Search Results (1,898)

This study explored the feasibility of utilizing tunnel muck-derived recycled granite aggregates (RGAs) in surface-layer asphalt mixtures via hybrid with basalt aggregates. Firstly, RGAs, including coarse aggregates (RGCAs) and fine aggregates (RGFAs), were prepared using a production method integrated with multi-cleaning technology. Then, the material properties of RGAs and RGA–basalt hybrid aggregates with varying RGA volume proportions were investigated. Finally, asphalt mixtures with these hybrid aggregates were designed and their engineering performance was evaluated. Basalt aggregates and their corresponding asphalt mixture served as the control group. Results suggest that since RGAs are rich in quartz and their SiO₂ content is as high as 70.88%, they are acidic aggregates. Employing multi-cleaning technology is a guaranteed method of obtaining RGAs with low mud content. The main conventional technical indexes of RGAs and all hybrid aggregates with 40–70% RGA volume proportions meet the requirements of Chinese technical specifications. Asphalt mixtures incorporating RGAs exhibit slightly higher voids in the mineral aggregates (VMAs) than the control group, indicating that RGAs modify the interlocking skeleton and contact states of aggregates. Blending RGAs with basalt to form hybrid aggregates is an effective way to achieve full-gradation utilization of tunnel muck-derived RGAs in the surface-layer asphalt mixtures. Without additional enhancement measures, a 40% RGA volume proportion in hybrid aggregates is recommended. For a higher RGA recycling rate, combining RGAs with cement is advised, maintaining 70% RGA volume proportion and 50% cement content of total filler volume. When external basalt aggregates are transported over a distance of 50–200 km, applying these schemes to local asphalt pavement surface layers can achieve at least 26.56% aggregate cost savings. Full article

Multi-agent systems (MAS) typically model interaction topologies using directed or undirected graphs when analyzing consensus convergence rates. However, as system complexity increases, purely directed or undirected networks may be insufficient to capture interaction heterogeneity. This paper adopts hybrid networks as interaction topology to investigate strategies for improving consensus convergence rates. We propose the Hermitian Kirchhoff index, a novel metric based on resistance distance, to quantify the consensus convergence rates and establish its theoretical justification. We then examine how adding or removing edges/arcs affects the Hermitian Kirchhoff index, employing first-order eigenvalue perturbation analysis to relate these changes to algebraic connectivity and its associated eigenvectors. Numerical simulations corroborate the theoretical findings and demonstrate the effectiveness of the proposed approach. Full article

The prediction of stock prices is challenging due to their volatility, irregular patterns, and complex time-series structure. Reliably forecasting stock market data plays a crucial role in minimizing financial risk and optimizing investment strategies. However, traditional models often struggle to capture temporal dependencies and extract relevant features from noisy inputs, which limits their predictive performance. To improve this, we developed an enhanced recursive feature elimination (RFE) method that blends the importance of impurity-based features from random forest and gradient boosting models with Kendall tau correlation analysis, and we applied SHapley Additive exPlanations (SHAP) analysis to externally validate the reliability of the selected features. This approach leads to more consistent and reliable feature selection for short-term stock prediction over 1-, 3-, and 7-day intervals. The proposed deep learning (DL) architecture integrates a temporal convolutional network (TCN) for long-term pattern recognition, a gated recurrent unit (GRU) for sequence capture, and multi-head attention (MHA) for focusing on critical information, thereby achieving superior predictive performance. We evaluate the proposed approach using daily stock price data from three leading companies—HDFC Bank, Tata Consultancy Services (TCS), and Tesla—and two major stock indices: Nifty 50 and S&P 500. The performance of our model is compared against five benchmark models: temporal convolutional network (TCN), long short-term memory (LSTM), GRU, Bidirectional GRU, and a hybrid TCN–GRU model. Our method consistently shows lower error rates and higher predictive accuracy across all datasets, as measured by four commonly used performance metrics. Full article

Background: Key performance indicators (KPIs) are widely used to evaluate embryologist performance in IVF laboratories, yet they are sensitive to patient demographics, treatment indications, and case allocation. Artificial intelligence (AI) offers opportunities to benchmark personal KPIs against context-aware expectations. This study evaluated whether personal CPR-based KPIs are measurable or distorted when compared with AI-derived predictions. Methods: We retrospectively analyzed 474 ICSI-only cycles performed by a single senior embryologist between 2022 and 2024. A Random Forest trained on 1294 institutional cycles generated AI-predicted clinical pregnancy rates (CPRs). Observed and predicted CPRs were compared across age groups, BMI categories, and physicians using cycle-level paired comparisons and a grouped calibration statistic. Results: Overall CPRs were similar between observed and predicted outcomes (0.31 vs. 0.33, p = 0.412). Age-stratified analysis showed significant discrepancy in the >40 group (0.11 vs. 0.18, p = 0.003), whereas CPR in the 35–40 group exceeded predictions (0.39 vs. 0.33, p = 0.018). BMI groups showed no miscalibration (p = 0.458). Physician-level comparisons suggested variability (p = 0.021), while grouped calibration was not statistically significant (p = 0.073). Conclusions: Personal embryologist KPIs are measurable but influenced by patient and physician factors. AI benchmarking may improve fairness by adjusting for case mix, yet systematic bias can persist in high-risk subgroups. Multi-operator, multi-center validation is needed to confirm generalizability. Full article

The large-scale utilization of wind and solar energy is crucial for achieving carbon neutrality targets. However, as extensive wind and solar power generation is integrated via power electronic devices, the inertia level of power systems continues to decline. This leads to a significant reduction in the system’s frequency regulation capability, posing a serious threat to frequency stability. Optimizing the system is an essential measure to ensure its safe and stable operation. Traditional optimization approaches, which separately optimize transmission and distribution systems, may fail to adequately account for the variability and uncertainty of renewable energy sources, as well as the impact of inertia changes on system stability. Therefore, this paper proposes a two-layer optimization method aimed at simultaneously optimizing the operation of transmission and distribution systems while satisfying minimum inertia constraints. The upper-layer model comprehensively optimizes the operational costs of wind, solar, and thermal power systems under the minimum inertia requirement constraint. It considers the operational costs of energy storage, virtual inertia costs, and renewable energy curtailment costs to determine the total thermal power generation, energy storage charge/discharge power, and the proportion of renewable energy grid connection. The lower-layer model optimizes the spatiotemporal distribution of energy storage units within the distribution network, aiming to minimize total network losses and further reduce system operational costs. Through simulation analysis and computational verification using typical daily scenarios, this model enhances the disturbance resilience of the transmission network layer while reducing power losses in the distribution network layer. Building upon this optimization strategy, the model employs multi-scenario stochastic optimization to simulate the variability of wind, solar, and load, addressing uncertainties and correlations within the system. Case studies demonstrate that the proposed model not only effectively increases the integration rate of new energy sources but also enables timely responses to real-time system demands and fluctuations. Full article

Leveraging multi-source remote sensing datasets and dynamic groundwater monitoring well observations, this study explores the multiscale spatiotemporal linkages of groundwater storage changes and land deformation in North China Plain (NCP) after the South-to-North Water Diversion Project (SNWDP). Firstly, we employed Gravity Recovery and Climate Experiment (GRACE) and interferometric synthetic aperture radar (InSAR) technology to estimate groundwater storage (GWS) and land deformation. Secondly and significantly, we proposed a novel GRACE statistical downscaling algorithm that integrates a weight allocation strategy and GWS estimation applied with InSAR technology. Finally, the downscaled results were employed to analyze spatial differences in land deformation across typical ground fissure areas. The results indicate that (1) between 2018 and 2021, groundwater storage in the NCP exhibited a declining trend, with an average reduction of −3.81 ± 0.53 km³/a and a maximum land deformation rate of −177 mm/a; (2) the downscaled groundwater storage anomalies (GWSA) showed high correlation with in situ measurements (R = 0.75, RMSE = 2.91 cm); and (3) in the Shunyi fissure area, groundwater storage on the northern side increased continuously, with a maximum growth rate of 28 mm/a, resulting in surface uplift exceeding 70 mm. Full article

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article

Deep coalbed methane (CBM) resources possess significant potential. However, their development is challenged by geological characteristics such as high in situ stress and low permeability. Furthermore, existing production strategies often prove inadequate. In order to achieve long-term stable production of deep coalbed methane reservoirs and increase their final recoverable reserves, it is urgent to construct a scientific and reasonable drainage system. This study focuses on the deep CBM reservoir in the Daning-Jixian Block of the Ordos Basin. First, a thermal–hydraulic–mechanical (THM) multi-physics coupling mathematical model was constructed and validated against historical well production data. Then, the model was used to forecast production. Finally, key control measures for enhancing well productivity were identified through production strategy adjustment. The results indicate that controlling the bottom-hole flowing pressure drop rate at 1.5 times the current pressure drop rate accelerates the early-stage pressure drop, enabling gas wells to reach the peak gas production earlier. The optimized pressure drop rates for each stage are as follows: 0.15 MPa/d during the dewatering stage, 0.057 MPa/d during the gas production rise stage, 0.035 MPa/d during the stable production stage, and 0.01 MPa/d during the production decline stage. This strategy increases peak daily gas production by 15.90% and cumulative production by 3.68%. It also avoids excessive pressure drop, which can cause premature production decline during the stable phase. Consequently, the approach maximizes production over the entire life cycle of the well. Mechanistically, the 1.5× flowing pressure drop offers multiple advantages. Firstly, it significantly shortens the dewatering and production ramp-up periods. This acceleration promotes efficient gas desorption, increasing the desorbed gas volume by 1.9%, and enhances diffusion, yielding a 39.2% higher peak diffusion rate, all while preserving reservoir properties. Additionally, this strategy synergistically optimizes the water saturation and temperature fields, which mitigates the water-blocking effect. Furthermore, by enhancing coal matrix shrinkage, it rebounds permeability to 88.9%, thus avoiding stress-induced damage from aggressive extraction. Full article

This study presents a case study of a holistic, psychosocial hazard management intervention program in a project-based, remote workforce in the Australian construction industry. There is a dearth of research on targeted, integrative, multi-level wellbeing interventions, and we seek to address this gap. Given the high rates of psychological distress and suicide in construction, understanding these hazards and the responses needed to manage them is critical for prevention. Data were collected from workers before and after the implementation of an intervention using an empirically validated measure of the work environment underpinned by the job demands–resources framework to evaluate exposure to psychosocial hazards, and mental health indicators, including psychological distress and suicidal ideation. Results revealed that job demands and resources improved following the change initiative, and workers reported significantly lower levels of psychological distress compared to workers on similarly diverse remote sites. The findings highlight the need for targeted mental health interventions addressing specific workplace psychosocial risks that adopt a holistic approach to change across all levels of an organisation. The study contributes to a nuanced understanding of psychosocial risks in construction and informs strategies to mitigate mental health harms in high-risk occupational settings. Full article

Quantum secret sharing is a cryptographic scheme that enables the secure storage and reconstruction of quantum information. While the theory of secret sharing is mature in its development, relatively few studies have explored the performance of quantum secret sharing on actual devices. In this work, we provide a pedagogical description of encoding and decoding circuits for different secret sharing codes, and we test their performance on IBM’s 127-qubit Brisbane system. We evaluate the quality of the implementation by performing a SWAP test between the decoded state and the ideal one, as well as by estimating how well the code preserves entanglement with a reference system. The results indicate that a ((3,5)) threshold secret sharing scheme and a non-threshold 7-qubit scheme perform similarly based on the SWAP test and entanglement fidelity, with both attaining a roughly 70–75% pass rate on the SWAP test for the reconstructed secret. We also investigate one implementation of a ((2,3)) qutrit threshold scheme and find that it performs the worst of all, which is expected due to the additional number of multi-qubit gate operations needed to encode and decode qutrits. A comparison is also made between schemes using mid-circuit measurement versus delayed-circuit measurement. Full article

Given the strong coupling between electricity flow and carbon flow, promoting the low-carbon transformation of the energy sector is a crucial measure to actively responding to climate challenges. As a pivotal hub linking the electricity market with the carbon market, promoting electricity–carbon coordinated scheduling of Virtual Power Plants (VPPs) is of great significance in expediting the energy transition process. Based on the introduction of carbon potential, this manuscript constructs a VPP electricity–carbon coordinated scheduling model that incorporates various typical elements, including renewable energy units and demand response. Furthermore, this paper utilizes Brain Storm Optimization (BSO) to improve the Snow Ablation Optimizer (SAO) algorithm and applies the improved algorithm to solve the model developed in this manuscript. Finally, an analysis was conducted using a small-scale VPP project in eastern China, and the results are the following: Firstly, the SAO improved by BSO demonstrates a significant enhancement in solution efficiency. In particular, for the cases presented in this manuscript, the algorithm’s convergence speed increased by 42.85%. Secondly, under the multi-market conditions and with real-time carbon potential, VPPs will possess greater flexibility in scheduling optimization and stronger incentives to fully explore their emission reduction potential through collaborative electricity–carbon scheduling, thereby improving both economic and environmental performance. However, constrained by factors such as the currently low carbon price level, the extent of improvement in VPPs’ performance under real-time carbon potential, compared to fixed carbon potential, remains relatively limited, with a 1.07% increase in economic benefits and a 2.63% reduction in carbon emissions. Thirdly, an increase in carbon prices can incentivize VPPs to continuously tap into their emission reduction potential, but beyond a certain threshold (120 CNY/t in this case study), the marginal contribution of further carbon price increases to emission reductions will progressively decline. Specifically, for every 20-yuan increase in the carbon price, the carbon emission reduction rate of VPPs drops below 1%. Full article

Brain tumors usually appear as masses formed by localized abnormal cell proliferation. Although complete removal of tumors is an ideal treatment goal, this process faces many challenges due to the aggressive nature of malignant tumors and the need to protect normal brain tissue. Therefore, early diagnosis is crucial to mitigate the harm posed by brain tumors. In this study, the classification accuracy is improved by improving the ResNet50 model. Specifically, the image is preprocessed and enhanced firstly, and the image is denoised by fractional calculus; then, transfer learning technology is adopted, the ECA attention mechanism is introduced, the convolutional layer in the residual block is optimized, and the multi-scale convolutional layer is fused. These optimization measures not only enhance the model’s ability to grasp the overall details but also improve its ability to recognize micro and macro features. This allows the model to understand data features more comprehensively and process image details more efficiently, thereby improving processing accuracy. In addition, the improved ResNet50 model is combined with EfficientNetB0 to further optimize performance and improve classification accuracy by utilizing EfficientNetB0’s efficient feature extraction capabilities through feature fusion. In this study, we used a brain tumor image dataset containing 5712 training images and 1311 validation images. The optimized ResNet50 model achieves a verification accuracy of 98.78%, which is 3.51% higher than the original model, and the Kappa value is also increased by 4.7%. At the same time, the lightweight design of the EfficientNetB0 improves performance while reducing uptime. These improvements can help diagnose brain tumors earlier and more accurately, thereby improving patient outcomes and survival rates. Full article

This study presents an experimental analysis of high-pressure liquid and gas gate valve leakage under multiple operating conditions, based on the variation patterns of ultrasonic signals. Focusing on a multi-physics field analysis of gate valve internal leakage and corresponding experiments, this research illustrates the acoustic wave characteristics of gate valves across diverse working media, pressures, internal leakage defect sizes, and valve diameters. By drawing upon both fluid mechanics and acoustics theory, an analytical approach suited to high-pressure gate valve leakage issues is devised. Separate high-pressure gate valve leakage test platforms for liquid and gas environments were designed and constructed, enabling 126 groups of tests under varying conditions, which include one measurement per condition of the valve size, defect size, and pressure value. These experiments examine the quantitative correlation of internal leakage flow rates and ultrasonic signal measurements under different situations. In addition, the distinct behaviors and principles exhibited by high-pressure liquid gate valves and gas gate valves are compared. The findings provide theoretical and technical support for quantifying high-pressure gate valve leakage. The study analyzes the theoretical basis for the generation of ultrasonic signals from valve internal leakage, providing specific experimental data under various operating conditions. It explains the various observations during the experiments and their principles. The conclusions of this research have practical engineering value and provide important references for future studies. Full article

Carbon emissions in the building materialization stage are highly significant and concentrated. Quantification at this stage is essential for assessing carbon reduction potential, guiding energy-saving strategies, and supporting China’s “dual carbon” goals in the construction sector. Distinct from conventional environmental and energy economics analytical approaches, the building carbon emissions in the materialization stage (BCEMS) in 30 provinces of China from 2010 to 2021 were calculated using multi-source data, and the characteristics of their spatio-temporal evolution were analyzed. The key influencing factors were identified using a geographic detector, and their spatial heterogeneity was analyzed with the Geographically and Temporally Weighted Regression (GTWR) model from a geographical analysis perspective. The results indicated the following: (1) From 2010 to 2021, BCEMS exhibited a trend of an “initial increase followed by a decrease and subsequent fluctuation”, with an average annual growth rate of 4.28%. Building materials were the largest contributor to BCEMS, particularly cement and steel. Spatially, the emissions displayed a pattern of “higher in the east, lower in the west”. High–high-agglomeration areas remained stable over time, primarily in Zhejiang and Fujian provinces, while low–low-agglomeration areas were concentrated in Xinjiang. (2) Single-factor detection revealed that fixed assets, population density, and the liabilities of construction enterprises were the dominant factors driving the emissions’ spatial evolution. Two-factor interaction detection identified the economic society and the construction industry as the key influencing domains. (3) The economic development level and the total population showed a positive correlation with BCEMS, with the effect intensity increasing from west to east. The urbanization level and fixed assets also generally showed a positive correlation with BCEMS; however, their effect intensity initially increased positively from west to east and then turned into a negative enhancement. The findings provide references for implementing regionally differentiated carbon reduction measures and promoting green and low-carbon urban transformation in China’s construction industry. Full article

This paper proposes an integrated OFDM signal system combining sidetone signals for communication and measurement, addressing the challenges of system complexity, resource waste, and interference caused by separated communication and measurement functions in traditional LEO satellite systems. The proposed approach effectively combines sidetone signals with OFDM technology, utilizing different short-period coprime pseudorandom codes as pilots to form composite ranging codes, while inserting multi-frequency sidetone signals at specific subcarrier points for precise ranging. A dual-mode channel estimation algorithm is designed to merge the channel estimation results from ranging pilots and sidetone signals, significantly enhancing system performance. Additionally, an adaptive ranging mode switching mechanism based on error thresholds achieves dynamic balance between ranging accuracy and spectral efficiency. Simulation results demonstrate that the proposed system can reduce bit error rate to approximately 10⁻³ at 6 dB SNR, saving about 3 dB of transmission power compared to conventional pilot methods, while achieving centimeter-level ranging accuracy of approximately 0.02 m, improving precision by 3–4 orders of magnitude over traditional pilot methods. The proposed scheme provides a high-precision, high-efficiency integrated solution for LEO satellite communication systems. The theoretical performance assumes idealized conditions, with practical deployment requiring comprehensive error modeling for hardware imperfections and environmental variations. Full article