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A series of fire experiments in a 1/10 scale model tunnel with a lateral open shaft were conducted. Analysis was performed to explore the maximum excess temperature and longitudinal temperature decay under the influence of a mechanical exhaust system with a lateral open shaft. Three different pool sizes and numerous extraction rates were considered. The experimental results yielded intriguing insights into the correlations between the rate of smoke extraction and the ceiling temperature. The variations in the temperature distribution of ceiling smoke upstream and downstream the fire source is different under the induced longitudinal velocity, especially for the near the fire source area. An analysis of the maximum excess temperature was conducted by inducing the heat loss coefficient . It is 0.85 (0.71) for the induced dimensionless longitudinal velocity , which indicates the effect of a large velocity on the smoke heat loss. Then, a modified model of the maximum excess temperature was given for a tunnel utilizing lateral open shaft smoke extraction. In addition, a simple model was proposed to capture ceiling temperature decay, where the decay coefficients upstream and downstream of the fire source are proportional to () for . The research results have certain guiding significance for the arrangement of fire protection, fire monitoring and early warning devices in mountain tunnels. [ABSTRACT FROM AUTHOR]

Quantitation of damping is of great significance for the design and condition assessment of wind turbines. The authors' previous theoretical and numerical studies showed that compared with damping ratios, a modal aerodynamic damping matrix can better describe the damping coupling in the fore‐aft (FA) and side‐side (SS) tower motions. In the present study, an improved damping identification method was first proposed to identify this damping matrix with artificial exciters and then verified by using OpenFAST simulations under different excitation frequencies, excitation force amplitudes, and turbulent wind fields. Following the numerical study, a scaled wind turbine model with a geometric scale ratio of 1/75 was carefully designed based on the NREL 5‐MW wind turbine prototype, in which the scaled blade design follows the rule in thrust coefficient similarity. An identification study was performed with this scaled model by a series of wind tunnel tests. The modal aerodynamic damping matrix was identified under steady‐state harmonic excitation in the operating state and compared with the identified results by a free decay method and the theoretical values. The results experimentally confirm the correctness of the aerodynamic damping matrix theory under uniform wind and the feasibility of the improved identification method in practice. [ABSTRACT FROM AUTHOR]

Protein glycosylation, a vital post-translational modification, is pivotal in various biological processes and disease pathogenesis. Computational approaches, including protein language models and machine learning algorithms, have emerged as valuable tools for predicting O-GlcNAc sites, reducing experimental costs, and enhancing efficiency. However, the literature has not reported the prediction of O-GlcNAc sites through the evolutionary scale model (ESM). Therefore, this study employed the ESM-2 model for O-GlcNAc site prediction in humans. Approximately 1100 O-linked glycoprotein sequences retrieved from the O-GlcNAc database were utilized for model training. The ESM-2 model exhibited consistent improvement over epochs, achieving an accuracy of 78.30%, recall of 78.30%, precision of 61.31%, and F1-score of 68.74%. However, compared to the traditional models which show an overfitting on the same data up to 99%, ESM-2 model outperforms in terms of optimal training and testing predictions. These findings underscore the effectiveness of the ESM-2 model in accurately predicting O-GlcNAc sites within human proteins. Accurately predicting O-GlcNAc sites within human proteins can significantly advance glycoproteomic research by enhancing our understanding of protein function and disease mechanisms, aiding in developing targeted therapies, and facilitating biomarker discovery for improved diagnosis and treatment. Furthermore, future studies should focus on more diverse data types, longer protein sequence lengths, and higher computational resources to evaluate various parameters. Accurate prediction of O-GlcNAc sites might enhance the investigation of the site-specific functions of proteins in physiology and diseases. [ABSTRACT FROM AUTHOR]

The advent of large-scale distributed generation (DG) has introduced several challenges to the voltage control of active distribution networks (ADNs). These challenges include the heterogeneity of control devices, the complexity of models, and their inherent fluctuations. To maintain ADN voltage stability more economically and quickly, a data-driven ADN voltage control scheme is proposed in this paper. Firstly, based on the multi-run state sensitivity matrix, buses with similar voltage responses are clustered, and critical buses are selected to downsize the scale of the model. Secondly, a linear voltage-to-power dynamics model in high-dimensional state space is trained based on the offline data of critical bus voltages, DGs, and energy storage system (ESS) outputs, utilizing the Koopman theory and the Extended Dynamic Mode Decomposition (EDMD) method. A linear model predictive voltage controller, which takes ADN stability and control cost into account, is also proposed. Finally, the effectiveness and applicability of the method are verified by applying it to an improved 33-bus ADN system. The proposed control method can respond more quickly and accurately to the voltage fluctuation problems caused by source-load disturbances and short-circuit faults. [ABSTRACT FROM AUTHOR]

In this paper, the influence of the structure of the bottom of the ladle with ceramic dam or set of dams on the mixing process was assessed, determining the mixing time required to achieve the level of 95% chemical homogenization. The 0.1 scale water model was used for the physical experiments. The numerical simulations were carried out in the Ansys-Fluent 12.1 software for a 1:1 scale ladle and the behavior of hot metal—nitrogen system. The research focused on three issues, i.e., the influence of the flow rate of technical gas, the influence of the position of the top injection lance, and the influence of the type of dam mounted in the ladle bottom. Finally, the use of a semi-circle dam or set of dams in the ladle bottom together with the top lance being set to a lower depth resulted in a significant reduction in the total mixing time of the liquid metal by 42% and 50%, respectively, without increasing the nitrogen flow rate. [ABSTRACT FROM AUTHOR]

This paper introduces a method for computing eigenvalues and eigenvectors of a generalized Hermitian, matrix eigenvalue problem. The work is focused on large scale eigenvalue problems, where the application of a direct inverse is out of reach. Instead, an explicit time-domain integrator for the corresponding wave problem is combined with a proper filtering and a Krylov iteration in order to solve for eigenvalues within a given region of interest. We report results of small scale model problems to confirm the reliability of the method, as well as the computation of acoustic resonances in a three dimensional model of a hunting horn to demonstrate the efficiency. • A new method for the computation of eigenvalues/resonances based on time-domain methods. • Solve large scale eigenvalue problems with very low memory requirement. • Compute eigenvalues only in a region of interest. • Computation of acoustic resonances of a hunting horn. [ABSTRACT FROM AUTHOR]

In coal masses, joints serve as crucial components influencing mechanical responses and failure mechanisms. The joint distribution complicates the acquisition of physical parameters for engineering-scale coal masses, and traditional laboratory or in situ tests often suffer from inaccuracies and high costs. Therefore, examination of the dynamic properties of engineering-scale coal mass is generally performed by numerical simulations. This paper proposes a model construction approach using two-dimension Particle Flow Code (PFC2D) software to study the mechanical properties of engineering-scale jointed coal mass, addressing the limitations of conventional models by integrating scale effects, accuracy, and computational efficiency. Firstly, the distribution characteristics and mechanical parameters of the joints in the coal mass were obtained based on field statistics and laboratory experiments. The parameters of the laboratory-scale model were calibrated by the numerical matching method. The discrete element model for the engineering-scale coal mass was constructed by the step-by-step matching method. The confining pressure effect on the coal mass under a biaxial loading condition was studied, while the strength change, fissure evolution, and failure mechanism under different confining pressures and fissure degrees were investigated. Based on the simulation results, a quantitative relationship was established between the mechanical parameters, fissure degree, and confining pressure under compression conditions. Ultimately, the failure zone ahead of the working face and the distribution of the abutment pressure were assessed using the mechanics parameters of coal masses with diverse joint distributions. [ABSTRACT FROM AUTHOR]

The complexity of flood risk models is intrinsically linked to a variety of sources of uncertainty (hydrology, hydraulics, exposed assets, vulnerability, coping capacity, etc.) that affect the accuracy and reliability of the analyses. Estimating the uncertainties associated with the different components allows us to be more confident in the risk values on the ground, thus providing a more reliable assessment for investment, insurance and flood risk management purposes. In this study, we investigate the flood risk of the entire Central Apennines District (CAD) in Central Italy using the laRgE SCale inUndation modEl – Flood Risk (RESCUE-FR), focusing on the interaction between the uncertainty in the hydraulic Manning parameter and the risk variability. We assess the coherence between the quantile flood risk maps generated by our model and the official risk maps provided by the Central Apennines District Authority (CAD Authority) and focusing on three specific zones within the CAD region. Thus, RESCUE-FR is used to estimate the expected annual damage (EAD) and the expected annual population affected (EAPA) across the CAD region and to conduct a comprehensive uncertainty analysis. The latter provides a range of confidence of risk estimation that is essential for identifying vulnerable areas and guiding effective mitigation strategies. [ABSTRACT FROM AUTHOR]

Effective management of malignant weeds is critical to soybean growth. This study focuses on addressing the critical challenges of targeted spraying operations for malignant weeds such as Cirsium setosum, which severely threaten soybean yield in soybean fields. Specifically, this research aims to tackle key issues in plant protection operations, including the precise identification of weeds, the lightweight deployment of segmentation models, real-time requirements for spraying operations, and the generalization ability of models in diverse field environments. To address these challenges, this study proposes an improved weed instance segmentation model based on YOLOv8s-Seg, named FCB-YOLOv8s-Seg, for targeted spraying operations in soybean fields. The FCB-YOLOv8s-Seg model incorporates a lightweight backbone network to accelerate computations and reduce model size, with optimized Squeeze-and-Excitation Networks (SENet) and Bidirectional Feature Pyramid Network (BiFPN) modules integrated into the neck network to enhance weed recognition accuracy. Data collected from real soybean field scenes were used for model training and testing. The results of ablation experiments revealed that the FCB-YOLOv8s-Seg model achieved a mean average precision of 95.18% for bounding box prediction and 96.63% for segmentation, marking an increase of 5.08% and 7.43% over the original YOLOv8s-Seg model. While maintaining a balanced model scale, the object detection and segmentation accuracy of this model surpass other existing classic models such as YOLOv5s-Seg, Mask-RCNN, and YOLACT. The detection results in different scenes show that the FCB-YOLOv8s-Seg model performs well in fine-grained feature segmentation in complex scenes. Compared with several existing classical models, the FCB-YOLOv8s-Seg model demonstrates better performance. Additionally, field tests on plots with varying weed densities and operational speeds indicated an average segmentation rate of 91.30%, which is 6.38% higher than the original model. The proposed algorithm shows higher accuracy and performance in practical field instance segmentation tasks and is expected to provide strong technical support for promoting targeted spray operations. [ABSTRACT FROM AUTHOR]

Symmetric lateral inlet pumping stations are commonly utilized for water lifting in agricultural multi-crop irrigation districts, but they often share non-ideal flow patterns, which can easily cause pump vibration and sediment deposition. In this paper, a symmetrical lateral pumping station in an irrigation district is taken as the research object, and CFD (Computational Fluid Dynamics) technology is used to study it. The model test used a model scale ratio of λ L = 1:18. Results: By comparing the CFD data and test data, the average relative error for the left station is found to be 3.213%, while that for the right station is 5.107%, indicating that the numerical simulation method is reliable. Six different rectification measures are proposed, the cross sectional flow pattern of the pumping station is observed, and the longitudinal profile of axial velocity distribution in the sump is analyzed. The velocity-weighted average angle and hydraulic loss of each case study are also analyzed. The flow operates smoothly in case study 7. The vortex in the approach channel disappears when the columns and bottom sill are finally installed. Compared to the original case study, the velocity-weighted average angle at the 5# station in case study 7 increased by 14%, and it increased by 13.9% at station #9. The flow became more stable, and hydraulic losses were minimized. The simulated hydraulic loss in case study 7 decreased by 14.2%. These findings can serve as a reference for similar pump station projects. [ABSTRACT FROM AUTHOR]

The technology of pantograph sinking in the cavity is generally adopted in the new generation of high-speed trains in China for aerodynamic noise reduction in this region. This study takes a high-speed train with a 4-car formation and a pantograph as the research object and compares the aerodynamic acoustic performance of two scale models, 1/8 and 1/1, using large eddy simulation and Ffowcs Williams–Hawkings integral equation. It is found that there is no direct scale similarity between their aeroacoustic performance. The 1/1 model airflow is separated at the leading edge of the panhead and reattached to the panhead, and its vortex shedding Strouhal number (St) is 0.17. However, the 1/8 model airflow is separated directly at the leading edge of the panhead, and its St is 0.13. The cavity's vortex shedding frequency is in agreement with that calculated by the Rooster empirical formula. The two scale models exhibit some similar characteristics in distribution of sound source energy, but the energy distribution of the 1/8 model is more concentrated in the middle and lower regions. The contribution rates of their middle and lower regions to the radiated noise in the two models are 27.3% and 87.2%, respectively. The peak frequencies of the radiated noise from the 1/1 model are 307 and 571 Hz. The 307 Hz is consistent with the frequency of panhead vortex shedding, and the 571 Hz is more likely to be the result of the superposition of various components. In contrast, the peak frequencies of the radiated noise from the 1/8 scale model are 280 and 1970 Hz. The 280 Hz comes from the shear layer oscillation between the cavity and the bottom frame, and the 1970 Hz is close to the frequency at which the panhead vortex sheds. This shows that the scaled model results need to be corrected before applying to the full-scale model. [ABSTRACT FROM AUTHOR]

In order to achieve the purposes of the transportation and installation of the integrated wind turbine with the mono-column composite bucket foundation, an integrated floating platform has been developed. Taking the integrated towing process of the multi-body coupling system as the research object, a model test with a scale ratio of 1:80 is designed. The influences of environmental factors such as significant wave height, wave period, and the wave incident angle on the motion response of the integrated wind turbine and the floating platform during the floating-transport process, the pressure inside the compartment, and the towing cable force have been studied. The research findings are as follows: (1) Wave height and wave period have a great influence on the integrated wind turbine and the floating platform. With the increase in wave height and wave period, the motion response of the integrated wind turbine and the floating platform increases, the air pressure fluctuation in the bucket foundation compartment becomes more and more intense, and the towing cable force required for towing also increases and fluctuates more violently. (2) When the wave incident angle changes from 0° to 45°, the pitching motion frequency of the integrated wind turbine significantly increases, the overall fluctuation of the compartment pressure in the bucket foundation compartment decreases, the motion response of the floating platform becomes larger, and the towing cable force required for towing also increases accordingly. [ABSTRACT FROM AUTHOR]

Scaling is a significant challenge in oilfield production gathering and transportation stations, and it not only constrains the economic efficiency but also affects the development of oil and natural gas. This study proposes a scaling prediction model based on chemical experimental analysis and reservoir dynamic analysis methods for the gathering and transportation stations in the Changqing Oilfield. The objective of this study is to provide technical support for the oilfield to advance precise management and achieve cost reduction and efficiency enhancement. Initially, the water quality and scale samples of the oilfield were tested and analyzed using Inductively Coupled Plasma (ICP), Ion Chromatography (IC), and Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS), and the distribution and patterns of scaling in the gathering and transportation pipelines were studied. Based on this, using the test data and the production liquid ratio of each development layer at the gathering and transportation stations, a reservoir dynamic correlation method was employed to construct a prediction model for the development layer with the highest similarity to the tested water samples at the stations and the types of scale samples. The results indicate that this prediction method can effectively reduce the scaling rate and provide guidance for the anti-scaling process in the Changqing Oilfield. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven kinematic model for the end-effector pose control applied to a variety of manipulator robots, focusing on the entire end-effector's pose (position and orientation). The measured signals of the full pose and their computed derivatives, along with a linear combination of an estimated Jacobian matrix and a vector of joint velocities, generate a model estimation error. The Jacobian matrix is estimated using the Pseudo Jacobian Matrix (PJM) algorithm, which requires tuning only the step and weight parameters that scale the convergence of the model estimation error. The proposed control law is derived in two stages: the first one is part of an objective function minimization, and the second one is a constraint in a quasi-Lagrangian function. The control design parameters guarantee the control error convergence in a closed-loop configuration with adaptive behavior in terms of the dynamics of the estimated Jacobian matrix. The novelty of the approach lies in its ability to achieve superior tracking performance across different manipulator robots, validated through simulations. Quantitative results show that, compared to a classical inverse-kinematics approach, the proposed method achieves rapid convergence of performance indices (e.g., Root Mean Square Error (RMSE) reduced to near-zero in two cycles vs. a steady-state RMSE of 20 in the classical approach). Additionally, the proposed method minimizes joint drift, maintaining an RMSE of approximately 0.3 compared to 1.5 under the classical scheme. The control was validated by means of simulations featuring an UR5e manipulator with six Degrees of Freedom (DOF), a KUKA Youbot with eight DOF, and a KUKA Youbot Dual with thirteen DOF. The stability analysis of the closed-loop controller is demonstrated by means of the Lyapunov stability conditions. [ABSTRACT FROM AUTHOR]

Accurate estimation of forest aboveground carbon stock (AGC) is essential for understanding carbon accounting and climate change. In previous studies, the extracted factors, such as spectral textures, vegetation indices, and textural features, were used to estimate the AGC. However, few studies examined how different factors affect estimation accuracy in detail. Meanwhile, there are also many uncertainties in the collection and processing of the field data. To quantify the various uncertainties in the process of AGC estimation, we used the random forest (RF) to establish estimation models based on field data and Sentinel-1/2 images in Shangri-La. The models included the band information model (BIM), the vegetation index model (VIM), the texture information model (TIM), the Sentinel-2 factor model (S-2M), and the Sentinel-1/2 factor model (S-1/2M). Then, uncertainties resulting from the plot scale and estimation models were calculated using error equations. Our goal is to analyze the influence of different factors on AGC estimation and to assess the uncertainty of plot scale and estimation models quantitatively. The results showed that (1) the uncertainty of the measurement was 3.02%, while the error of the monocarbon stock model was the main uncertainty at the plot scale, which was 9.09%; (2) the BIM had the lowest accuracy (R2 = 0.551) and the highest total uncertainty (22.29%); by gradually introducing different factors in the process of modeling, the accuracies improved significantly (VIM: R2 = 0.688, TIM: R2 = 0.715, S-2M: R2 = 0.826), and the total uncertainty decreased to some extent (VIM: 14.12%, TIM: 12.56%, S-2M: 10.79%); (3) the S-1/2M with the introduction of Sentinel-1 synthetic aperture radar (SAR) data has the highest accuracy (R2 = 0.872) and the lowest total uncertainty (8.43%). The inaccuracy of spectral features is highest, followed by vegetation indices, while textural features have the lowest inaccuracy. Uncertainty in the remote-sensing-based estimation model remains a significant source of uncertainty compared to the plot scale. Even though the uncertainty at the plot scale is relatively small, this error should not be ignored. The uncertainty in the estimation process could be further reduced by improving the precision of the measurement and the fitting of the monocarbon stock estimation model. [ABSTRACT FROM AUTHOR]

Marine propeller design requirements have risen in quantity and quality in recent decades. Reduced propeller cavitation is targeted to ensure that comfort requirements and environmental regulations are met. This paper presents the development of a mesh refinement process for the numerical prediction of tip vortex cavitation (TVC) using the commercial CFD package STAR-CCM+. Given the strong dependence on the mesh resolution within the areas of interest, mesh refinement and the use of field functions for adaptive meshing were demonstrated. The developed numerical model was substantiated against relevant published test data. Subsequently, the validated mesh refinement process was extended to scaled-up models representing medium- and full-scale propellers. The results showed that this process can be applied to CFD simulations to capture the minimum pressure within a tip vortex core. This process is also applicable to different types of hydrodynamic propulsors at both model scale and full scale. Additionally, the cavitation inception scaling law was evaluated for all small-scale and full-scale models, and it was found that the scaling parameter obtained using the developed refinement process was somewhat close to that obtained using existing methods. It is expected that the mesh refinement process developed in this study can be used to investigate the effect of scaling on tip vortex cavitation inception. [ABSTRACT FROM AUTHOR]

Urgent action is needed to address climate change, land degradation, and biodiversity loss. The Regreening Africa project (2017–2023), recently recognized as a UN World Restoration Flagship, aimed to reverse land degradation over large areas of land for the triple benefit of people, biodiversity, and climate in eight African countries. Based on projections and early lessons learned, the project sought to identify sustainable scaling models to achieve its ambitious targets. The so-called "Asset-Based Community-driven Development (ABCD) in Regreening" project aimed to demonstrate the positive contribution of deliberate community engagement and co-design. The project introduced ABCD sessions to 30 purposively selected community groups in the Regreening intensification sites in western Kenya. ABCD combines a unique set of framings, methods, and processes that focus on people's assets and agency, and emphasizes the importance of their attitudes toward self and others for sustainable behavior change. To evidence that ABCD intrinsically contributes to sustainable adoption and scaling of Regreening practices, the project developed the F-ACT+ tool to assess the alignment between ABCD and agroecological practices, and collected baseline and endline data from 300 project and 300 non-project participants. Results showed accelerated agroecological integration among ABCD project participants. ABCD participants showed significant improvements in nine agroecological principles and eight system components, particularly in the economic diversification, social values and diets, and knowledge co-creation principles, as well as in the pest and disease, household, and value chain system components. Summary ATT between ABCD and non-ABCD respondents was positive and significant in 10 principles and eight system components. The results support the synergistic contribution of ABCD to projects targeting sustainable behavior change at the individual and collective levels. Due to its focus on outcomes, this study provided limited insight into the specific mechanisms of ABCD, which are the subject of a separate publication on parallel theory-based contribution analysis work. [ABSTRACT FROM AUTHOR]

This paper presents the theoretical equations for computing the critical performance parameters of self-centering piers with and without energy-dissipating (ED) members. A structural fuses-based design method was proposed for developing self-centering piers with ED members, and two scale models of the self-centering piers with and without ED members were designed and constructed. The quasi-static tests and numerical simulations were conducted to verify the rationality of the theoretical derivation, the proposed design method, and the advantages of ED members on the seismic performance of the self-centering piers. The findings indicate that ED members undergo plastic deformation and dissipate energy before the ED bars yield, meeting the structural fuse concept. The theoretical performance parameters of specimens SCP and SCP-EDM matched well with the experimental and numerical results, such as the yield displacement, stiffness, and strength. Also, the ED members remarkably increase the strength, stiffness, and ED levels of self-centering piers. [ABSTRACT FROM AUTHOR]

Small-scale rock cutting tests serve as a simple approach to evaluate the performance of tunnel boring machines (TBMs), but the feasibility of this method requires further investigation. Herein, a small-scale rotary cutting machine is developed to conduct rock cutting tests, and the cutting performance is investigated. The results indicate that similar cutting performance can be achieved through both small-scale and full-scale tests. The critical penetration depth for effective rock cutting by the cutter is 0.5 mm, below which the cutter grinds against the rock. The optimal ratio of cutting spacing to penetration depth obtained in small-scale tests is 4.47. A result within the empirical range can be achieved by multiplying the optimal small-scale cutting parameters with the scale coefficient, demonstrating the feasibility of using small-scale tests to guide the design of cutter heads. Based on the test result, the scale prediction model is constructed to predict the full-scale cutting force. The predictive capability of the proposed model and CSM model is validated using 72 sets of full-scale test data involving the same types of rock, and the predictions of the proposed model are closer to the test data. [ABSTRACT FROM AUTHOR]

High-resolution planetary remote sensing imagery provides detailed information for geomorphological and topographic analyses. However, acquiring such imagery is constrained by limited deep-space communication bandwidth and challenging imaging environments. Conventional super-resolution methods typically employ separate models for different scales, treating them as independent tasks. This approach limits deployment and real-time applications in planetary remote sensing. Moreover, capturing global context is crucial in planetary remote sensing images due to their contextual similarities. To address these limitations, we propose Discrete Cosine Transform (DCT)–Global Super Resolution Neural Operator (DG-SRNO), a global context-aware arbitrary-scale super-resolution model. DG-SRNO achieves super-resolution at any scale using a single framework by learning the mapping between low-resolution (LR) and high-resolution (HR) function spaces. We mathematically prove the global receptive field of DG-SRNO. To evaluate DG-SRNO's performance in planetary remote sensing tasks, we introduce the Ceres 800 dataset, a planetary remote sensing super-resolution dataset. Extensive quantitative and qualitative experiments demonstrate DG-SRNO's impressive reconstruction capabilities. [ABSTRACT FROM AUTHOR]

This study explores the relationship between surface roughness and characteristic scale through theoretical analyses and experimental data. Considering the concavity and convexity of cycloidal gear tooth profile curvatures, a curved surface contact coefficient model suitable for both inner and outer contacts, with either equal or unequal curvatures, has been developed. Moreover, a fractal contact model is constructed for cycloidal pinwheels. This model considers scale correlation and analyzes the influence of microscopic parameters on the elastic-plastic critical grade and the behavior of the contact area. Additionally, the influence of the micro-convex body frequency index on surface contact performance is explored to provide theoretical insights for improving the elastic contact ratio and reducing wear on contact surfaces. [ABSTRACT FROM AUTHOR]

Researchers often combine both positively and negatively worded items when constructing Likert scales. This combination, however, may introduce method effects due to the variances in item wording. Although previous studies have tried to quantify these effects by using factor analysis on scales with different content, the impact of varied item wording on participants' choices among specific options remains unexplored. To address this gap, we utilized four versions of the Undergraduate Learning Burnout (ULB) scale, each characterized by a unique valence of item wording. After collecting responses from 1,131 college students, we employed unidimensional, multidimensional, and bi-factor Graded Response Models for analysis. The results suggested that the ULB scale supports a unidimensional structure for the learning burnout trait. However, the inclusion of different valences of wording within items introduced additional method factors, explaining a considerable degree of variance. Notably, positively worded items demonstrated greater discriminative power and more effectively counteracted the biased outcomes associated with negatively worded items, especially between the "Strongly Disagree" and "Disagree" options. While there were no substantial differences in the overall learning burnout traits among respondents of different scale versions, slight variations were noted in their distributions. The integration of both positive and negative wordings reduced the reliability of the learning burnout trait measurement. Consequently, it is recommended to use exclusively positively worded items and avoid a mix in item wording during scale construction. If a combination is essential, the bi-factor IRT model might help segregate the method effects resulting from the wording valence. [ABSTRACT FROM AUTHOR]

Compared with a stationary fire, the spread of smoke in a moving fire source is more complex, which means hazardous consequences. The study of smoke flow and distribution of ceiling temperature is essential for effective emergency response strategies and efficient execution of rescue operations. To study the smoke flow characteristics of a tunnel fire with a moving fire source, we constructed a 1:20 scale tunnel model and conducted experiments with a moving fire source. Six different moving velocities (0.1, 0.15, 0.3, 0.5, 0.8, and 1.0 m/s) were selected for the experiment. The focus of the experiment was to analyze the stability of the smoke and the temperature distribution of the tunnel ceiling under the moving conditions of different fire sources. The results show that when a fire source velocity is below 0.3 m/s, countercurrent smoke occurs in the tunnel. The smoke layer is stable, and the temperature in the center of the tunnel exceeds the sidewall temperatures. When the velocity reaches 0.3 m/s, smoke inversion disappears, the smoke layer begins to become unstable, and the temperature of the tunnel increases while a central zone of low temperature is formed. The faster the fire moves, the longer the central low-temperature zone will be in the tunnel. [ABSTRACT FROM AUTHOR]

To address the issues of target feature blurring and increased false detections caused by high compression rates in deepfake videos, as well as the high computational resource requirements of existing face extractors, we propose a lightweight face extractor to assist deepfake detection, GCS-YOLOv8. Firstly, we employ the HGStem module for initial downsampling to address the issue of false detections of small non-face objects in deepfake videos, thereby improving detection accuracy. Secondly, we introduce the C2f-GDConv module to mitigate the low-FLOPs pitfall while reducing the model's parameters, thereby lightening the network. Additionally, we add a new P6 large target detection layer to expand the receptive field and capture multi-scale features, solving the problem of detecting large-scale faces in low-compression deepfake videos. We also design a cross-scale feature fusion module called CCFG (CNN-based Cross-Scale Feature Fusion with GDConv), which integrates features from different scales to enhance the model's adaptability to scale variations while reducing network parameters, addressing the high computational resource requirements of traditional face extractors. Furthermore, we improve the detection head by utilizing group normalization and shared convolution, simplifying the process of face detection while maintaining detection performance. The training dataset was also refined by removing low-accuracy and low-resolution labels, which reduced the false detection rate. Experimental results demonstrate that, compared to YOLOv8, this face extractor achieves the AP of 0.942, 0.927, and 0.812 on the WiderFace dataset's Easy, Medium, and Hard subsets, representing improvements of 1.1%, 1.3%, and 3.7% respectively. The model's parameters and FLOPs are only 1.68 MB and 3.5 G, reflecting reductions of 44.2% and 56.8%, making it more effective and lightweight in extracting faces from deepfake videos. [ABSTRACT FROM AUTHOR]

Background: Depression is a pervasive mental health condition, particularly affecting older adults, where early detection and intervention are essential to mitigate its impact. This study presents an explainable multi-layer dynamic ensemble framework designed to detect depression and assess its severity, aiming to improve diagnostic precision and provide insights into contributing health factors. Methods: Using data from the National Social Life, Health, and Aging Project (NSHAP), this framework combines classical machine learning models, static ensemble methods, and dynamic ensemble selection (DES) approaches across two stages: detection and severity prediction. The depression detection stage classifies individuals as normal or depressed, while the severity prediction stage further classifies depressed cases as mild or moderate-severe. Finally, a confirmation depression scale prediction model estimates depression severity scores to support the two stages. Explainable AI (XAI) techniques are applied to improve model interpretability, making the framework more suitable for clinical applications. Results: The framework's FIRE-KNOP DES algorithm demonstrated high efficacy, achieving 88.33% accuracy in depression detection and 83.68% in severity prediction. XAI analysis identified mental and non-mental health indicators as significant factors in the framework's performance, emphasizing the value of these features for accurate depression assessment. Conclusions: This study emphasizes the potential of dynamic ensemble learning in mental health assessments, particularly in detecting and evaluating depression severity. The findings provide a strong foundation for future use of dynamic ensemble frameworks in mental health assessments, demonstrating their potential for practical clinical applications. [ABSTRACT FROM AUTHOR]

Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance into unobserved areas or periods of time. However, such models often poorly predict the distribution of animal space‐use intensity beyond the place and time of data collection, presumably because space‐use behaviors vary between individuals and environmental contexts. Similarly, ecological inference based on habitat selection models could be muddied or biased due to unaccounted individual and context dependencies. Here, we present a modeling workflow designed to allow transparent variance‐decomposition of habitat‐selection patterns, and consequently improved inferential and predictive capacities. Using global positioning system (GPS) data collected from 238 individual pronghorn, Antilocapra americana, across three years in Utah, USA, we combine individual‐year‐season‐specific exponential habitat‐selection models with weighted mixed‐effects regressions to both draw inference about the drivers of habitat selection and predict space‐use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter‐ and intra‐individual components. We then used the results to predict population‐level, spatially and temporally dynamic, habitat‐selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30 × 30 m resolution but an extent of 220 000 km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models – variability in habitat selection – into a tool to understand and predict species‐habitat associations across space and time. [ABSTRACT FROM AUTHOR]

This research investigated dynamic vibration control in a monopile-supported wind turbine system through strategically placed resonators along its length. Four configurations were analyzed: Case 1 (conventional wind turbine model), Case 2 (resonators in the turbine tower), Case 3 (resonators in the monopile), and Case 4 (resonators in both tower and monopile). Utilizing spectral element formulations, the dynamic stiffness matrix was derived for the wind turbine system, embedded in cohesionless soil with frequency-dependent viscoelastic springs. Analytical responses at the tower top and monopile head under dynamic harmonic excitation were computed, and transmittance was determined as the logarithmic ratio of responses to input excitation. Experimental dynamic tests were conducted on a 3D-printed wind turbine scaled model, yielding displacement responses at the tower top and monopile head. Comparison with analytically determined transmittance values reveals consistent agreement across four configurations. Case 4 demonstrates substantial response reductions, highlighting the efficacy of resonators in both tower and monopile. Transmittance plots indicate notable response reduction near resonant frequencies, showcasing local resonance phenomena. Furthermore, the tower and monopile head responses are substantially reduced when the excitation frequency surpasses the resonator's natural frequencies. This underscores the dynamic vibration control capabilities of periodically positioned resonators within the wind turbine scaled model. The research also explores the influence of parameters such as resonator mass ratio, soil shear modulus, and pile and tower slenderness ratio on transmittance plots. [ABSTRACT FROM AUTHOR]

Urbanization is inevitably accompanied by drastic changes in regional land use and therefore presents an evident influence on ecosystems. Habitat quality (HQ) reflects the ability of a habitat to provide suitable conditions for the survival of an individual or population, and clarifying the interrelationships between urbanization level (UL) and HQ provides insights into sustainable urbanzation and ecosystems conservation. Much attention has focused on how urbanization related to HQ at multi-scales, but few studies have analyzed the interactive coercive relationships between UL and HQ on the global county level. To address this gap, we adopted the bivariate spatial autocorrelation and coupling coordination degree (CCD) model to determine their interactive coercive relationships at the county level globally. Results showed that the global average UL was 0.0807, 0.0838, and 0.0857 in 2000, 2010, and 2020, respectively, with a continuously increasing trend. The global average HQ was 0.6186, 0.6133, and 0.6111 for 2000, 2010, and 2020, reflecting opposite declining trends. The Moran's I of population urbanization and HQ in 2000, 2010, and 2020 globally remained negative but showed an increasing trend, with values of −0.189, −0.228, and −0.254, respectively, while those of economic urbanization and HQ and land urbanization and HQ also remained negative and exhibited a similar increasing trend. The spatial autocorrelations for UL and HQ in different dimensions indicated that deserts and plateaus functioned as catchment areas with low UL and HQ. The CCD between UL and HQ in each county globally ranged from 0 to 0.70, while CCD between land urbanization and HQ was the most optimal at 0–0.90. The CCD between population urbanization and HQ was similar to that of economic urbanization in terms of spatial distribution. Nevertheless, the CCD of land urbanization in HQ exhibited the greatest degree of coordination among the three urbanization dimensions during the study period. These findings provided important support for sustainable urbanization and ecosystem protection globally. [ABSTRACT FROM AUTHOR]

Against the backdrop of rapid global economic development, the Beijing-Tianjin-Hebei (BTH) region, a pivotal economic hub and environmentally sensitive area in China, faces significant challenges in sustaining its landscape ecosystem. Given the region's strategic importance and vulnerability to environmental pressures, this study investigated the intricate relationships between landscape ecological risk, urban expansion, and economic growth (EG) in the BTH region. Utilizing the landscape as the focal point, we constructed a decoupling model at the grid scale to explore the decoupling relationship between the landscape ecological risk index (ERI), construction area growth (CAG), and EG. The results showed that (1) distinct stages and regional disparities were observed in the trends of ERI, CAG, and EG within the BTH region. The hot and cold spot patterns for these factors did not align consistently. (2) From 1995 to 2019, the coupling relationship between ERI, CAG, and EG in the BTH region underwent a fluctuating transition, initially moving from an undesirable state to an ideal state, and subsequently reverting to an undesirable state. Although the overall trends in these relationships showed some convergence, there were notable spatial distribution differences. (3) The spatial heterogeneity of the two decoupling relationships in the BTH region was relatively poor. Further analysis revealed that the evolution of these decoupling relationships was closely intertwined with regional policy shifts and adjustments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spatial scale is a key factor, which affects the accuracy of spatial expression and further influences the spatial planning of a research area. In order to help improve the efficiency and accuracy of optimal scale selection for all sizes of research areas, a universal two-layer theoretical framework for optimal scale selection was proposed in this paper. Port area was taken as an example to systematically clarify the application of the proposed framework, and the scale selection model for port spatial expression was established. Least-squares-based mean change point analysis was introduced into the model, and the concept of a comprehensive change point was proposed to form the criterion for optimal port scale selection. First, an appropriate scale domain was preliminarily determined by the upper scale selection model. Then, the lower scale selection model determined the final appropriate scale domain and took the corresponding scale of a minimum comprehensive change point as the final optimal scale for port spatial expression. Finally, a port area in Qingdao in eastern China was taken to verify the feasibility of the proposed model, and the optimal scale was suggested to be 14 m. The proposed framework in this paper helps ensure the accuracy of spatial expression and reduce spatial data redundancy, and it can provide the methodological references for planners to better spatialize a research area, which will guarantee the subsequent spatial planning work. [ABSTRACT FROM AUTHOR]

Corporate sustainability is a multifaceted concept with different definitions and measures proposed by the literature and the standards. This study aims to develop and validate a scale of corporate sustainability implementation that can face the complexity and multidimensionality of the concept. Specifically, the scale was designed around four dimensions (governance, prosperity, planet, and people), a three‐level structure, and a formative approach. The scale validation process entailed the engagement of experts, a survey of 303 Italian firms, and a case study application. The findings highlight the nature of the third‐order construct of sustainability implementation and the relevance of adding the governance dimensions to the most spread triple bottom line approach. As a theoretical contribution, this study argues that the missed recognition of these features could originate differences in previous sustainability measures. Further studies would apply the scale to measure firm sustainability in different contexts and in relationship with relevant drivers and outcomes. [ABSTRACT FROM AUTHOR]

Cavitation may occur in water-jet pump during operation of water-jet propulsion vessel, and once cavitation occurs, the tip clearance pulsating pressure of the impeller may be intensified, resulting in increased vibration of the water-jet propulsion unit. In this paper, the cavitation pulsating pressure characteristics at different positions in the pump are studied by experiment and numerical simulation, and the pulsating pressure characteristics in tip clearance are mainly researched. Based on Star-CCM+ commercial software, unsteady Reynolds-averaged Navier-Stokes equations(RANS) numerical simulation is carried out, and the feasibility of the numerical simulation method is verified by uncertainty analysis. The results show that the cavitation pulsating pressure near the leading edge of the impeller in the tip clearance is the largest. The variation of the tip clearance pulsating pressure with the intensification of cavitation is studied by numerical simulation, and its mechanism is revealed. A dimensionless coefficient of net positive suction head (CNPSH) is proposed, and the study shows that the cavitation pulsation pressure coefficients of pumps of different scales are equal when the working conditions are similar and the CNPSH are equal, which indicates that the cavitation pulsating pressure performance of full scale pump can be predicted by model scale. It is of great significance to evaluate the vibration performance of the full scale water-jet propulsion. [ABSTRACT FROM AUTHOR]

Empirical maturation models (e.g., Johnson and Rhodin models) for glomerular filtration rate (GFR) are commonly used as scaling factors for predicting pediatric renal clearance, but their predictive performance for drugs featured with tubular reabsorption is poorly understood. This study investigated the adequacy of GFR‐based scaling models for predicting pediatric renal clearance in drugs with passive tubular reabsorption by comparing with a mechanistic kidney model (Mech‐KiM) that encompasses the physiological processes of glomerular filtration, tubular secretion, and reabsorption. The analysis utilized hypothetical drugs with varying fractions of tubular reabsorption (Freabs), alongside the model drug metronidazole, which has a Freabs of 96%. Our simulations showed that when Freabs is ≤70%, the discrepancies between the GFR‐based scaling methods and the Mech‐KiM model in predicting pediatric renal clearance were generally within a twofold range throughout childhood. However, for drugs with substantial tubular reabsorption (e.g., Freabs > 70%), discrepancies greater than twofold were observed between the GFR‐based scaling methods and the Mech‐KiM model in predicting renal clearance for young children. In neonates, the differences ranged from 5‐ to 10‐fold when the adult Freabs was 95%. Pediatric physiologically based pharmacokinetic (PBPK) modeling of metronidazole revealed that using a GFR‐based scaling method (Johnson model) significantly overestimated drug concentrations in children under 2 months, whereas utilizing the Mech‐KiM model for renal clearance predictions yielded estimates closely aligned with observed concentrations. Our study demonstrates that using GFR‐based scaling models to predict pediatric renal clearance might be inadequate for drugs with extensive passive tubular reabsorption (e.g., Freabs > 70%). [ABSTRACT FROM AUTHOR]

Researchers often combine both positively and negatively worded items when constructing Likert scales. This combination, however, may introduce method effects due to the variances in item wording. Although previous studies have tried to quantify these effects by using factor analysis on scales with different content, the impact of varied item wording on participants' choices among specific options remains unexplored. To address this gap, we utilized four versions of the Undergraduate Learning Burnout (ULB) scale, each characterized by a unique valence of item wording. After collecting responses from 1,131 college students, we employed unidimensional, multidimensional, and bi-factor Graded Response Models for analysis. The results suggested that the ULB scale supports a unidimensional structure for the learning burnout trait. However, the inclusion of different valences of wording within items introduced additional method factors, explaining a considerable degree of variance. Notably, positively worded items demonstrated greater discriminative power and more effectively counteracted the biased outcomes associated with negatively worded items, especially between the "Strongly Disagree" and "Disagree" options. While there were no substantial differences in the overall learning burnout traits among respondents of different scale versions, slight variations were noted in their distributions. The integration of both positive and negative wordings reduced the reliability of the learning burnout trait measurement. Consequently, it is recommended to use exclusively positively worded items and avoid a mix in item wording during scale construction. If a combination is essential, the bi-factor IRT model might help segregate the method effects resulting from the wording valence. [ABSTRACT FROM AUTHOR]

The development and validation of lateral control strategies for railway running gears with independently rotating driven wheels (IRDWs) are an active research area due to their potential to enhance straight-track centering, curve steering performance, and reduce noise and wheel–rail wear. This paper focuses on the practical application of theoretical models to a 1:5 scaled test rig developed by the German Aerospace Center (DLR), addressing the challenges posed by unmodeled phenomena such as hysteresis, varying damping and parameter identification. The theoretical model from prior work is adapted based on empirical measurements from the test rig, incorporating the varying open-loop stability of the front and rear wheel carriers, hysteresis effects, and other dynamic properties typically neglected in literature. A transparent procedure for identifying dynamic parameters is developed, validated through closed- and open-loop measurements. The refined model informs the design and tuning of a cascaded PI and PD controller, enhancing system stabilization by compensating for hysteresis and damping variations. The proposed approach demonstrates improved robustness and performance in controlling the lateral displacement of IRDWs, contributing to the advancement of safety-critical railway technologies. [ABSTRACT FROM AUTHOR]

Manure utilization in China is below optimum due to the inconsistency between farmers' behavior and their intention to apply manure to their crops. With increasing constraints on the availability of chemical fertilizer and their impact on the environment, understanding how farmers' perceptions affect their intention and behavior toward manure application is an issue for the implementation of sustainable agricultural development. This study uses primary survey data from 653 farmers. A probit model was used to assess the influence of cognition on farmers' intentions, behavior, and the inconsistency between them in the use of manure application. Heterogeneity analysis was conducted to identify differences between farmers with different education levels and operation scales. The results showed that enhancing subjective norms, perceived behavioral control, and behavioral attitudes could increase their intention and behavior. Furthermore, improving the perceived behavioral control and behavioral attitudes of farmers would reduce the inconsistency between behavior and intention. In addition, some differences were identified in the role of farmers' cognition with different education levels and operation scales. In general, it is necessary to enhance farmers' comprehensive cognition of the value of manure through publicity and popularization of technology, reduce farmers' perceptions about difficulties in applying manure through new policies and subsidies, and stimulate farmers' enthusiasm to participate in the practice. [ABSTRACT FROM AUTHOR]

Baseline-IX is a tailless aircraft design with compound wing attached to a short body, a transition between a straight, swept flying wing design and a blended wing-body. Baseline-IX planform was designed to deal with a small BWB UAV that is capable of cargo delivery and surveillance missions. The design is influenced by the requirement of cargo space to carry batteries medical and other emergency supplies in its fuselage with a nose-mounted mission camera with a wingspan under 2.0 meters. This paper focuses on studying the aerodynamic characteristics of the novel Baseline-IX, inspired by its predecessor, the Baseline-V. Aerodynamic characteristics of Baseline-IX were investigated and validated through numerical computational simulations and wind tunnel experiments. The maximum lift-to-drag ratio of Baseline-IX obtained through this study is 15.14 for 1:2.4 scaled model and 17.46 for 1:1 prototype. Numerical simulation and wind tunnel experiments' lift-to-drag ratio percentage difference is 4.92%. Baseline-IX's lift-to-drag ratio surpasses 14.09% and 24.28% over similar-missions UAV operating in the market while both are larger in size. Baseline-IX has the potential to be developed as a small, easy to carry cargo delivery and surveillance BWB UAV. [ABSTRACT FROM AUTHOR]

In regression analysis, joint modeling mean and dispersion is an essential tool in absence of the variance homogeneity. Moreover, it is known in the literature that the generalized normal (GN) distribution has some features that provide flexibility in modeling thanks to its shape parameter. This paper proposes a joint location and scale model of the GN distribution for modeling location and scale in the presence of heteroscedasticity. We provide maximum likelihood (ML) estimators for the parameters of the proposed model. We also give an estimation procedure to estimate all parameters simultaneously. For the application, some simulation study scenarios and a real-life example are carried out to prove the estimation performance of the proposed model. [ABSTRACT FROM AUTHOR]

Contact interface is essential for the dynamic response of the bolted structures. To accurately predict the dynamic characteristics of bolted joint structures, a fractal extension of the segmented scale model, i.e., the JK model, is proposed in this paper to comprehensively analyze the dynamic contact performance of engineering surfaces and revisit the multi-scale model based on the concept of asperities. The influence of asperity geometry, dimensionless material properties, and the elastic, elastoplastic, and full plastic mechanical models of a single asperity is established considering the asperity–substrate interaction. Then, a segmented scale contact model of rough surfaces is proposed based on the island distribution function in a strict sense. The mechanical contact process of determining rough surfaces is divided into small-scale, medium-scale, and large-scale stages. Moreover, cross-scale boundary conditions, i.e., al1′, al2′, and al3′, are provided through strict mathematical deduction. The results show that the real contact area and contact stiffness are positively correlated with fractal dimension and negatively correlated with fractal roughness. On a small scale, the contact damping decreases with an increase in load. In meso-scale and large-scale stages, the contact damping increases with the load. Finally, the reliability of the proposed model is verified by setting up three groups of modal vibration experiments. [ABSTRACT FROM AUTHOR]

Indoor radon is one of the most significant contributors to lung cancer after smoking. Mitigation strategies based on protecting buildings with radon barrier materials, combined with home ventilation or room pressurization, are regularly used. A scale model room made from a porous ignimbrite rich in radon precursors was used as an analogue to test the efficiency of fifteen airtight membranes to reduce radon levels, also in combination with room pressurization. The results of these experiments were considered together with previous ones to propose the scale model room approach as a tool for rapidly evaluating the performance of specially designed radon barrier materials, and for radiation exposure assessment. Relative reduction of indoor radon (RIR) ranges from −20 to −94%. The most effective materials were FPO membrane, single-component silane-terminated polymer membranes and synthetic resins. The presence of additives likely modified the composition and structure of some products, improving their radon barrier capacity. The introduction of room pressurization further reduced radon levels in the model room where the membranes were applied. The overpressure necessary to reach RIRs of the order of 85–90% is very low for materials that powerfully stop radon even without ventilation, but necessarily higher for poorer membranes. [ABSTRACT FROM AUTHOR]

Driven by the need for integrated management of groundwater (GW) and surface water (SW), quantification of GW–SW interactions and associated contaminant transport has become increasingly important. This is due to their substantial impact on water quantity and quality. In this review, we provide an overview of the methods developed over the past several decades to investigate GW–SW interactions. These methods include geophysical, hydrometric, and tracer techniques, as well as various modeling approaches. Different methods reveal valuable information on GW–SW interactions at different scales with their respective advantages and limitations. Interpreting data from these techniques can be challenging due to factors like scale effects, heterogeneous hydrogeological conditions, sediment variability, and complex spatiotemporal connections between GW and SW. To facilitate the selection of appropriate methods for specific sites, we discuss the strengths, weaknesses, and challenges of each technique, and we offer perspectives on knowledge gaps in the current science. [ABSTRACT FROM AUTHOR]

A single-use aircraft launch system using a small helium balloon concept was proposed to elevate the likelihood of successful high-altitude flight tests above 30 km. The system, capable of carrying a payload of 0.5 kg, has been developed and reported. Completing the flight within a range of 92 km, which represents the maximum communication distance between the airborne flight system and the ground station for operational phase control, is imperative. Therefore, a straightforward method for simulating flight trajectories was outlined to determine launch windows and suitable locations to ensure flight completion within uninhabited areas. Simulation studies revealed that the shortest landing distance was obtained during summer at the selected launch site. Additionally, a parameter study was conducted to assess the influence of varying ascent velocities on the targeted flight test altitude, which depends on the payload weight and helium gas volume. Consequently, the parameter study results established the constraints for flight model design. A demonstration flight was conducted in 2023 to validate the feasibility of the system for high-altitude flight experiments. This flight successfully released the flight model at 31 km and recovered flight data before landing on the sea surface. The proposed system concept offers an expedited approach to aircraft design for operating in thin air. It enables component function tests and scaled model flights as an alternative to employing large high-altitude balloons for high-altitude experiments. [ABSTRACT FROM AUTHOR]

Restricted by a metal-enclosed structure, the internal defects of large transformers are difficult to visually detect. In this paper, a micro-robot is used to visually inspect the interior of a transformer. For the micro-robot to successfully detect the discharge level and insulation degradation trend in the transformer, it is essential to segment the carbon trace accurately and rapidly from the complex background. However, the complex edge features and significant size differences of carbon traces pose a serious challenge for accurate segmentation. To this end, we propose the Hadamard production-Spatial coordinate attention-PixelShuffle UNet (HSP-UNet), an innovative architecture specifically designed for carbon trace segmentation. To address the pixel over-concentration and weak contrast of carbon trace image, the Adaptive Histogram Equalization (AHE) algorithm is used for image enhancement. To realize the effective fusion of carbon trace features with different scales and reduce model complexity, the novel grouped Hadamard Product Attention (HPA) module is designed to replace the original convolution module of the UNet. Meanwhile, to improve the activation intensity and segmentation completeness of carbon traces, the Spatial Coordinate Attention (SCA) mechanism is designed to replace the original jump connection. Furthermore, the PixelShuffle up-sampling module is used to improve the parsing ability of complex boundaries. Compared with UNet, UNet++, UNeXt, MALUNet, and EGE-UNet, HSP-UNet outperformed all the state-of-the-art methods on both carbon trace datasets. For dendritic carbon traces, HSP-UNet improved the Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and Class Pixel Accuracy (CPA) of the benchmark UNet by 2.13, 1.24, and 4.68 percentage points, respectively. For clustered carbon traces, HSP-UNet improved MIoU, PA, and CPA by 0.98, 0.65, and 0.83 percentage points, respectively. At the same time, the validation results showed that the HSP-UNet has a good model lightweighting advantage, with the number of parameters and GFLOPs of 0.061 M and 0.066, respectively. This study could contribute to the accurate segmentation of discharge carbon traces and the assessment of the insulation condition of the oil-immersed transformer. [ABSTRACT FROM AUTHOR]

The "dual carbon" goal has made it a mainstream trend for new energy stations (NESs) and energy storage stations (ESSs) to jointly participate in market regulation. This paper proposes a multiple time scale distributed optimization method for NESs and ESSs based on the alternate direction multiplier method (ADMM). By first considering the uncertainty of new energy output and the volatility of electricity market prices, a multi time scale revenue model is constructed for day-ahead, intraday, and real-time markets. Then, the objective function is built by maximizing the comprehensive market revenues and is simplified using the synergistic effect of NESs and ESSs. Next, the simplified objective function is solved by the ADMM, and the revenues are maximized while each energy meets the relevant constraints. Lastly, the 33-node network topology is used to illustrate the feasibility of the proposed method. The simulation results show that after optimization, the output of NESs and ESSs can coordinate work in day-ahead, intraday, and real-time markets, while the abandonment power of wind and light is significantly improved. [ABSTRACT FROM AUTHOR]

Earth's plate tectonic behaviour arises from lithospheric ductile weakening and shear-localization. The ubiquity of mylonites at lithospheric shear zones is evidence that localization is caused by mineral grain size reduction. Most lithospheric mylonites are polymineralic, suggesting that the interaction between mineral phases by Zener pinning promotes grain size reduction and weakening. Yet this interaction only occurs where mineral phases mix at the grain scale. Phase mixing and its effect on microstructure and strength have been shown in deformation experiments and natural field samples. Our theory for the interaction between phase mixing (treated as a stress driven diffusion) with two-phase grain damage has been compared to lab experiments. But using processes at the tiny grain-scale embedded within the small hand-sample and lab scales to model large-scale lithospheric processes, requires an upscaling scheme that captures the physics from micro- to macrostructures. For example, weakening from grain-damage in zones of mixing can lead to banded viscosity structure at the small scale that manifests as viscous anisotropy at the large scale. Here we provide a new framework for self-consistently upscaling from microscopic (grain) scales, to mesoscopic (petrological heterogeneity) scales to macroscopic (tectonic) scales. The first upscaling step models phase mixing and grain size evolution in a small 'mesoscopic' lab-scale volume or 'patch', which is equivalent to a point in the macroscopic space. Within this mesoscale patch, stress driven diffusive mixing is described by an analytical solution for mineral phase fraction, provided a minimalist Fourier representation of phase fraction, and a transformation to the patch frame of reference as well as to the principal stress directions at that point. The orientation and volume fraction of mixed-phase regions can then be extracted from the analytical solution for phase fraction. The grain size and viscosity in the mixed bands are determined by two-phase grain-damage theory; the unmixed zone properties follow from mono-phase grain damage theory. The mesoscale banded viscosity field leads to a macroscale anisotropic viscosity at that point in space. But, the evolution of properties at each macroscale point involves tracking only a few quantities (phase fraction, grain sizes) rather than modelling each patch of mesoscale space as its own 2-D or 3-D system. For the final upscaling, the anisotropic viscosity field is used in a macroscale lithosphere flow model. We show an example of this scheme for a lithospheric Rayleigh–Taylor drip driven by ridge-push compressive stress, which can cause anisotropic weakening via grain mixing and damage that may help initiate subduction and passive margin collapse. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Science & Technology (JCS&T) is the property of Journal of Computer Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The gain in short-wave infrared and mid-wave infrared HgCdTe avalanche photodiodes (APDs) with large diameters has been analyzed using an already established model based on an empirical expression proposed by Okuto–Crowell (OC) and a new model derived for the impact ionization in HgCdTe. This model is based on a simplified but physical description of the carrier dynamics during the multiplication. It is shown that OC model has limitations in giving a precise description of the measured avalanche gains, and that is difficult, in view of present available data, to derive a universal expression for the model parameters to predict the avalanche gain in HgCdTe APDs as a function of the Cd composition, operating temperature, and multiplication layer geometry. The new model is shown to give a better fit of the gain data, associated with a scaling of the model parameters with the band gap. [ABSTRACT FROM AUTHOR]