Search Results (8,495)

Predicting the evolution of microstructure and field quantities under varying processing and loading conditions is a central challenge in computational materials science and metal additive manufacturing (AM). While deep learning (DL) methods offer ultra-fast prediction capabilities post-training, existing models often struggle with poor spatial and temporal extrapolation, high parameter burdens, and an inability to effectively integrate diverse conditioning parameters alongside high-dimensional input fields. To address these bottlenecks, we propose a novel conditional generative adversarial network (CPGAN), which is designed to seamlessly ingest both initial fields and governing condition parameters. The CPGAN framework offers three distinct advantages: (1) it accurately maps the combined effects of initial states and process conditions onto evolved fields; (2) it demonstrates robust extrapolation capabilities across diverse spatial and temporal scales, including the unique ability to natively generate high-resolution rectangular domains; and (3) it achieves superior predictive accuracy and training stability compared to standard convolutional baselines by effectively suppressing spurious artifacts. We validate CPGAN’s performance against rigorous physics-based ground truths across three representative engineering applications: porosity evolution in selective laser sintering (SLS), spatial distribution of 2D von Mises stress fields in solid structures, and the spatiotemporal evolution of grain growth. The results confirm that CPGAN is a highly adaptable and efficient surrogate model, capable of simulating continuous structural and morphological evolutions even when driven by highly non-uniform spatial or temporal kinetics. Full article

Traditional over-the-air (OTA) testing typically requires the device under test (DUT) to be positioned at the geometric center of the anechoic chamber, which limits the flexible evaluation of modern wireless terminals. Although the spherical vector wave (SVW) method provides a rigorous electromagnetic mode expansion, its direct use in eccentric testing scenarios is prone to coefficient-domain overfitting. In the conventional coefficient-domain formulation, the increased involvement of high-order evanescent modes can lead to overfitting of physically insignificant coefficients, resulting in unstable and oscillatory reconstruction. To explain this behavior, an analytical periodicity model is developed and validated by numerical simulations, showing good agreement across all tested configurations. To overcome this limitation, this paper develops a unified 3D eccentric spatial–spectral composite operator for eccentric field synthesis by directly incorporating the three-dimensional offset into the field evaluation process. The proposed operator maps probe excitation weights to the translated 3D local test-zone field samples, thereby reformulating the synthesis problem from coefficient-domain fitting to field-domain matching. This field-domain formulation naturally downweights high-order modal components with negligible local-field contributions, thereby improving numerical stability. Numerical simulations in a 3D multi-probe anechoic chamber (MPAC) demonstrate that, under significant eccentric conditions, the conventional SVW method essentially fails, while the plane wave synthesis (PWS) method achieves less accurate reconstruction than the proposed scheme. In contrast, the proposed scheme maintains stable, oscillation-free reconstruction and consistently outperforms PWS by 5 to 15 dB across all evaluated scenarios. This work provides a promising solution for flexible 3D OTA evaluation of large-scale wireless terminals. Full article

The southeastern flank of Mt. Etna is affected by the presence of active faults capable of adapting to deformation through both seismic slip and aseismic creep, posing challenges for seismic microzonation and for land-use planning. Structural surveys in the urban area of San Gregorio di Catania revealed a ~1 km long, N–S trending secondary fracture zone with an extensional component, inducing progressive damage to buildings and infrastructure. To characterize this scarcely visible structure, passive seismic single-station surveys processed with Horizontal-to-Vertical Spectral Ratio (HVSR) tecnique were integrated with Multichannel Analysis of Surface Waves (MASW). The HVSR data enabled the mapping of the spatial distribution of resonance frequencies, tracking an anomalous trend in the seismic bedrock geometry and depth directly correlatable with the presence of the secondary fracture zone. Directional analyses exhibit systematic preferential orientations of resonance peaks near the fracture corridor, confirming a rigorous structural control and a tectonic origin for the recorded anomalies. Furthermore, reconstructed 2D impedance contrast sections show distinct discontinuities and a local westward dislocation of the main seismo-stratigraphic interface across the deformation zone. The lack of correlated instrumental seismicity supports the interpretation that the displacement is primary accommodated via aseismic fault creep. Methodologically, these findings demonstrate that the passive seismic method provides a highly effective, non-invasive approach for identifying hard-to-detect tectonic structures that remain unobliterated by dense urbanization. Ultimately, these results offer critical, actionable constraints for seismic microzonation and urban land-use setback zoning. Full article

Across the world’s electrical substations, water-treatment plants, and manufacturing lines, a single engineer with valid credentials and a laptop can today push new control logic to a programmable logic controller (PLC) and change the physical behaviors of safety-critical equipment within minutes. Firmware and ladder-logic updates on SCADA and industrial IoT systems are privileged operations: an attacker installing a malicious update controls the physical process. Existing protections concentrate install authority in a single party with no externally verifiable record; compromise of the vendor key, the engineering workstation, or any operator credential suffices to deliver an attacker-chosen payload to a PLC. CertiFlash binds every update to four independent approvals: a vendor signature, a FROST-Ed25519 threshold signature from an operator quorum, a per-session nonce from the PLC, and a monotonic counter. Every decision is recorded in an append-only Merkle transparency log. The PLC verifies the aggregate with a standard RFC 8032 Ed25519 verifier, requiring no FROST-specific device code. Four security properties (authenticity, authorization, rollback resistance, auditability) are machine-checked in Tamarin under a Dolev–Yao adversary with up to t − 1 compromised operators and corroborated through ten attack scenarios. The implementation runs with concurrent Modbus TCP and Siemens S7 traffic, blocks all attacks, signs in 27–192 ms (k = 3–10), keeps ML-DSA-65 within 6% of Ed25519 from 1 KiB to 10 MiB, and sustains 30.1 updates/s on 100 PLCs. The operator-quorum signature remains FROST-Ed25519: the design is partially post-quantum in the evaluated version. The framework maps to IEC 62443-3-3 SR 3.4 and NIS2 Article 21(2)(d–e). Full article

Following the PRISMA-ScR framework for scoping reviews, we systematically searched five databases (Scopus, IEEE Xplore, ScienceDirect, SpringerLink, Web of Science) using a Boolean query combining real-time processing, 3D reconstruction, and deformation modelling terms. From 86 records identified, 56 peer-reviewed publications (2009–2025) were retained after two-stage screening and organized into a unified taxonomy covering sensing modalities (RGB-D, LiDAR, tactile), reconstruction pipelines (volumetric fusion, NRSfM, neural radiance fields), and deformation models (FEM, PBD, mass-spring, GNN-based surrogates, differentiable simulators). Of the 56 included works, 60% were published between 2022 and 2025, confirming the field’s rapid growth. Neural and implicit representations account for 20% of contributions, FEM-based methods for 16%, and hybrid or application-specific pipelines for 21%. Four systemic gaps emerge: the absence of a unified physics-aware benchmark; unresolved speed–accuracy trade-offs (PBD achieves >30 FPS on desktop GPUs for 10³–10⁴ vertex meshes but lacks mapping to physical material constants (Young’s modulus, Poisson’s ratio), limiting material fidelity; full-order FEM ensures physically consistent stress–strain behavior but runs at only 1–10 FPS without order reduction; reduced-order FEM recovers interactive rates for low-frequency deformation modes); fragile handling of occlusions and multi-object contact; and limited end-to-end integration of sensing and simulation. The findings support the presentation of a research roadmap centered on model order reduction, differentiable physics, multimodal sensing fusion, and standardized evaluation protocols, with implications for robust digital twins of deformable environments. Full article

The global prevalence of type 2 diabetes mellitus (T2DM) has increased more than twofold over the last thirty years. T2DM is associated with multiple complications, among which diabetic encephalopathy and accompanying cognitive impairment have drawn considerable interest. This systematic review synthesizes findings from advanced diffusion magnetic resonance imaging (dMRI) studies (published from 2009 to 2025) on T2DM-related brain microstructural abnormalities. The most common technique, diffusion tensor imaging (DTI), consistently reveals reduced white-matter integrity (lower fractional anisotropy, higher diffusivity) associated with cognitive impairment. DTI-based network analysis further identifies disrupted structural network topology, characterized by reduced global and nodal efficiency. To overcome DTI’s limitations, newer techniques provide more specific insights: diffusion kurtosis imaging shows reduced tissue complexity in white matter, gray matter, and crossing-fiber regions via non-Gaussian modeling; neurite orientation dispersion and density imaging quantifies decreased neurite density; intravoxel incoherent motion assesses combined microstructural and microvascular alterations; diffusion spectrum imaging maps complex fiber architecture. These dMRI metrics may provide promising imaging markers for characterizing T2DM-related brain microstructural alterations. However, most available evidence remains cross-sectional, and further longitudinal, multicenter validation is required before these measures can be considered clinically validated biomarkers for prediction, diagnosis, or monitoring. Full article

Computed tomography angiography (CTA)-based segmentation of the superficial temporal arteries (STAs) and facial vessels (FVs) is important for neurosurgical and reconstructive planning. Nevertheless, segmentation of STAs and FVs remains challenging because of their small caliber, tortuous courses, and proximity to high-intensity bony structures. This study aims to develop a deep learning framework for accurate automated segmentation of these craniofacial vessels. A single-input 3D nnU-Net v2 model was trained using raw CTA volumes, while a Fusion-based Vesselness Map (FVM) was constructed from multiscale vessel-enhancement filters to emphasize small vascular structures and suppress irrelevant regions such as the skull and skin. Instead of being used as an additional input channel, the FVM was incorporated into the loss function as a spatial prior to guide the network toward vessel boundaries and distal branches. In 72 clinical cases, the FVM-guided model improved segmentation accuracy compared with a baseline model trained with Dice Focal Loss, particularly in boundary delineation. For the STAs, the Average Symmetric Surface Distance decreased from 6.543 mm to 2.941 mm. Qualitative evaluation further showed reduced segmentation noise and fewer false positives near bone and distal branches. These findings suggest that integrating classical vessel enhancement into deep learning supervision can improve morphologically consistent craniofacial vessel segmentation and support preoperative surgical planning. Full article

Medical image analysis is a cornerstone of modern healthcare, yet conventional single-modal deep learning often struggles with the unique physical constraints and structural variability inherent in data acquired from diverse medical sensors. Recently, Vision-Language Models (VLMs) have sparked a paradigm shift by bridging the semantic gap between visual sensor signals and clinical narratives. Following the PRISMA guidelines, 167 representative studies are systematically synthesized in this review to provide a comprehensive roadmap of VLM technological evolution and clinical utility. First, rather than treating VLMs as generic feature extractors, their underlying mechanisms are uniquely distilled into seven core operational principles, which are then explicitly mapped to downstream applications such as few-shot diagnosis, prompt-driven segmentation, and multi-task foundation models. To facilitate intuitive evaluation, a rigorous quantitative cross-comparison of current benchmark architectures is presented. Crucially, this review goes beyond highlighting successes by critically assessing prevalent clinical bottlenecks, including zero-shot segmentation failures, multi-modal hallucinations in diagnosing rare diseases, and the prohibitive computational complexity associated with 3D volumes and gigapixel whole slide images. Finally, a novel, forward-looking framework is proposed: the transition from static “image-text alignment” to dynamic “multi-source sensor-driven intelligence”. By addressing both physical sensor constraints and algorithmic limitations, this survey offers actionable insights for developing trustworthy, sensor-aware clinical diagnostic agents. Full article

Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics. Full article

Adversarial camouflage has attracted growing research attention owing to its ability to execute multi-view, persistent attacks in real physical environments, outperforming conventional single-view adversarial patches. However, most existing methods are confined to non-targeted attacks, which induce arbitrary incorrect detection results without specifying target categories. This ambiguity weakens attack destructiveness and stealthiness, posing limitations for security evaluation of real-world vision systems. To address this gap, we present TACT, an approach built upon the full-coverage physical camouflage pipeline. By replacing the original category supervision with a predefined target class, TACT redirects the optimization gradient to guide 3D texture toward the target category features. Such a scheme only employs the inherent feature alignment mechanism of off-the-shelf object detectors, without redesigning network modules, defining novel loss functions, or modifying the rendering pipeline. Extensive experiments across digital and physical domains validate its effectiveness: on seven mainstream general-purpose object detectors, TACT-person achieves an average targeted attack success rate of 51.91%, and delivers cross-architecture and cross-version transferability. In physical tests, TACT-bird reduces mAP50-95 by 59.87% on YOLOv8, yet a TCER–TASR gap suggests that the physical pipeline acts as a low-pass filter: coarse-grained target classes transfer robustly while fine-grained ones suffer feature collapse. These results confirm the viability of native supervision redirection and reveal an empirical pattern: coarse-grained target classes transfer more robustly through the physical pipeline than fine-grained ones, suggesting that target class feature granularity consistently influences physical-domain attack effectiveness. Full article

Field-Programmable Gate Array (FPGA) technology allows for the creation of unique hardware implementations based on mass-produced chips. The process of project prototyping for such systems using Hardware Description Languages (HDLs) remains complex, even with modern tools. The comparison of HDL coding styles, for example, a behavioral case statement against a structural binary-tree decomposition, shows that the choice is capable of affecting post-implementation timing and area. The performed study, using the open-source yosys/nextpnr toolchain, shows that the validity of such a comparison is decided by the fabric domain. Logic that falls through to generic Look-Up Table (LUT) mapping is governed by the mapper’s heuristic fixed point rather than by source intent: on the crossbar, the behavioral and structural netlists become identical in cell composition; on the priority encoder, the verdict reverses; and on the barrel shifter, the LUT area collapses, so the comparison does not isolate the coding-style variable. It was measured that the keep_hierarchy attribute restores a meaningful comparison at ~17% LUT cost (N = 8) and provides a structural invariant to the ABC mapper variant, but the behavioral result is mapper-sensitive and the N = 4 verdict reverses under the legacy -noabc9 mapper (Cohen’s d from +2.4 to −1.6). By contrast, logic that involves a dedicated primitive before LUT mapping—an adder bound to the carry chain or a multiplier bound to a DSP block—yields source-meaningful verdicts that do not reverse with a mapper. Replication on a second fabric (Lattice iCE40) confirms that this behavior is fabric- rather than vendor-specific. The main contribution of this work is the proposed first fabric-domain characterization of synthesis canonicalization as a methodological hazard for cross-HDL FPGA studies on open-source toolchains, which identifies the two-phase synthesis mechanism that delimits it and supplies a decision rule (inspect post-synthesis composition) to identify whether a given comparison is susceptible. Full article

Maintaining reliable communication while ensuring collision-free motion is a central challenge for mobile relay robots operating in indoor disaster environments, where abrupt non-line-of-sight (NLOS) degradation and narrow structural bottlenecks can severely disrupt multi-hop connectivity. To address this problem, this paper proposes a Gaussian Process-Driven Adaptive Tube Model Predictive Control (GP-ATMPC) framework for communication-preserving relay navigation. Gaussian process regression (GPR) is used to construct a probabilistic spatial radio map from sparse received signal strength indicator (RSSI) measurements, providing both the predicted channel mean and its uncertainty over unvisited regions. Motion uncertainty is represented by an adaptive ellipsoidal error tube whose radius varies with translational motion, angular motion, and localization uncertainty. Based on this tube model, both obstacle and communication constraints are tightened over the full closed-loop state tube via a tube-tightened lower confidence bound (LCB) that jointly accounts for radio-prediction and motion-tracking uncertainty. Across two indoor disaster environments and 50 Monte Carlo runs each, the proposed method attains the highest connectivity satisfaction rate among controllers that preserve a safe motion margin, with significantly fewer end-to-end connectivity violations than nominal and heuristic adaptive-margin MPC by a paired Wilcoxon test, while maintaining millisecond-level online solve times. A reactive connectivity-first baseline reaches slightly higher raw connectivity but at three to four times the near-collision rate and without feasibility or stability guarantees. The radio-prediction layer is further validated in a higher-fidelity Gazebo environment and on real indoor RSSI measurements, where it reconstructs the measured channel with a mean absolute error of about 2.1 dB. These results indicate that coupling spatial radio prediction with adaptive tube-based robust control provides an effective framework for resilient communication-aware relay navigation in degraded indoor environments. Full article

Diffusion models for 3D genome structures make inference an ensemble-generation and tiling problem. In the released ChromoGen workflow, millions of independent denoising trajectories are executed through a single-GPU path, while overlapping genomic windows are sampled without enforcing consistency of their shared physical interval. We introduce ParaChromo, a parallel inference framework for conditioned, tiled 3D genome diffusion workloads built around the trained diffusion U-Net and distance-map interface. ParaChromo organizes the workload into three inference-layer modules: a workload-dispatch module schedules region, guidance, and sample chunks across worker groups; an encoder-aware sharded-conditioning module scales and shards the EPCOT front end with FSDP while keeping the inner-loop U-Net replicated; and a seam-coherent tiled-synchronization module projects the shared 12-bead overlap of adjacent reverse chains in distance-map space. On eight A6000 GPUs, the combined reduced-step and task-parallel systems path raises throughput from $2.356\pm 0.003$ to $235.71\pm 1.120$ samples/s, a $100.04\pm 0.486$-fold gain over the released single-GPU baseline. The reduced-step setting is supported by a sweep from 50 to 1000 DDIM steps, where distance-distribution and Hi-C-based metrics remain stable across four chromosomes. For the synchronization module, the chr22 seam discrepancy falls from 150.9 pm to 7.9 pm, while matched internal and Hi-C-based quality metrics are preserved. The synchronized chr22 run also gives a chromosome-scale coordinate rendering over 32 paper-aligned tiles. Together, these results show that conditioned, tiled 3D genome diffusion can be executed as a scalable workload when throughput parallelism, sampler length, encoder placement, and spatial consistency are treated as separate but compatible constraints. Full article

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

Understanding hand–paddle interaction is essential for optimizing performance and preventing injury in kayaking, yet coaches still lack objective, practical tools. We present a soft, instrumented glove that measures and dynamically maps palmar pressure throughout the stroke cycle. A matrix of piezoresistive sensors is integrated into the glove and connected to dedicated electronics housed in a waterproof enclosure. A viscoelastic model converts sensor resistance into forces, enabling time-resolved 3D mapping of contact mechanics. Data are transmitted via Bluetooth Low Energy (BLE). Experimental validation on a kayak ergometer across multiple cadences demonstrated accurate measurements (per-sensor root mean square error (RMSE) of ±2 N), clear delineation of pull and push phases, evolving pressure distribution over the motion, and a peak total right-hand force of 186 N at high cadence. Beyond feasibility, these results position the glove as a practical training aid: it supports athlete-specific load monitoring and the early detection of potentially problematic movement patterns. Full article