Search Results (102)

Data movement constrains edge convolutional neural network (CNN) accelerators. We present Weight and Activation Compression for Tensor Traffic (WACT), a lossless framework for 8-bit signed integer (INT8) weights and consumer-facing activations. WACT calibrates a mode, tile geometry, and Rice parameter per invocation-aware tensor identity, freezes the policy, and uses a complete metadata- and alignment-inclusive comparison with strict uncompressed (RAW) fallback. Across five image-classification CNNs with one cold weight load and 320 activation passes per model, WACT reduced measured downstream occupied packet bytes by 44.48% (1.801×) over 78,339 events, with zero decoded-INT8 mismatches. A minimal aligned-RAW sensitivity changed this saving by 0.0293 percentage points. On MobileNetV2, FULL_WACT exceeded the best recalibrated single mode and a matched zero-value-compression-style baseline by at least 5.402 and 8.587 percentage points, respectively. A separate labeled 50,000-image validation measured FP32 and INT8 quantize–dequantize (QDQ) Top-1 accuracies of 72.148% and 71.392%, respectively; the latter used scales frozen from the canonical 32-image calibration subset, denoted S0. Explicitly bounded aggregate cold-start SCALE-Sim modeled-access projections were 11.00–17.70%. The complete WACT policy was evaluated in software, whereas the implemented hardware was limited to the single-tile Rice-mode encoder and decoder paths. The routed 28 nm CMOS blocks had standard-cell areas of 0.010942 and 0.010571 mm², passed post-layout SDF loopback, and consumed 4.939 mW of codec-logic power. The measured downstream-byte reduction therefore provides an energy-saving opportunity whose realization depends on codec overhead and the cost of memory movement in the target hierarchy. Full article

This work presents the first complete system for automated six degrees of freedom (6DOF) satellite pose estimation from spatially resolved, ground-based, adaptive optics (AO)-corrected imagery, addressing a key challenge in Space Domain Awareness (SDA). The approach mitigates the need for human labeling by directly regressing satellite orientation and position from blurry, noisy, and deeply shadowed imagery. A multi-stage deep neural network pipeline localizes the satellite, predicts pose, and optionally applies temporal filtering. Networks are trained exclusively on fully synthetic imagery generated from a CAD model, yet generalize effectively to real data, bridging the Sim2Real domain gap. On 137 real, human-labeled test images of Seasat, the model achieved a mean rotation error of $5^{°}$ and a mean image-plane translation error of 21 cm. Slant range error was quantitatively evaluated on synthetic data due to unknown real-sensor parameters. Qualitative evaluation of additional real Seasat imagery rated 177 of 199 predicted poses as “ground truth equivalent” or “high-confidence match,” with zero catastrophic failures. The system was extended to seven degrees of freedom (7DOF) for satellites with articulating components and demonstrated on real Hubble Space Telescope (HST) imagery, achieving ${5.5}^{°}$ rotation error, 51 cm image-plane translation error, and $8^{°}$ symmetry-adjusted solar array error on a 249-frame pass with causal temporal filtering. Across 586 real test images from Seasat and HST (captured over multiple decades under diverse conditions) the system consistently performed well. Full 6DOF performance was quantified on a high-fidelity wave optics (HFWO) synthetic test set of Seasat, where the model achieved ${8.4}^{°}$ mean rotation error, 34 cm image-plane translation error, and 1.4% line-of-sight range error at $r_0=6$ cm and 1031 km range. In a limited 200-image benchmark, the model demonstrated 48% lower mean rotation error than a single human labeler while operating ∼800× faster. It required <40 h and a single A100 GPU to generate data and train. The approach was also demonstrated for ARGOS, a smaller satellite with highly symmetric geometry. An exploratory General Image-Quality Equation-based image quality metric (AO-IQ) was introduced as an empirical correlate for pose accuracy. General-purpose models like GPT-4o and Depth Anything V2 failed across most SDA tasks, but rapid gains in vision-language models warrant continued monitoring. These results establish a new operational baseline for practical, real-time satellite pose estimation from AO SDA imagery. Full article

Crime offense descriptions are often recorded as unstructured text, making large-scale analysis and categorization difficult. This study proposes a semantic clustering framework for Thai crime offense descriptions using sentence embeddings, dimensionality reduction, and unsupervised clustering. Two datasets were obtained from Thonglor Metropolitan Police Station and Mueang Nonthaburi Police Station, Thailand. After preprocessing, the datasets contained 962 and 902 unique offense descriptions, respectively. Each description was transformed into a 768-dimensional embedding using SimCSE-PhayaThaiBERT. The embeddings were represented in Principal Component Analysis (PCA) Space and Uniform Manifold Approximation and Projection (UMAP) Space and clustered using K-Means, DBSCAN, HDBSCAN, and OPTICS. The results showed that UMAP Space generally provided more useful clustering results than PCA Space. Although DBSCAN achieved the highest internal clustering scores, it classified most records as noise. In contrast, HDBSCAN provided a more balanced result by maintaining strong clustering quality while retaining more records for interpretation. Qualitative analysis showed that the discovered clusters corresponded to meaningful offense categories. The proposed framework can support exploratory analysis of Thai crime records without requiring manually labeled data. Full article

Per- and polyfluoroalkyl substances (PFASs) are persistent synthetic chemicals of global concern. While most research has focused on their occurrence and toxicity, far less attention has been paid to their impacts on the global carbon cycle. This review synthesizes current evidence on how PFASs influence carbon cycling across soils, aquatic systems, and the atmosphere. In soils, PFASs alter organic carbon inputs by affecting plant biomass and root exudates and shift microbial community composition and enzyme activities, thereby modulating organic matter decomposition. In aquatic ecosystems, PFASs biologically impair carbon sequestration by inhibiting plankton, and abiotically interact with extracellular polymeric substances to prolong the cycling of dissolved organic carbon. The atmosphere acts as a key mediator as follows: thermal treatment of PFASs generates perfluorocarbons, potent greenhouse gases that exacerbate global warming and further disturb carbon cycling. Despite clear disruptive effects, major knowledge gaps remain. Future research should use quantitative structure–property relationship modeling to assess PFAS alternatives (e.g., PFHxS), and employ advanced molecular tracking (e.g., isotopic labeling, NanoSIMS) and machine learning to unravel nonlinear PFAS–carbon dynamics. Improved detection technologies are needed to identify greenhouse gas byproducts from PFAS thermal treatment. Ultimately, deploying high-resolution flux observation networks and integrating PFAS dynamics into Earth system models and carbon-accounting frameworks are critical for predicting carbon–climate feedback and supporting global carbon neutrality goals. Full article

With advances in robotic manipulation in recent years, tactile sensing has become increasingly important in scenarios where visual information is unreliable or insufficient. However, the development of learning-based tactile algorithms and policies is limited by the cost and time required to collect large-scale physical datasets. While robotic simulation offers an alternative for synthetic data generation, current simulation platforms, such as NVIDIA Isaac Sim, lack integrated capacitive tactile sensors. This paper presents a finite element method (FEM)-based digital twin of a capacitive tactile sensor and an extension for NVIDIA Isaac Sim that enables the real-time generation of synthetic tactile data directly within the simulation environment. The proposed system extracts nodal deformations from the Isaac Sim PhysX engine and uses a convolutional neural network (CNN) to predict synthetic tactile maps that replicate the response of the physical sensor. We further demonstrate adaptability by retraining the model from an initial sensor to a second capacitive sensor, the Robotiq TSF-85, operating under the same sensing principle. The complete generation pipeline executes in 8.04 ms per frame on the laptop configuration and 6.71 ms on the workstation, enabling real-time operation at 60 Hz on both, and at 120 Hz on the workstation. The similarity of the generated tactile data for the Robotiq TSF-85 is evaluated using complementary similarity metrics, achieving a mean Structural Similarity Index Measure (SSIM) of 0.727 ± 0.16 and a mean Pearson correlation of 0.87 ± 0.16 against real measurements. To demonstrate the utility of the proposed framework, a shape-recognition task (cylinder, sphere, cube) was performed using only synthetic tactile data and evaluated on real-world sensor data, achieving an accuracy of 69.3% with zero real training labels. By enabling integrated tactile simulation and synthetic data generation within Isaac Sim, this work provides a practical tool for tactile perception using capacitive tactile sensors. Full article

Peak annotation in Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a persistent bottleneck that typically requires the manual assignment of chemical formulas to hundreds of fragment ion peaks per spectrum. This work describes a physics-informed probabilistic framework that automates this task by combining five chemically motivated constraints—Gaussian mass accuracy, element composition priors, isotope pattern matching, nitrogen rule parity, and graded valence bounds—into a multiplicative belief score. We evaluate the framework on 643 ground-truth peaks from 151 compounds spanning both positive and negative ion modes, and we explicitly distinguish two regimes. As a scoring task—when the correct formula is present in the candidate list—the framework attains 52.3% Top-1 and 76.4% Top-3 accuracy, a 4.9-fold improvement over mass-only scoring. In fully automated end-to-end deployment, where candidates are generated de novo, Top-1 accuracy is 26.3%; the limiting factor is candidate generation rather than scoring, as only 46.5% of ground-truth formulas are currently produced by the database and combinatorial generator. Leave-One-Compound-Out Cross-Validation (59 compounds, 525 peaks) yields 51.8% Top-1 accuracy with fixed domain-knowledge weights, confirming generalization stability. Ablation analysis identifies element composition priors as the dominant non-mass constraint (−27.7 percentage points when removed), followed by isotope matching (−10.3 pp) and the nitrogen rule (−5.3 pp). The framework requires no labeled training spectra—relying instead on physically motivated priors and curated fragment databases—provides interpretable per-constraint scores (which represent relative rankings rather than calibrated probabilities), and supports polarity-specific configurations, offering a practical computational foundation for automated ToF-SIMS spectrum interpretation. Full article

Multitasking is pervasive in multimodal interaction, particularly within safety-critical domains like driving. Evaluating the impact of In-Vehicle Multimodal Interaction (IVMI) on drivers is critical, yet existing methods predominantly rely on post hoc subjective surveys or coarse unimodal monitoring. Grounded in Multiple Resource Theory and following a Research through Design methodology, we operationalized this theory into a non-intrusive tool chain that evaluates IVMI impact from multimodal behavioral signals (visual, touch, and driving) and supports real-time, objective evaluation in both simulated and real-world domains. To mitigate the Sim-to-Real gap, the method combines real-world multimodal data acquisition with a modality-decoupled cross-domain calibration. Its feasibility was evaluated through a simulator study ($n=27$) and a small-nscale real-world on-road pilot study ($n=3$). The results suggest that the tool chain effectively acquires high-fidelity data to support the previously developed evaluation model (Quadratic Weighted Kappa = 0.916) and achieves a preliminary calibration of cross-domain latent feature spaces. As its reference labels are behaviorally derived and share a common basis with the model inputs, this agreement indicates internal consistency rather than independent construct validation. Crucially, while multimodal interaction behaviors (visual and touch) exhibited relatively high cross-domain consistency, real-world driving behaviors showed systematic magnitude suppression. This finding is tentatively interpreted, as a hypothesis to be tested in future work, through the lens of Risk Homeostasis Theory, and highlights the necessity of monitoring multimodal interaction behaviors rather than relying solely on vehicle telemetry. Overall, this research develops and provides preliminary feasibility evidence for a theory-driven cross-domain tool chain, indicating its potential to objectively quantify multimodal interaction impacts in real-world multitasking contexts. Given the small, homogeneous on-road sample, these pilot-stage results should be read as feasibility evidence and a methodological basis for future large-scale, demographically diverse validation. Full article

Cyber-physical smart infrastructures integrate sensing devices, communication networks, control components, and service platforms, which makes them vulnerable to malicious activities that may evolve gradually through several attack stages. The objective of this study is to develop and evaluate a simulation-based cybersecurity framework capable of detecting a proposed novel multi-stage cyber attack and identifying its internal progression within a realistic smart infrastructure environment. To achieve this objective, a NetSim-based cyber-physical smart infrastructure was modeled to generate both normal operational traffic and staged malicious traffic. The generated traffic was captured, processed, labeled, and transformed into a stage-aware cybersecurity dataset. An artificial neural network (ANN) model was then trained and evaluated for two detection tasks: binary classification of normal versus attack traffic and multi-class classification of compromise, coordination, and execution attack stages. Twenty experimental configurations were designed to examine the model under progressively broader infrastructure contexts, including sensing, service, gateway, control, backbone, and full-span operational scenarios. The best binary testing performance was achieved in the eighteenth experimental configuration, representing a broad full-span infrastructure scenario, with 97.96% accuracy, 97.80% precision, 97.65% recall, 97.72% F1-score, and 1.06% false positive rate. For stage-aware multi-class detection, the ANN model achieved 96.97% accuracy, 96.36% macro-averaged precision, 96.20% macro-averaged recall, 96.28% macro-averaged F1-score, and 96.55% weighted F1-score. Macro-averaged metrics report the unweighted average performance across classes, while weighted F1-score accounts for class support. These results show that the proposed simulation-based framework can generate realistic attack-aware traffic data and support reliable ANN-based detection of both attack presence and attack-stage progression. Full article

Real-time cooperative control of quadrotor swarms in cluttered environments requires balancing formation maintenance, obstacle avoidance, inter-UAV safety, and per-step computational cost. This paper proposes a multilayer perceptron (MLP) surrogate for high-level objective-weight selection in a modified multi-objective pigeon-inspired optimization (modified MPIO) distributed controller. The proposed MLP surrogate learns the state-to-weight mapping of the online search and directly predicts the two-dimensional objective-weight vector, while the original flocking, gap-based obstacle-avoidance, and command generation rules are retained unchanged. The surrogate is trained from teacher-generated weight labels using randomized scenes, DAgger-based state aggregation, and risk-weighted supervision. On a fixed closed-loop benchmark, the proposed controller increases the true collision free rate from 48.00% to 86.89% and the safe success rate from 38.67% to 74.22% relative to modified MPIO, while reducing the mean per-step decision latency for the whole swarm from 8494.70 ms to 0.92 ms. The improvement is most pronounced in safety-related and runtime metrics, while the formation-related gain is comparatively modest. Ablation results show that the final benchmark performance is not explained by DAgger or risk weighting alone, and that the medium-sized surrogate provides the best safety-latency tradeoff among the tested network architectures. A qualitative AirSim case study further indicates that the same high-level surrogate controller can be executed in a higher-fidelity asynchronous multirotor simulator. Full article

Vision-based monitoring of personal protective equipment (PPE) is central to construction safety, yet robust detectors remain limited by scarce, privacy-constrained site imagery. Digital twin simulation can generate labeled synthetic data at scale, but Sim-to-Real gaps make the effective use of synthetic data under a fixed training budget unclear. We benchmark YOLOv11s, Faster R-CNN, and RT-DETR-L using a controlled real–synthetic mixing protocol comprising a fixed real-only test set (400 images), separate sampling pools (5760 real and 4000 synthetic images), and eleven training configurations of approximately constant size (∼1450 images before the validation split) with real fractions ranging from 0% to 100%. Using average recall (AR@100) as the primary safety-oriented metric, the original single-run benchmark shows non-linear architecture-dependent responses to data mixing: YOLOv11s and RT-DETR-L achieve their single-run peaks at G9 (90% real/10% synthetic), whereas Faster R-CNN performs best at G10 (100% real). To assess robustness for the most central YOLOv11s comparison, we further conduct a targeted supplementary repeated-seed analysis for G9 and G10 and re-evaluate all resulting checkpoints on the same fixed real-only test set. This supplementary analysis shows that G10 achieves higher mean performance and lower variance than G9 for YOLOv11s, indicating that the apparent single-run advantage of limited synthetic supplementation is not stable across reruns. However, this robustness check is limited to the central YOLOv11s G9-versus-G10 case and should not be interpreted as a comprehensive robustness validation across all configurations and detector families. Persistent errors on safety vests further indicate a materiality gap for deformable PPE. Overall, these findings suggest that synthetic supplementation can be useful in some settings, but its value is architecture-dependent, evaluation setting-sensitive, and should be interpreted cautiously under robustness-oriented evaluation. Full article

SUMOylation is a rapid and dynamic process that orchestrates the switch between complex assembly and disassembly and between protein stabilization and turnover, making it particularly suitable for regulating stress responses. While proteomic methodologies exist for analyzing SUMOylated proteins under stress conditions, methods/tools for visualizing polySUMOylation dynamics have not been established. Here, we develop a polySUMOylation tracking tool by fluorescently labeling the polySIM domains derived from RNF4, which can reliably track polySUMO location and relate polySUMOylation levels to puncta number and intensity under various stress conditions, such as serum starvation, oxidative stress, and genotoxic stress. Furthermore, we extend its utility for tracking polySUMOylation across multiple cellular contexts in both control and stressed states. Collectively, this tracking tool enables deeper investigation of polySUMOylation dynamics and advances our understanding of how polySUMOylation regulates cellular processes in stress responses and disease pathogenesis. Full article

The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglecting the bidirectional feedback between consumption patterns, generation mix, and public decision-making. This PRISMA 2020-compliant systematic review compared statistical, machine learning, and deep learning models for energy forecasting and machine learning and deep learning models for anomaly detection. Searches in Google Scholar and Scopus used seven targeted strings, restricted to peer-reviewed empirical studies (2022–2026; 2023–2026 for anomaly detection), indexed in Q1–Q3 JCR journals, excluding theoretical and non-benchmarked works. A six-item risk of bias questionnaire—with a threshold of four points—guided inclusion, yielding 60 articles. Addressing the first research question (RQ1) on comparative model performance, hybrid deep learning architectures optimized with bio-inspired metaheuristics achieved the highest forecasting accuracy ($R^2$ up to 0.9984), with metaheuristic optimization acting as a cost-reducing factor; statistical models remained competitive for long-horizon forecasting, while large-language-model-based approaches addressed data scarcity through few-shot learning. Addressing the second research question (RQ2) on smart grid optimization, predictive techniques reduce forecasting errors enabling real-time load adjustment and Demand Response, though a systematic asymmetry constrains their potential: consumption studies integrate socio-economic variables, whereas generation studies rely on meteorological inputs. Addressing the third research question (RQ3) on infrastructure security, supervised and unsupervised approaches detect anomalous operational states and support fault diagnosis, yet remain constrained by scarce labeled fault data and limited cross-regional validation; generative models such as GANs and diffusion models partially address this limitation by enabling Sim2Real strategies and realistic digital twin construction. Evidence is strongest for hybrid forecasting; certainty is lower for anomaly detection given reliance on experimental surrogates. No single paradigm achieves universal superiority. The primary finding is the consistent absence of integrated frameworks jointly modeling consumption, generation, anomaly detection, and public decision-making across the reviewed literature. This result reflects a structural limitation of the current state of the art, rather than a forward-looking research agenda. This study was funded by the ENIA International Chair on Trustworthy Artificial Intelligence European Recovery Plan; the protocol was not pre-registered. Full article

Continuous monitoring of the Stand-to-Sit (STS) transition serves as a critical indicator of lower-limb frailty in the elderly, for which millimeter-wave radar provides an ideal privacy-preserving, device-free sensing solution. However, robustly distinguishing between safe Controlled Sits (CSs) and dangerous Uncontrolled Descents (UDs) is severely hindered by the prohibitive cost of subjective expert scoring for fine-grained labels, alongside the pervasive “Clever Hans” effect where existing deep models overfit static environmental clutter rather than learning intrinsic human kinematics. To circumvent these bottlenecks, we formulate STS evaluation as a dynamic boundary detection problem and propose SCA-BiMamba, a linear-complexity bidirectional State-Space Model that utilizes actual fall events as extreme kinematic surrogates for UDs. This forces the network to learn a strict physical boundary between CS and physiological failure without subjective grading. Furthermore, we establish a stringent Real-to-Sim diagnostic audit as a core methodological contribution. By projecting models trained on noisy real-world data onto pure-kinematics simulations—incorporating stochastic temporal phase shifts, kinematic overlaps, and unified physiological tremors—we explicitly quantify feature disentanglement. This protocol serves as a formal ‘probing test’ to expose the ‘Clever Hans’ effect, ensuring the model relies on invariant human physics rather than transient environmental artifacts. Extensive experiments demonstrate that SCA-BiMamba achieves highly robust classification on real-world data (averaging 94.2% Macro F1 with 100.0% Uncontrolled Descent Recall), and achieves a highly robust 99.4% ± 1.1% Macro F1 in the simulated zero-shot transfer. We emphasize that this optimal performance reflects the successful abstraction of extreme kinematic boundaries, rather than a flawless resolution of all clinical complexities. Concurrently, it exhibits strict resistance to shortcut learning and sustains robust real-world scalability using merely 20% of the training data, thereby establishing a promising privacy-preserving boundary-based radar motion classification framework for distinguishing controlled sitting from extreme instability surrogates. Full article

Inter-turn short-circuit faults (ITSC) in the stator winding of large synchronous machines are among the most critical failures in power systems and may lead to severe insulation damage and unplanned outages. At the same time, such faults, due to their nature in critical industrial scenarios, make it difficult to collect sufficiently rich labeled datasets for data-driven and deep-learning-based diagnostic methods. Training diagnostic models purely on simulated signals often results in a severe domain shift between the digital twin and the physical machine due to nonlinearities, mechanical noise, and measurement imperfections, causing a significant degradation of performance when the model is deployed in practice. This paper proposes a hybrid diagnostic framework that combines a nonlinear physics-based digital twin of a synchronous machine, formulated using an extended Park’s transformation model with a dedicated fault loop, with a Domain-Adversarial Neural Network (DANN) driven by a minimal physics-guided feature vector composed of the 100 Hz and 200 Hz harmonic amplitudes of the excitation current. Simulated data from the digital twin are used as a labeled source domain, whereas test-bench measurements of the excitation current form an unlabeled target domain, enabling unsupervised sim-to-real transfer of the stator fault resistance. The proposed architecture achieves accurate regression of the stator fault-loop resistance on a laboratory machine without any labeled measurements of real faults. Experimental results demonstrate Mean Absolute Error (MAE) below 3% across the investigated fault severity range, significantly outperforming baseline approaches that lack domain adaptation. The industrial significance of this approach lies in its potential to facilitate a transition from reactive to predictive maintenance. By enabling early-stage detection, the framework allows power plant operators to avoid catastrophic failures and significantly reduce exceptionally high costs associated with unplanned outages and cascading grid disturbances. Full article

Angiogenesis is a spatially regulated hallmark of colorectal cancer (CRC) progression, yet current analytical frameworks fail to resolve how nutraceutical bioactive compounds interact with angiogenic signalling within the heterogeneous tumour microenvironment. This review advances a central hypothesis: that the spatial localisation of palm oil mill effluent (POME)-derived bioactive compounds within CRC tumour tissues is predictive of their functional anti-angiogenic activity. POME—the largest waste stream of palm oil processing—contains a chemically diverse array of bioactives, including tocotrienols, phenolics, carotenoids, and fatty acids, with reported antioxidant, anti-inflammatory, and anti-angiogenic properties. However, the existing evidence is predominantly derived from bulk in vitro analyses, limiting mechanistic conclusions about compound behaviour within spatially organised tumour architectures. To address this gap, we propose an integrated framework positioning mass spectrometry imaging (MSI)—across matrix-assisted laser desorption/ionisation (MALDI), desorption electrospray ionisation (DESI), and secondary ion mass spectrometry (SIMS) platforms—as the analytical bridge between compound localisation and angiogenic function. By enabling the label-free, spatially resolved co-localisation of POME-derived compounds with key angiogenic mediators, including VEGF, HIF-1α, and NF-κB, within intact CRC tissues, MSI provides a mechanistic platform that transcends the limitations of conventional molecular analyses. A four-component translational roadmap is outlined, encompassing POME bioactive profiling, spatial compound mapping, angiogenic co-localisation analysis, and functional validation. Critically, the existing evidence on oil palm-derived bioactives is appraised with respect to study quality, mechanistic depth, and translational limitations, identifying the most analytically tractable candidate compounds for spatial investigation. Collectively, this framework positions POME valorisation within a precision nutraceutical oncology paradigm, offering a spatially informed strategy for anti-angiogenic intervention in CRC while simultaneously addressing the environmental burden of palm oil processing waste. Full article