inflection over the full printable window; Binder_Efficiency ranked second (PDP range: 5.21 MPa), isolating cement grade and paste dilution as independent strength levers. Cross-conformal prediction provided finite-sample coverage guarantees without distributional assumptions (empirical coverage: 95.24%; conformity quantile: 3.83 MPa); bootstrap analysis put the epistemic component at a mean predictive SD of 0.946 MPa, a quarter of that quantile. External validation yielded $R^2=0.769$ (Pearson $R=0.923$, RMSE = 1.91 MPa), with 19 of 20 predictions (95.0%) within Bland–Altman 95% limits of agreement, confirming transfer to a source study withheld from model training. A graphical user interface packaging the 14-feature CatBoost pipeline supports mix design queries without programming. Full article

This paper proposes an improved Theta* path planning algorithm for mobile robot navigation in complex obstacle environments. The proposed method introduces a density-aware edge cost function that penalizes transitions into regions of high obstacle density while keeping the heuristic as the provably admissible Euclidean distance, improving both path smoothness and obstacle clearance. A direction-aware local obstacle density measure is designed to evaluate both the spatial concentration of obstacles and their angular orientation relative to the goal direction. A geometric weighting strategy imposes heavier penalties on obstacles located along the forward path toward the target, steering the search toward open regions without compromising the any-angle path capability inherent to Theta*. A line-of-sight sampling mechanism is introduced to accurately evaluate density for long-range jumps, and a precomputed density field reduces the per-query look-up cost. Simulation results demonstrate that the proposed method consistently achieves the fewest turning points and the lowest cumulative angle change and curvature among all compared algorithms, while maintaining competitive path lengths. Full article

Uncertain graphs are commonly used to model the uncertain relationships between entities that arise from experimental or measurement errors. In recent years, the analysis of uncertain graphs has attracted significant research attention, with the computation of $(k,\eta )$-cores emerging as a fundamental problem. However, existing studies on $(k,\eta )$-cores often neglect edge directions, resulting in weak correlations among vertices in the resulting subgraph. To address this limitation, we propose a direction-aware $(k,l,\eta )$-core model. Specifically, a $(k,l,\eta )$-core is defined as a maximal connected subgraph in which every vertex has a probability of at least $\eta$ of having in-degree $\ge k$ and out-degree $\ge l$. We first present an online algorithm based on a peeling strategy to compute $(k,l,\eta )$-cores. To improve query performance, we develop two indexing mechanisms, DUCS-E and DUCS, that accelerate query processing. DUCS-E stores probability information for all possible $(k,l,\eta )$-cores, enabling it to completely avoid redundant computations during query processing, but at the cost of large storage space. To mitigate this issue, we propose the lightweight DUCS index, which stores directional probability information separately, reducing storage overhead while still pruning many irrelevant vertices; however, it requires additional verification. To balance efficiency and storage, we further design a hybrid index that combines the strengths of both approaches. Finally, experimental evaluations on real-world datasets demonstrate the effectiveness of the proposed $(k,l,\eta )$-core model as well as the efficiency and scalability of our methods. Full article

The massive integration of “hydro–wind–solar hybrid” generation transmitted via AC/DC hybrid grids with High-Voltage Direct Current introduces unprecedented challenges to power system transient stability. Traditional Dynamic Security Assessment heavily relies on time-domain simulation and high-density Monte Carlo sampling, which suffer from massive computational burdens and are strictly prohibitive for intra-day operational dispatch. To address this long-standing bottleneck, this paper proposes a novel data-driven comprehensive security region assessment framework based on an Active Learning query strategy and a Support Vector Machine surrogate model. By seamlessly coupling Python with the DIgSILENT PowerFactory simulator, the proposed AL algorithm actively queries and evaluates only the critical operating points near the stability margin, effectively avoiding redundant simulations in obviously safe or unsafe zones. Extensive simulations under a severe N-1-1 contingency demonstrate that the proposed framework can accurately map the non-linear boundaries of both transient rotor angle and short-term voltage stability constraints. Furthermore, the framework’s scalability is rigorously validated in complex 3D high-dimensional operational spaces. By integrating an ϵ-greedy exploration strategy with a Label Flip Rate (LFR) early stopping criterion, the proposed algorithm successfully overcomes the curse of dimensionality. Quantitatively, the proposed method achieves nearly identical boundary resolution using merely 65 physical simulations, as opposed to the 40,000 evaluations required by conventional high-density grid scanning. The total computational time is drastically reduced from 28.6 h to approximately 2.8 min, yielding a remarkable acceleration of over 600 times, making it highly suitable for near-online dynamic security monitoring. Full article

In applications pertaining to sensor network, the Internet of Things, industrial monitoring, and cyber–physical security, graphs are increasingly being outsourced to clouds, where similarity search should be supported without exposing graph content, query graphs, update contents, or database evolution. Existing privacy-preserving graph similarity schemes mainly target static encrypted databases, and as such struggle to handle insertions, deletions, label updates, and long-running index maintenance. This paper proposes DFB-PPGSQ, a dynamic forward-private and epochal backward-private graph similarity matching scheme that moves branch-based lower-bound filtering into a structured encryption framework. DFB-PPGSQ uses epoch-local feature tokens, per-record occurrence handles, one-time update labels, update buffers, deletion tombstones, and shuffle-based branch–tree re-randomization to preserve pruning efficiency while making same-epoch tombstone, traversal, size, timing, and refresh leakage explicit. We formalize the system model, leakage functions, algorithms, and security interpretation, then implement a reproducible Python prototype with HMAC-SHA256 token generation and multi-profile dynamic sensor-topology workloads. Across five random seeds, DFB-PPGSQ keeps server-side filtering latency close to the static branch–tree baseline (46.60 ms versus 44.72 ms at 4000 graphs), avoids immediate full-rebuild updates (0.091 ms insertion and 0.092 ms label update), and keeps metadata-assisted cross-epoch token linkage below 5.6% attack success after refresh in additional industrial and campus IoT stress workloads. Storage, communication, exact GED refinement, side-channel hardening, and verifiable-result protection are treated as deployment costs and limitations rather than being included in the headline latency. Full article

The overset grid adaptive method offers an efficient approach for the computational modeling of steady or transient three-dimensional compressible flow problems. When implementing this approach on parallel distributed memory computing systems, its scaling, load balancing, and partitioning must be addressed. In this study, we present a parallel, three-dimensional, finite volume compressible Navier–Stokes solver with block-based adaptive mesh refinement capability. The overset grid system consists of an Octree forest governed adaptive Cartesian off-body grid and a pre-partitioned body-conforming grid. To create, manage, and efficiently handle the load balancing and partitioning of the off-body grid, the developed solver utilizes the open source library of p4est. The communication between the partitions of the p4est governed off-body grid and the body-conforming grid is established by using an efficient spatial query algorithm. The parallel performance of the developed solver is evaluated by solving two benchmark problems: steady supersonic flow over a semi-infinite blunt-nose cylinder and the transient interaction of an incident planar shock with a sphere in quiescent air. The results show that the solver accurately captures and tracks the resultant flow shock structures while exhibiting good scalable parallel performance. Full article

Trajectory Representations Learning (TRL) serves as a foundational technology for supporting intelligent transportation. However, models trained on real-world data often suffer from performance degradation caused by inherent spatiotemporal distribution bias, which reflects the heterogeneity of urban structures and human movement behaviors. This leads to representations that overfit to frequent patterns, resulting in weak robustness and limited generalization to sparse or atypical trajectories. To address these issues, this paper presents a novel perspective, anchor-guided balanced learning, and instantiates it with a framework, AnchorTRL. AnchorTRL introduces anchors to proactively construct a balanced semantic space instead of passively fitting the empirical data distribution. Specifically, AnchorTRL designs a spatiotemporal anchor identification algorithm to recognize trajectory anchors that comprehensively cover the data manifold. And, it proposes a calculation method to measure all trajectories’ semantic similarity with anchors. Additionally, it develops an anchor-based balanced sampling strategy to mitigate the dominance of frequent patterns and steer the model towards learning a more balanced representation. Finally, it constructs a multi-task contrastive learning objective with adaptive constraints to enhance the aggregation of semantically similar trajectories. Experimental results show that AnchorTRL outperforms existing baseline methods in tasks such as travel time estimation and similar trajectory queries, demonstrating its effectiveness and robustness. This research provides methodological support for constructing more reliable trajectory representation learning models, and offers new insights for optimizing intelligent transportation applications under spatiotemporal biases. Full article

Tiered Mobile Wireless Sensor Networks (TMWSNs), consisting of mobile sensor nodes and storage nodes, are widely used in various fields due to their scalability, energy efficiency, and flexibility. Most existing secure query algorithms assume that data packets generated by sensor nodes can always be delivered to storage nodes. This assumption does not hold in practice, where packets may be lost due to attacks or adverse communication conditions. This paper proposes a loss-tolerant wireless network model for TMWSNs and a novel threat model tailored to this scenario, in which packet-dropping attacks compromise the integrity of query results. To counter these attacks, we present a baseline integrity verification algorithm, the Neighbor Node-Forwarding Digest Algorithm (NNFDA). Each sensor generates a digest of its data and forwards it to neighboring nodes. These digests are then transmitted to storage nodes together with the neighbors’ data, thereby establishing a chained relationship among sensor data. The base station verifies query results using this relationship. The baseline algorithm, however, causes high communication overhead. To reduce this cost, we propose an improved version, NNFDA-BM (NNFDA with Bitmap), which optimizes digest generation and transmission. Experimental results show that NNFDA-BM verifies query result integrity effectively while achieving a significant reduction in communication overhead compared with the baseline algorithm. Full article

The quick transition of the healthcare industry to digital during the era of the Internet of Medical Things and Artificial Intelligence has ignited the demand for frameworks for data sharing while retaining safety and patient privacy. Centralized learning models place potentially sensitive patient data at risk of leakage, regulatory violation, and cyber-attacks which undermine receptivity and responsible ownership of big medical data. Federated learning is a novel paradigm that allows patients from various healthcare entities to train machine learning models while maintaining the ability to leverage their data without sharing their direct data. This study proposes a systematic literature review of approaches of privacy-preserving federated learning frameworks in healthcare applications. Following PRISMA guidelines, searches were conducted across Web of Science, Scopus, IEEE Xplore, ScienceDirect, PubMed, and ACM Digital Library with predefined query strings, explicit inclusion/exclusion criteria, and quality appraisal procedures. A total of 80 peer-reviewed studies, published from January 2015 to December 2025, were included in this systematic review, which examined cryptographic, architectural and algorithmic methods including differential privacy, homomorphic encryption, and Secure Multi-Party Computation, along with integrations using blockchain to enhance trust and confidence in distributed healthcare systems. The findings indicate a gradual shift towards hybrid privacy-preserving federated learning architectures which combined multiple security mechanisms to improve trust, confidentiality and robustness. Although significant progress has been achieved, the real-world deployment of such systems is heavily affected due to the challenges in communication efficiency, non-IID data distribution, adversarial attacks, and regulatory requirements. This research highlights future research directions for scalable, explainable and interoperable federated architectures that strike an optimal balance of privacy, utility and system performance for next-gen health intelligence. Trial registration: PROSPERO (CRD420261401073). Full article