Search Results (3,388)

We employed a global high-resolution inverse model to estimate sectoral methane emissions, integrating observations from the GOSAT-2 satellite for the first time, along with observations from the surface observation network. A similar set of inversions using GOSAT observations was carried out to evaluate the consistency between emissions estimates derived from these two satellites and to ensure that GOSAT-2 data could seamlessly integrate with the existing data series without disrupting the continuity of flux estimates. This analysis, covering the period from 2019 to 2022, utilized prior anthropogenic emissions data mainly from EDGAR v6 and incorporated additional natural sources and sinks as outlined by global methane budget, 2020. Our analysis reveals a general agreement between total methane emissions estimates from GOSAT and GOSAT-2. However, on a sectoral basis, we found notable regional differences in the flux estimates. While GOSAT inversion estimates ~8 Tg a⁻¹ more anthropogenic emissions for China and around 4 Tg a⁻¹ more wetland emissions for Brazil and Indonesia, the posterior error distribution suggests that GOSAT-2 inversion is closer to surface observations over Asia. These discrepancies are found in regions with significant differences in XCH₄ data from the two satellites, such as East Asia and North America, tropical South America, and tropical Africa. These regional biases persist due to limited representative surface reference sites for Level 2 bias correction. The relatively lower data volume from GOSAT also introduces seasonal biases in the flux estimates when the quality filtering of Level 2 data persistently reduces usable observations during certain seasons, resulting in inadequate representation of the seasonal cycle in regions such as East Asia. Similarly, in tropical South America, where the model is relatively under-constrained by the limited surface observations, the lower data volume of GOSAT-2 suffers. While the two inversions exhibit consistent overall performance across North America and Europe, the GOSAT-2-based inversion demonstrates a better performance over East Asia. Therefore, while the two satellite datasets are broadly consistent, considering the fact that the biases in the XCH₄ data overlap with regions under-constrained by surface observations, establishing additional surface reference measurement sites is desirable to ensure consistent inversion results. Full article

This study proposes a system identification (SI) technique based on the constrained recursive least squares (CRLS) method to model the dynamics of the P-SUROII. By simplifying the dynamic model in consideration of the inherent characteristics of underwater vehicles and minimizing the number of parameters to be estimated, the proposed approach aims to improve estimation accuracy. In addition, a simplified thruster input model was applied to quantify the actual thruster output and improve the reliability of the input data. To satisfy the persistent excitation (PE) condition during the estimation process, experiments incorporating various motion modes were designed, and free-running and S-shaped maneuvering tests were additionally conducted to validate the model’s generalization capability and prediction performance. The coefficients estimated using the CRLS method, which is robust to noise and bias, were evaluated using quantitative similarity metrics such as root mean squared error (RMSE) and mean absolute error (MAE), confirming their validity. The proposed method effectively captures the actual dynamics of the underwater vehicle and is expected to serve as a key enabling technology for the future development of high-performance control systems and autonomous operation systems. Full article

Long-term and accurate prediction of air pollutant concentrations can serve as a foundation for air pollution warning and prevention, which is crucial for social development and human health. In this study, we provide a model for predicting the concentration of air pollutants based on big data spatiotemporal correlation analysis and deep learning methods. Based on an improved temporal convolutional network (TCN) and a bi-directional gated recurrent unit (BiGRU) as the fundamental architecture, the model adds two attention mechanisms to improve performance: Squeeze and Excitation Networks (SENet) and Convolutional Block Attention Module (CBAM). The improved TCN moves the residual connection layer to the network’s front end as a preprocessing procedure, improving the model’s performance and operating efficiency, particularly for big data jobs like air pollution concentration prediction. The use of SENet improves the model’s comprehension and extraction of long-term dependent features from pollutants and meteorological data. The incorporation of CBAM enhances the model’s perception ability towards key local regions through an attention mechanism in the spatial dimension of the feature map. The TCN-SENet-BiGRU-CBAM model successfully realizes the prediction of air pollutant concentrations by extracting the spatiotemporal features of the data. Compared with previous advanced deep learning models, the model has higher prediction accuracy and generalization ability. The model is suitable for prediction tasks from 1 to 12 h in the future, with root mean square error (RMSE) and mean absolute error (MAE) ranging from 5.309~14.043 and 3.507~9.200, respectively. Full article

Background/Objectives: Current bone-based landmark approaches have shown variable accuracy and poor reproducibility. We validated a two-point “tuberculum sellae–anterior clinoid process” (TS–ACP) line traced on routine 3D-computed tomography angiography (CTA) for predicting distal dural ring (DDR) position and quantified the interobserver agreement. Methods: We retrospectively reviewed data from 85 patients (87 aneurysms) who were treated via clipping between June 2012 and December 2024. Two blinded neurosurgeons classified each aneurysm as extradural, intradural, or straddling the TS–ACP line. The intraoperative DDR inspection served as the reference standard. Diagnostic accuracy, χ² statistics, and Cohen’s κ were calculated. Results: The TS–ACP line landmarks were identifiable in all cases. The TS–ACP line classification correlated strongly with operative findings (χ² = 138.3, p = 6.4 × 10⁻²⁹). The overall accuracy was 89.7% (78/87), and sensitivity and specificity for identifying intradural aneurysms were 94% and 82%, respectively. The interobserver agreement was substantial (κ = 0.78). Nine aneurysms were misclassified, including four cavernous-sinus lesions that partially crossed the DDR. Retrospective fusion using constructive interference in steady-state magnetic resonance imaging corrected these errors. Conclusions: The TS–ACP line represents a rapid, reproducible tool that reliably localizes the DDR on standard 3D-CTA, showing higher accuracy than previously reported single-landmark techniques. Its high accuracy and substantial inter-observer concordance support incorporation into routine preoperative assessments. Because the method depends on only two easily detectable bony points, it is well-suited for automated implementation, offering a practical pathway toward artificial intelligence-assisted stratification of paraclinoid aneurysms. Full article

Currently, there is a lack of methods for detecting the mechanism of gas explosion propagation within flameproof enclosures and the dynamic behavior of flameproof enclosures under explosion impact. Therefore, this paper studies a method for detecting the vibration characteristics of coal mine explosion-proof equipment under internal gas explosions using laser Doppler. First, a model of gas explosion propagation and explosion transmission response in flameproof enclosures is established to reveal the mechanism of gas explosion transmission inside coal mine flameproof enclosures. Second, a laser Doppler measurement method for coal mine flameproof enclosures is proposed, along with a step-by-step progressive vibration characteristic analysis method. This begins with a single-frequency dimension analysis using the Fourier transform (FFT), extends to time–frequency joint analysis using the short-time Fourier transform (STFT) to incorporate a time scale, and then advances to a three-dimensional linkage of scale, time, and frequency using the wavelet transform (DWT) to solve the limitation of the fixed window length of the STFT, thereby achieving a dynamic characterization of the detonation response characteristics. Finally, a non-symmetric Gaussian impact load inversion model is constructed to validate the overall scheme. The experimental results show that the FFT analysis identified a 2000 Hz main frequency, along with the global frequency components of the flameproof enclosure vibration signal, the STFT analysis revealed the dynamic evolution of the 2000 Hz main frequency and global frequency over time, and the wavelet transform achieved higher accuracy positioning of the frequency amplitude in the time domain, with better time resolution. Finally, the experimental platform showed an error of less than 5% compared with the actual measured impact load, and the error between the inverted impact load and the actual load was less than 15%. The experimental platform is feasible, and the inversion model has good accuracy. The laser Doppler measurement method has significant advantages over traditional coal mine flameproof equipment measurement and analysis methods and can provide further failure analysis and prevention, design optimization, and safety performance evaluation of flameproof enclosures in the future. Full article

The rapid expansion of bike-sharing systems has introduced significant management challenges related to spatial-temporal demand fluctuations and inefficient e-fence capacity allocation. This study proposes a Spatio-Temporal Graph Attention Transformer Network (STGATN), a novel hybrid deep learning framework for dynamic demand forecasting in bike-sharing e-fence systems. The model integrates Graph Convolutional Networks to capture complex spatial dependencies among urban functional zones, Bi-LSTM networks to model temporal patterns with periodic variations, and attention mechanisms to dynamically incorporate weather impacts. By constructing a city-level graph based on POI-derived e-fences and implementing multi-source feature fusion through Transformer architecture, the STGATN effectively addresses the limitations of static capacity allocation strategies. The experimental results from Shenzhen’s Nanshan District demonstrate the performance, with the STGATN model achieving an overall Mean Absolute Error (MAE) of 0.0992 and a Coefficient of Determination (R²) of 0.8426. This significantly outperforms baseline models such as LSTM (R²: 0.6215) and a GCN (R²: 0.5488). Ablation studies confirm the model’s key components are critical; removing the GCN module decreased R² by 12 percentage points to 0.7411, while removing the weather attention mechanism reduced R² by nearly 5 percentage points to 0.8034. The framework provides a scientific basis for dynamic e-fence capacity management, advancing spatio-temporal prediction methodologies for sustainable transportation. Full article

This paper addresses the issue of the poor collision avoidance effect of underactuated surface vehicles (USVs) during local path tracking. A virtual ship group control method is suggested by using Freiner coordinates and a model predictive control (MPC) algorithm. We track the planned path using the MPC algorithm according to the known vessel state and build a hierarchical weighted cost function to handle the state of the virtual vessel, to ensure that the vessel avoids obstacles while tracking the path. In addition, the control system incorporates an Extended Kalman Filter (EKF) algorithm to minimize the state estimation error by continuously updating the ship state and providing more accurate state estimation for the system in a timely manner. In order to validate the anti-interference and robustness of the control system, the simulation experiment is carried out with the “Yukun” as the research object by adding the interference of wind and wave of level 6. The outcome shows that the algorithm suggested in this paper can accurately perform the trajectory-tracking task and make collision avoidance decisions under six levels of external interference. Compared with the original MPC algorithm, the improved MPC algorithm reduces the maximum rudder angle output value by 58%, the integral absolute error by 46%, and the root mean square error value by 46%. The improved control algorithm reduces the maximum rudder angle output value by 42% and the maximum rudder angle output value by 10%. The control method provides a new technical choice for trajectory tracking and collision avoidance of USVs in complex marine environments, with a reliable theoretical basis and practical application value. Full article

Secret image sharing (SIS) is an image protection technique based on cryptography. However, traditional SIS schemes have limited noise resistance, making it difficult to ensure reconstructed image quality. To address this issue, this paper proposes a robust SIS scheme based on QR codes, which enables the efficient and lossless reconstruction of the secret image without pixel expansion. Moreover, the proposed scheme maintains high reconstruction quality under noisy conditions. In the sharing phase, the scheme compresses the length of shares by optimizing polynomial computation and improving the pixel allocation strategy. Reed–Solomon coding is then incorporated to enhance the anti-noise capability during the sharing process, while achieving meaningful secret sharing using QR codes as carriers. In the reconstruction phase, the scheme further improves the quality of the reconstructed secret image by combining image inpainting algorithms with the error-correction capability of Reed–Solomon codes. The experimental results show that the scheme can achieve lossless reconstruction when the salt-and-pepper noise density is less than $d\le 0.02$, and still maintains high-quality reconstruction when $d\le 0.13$. Compared with the existing schemes, the proposed method significantly improves noise robustness without pixel expansion, while preserving the visual meaning of the QR code carrier, and achieves a secret sharing strategy that combines robustness and practicality. Full article

In high-voltage switchgear, partial discharge (PD) detection using six-degree-of-freedom (6-DOF) manipulators presents challenges. However, these involve inverse kinematics (IK) solution redundancy and the lack of synergistic optimization between end-effector positioning accuracy and energy consumption. To address these issues, a dual-layer adaptive optimization model integrating multiple algorithms is proposed. In the first layer, a spatio-temporal correlation particle memory-based particle swarm optimization BP neural network (STPSO-BP) is employed. It replaces traditional IK, while long short-term memory (LSTM) predicts particle movement trends, and trajectory similarity penalties constrain search trajectories. Thereby, positioning accuracy and adaptability are enhanced. In the second layer, a chaotic mapping-based simulated annealing (CM-SA) algorithm is utilized. Chaotic joint angle constraints, dynamic weight adjustment, and dynamic temperature regulation are incorporated. This approach achieves collaborative optimization of energy consumption and positioning error, utilizing cubic spline interpolation to smooth the joint trajectory. Specifically, the positioning error decreases by 68.9% compared with the traditional BP neural network algorithm. Energy consumption is reduced by 60.18% in contrast to the pre-optimization state. Overall, the model achieves significant optimization. An innovative solution for synergistic accuracy–energy control in 6-DOF manipulators for PD detection is offered. Full article

Addressing the limitations of traditional fatigue life prediction methods, which rely on extensive experimental data and incur high costs, and given the current absence of studies that employ deformation inhomogeneity parameters to construct fatigue-indicator parameter (FIP) for predicting low-cycle fatigue (LCF) life of metals in hydrogen environments, this study firstly explores how hydrogen pre-charging influences the LCF behavior of hot-rolled ribbed bar grade 400 (HRB400) steel via experimental and crystal plasticity simulation, and focus on the relationship between the fatigue life and the evolution of microscale deformation inhomogeneity. The experimental results indicate that hydrogen charging causes alterations in cyclic hysteresis, an expansion of the elastic range of the stabilized hysteresis loop, and a significant reduction in LCF life. Secondly, a novel FIP was developed within the crystal plasticity finite element method (CPFEM) framework to predict the LCF life of HRB400 steel under hydrogen influence. This FIP incorporates three internal variables: hydrogen embrittlement index, axial strain variation coefficient, and macroscopic stress ratio. These variables collectively account for the hydrogen charging effects and stress peak impacts on the microscale deformation inhomogeneity. The LCF life of hydrogen-charged HRB400 steel can be predicted using this new FIP. We performed fatigue testing under only one loading condition to measure the corresponding fatigue life and determine the FIP critical value. This helped predict fatigue life under different cyclic loading conditions for the same hydrogen-charged material. We compared the experimental data to validate the novel FIP to accurately predict the LCF life of hydrogen-charged HRB400 steel. The error between the predicted results and the measured results is limited to a factor of two. Full article

Received Signal Strength Indicator (RSSI) prediction is valuable for network planning and optimization as it helps determine the optimal placements of wireless access points and enables better coverage planning. It is also crucial for efficient handover management between cells or access points, reducing dropped connections and improving service quality. Additionally, RSSI prediction supports indoor positioning systems, power management optimization, and cost-efficient network deployment. Path loss models have historically served as the foundation for RSSI prediction, providing a theoretical framework for estimating signal strength degradation. However, modern machine learning approaches have emerged as a revolutionary solution for network optimization, providing more versatile and data-driven methods to enhance wireless network performance. In this paper, an adaptive machine learning framework integrating environmental sensing parameters such as temperature, relative humidity, barometric pressure, and particulate matter for RSSI prediction is proposed. Performance analysis reveals that RSSI values are influenced by environmental factors through complex, non-linear interactions, thereby challenging the conventional linear assumptions of traditional path loss models. The proposed model demonstrates improved predictive accuracy over the baseline, with relative increases in variance explained of 6.02% and 2.04% compared to the baseline model excluding and including environmental parameters, respectively. Additionally, the root mean squared error is reduced to 1.40 dB. These results demonstrate that cognitive methods incorporating environmental data can substantially enhance RSSI prediction accuracy in wireless communications. Full article

The estimation of soil hydraulic properties—such as water retention and hydraulic conductivity—is essential for irrigation management and agro-hydrological modeling. This study presents the development and application of SOILHP, a low-cost, IoT-integrated device designed to monitor laboratory evaporation experiments for the estimation of soil hydraulic properties using inverse modeling tools. SOILHP incorporates mini-tensiometers, a precision balance, microcontrollers, and cloud-based data logging via Google Sheets. SOILHP enables the remote, real-time acquisition of soil pressure head and mass variation data without the need for commercial dataloggers. Evaporation experiments were conducted using undisturbed soil samples, and inverse modeling with Hydrus-1D was used to estimate van Genuchten–Mualem parameters. The optimized parameters showed low standard errors and narrow 95% confidence intervals, demonstrating the robustness of the inverse solution, confirming the device’s sensors accuracy. Forward simulations of internal drainage were performed to estimate the field capacity under different drainage flux criteria. The field capacity results aligned with values reported in the literature for tropical soils. Overall, SOILHP proved to be a reliable and economically accessible alternative for monitoring evaporation experiments aimed at fitting parameters of analytical functions that describe water retention and hydraulic conductivity properties within the soil pressure head range relevant to agriculture. Full article

To gain deeper insights into the influence of pipe parameters on water hammer properties and achieve the accurate simulation of the hydraulic transient process in pipeline systems, the Finite Volume Method (FVM) is adopted. The solution scheme, incorporating a second-order MUSCL-type reconstruction, is derived, and the numerical solution process is detailed. For enhanced accuracy, the unsteady friction term is included in the numerical solution of the governing water hammer equations. The method is validated through a comparison with experimental data and the verification of mesh and Courant number independence, confirming both its efficiency and accuracy. The calculation error of the peak water head is less than 5%. Finally, an engineering case is studied to investigate valve arrangement and operation. Optimization yields the optimal valve position and operating parameters. This analysis provides valuable reference for pipeline system design. Full article

This study examines the influence of artificial intelligence (AI) system transparency, cognitive load, response bias, and individual values on perceived AI decision integrity. Using a quantitative approach, data were collected through surveys and analyzed via SEM-PLS. The findings highlight that AI transparency and familiarity significantly impact users’ trust and perception of decision fairness. Response biases were found to be increased by the cognitive load and decision fatigue, affecting decision integrity. This study identifies mediating effects of sensitivity to errors and response bias in AI-driven decision-making. Practical implications imply that lowering the cognitive load and increasing transparency will help to increase the acceptance of AI, and incorporating ethical considerations into AI system design helps to minimize bias. This study contributes to AI ethics by emphasizing fairness, explainability, and user-centered trust mechanisms. Future research should explore AI decision-making across industries and cultural contexts. The findings of this study offer managerial, theoretical, and practical insights into responsible AI deployment. Full article

In many practical applications, data collected over time often exhibit autocorrelation, which, if unaccounted for, can lead to biased or misleading statistical inferences. To address this issue, we propose a varying-coefficient additive model for density-valued responses, incorporating a functional auto-regressive (FAR) error process to capture serial dependence. Our estimation procedure consists of three main steps, utilizing spline-based methods after mapping density functions into a linear space via the log-quantile density transformation. First, we obtain initial estimates of the bivariate varying-coefficient functions using a B-spline series approximation. Second, we estimate the error process from the residuals using spline smoothing techniques. Finally, we refine the estimates of the additive components by adjusting for the estimated error process. We establish theoretical properties of the proposed method, including convergence rates and asymptotic behavior. The effectiveness of our approach is further demonstrated through simulation studies and applications to real-world data. Full article