Search Results (677)

The relict bedding and slaty cleavage structure in slate directly influences the crushing characteristics and strength properties of slate aggregates. When slate aggregates are used in asphalt concrete, it may have risks of insufficient resistance to crushing and uncertain long-term durability. In order to investigate the crushing behavior of slate coarse aggregates in asphalt mixtures, a comparative study was conducted using limestone and basalt aggregates as reference. Various tests were carried out including crushing value tests, single-particle compression crushing tests, Marshall compaction resistance tests, and gyratory compaction resistance tests. The crushing patterns, crushing strength, and gradation changes of slate aggregates after crushing were systematically examined. Based on the Weibull distribution function, the statistical distribution of single-particle crushing strength was analyzed. Additionally, the particle distribution patterns were studied for single-sized aggregates, blended aggregates, and asphalt mixtures after these were subjected to crushing under Marshall compaction and gyratory compaction. The test results indicated that the crushing value of slate coarse aggregates was 9.2%, which indicates superior crushing resistance compared to traditional limestone and basalt. After long-term exposure to water immersion at 60 °C, high-pressure steam treatment, and heating at 220 °C, the increase in crushing value of slate coarse aggregates was less than 1.5%, indicating excellent water and heat resistance. The two-point and four-point crushing strengths of single particles of slate coarse aggregates were higher than those of limestone and basalt coarse aggregates, and the single-particle compression crushing strength followed the Weibull distribution pattern. Both single-sized and blended slate aggregates exhibited lower proportions of crushing during Marshall and gyratory compaction compared to basalt and limestone aggregates. Asphalt mixtures prepared with slate coarse aggregates also demonstrated better crushing resistance than those made with basalt and limestone, confirming that the bedding structure of slate does not cause excessive crushing in asphalt mixture. The obtained findings were limited to the tested slate aggregates from a single quarry and thus necessary performance verification should be conducted on slate aggregates from other sources before practical engineering applications. Full article

In recent years, the rapid development of commercial satellite projects, such as low-Earth orbit (LEO) communication and remote sensing constellations, has driven the satellite industry toward low-cost, rapid development, and large-scale deployment. Commercial off-the-shelf (COTS) components have been widely adopted across various commercial satellite platforms due to their advantages of low cost, high performance, and plug-and-play availability. However, the space environment is complex and hostile. COTS components were not originally designed for such conditions, and they often lack systematically flight-verified protective frameworks, making their reliability issues a core bottleneck limiting their extensive application in critical missions. This paper focuses on COTS solid-state drives (SSDs) onboard the Jilin-1 KF satellite and presents a full-lifecycle reliability practice covering component selection, system design, on-orbit operation, and failure feedback. The core contribution lies in proposing a full-lifecycle methodology that integrates proactive design—including multi-module redundancy architecture and targeted environmental stress screening—with on-orbit data monitoring and failure cause analysis. Through fault tree analysis, on-orbit data mining, and statistical analysis, it was found that SSD failures show a significant correlation with high-energy particle radiation in the South Atlantic Anomaly region. Building on this key spatial correlation, the on-orbit failure mode was successfully reproduced via proton irradiation experiments, confirming the mechanism of radiation-induced SSD damage and providing a basis for subsequent model development and management decisions. The study demonstrates that although individual COTS SSDs exhibit a certain failure rate, reasonable design, protection, and testing can enhance the on-orbit survivability of storage systems using COTS components. More broadly, by providing a validated closed-loop paradigm—encompassing design, flight verification and feedback, and iterative improvement—we enable the reliable use of COTS components in future cost-sensitive, high-performance satellite missions, adopting system-level solutions to balance cost and reliability without being confined to expensive radiation-hardened products. Full article

The present study describes the ten-year (2014–2024) validation of a Class 100,000ISO 8 qualified air system used in the manufacture of non-sterile pharmaceutical dosage forms in a GMP-certified facility. The lifecycle evaluation included design, installation, qualification, continuous operation, environmental monitoring, cleaning and disinfection verification, and annual third-party validation. The system was assessed for critical parameters, including air renewal rates, airflow directionality, the integrity of high-efficiency particulate air (HEPA) filters and ultra-low penetration air (ULPA) filters, environmental recovery times, and non-viable particle counts. Particle monitoring focused on 0.5 $\mathsf{\mu }$m and 1.0 $\mathsf{\mu }$m channels within the 0.5–5 $\mathsf{\mu }$m range specified by ISO 14644-1 for ISO 8 areas. The 0.5–1.0 $\mathsf{\mu }$m range was prioritized because it provides higher statistical representativeness for evaluating filter performance and controlling fine particulate dispersion, which is particularly relevant in non-sterile pharmaceutical production, while larger particles (>5 $\mathsf{\mu }$m) are more critical in aseptic processes. The influence of personnel and air exchange rates on cleanliness was also assessed during the final years of the study. Results demonstrate that continuous, systematic validation ensures the controlled environmental conditions required for pharmaceutical production and supports the sustained quality and safety of the finished products. This study provides a technical reference for engineers, pharmacists, and quality professionals involved in cleanroom design, qualification, and regulatory compliance. Full article

Direct retrieval of Total Phosphorus (TP) from remote sensing is not possible because TP is not optically active. Unlike optically active parameters, TP does not exhibit spectral signals and relies on indirect correlations with Optically Active Constituents (OACs) such as Chl-a and suspended solids. Existing approaches often rely solely on spectral reflectance while neglecting the environmental variables, such as temperature, that can affect the correlations between OACs such as Chl-a and temperature. To address this, this study integrates satellite-derived Land Surface Temperature (LST) with Landsat 8/9 spectral features, utilizing LST as a spatial proxy for the aquatic thermodynamic environment. Focusing on the Dongjiang River, a subtropical river in China, a machine learning framework was constructed based on in situ measurements collected from 2020 to 2023. Feature selection using Pearson’s correlation and Random Forest importance identified the optimal combination of spectral bands and thermal inputs. The results from the model revealed the following: (1) annual mean TP concentrations in the delta were higher than in the main channel, with more pronounced seasonal fluctuations; (2) statistical verification (Wilcoxon signed-rank test, p < 0.01) confirmed that incorporating LST yielded a certain reduction in retrieval error compared to the spectral-only model; (3) the most influential predictors for TP estimation were a combination of the blue, green, and red spectral bands along with LST; (4) models incorporating LST achieved significantly higher accuracy than those based solely on spectral reflectance, with improved R² and RMSE values across most TP concentration ranges (except for 0.04–0.06 mg/L). These findings demonstrate that integrating LST with spectral features enhances the accuracy of remote sensing-based TP retrieval in rivers, offering new opportunities for improved large-scale water quality monitoring. Full article

To address the bottlenecks of traditional jaw crusher liner wear detection—high safety risks, insufficient precision, and limited full-range analysis—this paper proposes a non-contact, high-precision wear analysis method based on binocular vision and deep learning. At its core is the integration of the state-of-the-art FoundationStereo zero-shot stereo matching algorithm, following scenario-specific adaptations, into the 3D reconstruction of industrial liners for wear analysis. A novel wear quantification methodology and corresponding indicator system are also proposed. After calibrating the ZED2 binocular camera and fine-tuning the algorithm, FoundationStereo achieves an Endpoint Error (EPE) of 0.09, significantly outperforming traditional algorithms. To meet on-site efficiency requirements, a “single-view rapid acquisition + CUDA engineering acceleration” strategy is implemented, reducing point cloud generation latency from 165 ms to 120 ms by rewriting kernel functions and optimizing memory access patterns. Geometric accuracy verification shows a Mean Absolute Error (MAE) ≤ 0.128 mm, fully meeting industrial measurement standards. A complete process of “3D reconstruction–model registration–quantitative analysis” is constructed, utilizing three core indicators (maximum wear depth, average wear depth, and wear area ratio) to characterize liner wear. Statistical results—such as an average maximum wear depth of 55.05 mm—are highly consistent with manual inspection data, providing a safe, efficient, and precise digital solution for the predictive maintenance and intelligent operation and maintenance (O&M) of liners. Full article

This study examines the tension between “substance” and “form” in standardized sustainability reporting within an emerging market context. Using 21,964 firm-year observations from Chinese A-share listed companies (2018–2023), we investigate whether the adoption of the Global Reporting Initiative (GRI) framework enhances substantive Environmental, Social, and Governance (ESG) and creates firm value. While baseline regressions suggest a positive link between GRI and ESG performance, rigorously applying Propensity Score Matching (PSM) reveals a critical nuance: the effect of mere framework adoption attenuates after controlling for selection bias, whereas independent assurance remains a robust driver of substantive governance quality. Furthermore, mediation analysis using bootstrap resampling documents a distinct “Labeling Effect”: GRI adoption directly enhances market valuation (Tobin’s Q), yet the indirect path via ESG scores is statistically insignificant. This indicates that investors utilize GRI as a heuristic signal of legitimacy rather than pricing granular performance metrics. We also identify a “Valuation Latency”, where substantive ESG improvements significantly boost operational profitability (ROA) but are not yet fully incorporated into stock prices. Heterogeneity analysis shows these effects are stronger for non-state-owned enterprises (Non-SOEs), supporting the view that private firms leverage standardized reporting and verification to mitigate legitimacy deficits. These findings provide empirical evidence for regulators and investors to distinguish between the “label” of adoption and the “substance” of verification. Full article

To address traffic safety hazards from asphalt pavement icing in Xinjiang’s cold regions and inefficiencies of conventional deicing and imprecise geothermal deicing systems, this study focused on local asphalt surfaces. Using “outdoor qualitative screening and indoor quantitative verification”, key variables were identified via controlled tests and their coupling effects on the time to complete icing were quantified through an L16(4⁴) orthogonal test (a 4-factor, 4-level design encompassing 16 test groups). A Backpropagation (BP) neural network model (3 inputs, 5 hidden neurons, and a learning rate of 0.7) optimized with 64 datasets was established to predict the time to complete icing of asphalt pavements, achieving a prediction accuracy (PA) of 90.7% for the time to complete icing and a mean error of merely 0.71 min. Dynamic icing risk thresholds (high/medium/low) were established via K-means clustering and statistical tests, enabling data-driven precise activation and on-demand regulation of geothermal deicing systems. This resolves energy waste and deicing delays, offering technical support for efficient geothermal utilization in cold-region transportation infrastructure, and provides a scalable “factor screening + model prediction” framework for asphalt pavement anti-icing practice. Full article

Mud loach (Misgurnus mizolepis) is a freshwater fish widely farmed in inland aquaculture owing to its nutritional value. However, failure to distinguish Chinese from Korean mud loach negatively affects the distribution economy and food safety regulation. Untargeted profiling was previously used to determine the origin of mud loaches, and N-acetylhistidine and anserine were selected as biomarker candidates. However, their quantitative verification and practical applicability for origin discrimination have not been thoroughly investigated. In this study, mud loaches of different geographical origins were analyzed using liquid chromatography-ultraviolet and liquid chromatography-tandem mass spectrometry to quantify the two metabolites, followed by statistical and receiver operating characteristic (ROC) analyses to evaluate their discriminative performance. Compared with Korean mud loaches, Chinese mud loaches showed significantly higher concentrations of both metabolites. The area under the curve values for N-acetylhistidine and anserine were 0.88 and 0.89, respectively, reflecting high sensitivity and specificity for discriminating between Korean and Chinese mud loaches. Cutoff values were established for reliably distinguishing the geographical origin of mud loaches. The established approach based on N-acetylhistidine and anserine can be used to determine the geographical origin of mud loach. Full article

Low-level wind shear (LLWS) is a critical aviation hazard that can cause flight disruptions and pose significant safety risks. Despite its operational importance, forecasting LLWS remains a challenging task. To improve LLWS prediction, probabilistic forecasting approaches based on ensemble prediction systems are increasingly used. In this study, LLWS forecasts were generated using a high-resolution, limited-area ensemble model, which allows for the representation of forecast uncertainty and variability in atmospheric conditions. Forecasts for Incheon International Airport were generated twice daily over the period from December 2018 to February 2020. To enhance forecast skill, statistical post-processing techniques, specifically Ensemble Model Output Statistics (EMOS), were applied and calibrated using Aircraft Meteorological Data Relay (AMDAR) observations. Prior to calibration, rank histograms were examined to assess the reliability and distributional consistency of the ensemble forecasts. Forecast performance was evaluated using commonly applied probabilistic verification metrics, including the mean absolute error (MAE), the continuous ranked probability score (CRPS), and probability integral transform (PIT). The results indicate that ensemble forecasts adjusted through statistical post-processing generally provide more reliable and accurate predictions than the unprocessed raw ensemble outputs. Full article

The avalanche speed is an important indicator for measuring the intensity of avalanches, and its measurement method is relatively complex. In practical engineering, empirical formulas based on statistics are usually adopted. However, research on the applicability of existing calculation methods in different regions is still insufficient, and further verification and improvement are urgently needed. Based on the integrated space–air–ground field survey data, this study uses RAMMS::AVALANCHE to conduct dynamic numerical simulations of 78 avalanche events in the Qiet’ akesu Gully of the Wenquan to Horgos transportation corridor in the Western Tianshan Mountains during the winter of 2023–2024, analyses the avalanche movement process, and compares the calculation results of the numerical tests of avalanche movement speed with empirical formulas. The results indicate that the velocities calculated using Formula A and Formula B are generally overestimated, approaching approximately 1.5 times the reference value. The mean absolute percentage error of Formula A (19.46%) is lower than that of Formula B (48.27%). In contrast, Formula C exhibits a significantly lower mean absolute percentage error (8.42%) compared with the other two methods, and its results remain stably around one-half of the reference value. Based on these findings, a comprehensive estimation strategy is proposed: twice the value calculated by Formula C is adopted as the primary reference, while two-thirds of the value from Formula A is taken into consideration, and the larger of the two is selected as the final estimated velocity. This strategy ensures the robustness of the results while effectively avoiding the potential overestimation or underestimation associated with reliance on a single empirical formula. This study provides a scientific basis for highway route selection and the placement of avalanche mitigation measures in high-altitude mountainous areas, and offers technical support for the construction and operational safety of infrastructure along the G219 Wenquan–Horgos transportation corridor. Full article

For the problem of online real-time prediction of copper particle content in the lubricating oil of the main spindle-bearing system of mining equipment, the traditional direct detection method is costly and has insufficient real-time performance. To this end, this paper proposes an indirect prediction method based on data-driven neural networks. The proposal of this method is based on a core assumption: during the stable wear stage of the equipment, there exists a modelable statistical correlation between the copper particle content in the oil and the total amount of non-ferromagnetic particles that are easy to measure online. Based on this, a neural network prediction model was constructed, with the online metal abrasive particle sensor signal (non-ferromagnetic particle content) as the input and the copper particle content as the output. The experimental data are derived from 100 real oil samples collected on-site from the lubrication system of the main shaft bearing of a certain mine mill. To enhance the model’s performance in the case of small samples, data augmentation techniques were adopted in the study. The verification results show that the average prediction accuracy of the proposed neural network model reaches 95.66%, the coefficient of determination (R²) is 0.91, and the average absolute error (MAE) is 0.3398. Its performance is significantly superior to that of the linear regression model used as the benchmark (with an average accuracy of approximately 80%, R² = 0.71, and the mean absolute error (MAE) = 1.5628). This comparison result not only preliminarily verified the validity of the relevant hypotheses of non-ferromagnetic particles and copper particles in specific scenarios, but also revealed the nonlinear nature of the relationship between them. This research explores and preliminarily validates a low-cost technical path for the online prediction of copper particle content in the stable wear stage of the main shaft bearing system, suggesting its potential for engineering application within specific, well-defined scenarios. Full article

Global Navigation Satellite System (GNSS) signals are highly vulnerable to spoofing attacks, which can cause positioning errors and pose serious threats to user receivers. Therefore, the development of efficient and reliable spoofing detection techniques has become an urgent requirement for ensuring GNSS security. In spoofing attacks, attackers introduce additional bias in the Doppler shift. However, detection methods that rely on extracting this deviation from raw measurements suffer from limited practicality, and existing alternative detection schemes based on position, velocity, and time (PVT) information exhibit poor adaptability to diverse scenarios. To address these limitations, this paper proposes a spoofing detection method based on the self-consistency verification of the receiver’s clock state (SCV-RCS). Its core statistic is the cumulative difference between the estimated clock bias and the bias obtained by integrating clock drift. By monitoring this consistency, SCV-RCS identifies anomalies in pseudorange and Doppler observations without complex bias extraction or auxiliary hardware, ensuring easy deployment. Simulation and experimental results demonstrate the method’s effectiveness across diverse spoofing scenarios. It achieves the fastest alarm delay of ≤2 s while providing continuous alerting capability in full-channel and partial-channel spoofing. This study provides a robust and reliable solution for GNSS receivers operating in complex spoofing environments. Full article

This paper identifies theoretical vulnerabilities in quantum integrity verification by demonstrating that Bell inequality (BI) violations, central to the detection of quantum entanglement, can align with predictions from hidden variable theories (HVTs) under specific measurement configurations. By invoking a Heisenberg-inspired measurement resolution constraint and finite-resolution positive operator-valued measures (POVMs), we identify “convergence vicinities” where the statistical outputs of quantum and classical models become operationally indistinguishable. These results do not challenge Bell’s theorem itself; rather, they expose a vulnerability in quantum integrity frameworks that treat observed Bell violations as definitive, experiment-level evidence of nonclassical entanglement correlations. We support our theoretical analysis with simulations and experimental results from IBM quantum hardware. Our findings call for more robust quantum-verification frameworks, with direct implications for the security of quantum computing, quantum-network architectures, and device-independent cryptographic protocols (e.g., device-independent quantum key distribution (DIQKD)). Full article

Deep learning-based radar detection often suffers from poor cross-device generalization due to hardware heterogeneity. To address this, we propose a unified framework that combines rigorous calibration with adaptive temporal modeling. The method integrates three coordinated steps: (1) ensuring precise spatial alignment via improved Perspective-n-Point (PnP) calibration with closed-loop verification; (2) unifying signal statistics through multi-range bin calibration and chirp-wise Z-score standardization; and (3) enhancing feature consistency using a lightweight global–temporal adapter (GTA) driven by global gating and three-point attention. By combining signal-level standardization with feature-level adaptation, our framework achieves 86.32% average precision (AP) on the ROD2021 dataset. It outperforms the E-RODNet baseline by 22.88 percentage points with a 0.96% parameter increase, showing strong generalization across diverse radar platforms. Full article

While there is currently a significant opportunity for the construction of photovoltaic solar farms in the Southeastern United States, there is also a need for proper spatial planning that has not been adequately addressed by the existing literature. The objective of this study is to examine the adaptability of geographic information system-based multiple criteria decision analysis models developed for foreign contexts to the United States. This was accomplished through the application of a model developed originally for Thailand to the study area of Bibb County, Georgia, United States. Model results were analyzed to identify trends and provide concrete recommendations for future work. Using a six-rank classification scheme, 93% of Bibb County was found to have moderate suitability, while 5% and 2% had moderate-to-low and moderate-to-high suitability, respectively. Of the 11 model criteria, land usage and power line distance were found to have the largest impact on the area’s suitability. Statistical analysis identified positive trends indicating that these criteria explained 21% and 10% of the variance in the model’s output, respectively. Empirical verification proved the model structure to be viable for application in the Southeastern United States; however, additional examination of the model’s results found that there is room to improve the model for the local context. These improvements could potentially be realized through the reweighting of criteria and the re-establishment of evaluation benchmarks, allowing for the development of a truly robust model for the region. Full article