Search Results (8,279)

In LiDAR-camera fusion measurement, conventional nearest-neighbor back-projection methods are limited by discrete point-cloud sampling and struggle to accurately recover the spatial position corresponding to image measurement points under sparse point-cloud conditions. Moreover, targets in complex industrial environments often exhibit diverse local geometric structures, making it difficult for a single surface model to effectively describe different surface characteristics. This paper proposes a Geometry-Guided Adaptive Ray-Surface Intersection measurement method, which replaces the traditional image-to-discrete-point mapping with ray-to-continuous-surface intersection. A spatial viewing ray is constructed from the image measurement point and camera optical center, and local neighborhood points are extracted through nearest projected-point back-projection. Based on curvature and normal-vector statistical features, local surfaces are classified into four geometric categories, and corresponding surface models are adaptively constructed using plane fitting, curved surface fitting, voxel-assisted plane searching, or bilinear patch fitting. The three-dimensional coordinates are then obtained through ray-model intersection. Experiments were conducted on multiple target types in an industrial equipment warehouse over a range of 5–20 m. The results show that the proposed method reduces the average measurement error from 5.63 cm of the nearest-neighbor method to 1.81 cm. When the point-cloud density decreases to 25%, the average absolute error increases by only 26.5%, compared with 92.6% and 58.7% for the nearest-neighbor and interpolation methods, respectively. These results demonstrate the effectiveness and robustness of the proposed method for LiDAR-camera fusion measurement under sparse point-cloud conditions. Full article

Clinical decision making in Acute Myeloid Leukemia (AML) critically relies on rapid genomic characterization. To better understand the AML diagnostic landscape in Canada, the Canadian Leukemia Study Group (CLSG) conducted a survey of laboratory hematology leadership (n = 18) at 16 laboratories across 10 provinces, administered using Google Forms in September 2024. Nearly all surveyed sites were equipped to deliver a full suite of testing platforms through existing on-site infrastructure or laboratory partnerships. Reporting practices varied in terms of genomic integration into bone marrow results and the use of AML classification systems. Turn-around-time (TAT) targets were predominantly determined through internal institutional consensus (62%) or recommendations by provincial cancer agencies/international groups (44%). TAT reduction was a top priority for 56% of laboratories, suggesting timely biomarker results to be an active area for improvement. Various treatment-determining biomarkers were frequently assessed as rapid-tests (defined as a 5-day TAT), including FLT3-ITD (69%), FLT3-TKD (56%), and NPM1 (56%), while others such as IDH1 and TP53 were rapid at a limited number of laboratories. Respondents demonstrated a strong shared interest in joint projects such as the validation of AML measurable residual disease (MRD) assays (56%). There was also unanimous support for establishing CLSG AML laboratory consensus guidelines. This survey documents the current state of Canadian AML laboratories and provides a foundation for future shared development projects. Full article

Avian infectious bronchitis virus (IBV) is widely distributed worldwide and causes substantial economic losses to the poultry industry. Because IBV undergoes frequent mutation, prevention and control of infection remain challenging. Immunization is an important measure for the prevention and control of IB. Therefore, there is an urgent need for a rapid, sensitive, specific, and convenient method for the detection of antibodies against IBV. In this study, we firstly developed an indirect colloidal gold immunochromatographic strip for the rapid detection of antibodies against IBV based on a conserved epitope peptide. The recombinant epitope peptide recognized by N2D5 monoclonal antibody (mAb) against the N protein of IBV was expressed as a GST fusion protein (GST-N2D5) based on the conserved antigenic epitope previously identified in our laboratory. Colloidal gold-labeled GST-N2D5 was used as the detection reagent to generate visual signals. Rabbit anti-chicken IgY and mouse anti-GST mAb were immobilized on the nitrocellulose membrane as the test line (T line) and control line (C line), respectively. The optimal pH and optimal protein concentration for conjugation of gold nanoparticles (AuNPs) with GST-N2D5 were pH 8.5 and 72 µg/mL, respectively. Specificity was evaluated using common avian pathogens, and no cross-reactivity was observed. The detection limit of the strip for IBV-positive serum was 1:180. In addition, the assay showed good reproducibility and stability, and results could be observed within 5 min without any specialized equipment. Clinical chicken serum samples were tested using both the developed strip and an enzyme-linked immunosorbent assay (ELISA), and the strip showed high agreement with the ELISA. In conclusion, the established immunochromatographic strip is rapid, sensitive, specific, and easy to operate, and therefore has potential as an on-site tool for the rapid detection of antibodies against IBV, particularly in resource-limited settings. Full article

Pesticides pose significant hazards to aquatic ecosystems and public health; therefore, understanding their fate in aquatic systems is critically important. Photochemical processes driven by direct and indirect photolysis, particularly hydroxyl radical (^•OH) reactions, play a pivotal role in the transformation of these contaminants in natural waters. This study employed an efficient and selective ^•OH production technique using a high-power UV light-emitting diode (UV-LED) combined with nitrite photolysis to determine the second-order reaction rate constants between ^•OH and selected pesticides (k_X,OH). This approach enabled reliable estimation of the indirect photodegradation rate constants (k_IP) for the selected pesticides in aquatic systems. In addition, direct photodegradation rate constants (k_DP) of the selected pesticides were determined under simulated sunlight conditions using a solar simulator equipped with a 500 W xenon lamp. The photochemical half-lives of selected pesticides in river water and seawater were calculated from k_DP and kIP under assumed steady-state HO^• concentrations. The results demonstrated that direct photolysis rate constants of the selected pesticides ranged from 1.62 × 10⁻⁷ to 5.52 × 10⁻⁴ s⁻¹. The second-order reaction rate constants between the investigated pesticides and ^•OH ranged from 0.045 × 10⁹ to 14.8 × 10⁹ M⁻¹ s⁻¹. Estimated half-lives under direct photolysis in seawater ranged from hours to days, whereas half-lives attributed to indirect photolysis in seawater extended to several years. In contrast, half-lives of pesticides in river water ranged from hours to days for indirect photolysis. Under the assumed steady-state ^•OH concentrations, direct photolysis generally produced shorter calculated half-lives than the ^•OH pathway in seawater, whereas the higher assumed ^•OH concentration substantially reduced the calculated indirect-photolysis half-lives in river water. These findings should be interpreted as condition-specific kinetic comparisons rather than direct measurements of environmental persistence. Full article

Recent studies have witnessed significant advances in unified multi-weather image restoration, which aims to handle diverse weather degradations within a single model. In this work, we observe that rain, haze, and snow restoration exhibit substantial differences in both degradation characteristics and learning dynamics, making straightforward joint optimization prone to performance imbalance and ineffective knowledge transfer. To this end, we propose UMWIR-Net, a unified multi-weather image restoration network equipped with an Asymmetric Task Collaborative Learning strategy. ATCL consists of Intra-Task Difficulty Optimization and Inter-Task Contribution Scheduling. Specifically, Intra-Task Difficulty Optimization jointly models the remaining restoration error and recent learning progress to dynamically estimate the optimization difficulty of each weather task, thereby assigning larger weights to slowly converging and under-optimized tasks. Inter-Task Contribution Scheduling measures the directional influence of a source-task update on the validation objective of a target task, constructs an asymmetric task-contribution matrix, and accordingly promotes tasks that provide stronger transferable knowledge while compensating those that benefit less from collaborative learning. In this manner, different weather restoration tasks collaborate selectively and asymmetrically, allowing the model to exploit complementary knowledge across tasks and improve overall restoration performance. Furthermore, UMWIR-Net adopts a wavelet-based Transformer backbone to capture low- and high-frequency information, enabling effective modeling of both global structures and local details for diverse weather restoration. Extensive experiments on multi-weather image restoration datasets show that UMWIR-Net achieves state-of-the-art performance and delivers more balanced restoration quality across rain, haze, and snow removal. Full article

The transition from early non-standalone 5G deployments to 5G Standalone and, more recently, 5G-Advanced has turned mobile networks into flexible, programmable infrastructures capable of supporting private, industrial, and research-oriented deployments for the development of beyond-5G applications and architectures. Evaluating these networks’ capabilities, however, remains challenging because commercial platforms often provide limited access to internal interfaces, radio parameters, and network measurements. This paper presents an open-source private 5G SA testbed for beyond-5G application validations built using Open5GS, srsRAN, Ettus USRP N310 software-defined radio, programmable SIM cards, and commercial 5G customer-premise equipment. The platform is deployed in a semi-anechoic chamber. End-to-end operation is validated through subscriber registration, authentication, PDU session establishment, and external data connectivity. The performance of the implemented 5G network is evaluated using throughput, block error rate, modulation and coding scheme, and gNB trace logs. Unlike previous open-source 5G testbeds that primarily use RF waveguides, individual network components, or a limited set of radio configurations, the proposed platform combines COTS SIM-based UE operation with a controlled over-the-air evaluation of FDD/TDD and multiple antenna configurations and correlates application-level throughput with internal gNB radio metrics. For FDD downlink operation, the average throughput increased by approximately 74% from 1 × 1 to 2 × 2 and by a further 57% from 2 × 2 to 4 × 4, although the additional peak-throughput gain from 2 × 2 to 4 × 4 remained limited. The platform provides a reproducible environment for validating beyond-5G mechanisms, comparing network configurations, and studying the behavior of future open-source 5G SA systems under controlled conditions. Full article

As offshore wind development moves toward deeper waters, floating offshore wind turbines have become essential for carbon-neutral energy systems. This study experimentally investigates the hydrodynamic coefficients of typical column–heave-plate components under forced oscillations, focusing on the influence of pitch-induced inclination. A circular column without a heave plate and a circular column equipped with a hexagonal heave plate were tested under heave and surge motions with varying periods, amplitudes, and static inclination angles. The static inclinations were used to represent the attitude variation of platform components during large-amplitude pitch responses. Added mass and damping coefficients were identified using the least squares method. The results show that for the heave-plate-equipped column, increasing the inclination from 0° to 5° and 10° reduced the nondimensional heave added mass by approximately 4.3% and 5.9%, respectively, and reduced the nondimensional heave damping by approximately 7.1% and 6.8%. The corresponding reductions in surge added mass were approximately 5.3% and 10.5%, whereas the reductions in surge damping reached approximately 8.2% and 16.4%, indicating that the surge damping is most sensitive to static inclination. These variations may be associated with the altered geometric projection and disturbed flow symmetry of the inclined component, which may affect the attached-fluid volume and energy-dissipation process during forced oscillation. Future studies should further verify the corresponding local separation and vortex-formation mechanisms through detailed flow-field measurements, PIV, or CFD. Full article

Maritime transport is a major component of global supply chains, but reducing its atmospheric emissions remains essential to improving the environmental sustainability of shipping. This study analyzes onboard emission data reported for the MV Ilshin Green Iris under real-world operating conditions to assess how fuel selection affects the direct-emission performance of a dual-fuel marine propulsion system. The vessel is equipped with a MAN B&W 6G50ME-C9.5-GI engine employing high-pressure dual-fuel (HPDF) technology. A quantitative comparison between heavy fuel oil (HFO) and liquefied natural gas (LNG) was performed at 50% of the maximum continuous rating (MCR). At 50% MCR, LNG reduced CO₂ emissions by 27.0%, NOx emissions by 20.7%, and CO emissions by 18.2% relative to HFO, while PM showed an indicative reduction of approximately 69%; its precise magnitude remains uncertain because a complete PM uncertainty budget was unavailable. Over the 900 s measurement period, the estimated reduction in CO₂ mass was 154 kg. During LNG operation, the specific CH₄ emission at 50% MCR was approximately 0.6 g/kWh. Using a 100-year global warming potential of 29.8 for fossil CH₄, this corresponds to approximately 17.9 g CO₂-eq/kWh, equivalent to about 10.5% of the direct CO₂ reduction between HFO and LNG at this operating point. The results are representative of the analyzed stabilized operating point rather than of the vessel’s complete operational profile. The main contribution of this study is a structured matched-load analysis of HFO and LNG emissions from the same HPDF marine engine. The analysis combines measurement-derived specific emissions with energy-based mass estimates, methane-related limitations, data-quality considerations, and regulatory and sustainability implications. Because both fuels were evaluated in the same engine at the same 50% MCR operating point, the study provides a consistent basis for assessing fuel-related differences within the limits of the available dataset. Full article

Regenerative aluminum melting serves as a core process in recycled aluminum production. In the regenerative aluminum melting process, the furnace temperature is a key variable which affects product performance and energy costs. The extreme in-furnace temperature necessitates sensors equipped with protective jackets, which increases measurement costs and severely compromises real-time monitoring capability. Accordingly, accurate furnace temperature prediction is highly valuable for regenerative aluminum melting. In regenerative aluminum melting furnaces, periodic burner nozzle commutation and frequent material charging and discharging lead to complex and time-varying operating conditions, posing considerable challenges to high-precision furnace temperature prediction. To address these issues, a condition-adaptive multi-scale convolution-enhanced Transformer (CA-MC-Transformer) model is proposed for furnace temperature prediction. Firstly, an agglomerative hierarchical clustering algorithm based on the weighted dynamic time warping (WDTW) distance is designed to perform unsupervised clustering on historical process data, thereby extracting physically interpretable prior labels for macroscopic operating conditions. Secondly, multi-scale dilated causal convolutions are utilized to capture local dynamic features at diverse temporal resolutions. A soft attention mechanism is further introduced to dynamically assign fusion weights to condition embeddings and local features, enabling condition-adaptive feature reconstruction. Finally, the fused adaptive features are fed into an encoder-only Transformer network to capture the global long-range temporal dependencies and achieve accurate furnace temperature prediction. Comparative experiments conducted on real operational datasets from an aluminum plant verify that the proposed method effectively eliminates the inherent tracking lag of conventional deep learning models, and substantially improves prediction accuracy and anti-noise robustness under complex and variable operating conditions. Full article

This single-site industrial case study compares a leased diesel shunting locomotive with a battery-electric shunting locomotive used for the same class of in-plant railway tasks at the Seza Cement Plant. The evidence base comprises plant leasing and fuel records, equipment specifications, site-reported electricity indicators, operator-reported operational observations, direct CO₂ calculations, and documented occupational safety and health (OSH) functions; it is not a controlled or statistically replicated time–motion experiment. The diesel system incurred a monthly lease cost of USD 10,000 and consumed approximately 1800 L/month, equivalent to 21,600 L/year. Cross-checking the direct CO₂ calculation with 2.692 and 2.683 kg CO₂/L factors gives 58.1 and 58.0 t CO₂/year, respectively. The approximately 24-month payback is treated as a plant-reported investment indicator and evaluated through a normalized sensitivity model because disaggregated costs for locomotive purchase, charging infrastructure, battery replacement, and historical maintenance are not available in the case-study dataset. Operational evidence is reported descriptively: the 20–40% reduction in task time is an operator-reported range rather than a statistical mean; the 7–9 min value refers to the complete 10-wagon weighing maneuver; and 25 loaded wagons (approximately 1450 t) represents the maximum documented field movement rather than a manufacturer-rated capacity. A force-balance check shows that this maximum movement is feasible only if total equivalent resistance remains below approximately 5.41 N/kN, using the 77 kN catalog tractive effort as an upper bound. The battery-electric locomotive produces no local exhaust emissions at the point of use and incorporates SIL 2 remote-control functions, a deadman function, emergency-stop controls, camera support, lighting, and warning systems; these features indicate risk-control capability but do not constitute a measured accident-rate reduction. The study therefore contributes facility-scale, evidence-bounded information for low-speed, repetitive industrial shunting within a defined operating area rather than a general proof of battery-electric superiority across railway applications. Full article

Electrochemical parameter identification is crucial for evaluating the electrochemical processes in proton exchange membrane water electrolysis (PEMWE). Conventional characterization techniques-including polarization-curve fitting, electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and current interruption (CI)-face significant limitations for rapid diagnostics under high-current dynamic operation, arising from constraints in instrument current rating, measurement time, zero-current control, and noise amplification in numerical differentiation. In this study, we present a simple current step (CS) method to accurately identify key electrochemical parameters and perform overpotential breakdown by using a simplified equivalent circuit model with a current source. To address the numerical instability in derivative calculation caused by sampling noise during voltage transient analysis, a physics-informed neural network (PINN) is introduced to enhance signal smoothness while guaranteeing physical consist ency. Compared with standard characterization, the proposed CS-PINN method demonstrates high accuracy, with an error of less than 2% in overpotential breakdown, less than 5.3% in ohmic resistance, and 2.8% in the Tafel slope (at 5 A/cm²). These results confirm that the CS-PINN method provides a fast, accurate, and equipment-friendly route for rapid electrochemical parameter identification in PEMWE. Full article

To address the limited cooling range of ventilation in deep, high-temperature development headings and the lack of coordinated design between coil-based cooling and the ventilation system, this study proposes a coupled “twin-forcing–coil” cooling scheme. Building on conventional overlap (forcing–exhausting) ventilation, a rear-mounted second forcing duct is added to the conventional overlap (force–exhaust combined) auxiliary ventilation system, forming a dual-duct forcing, single-exhausting configuration—hereafter termed the “twin-forcing–single-exhausting” (TFSE) system—that provides a booster (relay) air supply to mitigate the along-path attenuation of cooling capacity and the short-circuiting of cold air; an in situ heat-exchange coil wall further provides supplementary cooling where ventilation-based temperature control weakens. Using a development heading at the 790 m level of a metal mine in Yunnan as the engineering background, a three-dimensional numerical model coupling the roadway, ventilation system, and coil wall was established and validated against nine field monitoring points, showing average relative errors of approximately 1% for temperature and 2–3% for humidity, comparable to the measurement uncertainty of the field instrumentation. Because the numerical model does not account for evaporative and condensation phase-change processes, two supplementary development headings with standing water at the face were used for validation; results showed that model error increases with water accumulation and heading length, indicating the model’s applicability is limited to conditions with intact surrounding rock and minimal seepage. Six operating cases were designed with duct placement and coil spacing as variables. Results show that single-duct ventilation cooling decays markedly beyond 30 m from the face, whereas twin-forcing booster (relay) air supply effectively extends the cooling range, reducing the 30–70 m section temperature by 2.7–2.9 K; the second duct should be positioned where the first duct’s cooling capacity begins to attenuate but is not yet depleted. Based on only two spacing configurations tested (10 m and 15 m), coil-staggered spacing showed limited effect on cooling performance under the field conditions examined; this preliminary finding requires validation across a broader range of spacings. Among the chilled-water conditions tested, an inlet temperature of 280.65 K and a flow velocity of 0.5 m/s offered a reasonable trade-off between cooling uniformity and economic efficiency. Under the boundary conditions and equipment parameters of this case, energy consumption estimates further indicate that the cooling effect per unit electricity consumption of twin-forcing ventilation is roughly 6–8 times that of coil-based cooling, primarily due to pumping losses over the ~240 m chilled-water delivery distance. This energy penalty indicates that coil-based cooling is better suited as a localized, short-distance supplementary measure rather than as a means of extending the cooling range over long distances. Full article

Hydrogenation reactors are critical equipment in the petrochemical industry, yet their material degradation is governed by coupled multi-mechanism damage. Current assessment practices largely neglect this complexity, remaining single-factor oriented and overlooking synergistic interactions and temporal evolution. This paper proposes a regionally differentiated, multi-level framework integrating 5 primary and 17 secondary indicators with a hybrid AHP-EWM weighting strategy that synthesizes expert knowledge and measured data. A multi-factor coupling correction coefficient is introduced to provide a preliminary estimate of the synergistic acceleration effect among damage mechanisms, while a GM(1,1) gray model enables dynamic trend prediction. Applied to a 25-year 2.25Cr-1Mo steel reactor, the method produces regional degradation values of 0.378, 0.607, and 0.533 for the base metal, welds, and cladding layer, respectively, with an overall baseline of 0.453 rising by 11% to 0.503 after coupling correction. Compared with exponential regression, ARIMA, and BP neural networks, GM(1,1) is selected for its balanced performance in small-sample fitting, extrapolation stability, and physical interpretability. Sensitivity analysis confirms stable degradation grading even with ±50% coupling coefficient variations. The proposed approach mitigates the underestimation inherent in conventional single-mechanism assessments and offers a quantitative tool for full-lifecycle risk management and predictive maintenance of hydrogenation reactors. Full article

High-resolution remote sensing object detection is a fundamental task in Earth observation. However, the storage, transmission, and downstream processing of high-resolution images impose substantial bandwidth, memory, and computational burdens. Compressed sensing (CS) samples and compresses the signals simultaneously, thereby reducing data transmission and storage overhead. Unfortunately, most existing CS-based pipelines require explicit image reconstruction before downstream inference, leading to heavy computational overheads and poor scalability for high-resolution remote sensing images (RSIs). This work focuses on post-acquisition image compression and explores algorithmically, rather than from a physical hardware implementation perspective, whether explicit image reconstruction is an indispensable intermediate step prior to object detection. To this end, we propose HR²SIOD-CL, an end-to-end compressed learning (CL) framework that performs object detection directly on CS measurements of high-resolution RSIs without explicit image reconstruction. HR²SIOD-CL integrates an entropy-driven content-aware adaptive sampling strategy and a measurement-domain detection backbone for multi-scale feature extraction. Although jointly optimized during training, the adaptive sampling and detection modules can be decoupled for flexible deployment. For fair comparison, an extra lightweight reconstruction network equipped with a single-step data-consistency correction is constructed as the baseline. Extensive experiments on the NWPU VHR-10 and DIOR datasets show that across various sampling ratios, HR²SIOD-CL surpasses the reconstruction-based detection method when integrated into two-stage detectors, and achieves comparable or superior detection performance to the reconstruction-based counterparts when integrated into single-stage detectors. Meanwhile, its computational overhead and GPU memory consumption are merely 6.97% and 35.74% of those of the reconstruction-based counterpart, respectively. These results indicate that CS measurements can function as an effective intermediate representation for object detection, and explicit image reconstruction is not a prerequisite when detection is the primary objective. Full article

Gears serve as core transmission components, and their wear evolution directly affects equipment stability and service life under long-duration complex loading. Especially under complex loading and long-term service conditions, the tooth surface topography undergoes continuous evolution. However, traditional wear prediction methods based on physical models or empirical formulas have significant limitations in addressing nonlinear problems involving multiple coupled variables. This study proposes a deep neural network (DNN)-based method for gear wear prediction. Geometric parameters, loading conditions, and surface topography characteristics are integrated as model inputs to enable point-by-point prediction of tooth-profile wear. Experimental results demonstrate that the proposed model achieves excellent predictive performance in the mild-wear regime, with a mean absolute error (MAE) below 2.5 × 10⁻⁴ mm, a root mean square error (RMSE) below 5.0 × 10⁻⁴ mm, and R² values ranging from 0.92 to 0.99. The model also achieves satisfactory prediction accuracy at previously unseen measurement positions and for previously unseen superfinished gear samples. The proposed DNN effectively learns implicit wear-evolution patterns from experimental data and exhibits strong generalization capability, providing a practical approach for gear health monitoring and predictive maintenance. Full article