Search Results (542)

Boaters use class ratings to describe whitewater difficulty on a scale from I to VI, with ratings assigned and refined through the judgment of experienced boaters. These practices are effective, but the addition of physical measurements could improve their reliability and facilitate whitewater comparisons across different rivers and flows. This exploratory study considers specific stream power (SSP), a physics-based metric describing energy transfer in rivers, as an indicator of whitewater difficulty. Data were collected at eight study sites on the Upper Deschutes River in Oregon using a camera-mounted DJI Phantom 4 RTK quadcopter. Sites were assessed at one or two flows and whitewater difficulty ranged from Class I flatwater to Class V cascading rapids. Stream slope and width data were derived from a combination of unmanned aerial vehicle (UAV) photogrammetry and aerial light detection and ranging (LiDAR), and SSP was calculated for each site. Whitewater class ratings were strongly associated (df = 6, p < 0.05) with site average SSP, r_s= 0.95, 95% CI [0.72, 1.00], site average slope, r_s = 0.95, 95% CI [0.72, 1.00], and within-site maximum SSP, r_s = 0.93, 95% CI [0.58, 1.00]. The within-site maximum slope, minimum width, and constriction ratio had significant but smaller relationships. Physical assessments of whitewater conditions may help support management decisions in dam removal, hydropower relicensing, or other instream flow negotiations. Full article

Automated classification of municipal solid waste remains challenging due to high variability in object appearance, deformation, contamination, and inconsistent consumer disposal behavior, creating a need for representative multimodal datasets that enable robust machine-learning models. In this work, we developed a custom edge-node reverse-vending-machine (RVM) data-collection station equipped with multimodal sensors (RGB-D and microphone array) to co-record RGB-D video streams and impact audio on a single local compute node within the same acquisition window. Strict data acquisition protocols were designed to ensure a consistent environment, controlled illumination, and uniform background conditions while capturing realistic variations associated with everyday Greek consumer waste. The resulting dataset comprises 3300 multimodal samples corresponding to 721 distinct household waste items. Each sample is fully annotated across six categories addressing features critical for recycling: Material (18 classes), Hue (16 classes), Top/Cap, Label, Contamination, and Deformation. In addition, Common Objects in Context (COCO) annotations were added to support segmentation and object detection problems. This dataset is intended to support research on multimodal waste recognition, sensor fusion, and resource-efficient classification, and to serve as a foundation for evaluating models targeting edge-deployed recycling systems. Full article

Microbial proteases are widely used for industrial proteolysis in detergents, food processing, hydrolysate production, product stabilization, and side-stream valorization. Their industrial value is not determined by general activity or degree of hydrolysis alone, but by the ability of a defined catalyst to operate reliably under process conditions and generate the intended product endpoint. This review frames microbial proteases as product-directed biocatalysts whose performance depends on the alignment of catalyst identity, process compatibility, substrate access, and product outcome. Production, maturation, and immobilization are considered determinants of the deployed enzyme form, while biocatalyst identity, process translation, product outcome, and endpoint refinement provide a framework for evaluation. Across protease classes, industrial utility is shaped by acid-window matching, operating-window engineering, catalyst definition, metal-state compatibility, and peptide-pool refinement. Data-guided workflows integrating protease discovery and engineering, cleavage prediction, peptidomic mapping, real-substrate validation, and enzyme-system optimization offer a path toward more predictable product-oriented industrial proteolysis. Full article

The Internet of Vehicles (IoV) relies on Basic Safety Messages (BSMs) for cooperative awareness, yet these broadcasts remain vulnerable to falsification, replay, flooding, Sybil-based, and motion-manipulation attacks. This paper proposes a temporal-window-aware physics-informed edge-oriented Intrusion Detection System (IDS) for 20-class IoV misbehavior detection under two simulation-based BSM observability regimes: ideal noise-free kinematics and realistic noise-inclusive observables reconstructed using the sensor-error components supplied separately by VeReMi Extension. Accordingly, “realistic” denotes a noise-inclusive simulation condition rather than real-world validation. From VeReMi Extension streams, the framework derives a compact 20-feature representation capturing kinematics, timing, replay cues, pseudonym dynamics, position-consistency residuals, zero-pattern behavior, and long-horizon motion indicators. These features are normalized with a training-only robust scaler, organized into sender-specific temporal windows, and classified using a lightweight three-layer stacked Long Short-Term Memory (LSTM) with residual temporal pooling. Four implementation variants are evaluated: dense Keras, default-optimized TensorFlow Lite, pruning-only Keras, and pruning-plus-compression TensorFlow Lite. Temporal sensitivity identifies $T=40$ as the best robustness–latency compromise under the realistic noise-inclusive regime. At $T=40$, the final pruned-and-compressed TensorFlow Lite model achieves $99.60\%$ accuracy and $99.09\%$ macro-F1 under ideal observability, and $99.38\%$ accuracy and $98.67\%$ macro-F1 under realistic noise-inclusive observability, with an $88.38$ KB footprint and $0.1283$ ms controlled-runtime latency. Large-scale Central Processing Unit (CPU) benchmarks on $\mathrm{150,000}$ noise-inclusive test sequences provide a platform-dependent runtime reference, with the pruned TensorFlow Lite model reaching $99.14\%$ accuracy, $98.18\%$ macro-F1, and $3.544$ ms average latency on a multi-core Intel Xeon CPU. To complement this high-throughput evaluation, edge-deployment potential is profiled using the official C++ TensorFlow Lite benchmark tool. When evaluated using a single CPU thread without batching, the final artifact achieves an unbatched per-sequence latency of $1.356$ ms, corresponding to less than $1.4\%$ of the standard 100 ms BSM generation interval. An architecture-width ablation identifies the $64/32/32$ recurrent stack as the performance–resource knee point: expanding it to $128/64/64$ improves validation macro-F1 by only $0.0019$ percentage points while increasing TensorFlow Lite footprint and latency by factors of $2.46$ and $2.32$, respectively. A training-time architecture-preserving feature-family ablation confirms that engineered descriptors are essential: raw kinematics alone reduce noise-inclusive macro-F1 from $98.67\%$ to $67.49\%$, with pseudonym dynamics and position-consistency cues producing the largest individual degradations. Full article

Ulva prolifera detection is of great significance for marine ecological monitoring and green tide disaster prevention and control. Current single-modal detection methods have inherent limitations. Optical RGB imagery is highly vulnerable to cloud occlusion, while synthetic aperture radar (SAR) data is contaminated by severe speckle noise. Furthermore, existing multimodal detection algorithms struggle to address the prominent multimodal feature heterogeneity between optical and SAR remote sensing data. To overcome these challenges, we develop a decoupled multimodal detection network (DMDNet) for fine-grained Ulva prolifera segmentation. First, a dual-branch feature extraction module with parallel alignment encoding is constructed to adapt to heterogeneous inputs of two modalities. Second, a dedicated convolutional layer unifies the dimensions of the two-modality feature streams. The processed features are subsequently passed to the encoder–decoder module, where the network exploits available features from both optical and SAR modalities for segmentation. Third, a multimodal comprehensive loss function is designed to mitigate the segmentation accuracy degradation caused by class imbalance and blurry target boundaries. In addition, a multimodal joint training strategy is adopted to train the model with optical and SAR samples simultaneously in each iteration. Equipped with a shared encoder and independent task-specific decoder heads, DMDNet accepts either a single optical image or a single SAR image as input and generates stable and reliable segmentation results. Comprehensive experiments are conducted on FIO-EP and CODC datasets, which demonstrate that DMDNet outperforms other baseline models. Full article

Prestressed and post-tensioned concrete structural elements constitute approximately 43.4% of modern bridge infrastructure, representing 58.2% of the total bridge surface area due to their long-span capabilities. Despite their structural efficiency, evaluating residual post-tensioning forces and diagnosing localized degradation within internally grouted tendons—such as localized stress corrosion cracking (SCC), grout voids, and moisture infiltration—remains a critical challenge due to geometric confinement and high material attenuation. This paper presents a non-destructive Structural Health Monitoring (SHM) methodology optimized for the continuous and periodic assessment of post-tensioned anchorage zones under operational traffic loads. The proposed Identification of Active Anomalies (IAA) system integrates the Acoustic Emission (AE) method with unsupervised machine learning to classify multi-mechanism structural degradation. By implementing a mathematically transparent k-means clustering framework initialized via the k-means++ heuristic, high-velocity multi-parameter AE data streams are partitioned within an n-dimensional Euclidean feature space. The scientific novelty of this work lies in its real-scale validation on an operational, highly complex cable-stayed bridge, establishing a previously unpublished acoustic signature database (the 2025 Signal Database). The empirical validity of the algorithm’s predictive boundaries was confirmed through forensic physical inspections and material sampling during a major structural rehabilitation in 2026, which corroborated the active corrosion states within heavily confined post-tensioned anchorage blocks. Furthermore, extracted AE pattern classes are explicitly correlated with structural crack opening widths, enabling real-time tracking of macro-defect propagation, anchorage slippage, and active micro-structural corrosion. Full article

Published quantum-streaming theory establishes quantum memory advantages for specific streaming problems; how a correlation-sensitive proxy model behaves under the structured dependence typical of sensor and telemetry streams, however, remains uncharted. We present a proxy-based diagnostic and hypothesis-generation framework for correlation-sensitive streaming analysis. Operational proxies for the refreshing time $\tau$ and repetition number r, introduced in this paper, are estimated on five synthetic non-IID regimes (IID, Markov switching, seasonal drift, burst repetition, and long-range dependence) and mapped onto a $(\tau ,r)$ sensitivity landscape—the paper’s central artifact. Three memory-bounded classical baselines (online SGD, averaged SGD, and Count-Min) supply empirical reference points; no quantum algorithm is implemented or simulated and all quantum curves are heuristic proxy estimates. Analytic and empirical refreshing-time estimates diverge by up to $125\times$ under long-range dependence (≈8× for Markov)—the two estimators answer different questions about temporal dependence. Using empirical $\tau$, the proxy model predicts a hypothesised proxy-favourable region under mild-to-moderate Markov correlation that closes as correlation strengthens: the mean curves cross at ${\rho }^{*}\approx 0.82$ under the operating constants ($C=2$, $\delta =0.05$; the crossing moves between ${\rho }^{*}\approx 0.38$ and beyond the sweep range across a C–$\delta$ grid, so only the ordinal reading is robust), and by $\rho =0.88$ the classical baseline exceeds the proxy estimate (Hodges–Lehmann difference $0.046$). Applied unchanged to two real NAB telemetry streams, the same estimators place NYC Taxi inside and Machine Temperature outside the hypothesised favourable region—an ordering that persists across all tested encoder resolutions—with imbalance-aware metrics guarding against majority-class artefacts. Ablations over the $\tau$ estimator, forward window, stream length, target function, and proxy constants preserve the regime ordering within each estimator family. Full article

Accurate semantic segmentation of large-scale outdoor LiDAR point clouds remains a challenging endeavor, primarily due to ambiguous class transitions at object interfaces, non-uniform sampling density across the surveyed area, and shared geometric signatures among distinct object categories. This paper proposes GFE-Net (Geometry-Enhanced Feature Extraction Network), a hierarchical encoder–decoder architecture that systematically improves per-point feature characterization through three complementary design contributions: First, to mitigate the shortcomings of conventional fixed-neighborhood queries in regions of variable point density, a Structure-Guided Neighborhood Adaptation (SGNA) module is devised. At its core lies a morphology-driven contextual gating (MCG) unit that synthesizes neighbor-wise calibration weights from hierarchical shape descriptors fused with elevation difference statistics, allowing the network to preferentially amplify morphologically congruent neighbors while dampening spurious or cross-boundary contributions. Second, to strengthen semantic discrimination beyond what spatial locality alone affords, a Local–Global Interactive Enhancement (LGIE) module is presented. The LGIE module simultaneously distills precise local structure through Euclidean-space neighborhood graphs and captures scene-wide co-activation patterns via compact bilinear factorization of the latent feature space, merging both streams through a residual refinement mechanism that markedly improves inter-class separability. Third, to enforce label consistency at object interfaces without relying on post-processing heuristics, a Neighborhood Prediction Consistency (NPC) loss is introduced. Built upon a Gaussian distance-decay weighting kernel, the NPC loss assigns progressively stronger penalties to label mismatches between a query point and its geometrically proximate neighbors, thereby promoting spatially coherent predictions and attenuating boundary noise. GFE-Net is rigorously benchmarked on two widely adopted large-scale datasets—S3DIS and SensatUrban—yielding OA/mIoU of 89.6%/73.1% and 93.3%/61.1%, respectively. These results demonstrate competitive performance under the reported protocols. Detailed ablation studies and computational profiling further substantiate the efficacy of each individual component. Full article

Next-generation waste degradation and valorization technologies are increasingly developed as integrated platforms for pollutant removal, resource recovery, and circular manufacturing. This review critically evaluates degradation and valorization routes for liquid, organic solid, and inorganic/electronic waste streams from an engineering perspective. For liquid waste, advanced oxidation processes, photocatalysis, electrochemical oxidation, plasma treatment, and hybrid systems are assessed with emphasis on radical utilization, photon and electron efficiency, catalyst stability, byproduct formation, and reactor hydrodynamics. For organic solid waste, biological, thermochemical, catalytic, enzymatic, and mechanical pathways are compared for agricultural residues, textile waste, and industrial polymers, including fermentation, pyrolysis, hydrogenolysis, solvolysis, enzymatic depolymerization, and mechanical recycling. Their practical viability depends strongly on feed purity, product selectivity, monomer or fuel recovery, and the energy and separation requirements of downstream processing. For inorganic and electronic waste, hydrometallurgical, pyrometallurgical, biohydrometallurgical, and physical separation routes are examined for the recovery of critical metals, mineral phases, and non-metallic fractions. Industrially mature integrated flowsheets generally offer greater feed tolerance, whereas emerging selective routes provide improved recovery potential but remain constrained by reagent consumption, reaction rate, and scale. Across all waste classes, the review identifies reactor design, process intensification, reaction–separation integration, techno-economic analysis, and life-cycle assessment as essential tools for translating laboratory performance into scalable, economically competitive, and environmentally sustainable processes. Full article

Synthetic Aperture Radar (SAR) backscatter serves as a key tool for tracking surface water dynamics; however, single-source data dependencies introduce systematic bias tied to the specific physical limitations of the signal. A primary challenge in SAR analysis is the backscatter ambiguity created by ‘water look-alike’ surfaces, which frequently result in false-positive water detections. We show that integrating interferometric repeat-pass coherence significantly enhances the robustness of hydrological mapping in environments where backscatter is prone to signal ambiguity. Using global-scale C-band Sentinel-1 (S1) VV-polarized one-year mosaics (December 2019 to November 2020), we first analyzed normalized backscatter and coherence signatures across major land cover and land use (LULC) classes. To benchmark the complementary value of these data streams, a tile-based minimum-error thresholding approach was applied to detect permanent water surfaces across five challenging global test sites. This evaluation was conducted without post-processing or masking to isolate the fundamental strengths of each dataset. The results indicate that coherence is an optimal complement to backscatter in arid and bare soil regions, where it vastly outperforms backscatter in mapping inland water surfaces. Crucially, since the spatial overlap of False Positives and False Negatives between datasets is minimal, the inherent complementarity of the datasets is proven here via a logical AND fusion rule, which significantly mitigates commission errors and yields substantial improvements in the aggregated F1-score and IoU performance. Analysis-ready L-band NISAR products could contribute to a more comprehensive approach for operational, large-scale surface water assessments. Full article

This paper investigates the Electrode Induction Gas Atomization (EIGA) of a Class 4 tungsten heavy alloy (WHA-4) containing 97.8 wt.% W, with the balance comprising Ni and Fe. Compared with conventional tungsten heavy alloys containing less than 95 wt.% W, the high tungsten content reduces the liquid-phase fraction and melt fluidity, making stable processing significantly more challenging. Powder-metallurgy-fabricated electrodes were atomized under varying resonant induction conditions to evaluate the effects of capacitance, frequency, and input power on melt stability. The results show that stable processing can be achieved through an optimum combination of capacitance and resonant frequency, providing sufficient energy to maintain a steady melt stream and uninterrupted atomization. Under optimized conditions, predominantly spherical powders with minimal defects were successfully produced. Microstructural characterization revealed that the Ni–Fe binder phase remained localized along tungsten grain boundaries and in isolated pockets between tungsten grains. X-ray diffraction and chemical analyses confirmed that the phase constitution and alloy composition of the electrode were preserved during atomization. Under non-optimum conditions, process instability was associated with incomplete melting, beard formation, unstable melt flow, and nozzle blockage. These findings provide new insights into the atomization behavior of high-tungsten heavy alloys and establish practical processing procedures for producing high-quality WHA-4 powders for advanced manufacturing applications. Full article

Scalable classroom process evidence can support teacher reflection and expert review, but manual observation is labor-intensive and direct eye tracking is often too intrusive for routine classroom use. This paper presents a single-camera classroom video analytics system that converts ordinary classroom recordings into reviewable process evidence for teacher reflection. The system combines three-class head detection, teacher association and tracking, six-dimensional head-pose estimation for frontal teachers, pose smoothing with a multiplicative extended Kalman filter, gaze-cone projection, student and region hit testing, four-class rule-based behavior segmentation, and evidence export. In the perception experiments, the three-class head detector achieved an mAP@0.50 of 0.96, while frontal-teacher head-pose estimation yielded a mean angular error of 7.55°. In behavior verification, the algorithm achieved 78.8% sample-wise accuracy and a weighted F1-score of 77.1% against adjudicated human labels. The complete pipeline processed a single 1080p video stream at 48.20 FPS under the reported setup. Because the system infers visual attention from head pose, its output should be interpreted as a visual-attention proxy rather than as a direct measure of eye gaze. Its behavior labels describe observable classroom process patterns and are intended to support human review, not to evaluate teaching quality or individual participants. The exported heatmaps, regional timelines, behavior timelines, annotated videos, and structured reports provide traceable evidence that can be checked against the source recording. Full article

Wireless sensing inside rotating industrial machines is challenging due to signal attenuation, multipath propagation, and continuous sensor motion. Smart particles equipped with wireless communication and inertial sensors provide a promising approach for monitoring such systems. However, the reliability of wireless signal transmission under rotational dynamics remains insufficiently understood, and systematic approaches for sensor selection are lacking. This paper presents a link-budget-normalised experimental characterisation of three commercial smart particles, namely MetaMotionS (BLE), WitMotion BLE, and WitMotion Wi-Fi, in a bare 300 mm diameter by 310 mm deep metallic drum fitted with six triangular lifters, at rest and at 16, 18, and 20 RPM, corresponding to 20.7–25.9% of the critical speed and Froude numbers of 0.043–0.067. Because raw received power conflates transmit power with channel behaviour, the comparison is expressed as excess path loss above free space together with second-order fading statistics. Two particles of the same protocol class differ by 23.9 dB, of which at most 4 dB is attributable to the transmission of power across the documented range of both radios, establishing that device implementation rather than protocol class governs the ranking. Two particles logged simultaneously through a single receiver observe one channel realisation, and the correlation between their signal fluctuations is not significantly different from zero at any speed (r = +0.064, −0.098, −0.083), indicating device-specific rather than environmental fading. Under rotation, the Wi-Fi particle holds an RSSI standard deviation of 1.96 dB against 6.27 and 6.58 dB for the two BLE particles (Welch ANOVA, p < 0.001; Games–Howell post hoc, all pairwise comparisons p < 0.001; |Cliff’s δ| > 0.96). A bounded, dimensionless multi-criteria selection procedure over link margin, signal variability, cross-speed consistency, packet delivery, and energy per delivered packet is introduced; the ranking is invariant under weighted-sum and TOPSIS aggregation but inverts once endurance carries a weight above 0.35, which quantifies the trade-off between link quality and battery life. Coupling between the wireless and motion streams is examined by folding both onto rotation phase. A rotation-locked component in RSSI is detected in one of nine sensor–speed combinations, with a maximum modulation amplitude of 0.92 dB. The RSSI logging cadence of approximately 1 Hz resolves the drum fundamental but lies below the Nyquist requirement for the dominant motion band at 0.91–1.04 Hz, so joint wireless–motion studies of rotating machinery require RSSI logging at 5 Hz or above on a clock shared with the inertial unit. Full article

Machine-learning intrusion detection is challenged by attacks resembling legitimate traffic and by class imbalance. This study evaluates Random Forest detection on the UNSW-NB15 dataset for five binary attack-versus-normal tasks: DoS, Exploit, Backdoor, Analysis, and Reconnaissance. Categorical attributes were label-encoded, features selected using mutual information, hyperparameters optimized by randomized search, decision thresholds tuned on validation data, and SMOTE applied to training data at a fixed target of 56,000 minority samples. SVM and KNN results are descriptive because the shared label-encoded representation precludes model-independent comparison. Without SMOTE, Random Forest achieved F1-scores of 0.9468 for DoS, 0.9504 for Exploit, 0.9839 for Backdoor, 0.9317 for Analysis, and 0.9720 for Reconnaissance. Fixed-SMOTE pipelines achieved 0.9539, 0.9516, 0.9668, 0.8370, and 0.9651, respectively. Thus, SMOTE improved DoS, marginally improved Exploit, and reduced performance for Backdoor, Analysis, and Reconnaissance. Confusion matrices showed fewer false positives and false negatives for DoS, while Analysis gained recall but produced substantially more false positives. These results compare separately optimized complete pipelines and do not isolate SMOTE’s effect. They characterize filtered target-attack-versus-normal streams, not operational mixed-attack traffic. Under this protocol, performance and precision–recall trade-offs were attack-dependent under the evaluated fixed-SMOTE experimental configuration, indicating that oversampling should be evaluated separately for each attack category. Full article

Ensuring high crop productivity while reducing nutrient losses and environmental impacts remains a central challenge of modern fertilization strategies. Controlled-release fertilizers (CRFs) have emerged as a key tool to synchronize nutrient availability with plant demand by embedding soluble nutrient sources within coatings or matrices that modulate water penetration and ion diffusion. This narrative review synthesizes recent advances in CRF materials and technologies, with particular emphasis on biodegradable and bio-based systems, hydrogels, nanostructured carriers, and extrusion-based matrix formulations. It outlines the main classes of coating and matrix materials, their release mechanisms, agronomic performance, and documented benefits for nutrient-use efficiency, crop yield and quality, and soil and water protection. The review also analyzes major scientific, technical, environmental, and economic barriers that currently limit the large-scale deployment of CRFs, including microplastic pollution from persistent polymer coatings and the difficulty of tailoring release profiles under variable field conditions. Emerging directions are highlighted, such as composite and multilayer bio-based coatings, superhydrophobic and stimuli-responsive structures, biochar- and compost-based matrices, nutrient recovery from waste streams, and integration with precision agriculture and sensor technologies. The paper identifies priorities for future research needed to translate promising CRF concepts into robust, field-validated and sustainable fertilization solutions. Full article