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The behavior of four algebraic closure models for anomalous electron transport is investigated using a fluid Hall thruster code. The models, which were selected because they have been previously described in the literature, are calibrated against a baseline experimental condition of a 9-kW-class magnetically shielded Hall thruster operating at 300 V and 15 A on xenon propellant. The extensibility of the models is then assessed by using this calibrated model to simulate three additional operating conditions—300 V and 30 A, 600 V and 15 A, and 300 V and 15 A operating on krypton propellant. The quality of the model prediction is quantified by comparing the model outputs to experimental measurements of discharge current, thrust, and ion velocity. It is found that while none of the models can predict the ion acceleration characteristics accurately, some compare favorably in terms of the scaling of thrust and discharge current across operating conditions. The limitations of the models are attributed to the coupling between the functional scaling of the closure models with respect to the local plasma properties and the fluid model. The role of the electron energy balance in this coupling is also highlighted. These results are discussed in the context of motivating improved closure models of the anomalous electron transport in Hall thrusters. [ABSTRACT FROM AUTHOR]

The evolution of statistical modelling has historically been constrained by the practical limitations of computation; early statistical modelling favoured models which could feasibly be estimated. As increased mathematical complexity often implies more intricate computation, statistical models have grown both mathematically and computationally more complex. However, paradoxically, sometimes conceptually simpler models present more computational challenges than complex ones, and these have historically been neglected. In the case of binary responses, logistic regression models are the gold standard; covariates are modelled additively on the log-odds scale. In the case of time-to-event responses, the Cox proportional hazards regression model has additivity on the log-hazard rate scale. Both of these methods are computationally convenient, and yet the scales on which covariates are modelled are far from intuitive. We demonstrate the use of alternative models which are computationally more complex, yet feasible, but in which modelling is on a more interpretable scale. In the case of binary responses, a more intuitive alternative is log-binomial regression, in which modelling is on the log-relative risk scale. In the case of time-to-event responses, distributional regression enables modelling on the time scale. While logistic regression and proportional hazards regression qualitatively both deliver the same conclusions as the alternative models, log-binomial and distributional regression provide more interpretable coefficients, which are readily estimated. [ABSTRACT FROM AUTHOR]

For essay-writing tests, challenges arise when scores assigned to essays are influenced by the characteristics of raters, such as rater severity and consistency. Item response theory (IRT) models incorporating rater parameters have been developed to tackle this issue, exemplified by the many-facet Rasch models. These IRT models enable the estimation of examinees' abilities while accounting for the impact of rater characteristics, thereby enhancing the accuracy of ability measurement. However, difficulties can arise when different groups of examinees are evaluated by different sets of raters. In such cases, test linking is essential for unifying the scale of model parameters estimated for individual examinee–rater groups. Traditional test-linking methods typically require administrators to design groups in which either examinees or raters are partially shared. However, this is often impractical in real-world testing scenarios. To address this, we introduce a novel method for linking the parameters of IRT models with rater parameters that uses neural automated essay scoring technology. Our experimental results indicate that our method successfully accomplishes test linking with accuracy comparable to that of linear linking using few common examinees. [ABSTRACT FROM AUTHOR]

This paper presents a learning based planner for computing optimized 3D printing toolpaths on prescribed graphs, the challenges of which include the varying graph structures on different models and the large scale of nodes & edges on a graph. We adopt an on-the-fly strategy to tackle these challenges, formulating the planner as a Deep Q-Network (DQN) based optimizer to decide the next 'best' node to visit. We construct the state spaces by the Local Search Graph (LSG) centered at different nodes on a graph, which is encoded by a carefully designed algorithm so that LSGs in similar configurations can be identified to re-use the earlier learned DQN priors for accelerating the computation of toolpath planning. Our method can cover different 3D printing applications by defining their corresponding reward functions. Toolpath planning problems in wire-frame printing, continuous fiber printing, and metallic printing are selected to demonstrate its generality. The performance of our planner has been verified by testing the resultant toolpaths in physical experiments. By using our planner, wire-frame models with up to 4.2k struts can be successfully printed, up to 93.3% of sharp turns on continuous fiber toolpaths can be avoided, and the thermal distortion in metallic printing can be reduced by 24.9%. [ABSTRACT FROM AUTHOR]

School value-added models are widely applied to study, monitor, and hold schools to account for school differences in student learning. The traditional model is a mixed-effects linear regression of student current achievement on student prior achievement, background characteristics, and a school random intercept effect. The latter is referred to as the school value-added score and measures the mean student covariate-adjusted achievement in each school. In this article, we argue that further insights may be gained by additionally studying the variance in this quantity in each school. These include the ability to identify both individual schools and school types that exhibit unusually high or low variability in student achievement, even after accounting for differences in student intakes. We explore and illustrate how this can be done via fitting mixed-effects location scale versions of the traditional school value-added model. We discuss the implications of our work for research and school accountability systems. [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]

It is now well-recognized that urbanization strongly impacts wildlife communities and populations. However, we typically do not know which feature(s) affect individual species most strongly, and this lack of understanding impedes theory development and effective planning for conservation and management goals. To address this knowledge gap, we evaluated how the abundance of ten mammal species responded to six different features of urbanization quantified at five spatial scales using data from 112 camera traps deployed for two years across a gradient of urbanization in New Hampshire, USA. We fit Bayesian abundance models to measure response to each feature and scale. There was no singular urban feature or spatial scale in the best model for all species. Rather, species responded uniquely to features across scales, and the scale of urban features in the best model also varied. Within a species, the magnitude and direction of response varied across features and scales, with only black bear (Ursus americanus), gray fox (Urocyon cinereoargenteus), and Virginia opossum (Didelphis virginiana) exhibiting a consistently significant unidirectional relationship with a single feature across all scales. Our results emphasize that species respond to specific urban features, thus a failure to include certain features can cause misleading inference about wildlife response to "urbanization". Therefore, researchers must carefully justify the choice of urban feature and spatial scale at which it is represented for each species of interest. An expanded inclusion of multiple urban features in wildlife research will inform management decisions and help attain conservation goals for species impacted by urbanization. [ABSTRACT FROM AUTHOR]

At long times, a fractional Brownian particle in a confining external potential reaches a non-equilibrium (non-Boltzmann) steady state. Here we consider scale-invariant power-law potentials V (x) ∼ | x | m , where m > 0, and employ the optimal fluctuation method (OFM) to determine the large- | x | tails of the steady-state probability distribution P (x) of the particle position. The calculations involve finding the optimal (that is, the most likely) path of the particle, which determines these tails, via a minimization of the exact action functional for this system, which has recently become available. Exploiting dynamical scale invariance of the model in conjunction with the OFM ansatz, we establish the large- | x | tails of ln ⁡ P (x) up to a dimensionless factor α (H , m) , where 0 < H < 1 is the Hurst exponent. We determine α (H , m) analytically (i) in the limits of H → 0 and H → 1, and (ii) for m = 2 and arbitrary H, corresponding to the fractional Ornstein-Uhlenbeck (fOU) process. Our results for the fOU process are in agreement with the previously known exact P (x) and autocovariance. The form of the tails of P (x) yields exact conditions, in terms of H and m, for the particle confinement in the potential. For H ≠ 1 / 2 , the tails encode the non-equilibrium character of the steady state distribution, and we observe violation of time reversibility of the system except for m = 2. To compute the optimal paths and the factor α (H , m) for arbitrary permissible H and m, one needs to solve an (in general nonlinear) integro-differential equation. To this end we develop a specialized numerical iteration algorithm which accounts analytically for an intrinsic cusp singularity of the optimal paths for H < 1 / 2 . [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]

Saliency maps are input-resolution matrices used for visualizing local interpretations of image classifiers. Their pixel values reflect the importance of corresponding image locations for the model's decision. Despite numerous proposals on how to obtain such maps, their evaluation remains an open question. This paper presents a carefully designed experimental procedure along with a set of quantitative interpretation evaluation metrics that rely solely on the original model behavior. Previously noticed evaluation biases have been attenuated by separating locations with high and low values, considering the full saliency map resolution, and using classifiers with diverse accuracies and all the classes in the dataset. We used the proposed evaluation metrics to compare and analyze seven well-known interpretation methods. Our experiments confirm the importance of object background as well as negative saliency map pixels, and we show that the scale of their impact on the model is comparable to that of positive ones. We also demonstrate that a good class score interpretation does not necessarily imply a good probability interpretation. DeepLIFT and LRP- ϵ methods proved most successful altogether, while Grad-CAM and Ablation-CAM performed very poorly, even in the detection of positive relevance. The retention of positive values alone in the latter two methods was responsible for the inaccurate detection of irrelevant locations as well. [ABSTRACT FROM AUTHOR]

AbstractThe Triarchic Model of Grit Scale (TMGS) was developed as an innovative measurement designed to evaluate general grit levels, encompassing perseverance of effort, consistency of interests, and adaptability to situations within a collectivism culture. The present study was undertaken with the aim of examining the factor structure, measurement invariance, empirical validity, and incremental validity of the TMGS among in a sample of Chinese adolescents (N = 997, 43.4% males, Mage = 16.64, SDage = 1.05). The results revealed that the original three-factor model of TMGS exhibited the best fit to the data, and supported partial scalar invariance across gender. Additionally, the internal consistency values of the TMGS scores ranged from marginal to acceptable, and the stability coefficients across time were acceptable. Moreover, the TMGS scores showed satisfactory criterion-related validity, correlating with scores of external criteria variables (e.g., Grit-S, self-control, and big five personality). Finally, the TMGS scores demonstrated superior incremental validity in predicting academic burnout compared to conscientiousness. Overall, although further studies are needed, our findings suggested that the TMGS demonstrated acceptable psychometric properties within a collectivist culture and may serve as a promising tool for assessing grit levels in Chinese adolescents. [ABSTRACT FROM AUTHOR]

This paper presents an existence result for a novel nonlinear Caputo q-fractional p-Laplacian epidemiological model on time scales. The proof of the main result leverages Arzelà–Ascoli and Schauder’s fixed point theorems. Additionally, a set of open questions is addressed to further enrich this field of valuable scientific research [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]

Context. We present VENICE, an operator-splitting algorithm to integrate a numerical model on a hierarchy of timescales. Aims. VENICE allows a wide variety of different physical processes operating on different scales to be coupled on individual and adaptive time-steps. It therewith mediates the development of complex multi-scale and multi-physics simulation environments with a wide variety of independent components. Methods. The coupling between various physical models and scales is dynamic, and realised through (Strang) operators splitting using adaptive time-steps. Results. We demonstrate the functionality and performance of this algorithm using astrophysical models of a stellar cluster, first coupling gravitational dynamics and stellar evolution, then coupling internal gravitational dynamics with dynamics within a galactic background potential, and finally combining these models while also introducing dwarf galaxy-like perturbers. These tests show numerical convergence for decreasing coupling timescales, demonstrate how VENICE can improve the performance of a simulation by shortening coupling timescales when appropriate, and provide a case study of how VENICE can be used to gradually build up and tune a complex multi-physics model. Although the examples provided here couple dedicated numerical models, VENICE can also be used to efficiently solve systems of stiff differential equations. [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]

To use the EQ-5D questionnaire with bolt-on dimensions in economic evaluation studies, new value sets are needed. In this study, we explored the feasibility of a new approach called the scaling factor model, which estimates bolt-on value sets using estimated EQ-5D dimensional weights. We designed a 2-arm study, inviting university students to value health states with and without bolt-on items using the composite time trade-off method. We selected 25 health states from an orthogonal array and added the 5 mildest EQ-5D states in the design. In arm 1, EQ-5D without self-care and standard EQ-5D states were valued, and in arm 2, standard EQ-5D states and EQ-5D with vision were valued. By arm, we compared the mean observed values of health states with and without bolt-on item. Next, by arm, we estimated value sets for the EQ-5D with bolt-on states using both standard model and scaling factor model. Model performances were compared in terms of prediction accuracy and correlation with likelihood-based mean values. Adding a five-level bolt-on to EQ-5D resulted in statistically lower values. This effect was consistent across 2 arms and bolt-on items. The scaling factor models outperformed the standard models in all statistics. The scaling factor model offers a methodologically viable and low-cost option for producing value sets for EQ-5D supplemented with bolt-on items. Future studies should further test this method using other bolt-on items and more relevant study populations. • Values for the EQ-5D with a bolt-on item cannot be derived from standard EQ-5D value sets. In contrast to the traditional method of independently estimating the value set, this study introduces an innovative approach called scaling factor model to estimate value sets for EQ-5D health states with a bolt-on item. • Compared with the standard modeling approach, the scaling factor model predicts the values of bolt-on health states more accurately. This finding suggests that bolt-on value sets can be estimated based on the standard EQ-5D value sets, thereby enhancing resource efficiency and maintaining dominance relationships among health states. • The use of the scaling factor model provides a cost-effective means to estimating the value set for EQ-5D health states with a bolt-on item. Another advantage of the scaling factor model is that it avoids potential inconsistencies between the value sets of original EQ-5D states and EQ-5D states with bolt-on items. [ABSTRACT FROM AUTHOR]

The modified suction caisson (MSC) is a novel type of foundation for offshore wind turbines. Model tests and numerical simulations were conducted on the MSCs in sand to explore the lateral bearing behavior and variations of the rotation center position. Results show that the MSC can effectively increase the lateral bearing capacity and limit rotation compared with the regular suction caisson. For the MSC, the yield envelope slope at the 1/100 scale is equivalent to the slope at the 1/50 scale. Equations for the yield envelopes under different model scales were obtained. The internal compartment diameter and external structure length have a significant influence on the MSC lateral bearing capacity. Optimal dimensions of the MSC to maximize the bearing capacity were obtained: the ratio of the internal compartment diameter to the external structure diameter is greater than 0.4, and the ratio of the external structure length to the internal compartment length is within the range of 0.2 to 0.6. The rotation center of the MSC moves downward along the caisson embedded depth and moves forward in the loading direction with increasing angular rotation. As the angular rotation reaches 0.02 rad, the position of the rotation center tends to be stable. [ABSTRACT FROM AUTHOR]

Engineering rock masses typically feature natural discontinuities that span different scales, with filled joints as macroscopic discontinuities and microdefects as mesoscopic ones. However, the mechanism of wave propagation in the filled joints has been unclear. This study develops a novel dual-scale equivalent rock model to investigate the wave propagation in jointed rock masses. Firstly, two combination models at different scales were developed by using thin layers and contact interfaces to simulate filled joints and microdefects, respectively. Subsequently, a wave propagation equation was derived through a time-domain recursive method, and validated against the experimental data and the predictions of traditional models. Finally, a parametric analysis was conducted on the frequency response of wave propagation in jointed rock masses. The results show that this novel model can describe the wave dispersion and attenuation in dual-scale discontinuous rock masses, and its equation exhibits robustness. It is revealed that two independent mechanisms induce the wave dispersion in a single joint: multiple wave reflections inside the joint and the viscosity of the filling material. The effect of microdefects on wave attenuation mainly depends on propagation distance, while the influence of filled joints on wave propagation can be determined by the quality factor, thickness, effective wave velocity, and density of the filling. The wave velocity of the filling has a significantly higher influence on the transmission coefficient than the filling density. This challenges the conventional concept that the wave impedance of filling is a basic parameter for estimating the transmission coefficient. Highlights: A novel dual-scale equivalent model was proposed for wave propagation in jointed rock masses. The multiple reflection effects at rock joints were revealed to be the main cause of wave dispersion. Using wave impedance of joint filling to predict the transmission coefficient was found to be inaccurate. The frequency response difference in macroscopic and mesoscopic discontinuities of rock masses was revealed. [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]

Often, reliability systems suffer shocks from external stress factors, stressing the system at random. These random shocks may have non ignorable effects on the reliability of the system. In this article, we provide sufficient (and necessary) conditions on components' lifetimes and their survival probabilities from random shocks for comparing the lifetimes of two parallel systems with independent components by means of usual stochastic and reversed hazard rate orders, when the matrix of parameters changes to another matrix in a mathematical sense. We also discuss stochastic comparisons of series systems with independent components under random shocks in the sense of usual stochastic and hazard rate orders, by using the concept of vector majorization and related orders. The results developed in this article generalize some known results in the literature. [ABSTRACT FROM AUTHOR]

Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300‐m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance‐based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300‐m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300‐m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation. [ABSTRACT FROM AUTHOR]

Due to the influence of the three-dimensional (3D) effect, the identification accuracy of 3D aerodynamic admittance functions (AAFs) would inevitably be influenced by the ratio of turbulent integral scale to model dimension (dimensionless integral scale). For this, the two-dimensional (2D) AAFs of a streamlined bridge deck in different turbulent flow fields are experimentally investigated, wherein the 2D AAF is determined by separating out the 3D effect from the 3D AAF. Consistent with the previous studies, the 3D AAFs exhibit significant flow field dependence, and are less than the Sears function in low-frequency range. Different from the 3D AAF, the 2D AAFs obtained in two turbulent flow fields are higher than the Sears function and have a good consistency in various turbulent flows. It can be therefore considered that the identification accuracy of 2D AAF of a streamlined bridge deck is not related to the dimensionless integral scale, which might avoid the possible deviation caused by the mismatch of integral scale in the wind tunnel test. An empirical formula for the 2D AAF of a streamlined bridge deck is then proposed by fitting the experimental data. In addition, to further examine the validity of above conclusion, a comparison between the 3D AAFs of a streamlined bridge deck measured in other wind tunnel tests and the predictions calculated with the present 2D AAF is also made. [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]

Global Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better‐informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open‐source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional‐refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm‐resolving model in precipitation predictions across scales. Plain Language Summary: This study explores the use of advanced weather and climate models, known as Next‐generation Global Storm Resolving Models (GSRMs), to improve our understanding of weather and climate events at different scales. This work is based on a cutting‐edge modeling framework called CAM‐MPAS, which is based on the open‐source Community Earth System Model (CESM2), to study tropical convection features over the Maritime Continent region. This study focused on examining storm‐scale features with a high‐resolution of 3 km, targeting specific numerical experiments during the active convection period in the winter of 2018. The study investigated the accuracy of representing convective systems, precipitation patterns, and tropical cyclone behaviors. Results indicate that the regional‐refined experiments provided more precise precipitation distributions, captured the diurnal cycle more accurately, and demonstrated better agreement with observations regarding tropical cyclone intensity and strength statistics. The findings suggest that refining the regional scale in storm‐resolving models can enhance precipitation predictions across scales. This work contributes to advancing the development and application of storm‐resolving models for better‐informed climate impact assessments. Key Points: The mean state and convective features, centered over the Maritime Continent, are examined at coarse and convection‐permitting scalesCoarse and refined meshes have different precipitation statistics and probability distributionsPrecipitation statistics, diurnal cycles, and tropical cyclone features are improved with regional refinement [ABSTRACT FROM AUTHOR]

In this paper, we show that loop-induced processes involving new physics particles can readily satisfy Landau Equation and trigger triangular singularities at high-energy colliders, leading to fully visible Standard Model final states. In addition, a t-channel triangular singularity can also occur during particle scattering processes that may extend to low momentum exchange. We discuss several typical scenarios in supersymmetric and extended Higgs models, and three types of final-state kinematic features at the collider. Four-particle vertices in new physics can play a significant role in high energy diagrams. We identify an "everything on shell" triangular singularity diagram only involving bosonic couplings, which has the potential to completely avoid large virtuality suppression. We further show that such a virtuality-free diagram is missing in the Standard Model at electroweak scale, and it becomes available in new physics models. [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]

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]

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]

Full-fledged computational modeling of Direct Reduction reactors encompasses single-pellets models and the step-wise scaling up to industrial-scale reactors. This study delves into the particle-resolved computational modeling of hydrogen-based direct reduction of 0.5 kg iron ore fixed-beds, as a scale-bridging step and focusing on the influence of pellet sizes and shapes. To investigate the effect of particle sizes, two beds with particles sized 10.0–12.5 and 12.5–16.0 mm were characterized and numerically reconstructed using the discrete element method. While to assess the effect of particle shapes, a third numerical bed was generated directly upon a high-resolution CT-scan of a real bed. Through reactive CFD simulations, we investigated the reduction process in these three beds using hydrogen as the reducing gas. Our findings reveal that the bed structure significantly impacts the reduction efficiency and overall conversion degree. This study emphasizes the importance of accurate bed reconstruction and provides critical insights for optimizing the hydrogen-based direct reduction process in industrial applications. • Three CFD simulations with varying pellet sizes and comparing spherical vs. real pellets. • Bed with larger pellets reduce slower than small-pellet beds of equivalent weight. • Real bed structure is more compact and rearranged compared to DEM-generated beds. • Shapes considerations impact flow fields locally, but global conversion is similar. • DEM-generated beds are representative enough for modeling the reduction. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of plausible object placement for the challenging task of realistic image composition. We propose DiffPop, the first framework that utilizes plausibility‐guided denoising diffusion probabilistic model to learn the scale and spatial relations among multiple objects and the corresponding scene image. First, we train an unguided diffusion model to directly learn the object placement parameters in a self‐supervised manner. Then, we develop a human‐in‐the‐loop pipeline which exploits human labeling on the diffusion‐generated composite images to provide the weak supervision for training a structural plausibility classifier. The classifier is further used to guide the diffusion sampling process towards generating the plausible object placement. Experimental results verify the superiority of our method for producing plausible and diverse composite images on the new Cityscapes‐OP dataset and the public OPA dataset, as well as demonstrate its potential in applications such as data augmentation and multi‐object placement tasks. Our dataset and code will be released. [ABSTRACT FROM AUTHOR]

Model ensembles may provide estimates of uncertainties arising from unknown initial conditions and model deficiencies. Often, the ensemble mean is taken as the best estimate, and quantities such as the mean‐squared error between model mean and observations decrease with the number of ensemble members. But the ensemble size is often limited by available resources, and so some idea of how many ensemble members that are needed before the error has saturated would be advantageous. The behaviour with ensemble size is often estimated by producing subsamples from a large ensemble. But this strategy requires that this large ensemble is already available. Fortunately, in many situations, the dependence on ensemble size follows simple analytical relations when the quantity under interest (such as the mean‐squared error between ensemble mean and observations) is calculated over many grid points or time points. This holds both for ensemble means and the related sampling variance. Here, we present such relations and demonstrate how they can be used to estimate the gain of increasing the ensemble. Whereas previous work has mainly focused on the size of the model ensemble, we recognize that uncertainties in observations play a role. We therefore also study the effect of using the mean of an ensemble of reanalyses. We show how the analytical relations can be used to estimate the point where the gain of increasing the size of the model ensemble is dwarfed by the gain of increasing the number of reanalyses. We demonstrate these points using two climate model ensembles: a large multimodel ensemble and a large single‐model initial‐condition ensemble. [ABSTRACT FROM AUTHOR]

To study the scale effect on propeller bearing forces in oblique flow, the propeller bearing forces of a fully appended four-screw ship that refers to a vessel propelled by four propellers are calculated and investigated. The findings reveal that scale effects in wake fields lead to an increase in the axial velocity of the propeller disk in the full-scale ship compared to the model, influenced by boundary layer flow. Moreover, asymmetric differences in the impact of oblique flow on the leeward and windward sides result in varied effects of positive and negative drift angles on the wakefield. The disparity in wake fields between inside and outside disks exacerbates unbalanced load distribution, with time-averaged loads showing an increase with drift angle for both propellers at full scale. Additionally, pronounced scale effects lead to variations in blade loads between model and full scale, with drift angles shifting the locations of single-blade load extremums. Unsteady bearing force components exhibit periodic fluctuations, with larger amplitudes at the blade passage frequency for the model scale propeller compared to the full scale, particularly evident under negative drift angles. The aforementioned findings can provide theoretical guidance for load balancing and vibration reduction of the four-screw ships. [ABSTRACT FROM AUTHOR]

Impact-driven pipe piles are commonly used in offshore structures, but vibratory driving is becoming more popular due to advantages regarding noise emissions, material fatigue and installation time. In offshore practice, crane-guided vibratory driving is used for maximum control over the installation process. Regarding the load-bearing behaviour of piles, the influence of the installation method is currently subject to research. To contribute to this question, comparative scale model investigations were carried out on impact-driven, jacked and vibratory-driven piles in dense sand. The tests focused on variations of vibratory installation parameters, including crane-guided and free vibratory driving. Based on measurements at the pile and in the soil, the influence of dynamic pile motions on the soil stress development could be analysed. The well-known increase of radial effective soil stresses due to impact driving could be reproduced. The tests showed that the same effects could be evoked by free vibratory driving if certain vibration parameters were met. Increased soil stresses may be beneficial for the lateral or axial pile behaviour, but can cause problems regarding drivability. A sequential application of crane-guided and free vibratory driving may optimise both the installation process and the bearing behaviour. [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]

Hydroelastic effects are particularly pronounced in ultralarge container ships due to their substantial size and high velocity. The computation of combined torsional and bending moments presents a challenge, exacerbated by warping effects. This study focuses on the investigation of wave loads, encompassing vertical, horizontal, and torsional moments, through model tests on a 20,000 TEU container ship. A 1/77 scale model featuring an open U‐shaped backbone was crafted to simultaneously simulate and measure longitudinal, horizontal, and torsional stiffness, along with these loads. Wave loads on the hull, including those resulting from springing effects, were calculated in regular waves, employing hydroelastic theory and accounting for hydrodynamic forces. The study also delves into the characteristics and analysis of spring effects within the wave loads on the hull. The insights garnered from this research contribute to the fatigue analysis and safety assessments of ship structures. [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]

Aim: This pilot study examines how rural and remote junior doctors' career decisions are influenced by collegial relationships within the discipline of general paediatrics. Methods: Social network analysis (SNA) was undertaken by structured interviews with 10 paediatricians working in regional towns in Western Australia. UNICET software was used to determine the interactions between individual networks to look for overlap and common influencers. Results: Ten rural paediatricians were interviewed. An individual was found to have key measures of centrality at the core of the entire social network of rural general paediatricians. This included a high degree of 'betweenness' (connections within social networks), and a high broker index (connections between separate areas of a network or between networks) demonstrated by that person combining three disconnected networks into a single coherent network. This central individual was a recently appointed consultant with links to senior paediatricians, peers and junior trainees, and may be instrumental in recruitment and retention in the rural paediatric workforce. Conclusion: Improving understanding of the impact of social networks, and decision‐making processes that influence rural career choices, can inform innovative solutions to develop sustainable strategies for recruiting and retaining the rural paediatric workforce. Applying this model on a larger scale may provide more data to support evidence‐based programmes that enable this within the Australian context. [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]

Within-person variation in cognitive performance is linked to pathological aging. Cognitive fluctuations have not been analyzed using cognitive process models, such as the diffusion model, to characterize which cognitive processes contribute to variability in cognition. We collected 21 daily assessments of attention and personality in younger adults, healthy older adults, and those with mild cognitive impairment. We employed mixed-effects location scale models to analyze group differences on mean diffusion parameters and daily variability. Discussion focuses on how these methods extend our understanding of how cognitive deficits might appear in aging and disease and the moderating influence of daily personality. [ABSTRACT FROM AUTHOR]

This research is an extension of the recent scale development efforts for the marker variable Attitude Toward the Color Blue (ATCB), which addresses the efficacy of multiple shorter permutations of the scale. The purpose of this study is to develop a shorter version of an ideal marker variable scale used to detect common method variance. Potential uses of the shorter version of ATCB include intensive longitudinal studies, implementation of experience sampling methodology, or any brief survey for which the original version might be cumbersome to implement repeatedly or appear very odd to the respondent when paired with only a few other substantive items. Study 1, uses all six-, five-, and four-item versions of ATCB in confirmatory factor analysis (CFA) marker technique tests on a bivariate relationship. Study 2 analyzes the best- and worst-performing versions of reduced lengths of the ATCB scale found in the first study on another bivariate relationship. Study 3 compares the original seven-item version, as well as randomly selected reduced length versions in a data set with 15 model relationships. Study 4 uses an experiment to determine the efficacy of providing respondents with one of three shorter ATCB scales in a model of three substantive variables. Our findings indicate that ATCB of different permutations and lengths can detect CMV successfully, and that researchers should choose the length of scale based on their survey length. We conclude that ATCB is adaptable for a variety of research situations, presenting it as a valuable tool for high-quality research. [ABSTRACT FROM AUTHOR]

Three-dimensional digital modeling at actual scales is essential for digitally preserving cultural relics. While 3D reconstruction using a monocular camera offers a cost-effective solution, the lack of scale information in the resulting models limits their suitability for geometric measurements. Objects with monotonous textures, such as batteries, pose additional challenges due to insufficient feature points, increasing positional uncertainty. This article proposes a method incorporating point and line features to address the scale ambiguity in multi-view 3D reconstruction using monocular cameras. By pre-measuring the lengths of multiple sets of real line segments, building a lookup table, and associating the line features in different images, the table was input into the improved reconstruction algorithm to further optimize the scale information. Experimental results on real datasets showed that the proposed method outperformed the COLMAP method by 70.82% in reconstruction accuracy, with a scale recovery reaching millimeter-level accuracy. This method is highly generalizable, cost-effective, and supports lightweight computation, making it suitable for real-time operation on a CPU. [ABSTRACT FROM AUTHOR]

The rise of artificial intelligence (AI) and in particularmodernmachine learning (ML) algorithms during the last decade has been met with great interest in the agricultural industry. While undisputedly powerful, their main drawback remains the need for sufficient and diverse training data. The collection of real datasets and their annotation are themain cost drivers of ML developments, and while promising results on synthetically generated training data have been shown, their generation is not without difficulties on their own. In this paper, we present a development model for the iterative, cost-efficient generation of synthetic training data. Its application is demonstrated by developing a low-cost early disease detector for tomato plants (Solanum lycopersicum) using synthetic training data. A neural classifier is trained by exclusively using synthetic images, whose generation process is iteratively refined to obtain optimal performance. In contrast to other approaches that rely on a human assessment of similarity between real and synthetic data, we instead introduce a structured, quantitative approach. Our evaluation shows superior generalization results when compared to using nontask-specific real training data and a higher cost efficiency of development compared to traditional synthetic training data. We believe that our approach will help to reduce the cost of synthetic data generation in future applications. [ABSTRACT FROM AUTHOR]