Search Results (3,440)

Achieving synergies between government-led and market-based policy instruments is critical to advancing Energy–Environmental Productivity and Sustainable Urban Development. This study investigates the effects of China’s dual-pilot energy policies (New Energy Demonstration Cities (NEDCs) and Energy Consumption Permit Trading (ECPT)) on urban environmental productivity (UEP) across 279 prefecture-level cities from 2006 to 2023. Utilizing a Non-Radial Directional Distance Function (NDDF) approach, combined with Difference-in-Differences (DID) estimation and spatial econometric models, the analysis reveals that these synergistic policies significantly enhance both comprehensive and net measures of UEP. Mechanism analysis highlights the roles of industrial restructuring, technological innovation, and energy transition in driving these improvements, while heterogeneity analysis indicates varying effects across different city types. Spatial spillover analysis further demonstrates that policy impacts extend beyond targeted cities, contributing to broader regional gains in UEP. These findings offer important insights for the design of integrated energy and environmental policies and support progress toward key Sustainable Development Goals (SDG 7, SDG 11, and SDG 12). Full article

Sonar is the primary tool used for detecting small targets at long distances underwater. Due to the influence of the underwater environment and imaging mechanisms, sonar images face challenges such as a small number of target pixels, insufficient data samples, and uneven category distribution. Existing target detection methods are unable to effectively extract features from sonar images, leading to high false positive rates and affecting the accuracy of target detection models. To counter these challenges, this paper presents a novel sonar small-target detection framework named DCE-Net that refines the YOLOv8 architecture. The Detail Enhancement Attention Block (DEAB) utilizes multi-scale residual structures and channel attention mechanism (AM) to achieve image defogging and small-target structure completion. The lightweight spatial variation convolution module (CoordGate) reduces false detections in complex backgrounds through dynamic position-aware convolution kernels. The improved efficient multi-scale AM (MH-EMA) performs scale-adaptive feature reweighting and combines cross-dimensional interaction strategies to enhance pixel-level feature representation. Experiments on a self-built sonar small-target detection dataset show that DCE-Net achieves an mAP@0.5 of 87.3% and an mAP@0.5:0.95 of 41.6%, representing improvements of 5.5% and 7.7%, respectively, over the baseline YOLOv8. This demonstrates that DCE-Net provides an efficient solution for underwater detection tasks. Full article

Sensor fusion in intelligent suspension systems constitutes a fundamental technology for optimizing vehicle dynamic stability, ride comfort, and occupant safety. By integrating data from multiple sensor modalities, this study proposes a hierarchical multi-sensor fusion framework for active suspension control, aiming to enhance control precision. Initially, a binocular vision system is employed for target detection, enabling the identification of lane curvature initiation points and speed bumps, with real-time distance measurements. Subsequently, the integration of Global Positioning System (GPS) and inertial measurement unit (IMU) data facilitates the extraction of road elevation profiles ahead of the vehicle. A BP-PID control strategy is implemented to formulate mode-switching rules for the active suspension under three distinct road conditions: flat road, curved road, and obstacle road. Additionally, an ant colony optimization algorithm is utilized to fine-tune four suspension parameters. Utilizing the hardware-in-the-loop (HIL) simulation platform, the observed reductions in vertical, pitch, and roll accelerations were 5.37%, 9.63%, and 11.58%, respectively, thereby substantiating the efficacy and robustness of this approach. Full article

In the present study, 96 teams’ performances across 48 matches in the group stage of the FIFA Club World Cup 2025 were analyzed. Teams scoring first won 62.5% of matches (p < 0.05), while goals were evenly distributed between halves (p > 0.05) and showed marginal variation across six 15 min intervals, peaking near the 30–45 and 75–90 min marks. Parametric analyses revealed a significant effect of match outcome on possession, with winning teams exhibiting higher average possession (53.3%) compared to losing and drawing teams. Non-parametric analyses identified significant differences between match outcomes for goals scored, attempts at goal, total and completed passes, pass completion rate, defensive line breaks, receptions in the final third, ball progressions, defensive pressures, and total distance covered. Winning teams scored more goals and registered more attempts on target than losing teams, although some metrics showed no significant difference between wins and draws. Logistic regression analysis identified attempts at goal on target, defensive pressures, total completed passes, total distance covered, and receptions in the final third as significant predictors of match success (AUC = 0.85), correctly classifying 80.2% of match outcomes. These results emphasized the crucial role of offensive accuracy and possession dominance in achieving success in elite football. Full article

Magnetite (Fe₃O₄) provides a protective corrosion layer in the steam generators of nuclear power plants. The presence of monoethanolamine (MEA) in coolant water has a beneficial effect on corrosion processes. In that context, the adsorption of MEA and ethanol–ammonium cation on the {111} surface of magnetite was studied using the molecular dynamics (MD) method. A modified version of the mechanical force field (ClayFF) was used. The systems were simulated at different temperatures (423 K; 453 K; 503 K). Surface coverage data were obtained from adsorption simulations; the root-mean-square deviation (RMSD) of the target molecules were calculated, and their minimum distance to the magnetite surface was traced. The potential and adsorption energies of MEA were calculated as a function of temperature. It has been established that the interaction between MEA and magnetite is due to electrostatic phenomena and the adsorption rate increases with temperature. A comparison was made with existing experimental results and similar MD simulations. Full article

In the evolving field of quantum computing, optimizing Quantum Error Correction (QEC) parameters is crucial due to the varying types and amounts of physical noise across quantum computers. Traditional simulators use a forward paradigm to derive logical error rates from inputs like code distance and rounds, but this can lead to resource wastage. Adjusting QEC parameters manually with tools like STIM is often inefficient, especially given the daily fluctuations in quantum error rates. To address this, we introduce MITS, a reverse engineering tool for STIM that automatically determines optimal QEC settings based on a given quantum computer’s noise model and a target logical error rate. This approach minimizes qubit and gate usage by precisely matching the necessary logical error rate with the constraints of qubit numbers and gate fidelity. Our investigations into various heuristics and machine learning models for MITS show that XGBoost and Random Forest regressions, with Pearson correlation coefficients of $0.98$ and $0.96$, respectively, are highly effective in this context. Full article

Background: In this work, we examine determinants of staff departure rates in the NHS, a critical issue for workforce stability and continuity of care. High turnover, particularly among clinical staff, undermines service delivery and incurs substantial replacement costs. Methods: Here, we analyse a unique dataset derived from Electronic Staff Records at Ashford and St. Peter’s NHS Foundation Trust, using a machine learning approach to move beyond traditional survey-based methods, to assess propensity to leave. Results: In addition to established predictors such as salary and length of service, we identify drivers of increased risks of staff exits, including the distance between home and workplace and, especially for medical staff, cost centre vacancy rates. Conclusions: These findings highlight the multifactorial nature of staff retention and suggest the potential of local administrative data to improve workforce planning, for example, through hyperlocal recruitment strategies. Whilst further work will be required to assess the generalisability of our findings beyond a single Trust, our analysis offers insights for NHS managers seeking to stabilise staffing levels and reduce attrition through targeted interventions beyond pay and tenure. Full article

Neuronal proteins synthesized locally in axons and dendrites contribute to growth, plasticity, survival, and retrograde signaling underlying these cellular processes. Advances in molecular tools to profile localized mRNAs, along with single-molecule detection approaches for RNAs and proteins, have significantly expanded our understanding of the diverse proteins produced in subcellular compartments. These investigations have also uncovered key molecular mechanisms that regulate mRNA transport, storage, stability, and translation within neurons. The long distances that axons extend render their processes vulnerable, especially when injury necessitates regeneration to restore connectivity. Localized mRNA translation in axons helps initiate and sustain axon regeneration in the peripheral nervous system and promotes axon growth in the central nervous system. Recent and ongoing studies suggest that axonal RNA transport, storage, and stability mechanisms represent promising targets for enhancing regenerative capacity. Here, we summarize critical post-transcriptional regulatory mechanisms, emphasizing translation in the axonal compartment and highlighting potential strategies for the development of new regeneration-promoting therapeutics. Full article

Based on the theoretical framework of the phase comparison method and the computational analysis of the interference model calculation analysis, this paper designs, implements, establishes, calibrates, and verifies an interference experimental platform. The proposed methodology validates the effectiveness and practical feasibility of multiantenna synthetic interference technology in real-world applications. Experimental results demonstrate that the developed system can achieve flexible and arbitrary interference angles with desired distortion characteristics through precise amplitude–phase modulation, enabling dynamic manipulation of phase plane angles. Furthermore, the system successfully synthesizes false target positions at distances exceeding five times the baseline length from the jamming platform center. Both mathematical computations and experimental validations confirm that this multiantenna synthetic interference technology represents an advanced electromagnetic countermeasure characterized by two-dimensional planar interference coverage and robust phase parameter tolerance, while also enabling artificial angular glint generation. This technology exhibits significant potential for practical engineering applications. Full article

Background/Objectives: The hippocampus is an essential part of the central nervous system (CNS); it plays a significant role in social–cognitive memory processing. Prenatal exposure to valproic acid (VPA) can lead to impaired hippocampal functions. In this study, we evaluated the effect of plumbagin (PLB) as a natural product on spatial learning and memory, neuro-morphological changes, and inflammation levels in a VPA-induced autism model during adolescence. Methods: Pregnant Wistar rats received a single intraperitoneal (i.p.) injection of VPA (600 mg/kg) or saline on gestational day 12.5. The male offspring were then categorized and assigned to five groups: Saline+DMSO-, VPA+DMSO-, and VPA+PLB-treated groups at doses of 0.25, 0.5, or 1 mg/kg. Spatial learning and memory were evaluated using the Morris water maze. Histopathological evaluations of the hippocampus were performed using Nissl and hematoxylin–eosin staining, as well as immunofluorescence. The pro-inflammatory cytokine levels were also quantified by quantitative real-time PCR. Results: The findings revealed that a VPA injection on gestational day 12.5 is associated with cognitive impairments in male pups, including a longer escape latency and traveled distance, as well as decreased time spent in the target quadrant. Treatment with PLB significantly enhanced the cognitive function, reduced dark cells, and ameliorated neuronal–morphological alterations in the hippocampus of VPA-exposed rats. Moreover, PLB was found to reduce astrocyte activation and the expression levels of pro-inflammatory cytokines. Conclusions: These findings suggest that PLB partly mitigates VPA-induced cognitive deficits by ameliorating hippocampal inflammation levels. Full article

Rolling bearings serve as the most widely utilized general components in drive systems for rotating machinery, and they are susceptible to regular malfunctions. To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. The suggested approach begins with the implementation of Variational Mode Decomposition (VMD) on the initial vibration signal. This is succeeded by a denoising process that utilizes the goodness-of-fit test based on the Anderson–Darling (AD) distance for enhanced accuracy. This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. Ultimately, the signal that has been denoised is utilized as input for the DWMFCNN model to recognize different kinds of rolling-bearing faults. Results from the experiments show that the suggested approach shows an improved denoising performance and a greater adaptability to changing working conditions. Full article

Directed grey-box fuzzers focus on testing specific target code. They have been utilized in various security applications, such as reproducing known crashes and identifying vulnerabilities resulting from incomplete patches. Distance-guided directed fuzzers calculate the distance to the target node for each node in a CFG or CG, which has always been the mainstream in this field. However, the distance can only reflect the relationship between the current node and the target node, and it does not consider the impact of the reaching sequence before the target node. To mitigate this problem, we analyzed the properties of the instrumented function’s call graph after selective instrumentation, and the complexity of reaching the target function sequence was estimated. Assisted by the sequence complexity, we proposed a two-stage function call sequence-based seed-scheduling strategy. The first stage is to select seeds with a higher probability of generating test cases that reach the target function. The second stage is to select seeds that can generate test cases that meet the conditions for triggering the vulnerability as much as possible. We implemented our approach in SEZZ based on SelectFuzz and compare it with related works. We found that SEZZ outperformed AFLGo, Beacon, WindRanger, and SelectFuzz by achieving an average improvement of 13.7×, 1.50×, 9.78×, and 2.04× faster on vulnerability exposure, respectively. Moreover, SEZZ triggered three more vulnerabilities than the other compared tools. Full article

This study investigates the critical link between child care accessibility and local labor force participation, addressing a gap in current research that often lacks local spatial granularity. While over half of the U.S. population resides in child care deserts, disproportionately affecting rural, low-income, and minority communities, the economic implications for local labor markets remain underexplored. Leveraging Michigan child care license data and Census tract-level demographic and employment characteristics, this research employs a spatial econometric approach to estimate the impact of geographic distance to child care facilities on labor supply using descriptive data. Our findings consistently demonstrate that increased distance to child care is significantly associated with reduced labor force participation. While female labor force participation is lower in areas with constrained access to child care, we also found that households with two parents are also less likely to have full labor force participation when access to child care is constrained. The cost-effective framework used here can be replicated to identify specific communities most impacted by child care-related employment disruptions. The analytical findings can be instrumental in targeting and prioritizing child care policy interventions. Full article

This study investigated the effects of a single-night sleep extension protocol on physical performance and cognitive function in physically active university students across different times of day. Using a within-subjects, counterbalanced crossover design, 24 physically active university students (17 males, 7 females; age: 22.7 ± 1.6 years) completed performance assessments under normal-sleep and sleep-extension conditions. Participants’ sleep was monitored via wrist actigraphy, and a comprehensive assessment battery comprising vertical jumps, Y-Balance tests, medicine-ball throws, 5 m shuttle-run tests, reaction-time tests, and digit-cancellation tests was administered at baseline (8 PM), morning (8 AM), and afternoon (4 PM). Sleep extension increased total sleep time by approximately 55 min (531.3 ± 56.8 min vs. 476.5 ± 64.2 min; p < 0.001, d = 0.91). Significant improvements were observed in 5 m shuttle-run performance at 8 AM (best distance: 102.8 ± 11.9 m vs. 93.3 ± 8.5 m, p < 0.001, d = 0.93; fatigue index: 13.1 ± 8.3% vs. 21.2 ± 9.5%, p < 0.001, d = 0.90), squat-jump heights (28.2 ± 8.0 cm vs. 26.3 ± 7.2 cm, p = 0.005, d = 0.25), simple reaction time (252.8 ± 55.3 ms vs. 296.4 ± 75.2 ms, p < 0.001, d = 0.66), and digit-cancellation performance (67.6 ± 12.6 vs. 63.0 ± 10.0 targets, p = 0.006, d = 0.40). Sleep extension significantly enhances both physical and cognitive performance in physically active individuals, with effects more pronounced during morning hours, partially attenuating typical circadian performance decline and establishing sleep extension as an effective, non-pharmacological strategy for optimizing performance capabilities. Full article

The implementation of an Indoor Positioning System (IPS) in a three-storey residential building employing WiFi signals that can also be used to track indoor movements is presented in this study. The movement of inhabitants is monitored through an Android smartphone by detecting the Received Signal Strength Indicator (RSSI) signals from WiFi Anchor Points (APs).Indoor movement is detected through a successive estimation of a target’s multiple positions. Using the K-Nearest Neighbors (KNN) and Particle Swarm Optimization (PSO) algorithms, these RSSI measurements are trained for estimating the position of an indoor target. Additionally, the Density-based Spatial Clustering of Applications with Noise (DBSCAN) has been integrated into the PSO method for removing RSSI-estimated position outliers of the mobile device to further improve indoor position detection and monitoring accuracy. We also employed Time Reversal Resonating Strength (TRRS) as a correlation technique as the third method of localization. Our extensive and rigorous experimentation covers the influence of various weather conditions in indoor detection. Our proposed localization methods have maximum accuracies of 92%, 80%, and 75% for TRRS, KNN, and PSO + DBSCAN, respectively. Each method also has an approximate one-meter deviation, which is a short distance from our targets. Full article