Search

This study focuses on the use of bio-based materials for structural purposes in the packaging field, which requires the identification of their mechanical properties at a representative scale. The mechanical properties of bio-based materials are more variable than those of traditional composite materials. In a standard characterization approach using elemental coupons under uniaxial loading, the variability depends on the chosen representative elementary volume (REV), free edges, boundary conditions, etc. for elastic properties that are not identified for representative working conditions; this could lead to the ineligibility of these bio-based materials as structural materials. This paper contributes to the debate on how to study the response of bio-based materials within a structure, here a packaging structure as a logistic unit (LU) subjected to a compressive load simulating storage and stacking conditions. In the set of tools and methods for the design of packaging materials made of bio-based materials, an elastic nonlinear geometric finite element model (FEM) and an experimental approach are presented. The FEM allows the numerical identification of zones of interest within the LU. Inevitably, the FEM classically requires input data which are elastic properties of the equivalent homogeneous material. The design of the FEM is based on a calculation-test approach using an existing reference LU and it can be summarized in two main steps. The first step concerns the development of a FEM able to restore the experimental conditions of vertical compression imposed by transport standards for packaging. The second step is based on updating the input properties of the FEM by reverse identification, to achieve the representative working condition properties, using experimental results obtained on the existing reference LU. For the reverse identification a multi-scale investigation is mandatory. For this purpose, the linear elastic part of the load/vertical displacement curves (at the LU stiffness scale) and the displacement and strain fields measured (at the local LU scale) by 3D digital image correlation (3D DIC) are evaluated. Then, FEM property updating is carried out by reducing the deviation of displacement/strain fields between FEM and experimentally measured results (3D DIC). Finally, we explain how FEM and 3D DIC help in decision-making by allowing the recognition of zones of interest in a phase of design of new LUs with the concept of Multi-Instrumented Technological Evaluator (MITE). [ABSTRACT FROM AUTHOR]

Matrix (including interface) failure is a typical form of failure in the composite laminates for cryogenic storage tanks causing functionally useless of the structure. In this work, a low-cost multiscale model based on the Self-consistent Clustering Analysis (SCA) method is developed to predict the matrix failure process of the composite storage tanks. First, a reduced order model modeling method with fiber/matrix interfaces is proposed. Then, in conjunction with Progressive Failure Analysis (PFA), a reduced-order model is used to predict matrix failure and interface failure of the composites, mesh sensitivity analyses were carried out, and strength damage envelopes were obtained for unidirectional composites subjected to a combination of transverse stresses and in-plane shear. Compared with the prediction results of the Puck criterion, the errors in predicting damage strength for compressive-shear load and tensile-shear load do not exceed 10% and 30%, respectively. And finally, the thermal-mechanical load analysis of the composite storage tanks is carried out and the effect of homogenous temperature field and heterogenous temperature field on the load-bearing performance of the composite structure is analyzed. The method is proved to have good accuracy and to be very efficient. Its main feature is the ability to rapidly predict the stiffness and strength properties of composite structures under different environmental conditions, in agreement with experimental results, showing the potential to reduce the time and cost required for structural design. [ABSTRACT FROM AUTHOR]

Highlights 2.5D woven composite material inevitably produces void defects in its production process, which will seriously reduce its mechanical properties and reduce its service life. In this paper, the effects of void defects on the mechanical properties of 2.5D woven composites were studied by multi‐scale analysis. An improved Halpin‐Tsai semi‐empirical model is proposed to calculate the elastic properties of yarns with porous defects and verified by finite element method. A microscale representative volume unit (RVE) for predicting the elastic constants of composites with pore defects is established. Theoretical analysis and finite element analysis were used to verify the micro scale, and finite element analysis and experiment were used to verify the micro scale. The effect of porosity on the elastic properties of micro‐scale RVE was studied in detail. The results show that the model is reasonable and accurate in predicting the mechanical properties of yarns and composites. In addition, the effect of porosity on the mechanical properties of 2.5D woven composites is significant. An improved Halpin‐Tsai semi‐empirical model is proposed, which makes the microscale theoretical analysis of 2.5D woven composites better consistent with the finite element analysis. The void position obstructs the stress transfer of the matrix, and the stress concentration phenomenon also occurs. The void content has an effect on the mechanical properties of composites at both micro and micro scales. [ABSTRACT FROM AUTHOR]

Two different bandpass filtering methods have been used in the literature to study turbulence energy cascade simultaneously in physical and scale spaces. One of them is the derivative-based filter by Leung et al. [J Fluid Mech. 2012;V710:453–481] and the other is the difference-based filter by Motoori and Goto [J Fluid Mech. 2021;V911:A27]. The differences in their definitions lead to a variability in the quantitative results across the literature. In this work, we compare the two filtering methods and show that the difference-based filter can be related to the derivative-based filter through Taylor's approximation. Within the truncation error, the deviation between the two filters depends on the ‘filter width’, ‘a’, which is an input to the difference-based filter. The apparent reason for the deviations is that for large ‘a’, the difference-based filter extracts scales in a wider bandwidth compared to the derivative-based filter, which corresponds to the limiting case of the difference-based filter as $ a \rightarrow 1 $ a→1. Thus, one must be careful while choosing a filter based on their objectives, and while comparing results based on different filter designs. It is hoped that the present work will help reconcile the quantitative variations across the relevant literature. [ABSTRACT FROM AUTHOR]

A method for the oxidation life analysis of SiC/SiC composite structures was proposed to predict the oxidation life of ceramic matrix composite (CMC) components. In this paper, multiscale diffusion–oxidation simulation of the CMCs' structure can be carried out by establishing the equivalent diffusion coefficient model, to get the microscopic oxidation morphology parameters at each point. Based on the results, the residual stiffness is calculated with the homogenization method. Then, the residual stiffness is taken as the material parameter of element to calculate the stress distribution after oxidation in the CMC structure. And the residual strength of CMCs was calculated by progressive damage analysis. The oxidation life of CMC component is analyzed by judging the failure condition of each element. Finally, the oxidation life prediction of the CMC adjustment sheet under specific loading environment is realized, which is about 1 200 h. The results show that this method is feasible for the calculation of hot end parts under actual complex loads. [ABSTRACT FROM AUTHOR]

Based on the far field function of electric field, a new glass carbon fiber gradient-stitched structure is proposed and designed in the paper. In the frequency range of 8–28 GHz, the electric field distribution of transverse electric and magnetic field (TEM) waves incident obliquely on the surface of the structure by optimizing the glass fiber fabric units. It is confirmed that the surface has the function of suppressing electromagnetic wave reflection. The electrical performance simulation and testing results of the structure show that the radar cross section (RCS) is reduced by 90% in the frequency range of 12.3–26.5 GHz. In addition, a multi-scale analysis model is established to quickly predict the tensile modulus and strength of the composite material. The strength and modulus reached 26.9 GPa and 403 MPa, respectively. The simulating and testing results of mechanical properties are in good agreement. [ABSTRACT FROM AUTHOR]

The China-India border dispute is the biggest obstacle to the development of China-India relations. The existing studies on the China-India border dispute do not pay enough attention to geographical factors and lack comprehensive analysis of multi-agent and multi-scale, so it is difficult to grasp the essential characteristics and overall situation of the China-India border issue. This paper proposes a multi-scale geopolitical analysis framework to analyze the competitive nature of contemporary China-India border dispute under the joint action of multi-scale and multi-agent. The main conclusion is that the contemporary China-India border dispute is a complex problem caused by multi-scale spatial superposition and multi-agent power game. On the global scale, the China-India border dispute is an important part of the strategic interaction be‐ tween the United States and India to contain China; At the regional scale of South Asia, China-India border dispute is a land blockade for India and its partner countries to restrict China's expansion of influence in South Asia; On the bilateral scale of China and India, India regards the China-India border dispute as a strategic fulcrum to seek land advantages over China, and tries to gain strategic advantages over China on both land and sea; At the national internal scale, the China-India border dispute is a competition between China and India in the border transformation mechanism; At the border area scale, the China-India border dispute is a competition between the two countries in terms of their ability to effectively manage border areas. The national inter‐ nal scale is the core scale that determines the China-India border dispute, but the possibility of the two countries reaching a reasonable compromise on this scale is still very small in the short term. The current situation of the global, South Asian region, and bilateral scale is not conducive to a rational solution of the China-India border dispute. The border area scale is the main focus of the China-India border dispute for a long time. [ABSTRACT FROM AUTHOR]

For industry image data, this paper proposes an image classification method based on stochastic configuration networks and multi-scale feature extraction. The multi-scale features are extracted from images of different scales using deep 2DSCN, and the hidden features of multiple layers are also connected together to obtain more informational features. The integrated features are fed into SCNs to learn a classifier which improves the recognition rate for different categories. In the experiments, a handwritten digit database and an industry hot-rolled steel strip database are used, and the comparison results demonstrate the proposed method can effectively improve the classification accuracy. [ABSTRACT FROM AUTHOR]

An HFMD outbreak spread over the city of Hải Phòng from summer 2011 to autumn 2012. This epidemic was chosen because it was the very first HFMD epidemic in North Vietnam, eliminating thus interferences with previous outbreaks. This epidemic displayed three separate waves. A complete dataset was collected for more than 9500 patients during this period, which enabled us to analyze this epidemic at different scales. Access to the healthcare system was crucial during this period, which was possible due to a reorganization of the system in February–March 2012. An analysis at the commune level enabled us to track the epidemic along certain communication routes. The three-waves structure reveals a wide disparity at the district level. We developed a mathematical model showing high accuracy at the adjustment of data for both the total number of cases and for the number of cases per week. As a consequence, the model was able to accurately determine the dates of the beginning and end of each wave and to show that they overlapped. Using mathematical functions associated with this model, it was possible to calculate the probability for a patient to belong to a specific wave. [ABSTRACT FROM AUTHOR]

Imogolite nanotubes (INTs) are predicted as a unique 1D material with spatial separation of conduction and valence band edges but their large band gaps have inhibited their use as photocatalysts. The first step toward using these NTs in photocatalysis and exploiting the polarization‐promoted charge separation across their walls is to reduce their band gap. Here, the modification of double‐walled aluminogermanate INTs by incorporation of titanium into the NT walls is explored. The precursor ratio x = [Ti]/([Ge]+[Ti]) is modulated between 0 and 1. Structural and optical properties are determined at different scales and the photocatalytic performance is evaluated for H2 production. Although the incorporation of Ti atoms into the structure remains limited, the optimal condition is found around x = 0.4 for which the resulting NTs reveal a remarkable hydrogen production of ≈1500 µmol g−1 after 5 h for a noble metal‐free photocatalyst, a 65‐fold increase relative to a commercial TiO2‐P25. This is correlated to a lowering of the recombination rate of photogenerated charge carriers for the most active structures. These results confirm the theoretical predictions regarding the potential of modified INTs as photoactive nanoreactors and pave the way for investigating and exploiting their polarization properties for energy applications. [ABSTRACT FROM AUTHOR]

Soil arching is significantly influenced by relative density, while its mechanisms have barely been analyzed. A series of DEM numerical simulations of the classical trapdoor test were carried out to investigate the multi-scale mechanisms of arching development and degradation in granular materials with different relative density. For analysis, the granular assembly was divided into three zones according to the particle vertical displacement normalized by the trapdoor displacement δ. The results show that before the maximum arching state (corresponding to the minimum arching ratio), contact forces between particles in a specific zone (where the vertical displacement of particles is larger than 0.1δ but less than 0.9δ) increase rapidly and robust arched force chains with large particle contact forces are generated. The variation in contact forces and force chains becomes more obvious as the sample porosity decreases. As a result, soil arching generated in a denser particle assembly is stronger, and the minimum value of the arching ratio is increased with the sample porosity. After the maximum arching state, the force chains in this zone are degenerated gradually, leading to a decrease in particle contact forces in microscale and an increase in the arching ratio in macroscale. The recovery of the arching ratio after the minimum value is also more significant in simulations with a larger relative density, as the degeneration of contact force chains is more obvious in denser samples. These results indicate the importance of contact force chain stabilities in specific zones for improving soil arching in engineering practice. [ABSTRACT FROM AUTHOR]

This paper is devoted to a mathematical model with diffusion and cross-diffusion to describe the interaction between vegetation and soil water. First, the existence of Hopf bifurcation and cross-diffusion-driven Turing instability are discussed. Then, based on the nonlinear analysis, we obtain the exact parameters range for stationary patterns and show the dynamical behavior near Turing bifurcation point. It is found that the model has the properties of gap, strip and spot patterns. Moreover, the small water-uptake ability of vegetation roots promotes the growth of vegetation and the transitions of vegetation pattern. But with the continuous increase of the water-uptake ability of vegetation roots, the local vegetation biomass density increases and the isolation between vegetation patches also increases, which may induce the emergence of desertification. In addition, our results reveal that the water consumption rate induces the transitions of vegetation pattern and prohibits the increase of vegetation biomass density. [ABSTRACT FROM AUTHOR]

Urbanization is a major cause of global insect declines, yet some species can persist, and even thrive, in cities.Research on butterflies frequently report reduced diversity in urban habitats compared to rural ones, but less is known about whether urbanization favours butterflies with specific functional traits. Further, few studies have evaluated whether urbanization leads to the biotic homogenization of butterfly communities, despite being frequently reported for other taxa.Here, we investigate how butterfly community composition changes along an urban gradient by surveying 44 sites around Montréal, Quebec, Canada. We test the hypothesis that urban butterfly communities are homogenized at the taxonomic and functional levels.We found clear differences in the structure of urban versus rural butterfly communities with urban areas favouring a few, highly abundant, non‐native species. These shifts were defined by losses of native species richness and abundance combined with increases in non‐native species abundance. For most of the butterfly community, species with longer flight periods were more common in urban areas. Finally, urban communities were homogenized at the taxonomic and functional levels as demonstrated by reductions in beta diversity and variation in several key functional traits (wingspan, larval diet breadth, oviposition style) in urban habitats compared to rural ones.Overall, urban habitats in this region support less diverse butterfly communities that are homogenized. Since urban areas are growing worldwide, a better understanding of how biotic homogenization arises and what its consequences are will be key to guiding future conservation efforts. [ABSTRACT FROM AUTHOR]

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions. Synopsis: PILOT is a computational framework of analysis of multi-scale single cell or pathomics data measured over distinct patients. It allows the estimation of sample-level clustering and trajectories Statistical methods allow the interpretation of results, i.e., association of clusters/trajectories with cell clusters, genes and tissue structures. PILOT is showcased in scRNA-seq of myocardial infarction and pathomics data of kidney IgA nephropathy. PILOT is a computational framework of analysis of multi-scale single cell or pathomics data measured over distinct patients. [ABSTRACT FROM AUTHOR]

Temperature is one of the most important factors significantly affecting damage detection performance in civil engineering. A new method called the Adaptive Bandwidth Filter Algorithm (ABFA) is proposed in this paper to separate the temperature effect from quasi-static long-term structural health monitoring data. The Adaptive Bandwidth Filter Algorithm (ABFA) is referred to as an algorithm of automatically adjusting the frequency bandwidth filter via the particle swarm optimization (PSO) algorithm. Considering the obvious multi-scale feature of the collected data of civil structure, the acquired time series are divided into different time scales (for example, day, month, year, etc.), and these scales in the frequency domain correspond to the center frequencies of the adaptive bandwidth filter. The temperature effect on structure responses across different time scales is thereafter explored by adaptively adjusting the frequency bandwidth of the filter based on the known center frequencies of different scales. The relationship between the temperature and the structure responses is established through statistical regression facilitated by sufficient in situ monitoring data. Simulation and experiment results show the very promising performance of the proposed algorithm and decouple the temperature effect accurately from the contaminated data; thus an enhanced capability of damage detection is achieved. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-scale structure that integrates deep neural networks and Transformer features, aiming to address the issue of performance degradation in salient object detection networks when objects of different sizes appear in the same scene. When dealing with objects of different scales, the performance of existing methods often fluctuates due to the contradiction between sampling depth and receptive field size. To tackle this challenge, three different sampling rates were adopted to sample the feature maps,and the Transformer module was used to learn global context information.This method enables the effective fusion of the characteristics of Convolutional Neural Networks (CNNs) and Transformer networks,thereby innovatively proposing a salient object detection strategy for multi-scale objects.Experimental results on three public datasets,UHRSD-TE,DUT-OMRON,and DUTS-TE,demonstrate that this method performs excellently in the task of salient object detection for objects of different sizes in the same scene. [ABSTRACT FROM AUTHOR]

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integration encoding strategy for texture classification. The essence of this strategy is to introduce the multi-scale local texture information in the view of thresholding. Based on this, we propose the local multi-scale center pattern, local multi-scale sign pattern, and local multi-scale magnitude pattern to extract and describe the multi-scale local texture information. Then, the three sub-patterns are jointly combined to generate the final descriptor for texture classification tasks. The experimental results on three popular texture databases significantly demonstrate that the proposed texture descriptor is very discriminative and powerful for visual texture classification tasks. [ABSTRACT FROM AUTHOR]

The conceptual design and engineering of an integrated catalytic reactor requires a thorough understanding of the prevailing mechanisms and phenomena to ensure a safe operation while achieving desirable efficiency and product yields. The necessity and importance of these requirements are demonstrated in this investigation in the case of novel membrane-assisted reactors tailored for CO2 hydrogenation. Firstly, a carbon molecular sieve membrane was developed for simultaneous separation of CO2 from a hot post-combustion CO2-rich stream, followed by directing it along a packed-bed of hybrid CuO-ZnO/ZSM5 catalysts to react with hydrogen and produce DiMethyl Ether (DME). The generated water is removed from the catalytic bed by permeation through the membrane which enables reaction equilibrium shift towards more CO2-conversion. Extra process intensification was achieved using a membrane-assisted reactive distillation reactor, where similarly several such parallel membranes were erected inside a catalytic bed to form a reactive-distillation column. This provides the opportunity for a synchronized separation of CO2 and water by a membrane, mixing the educts (i.e., hydrogen and CO2) and controlling the reaction along the catalytic bed while distilling the products (i.e., methanol, water and DME) through the catalyst loaded column. The hybrid catalyst and carbon molecular sieve membrane were developed using the synthesis methods and proved experimentally to be among the most efficient compared to the state-of-the-art. In this context, selective permeation of the membrane and selective catalytic conversion of hybrid catalysts under the targeted operating temperature range of 200–260 °C and 10–20 bar pressure were studied. For the membrane, the obtained high flux of selective CO2-permeation was beyond the Robeson upper bound. Moreover, in the hybrid catalytic structure, a combined methanol and DME yield of 15% was secured. Detailed results of catalyst and membrane synthesis and characterization along with catalyst test and membrane permeation tests are reported in this paper. The performance of various configurations of integrated catalytic and separation systems was studied through an experimentally supported simulation along with the systematic analysis of the conceptual design and operation of such reactive distillation. Focusing on the subnano-/micro-meter scale, the performance of sequential reactions while considering the interaction of the involved catalytic materials on the overall performance of the hybrid catalyst structure was studied. On the same scale, the mechanism of separation through membrane pores was analyzed. Moreover, looking at the micro-/milli-meter scale in the vicinity of the catalyst and membrane, the impacts of equilibrium shift and the in-situ separation of CO2 and steam were analyzed, respectively. Finally, at the macro-scale separation of components, the impacts of established temperature, pressure and concentration profiles along the reactive distillation column were analyzed. The desired characteristics of the integrated membrane reactor at different scales could be identified in this manner. [ABSTRACT FROM AUTHOR]

The structure and diversity of plant communities respond to changes in landscape structure and vary with spatial scale, stand age and plant size. Therefore, it is important to identify the scale (grain size and extent) at which secondary forest attributes of large and/or small plants and landscape structure are more closely associated. We performed multi-scale analyses in which different grain sizes and extents were assessed to determine the most appropriate spatial scale for assessing the association of large/small tree aboveground biomass and species richness with successional age and landscape structure using regression analysis. AGB and species richness were more strongly associated with landscape structure when large grain sizes (500 m2 ) were used, with R 2 values between 0.31 and 0.43. Variation in AGB and species richness was explained primarily by successional age and landscape structure, respectively. At large extents, successional age was related to the AGB of large trees (R 2 = 0.43); at intermediate extents, landscape structure was related to the species richness of large trees (R 2 = 0.31). The approach and results of this study may facilitate the identification of appropriate areas and scales for the maintenance or restoration of tree diversity, carbon storage, and the provision of ecosystem services in tropical dry forests. [ABSTRACT FROM AUTHOR]

Advances in 3D-printing technology have made it possible to manufacture structures containing periodic microstructures (lattice structures) and obtain superior mechanical performance that cannot be provided with uniform solids. To achieve the best performance, microstructures with different properties should be optimally designed and arranged in the right places within the macrostructure. However, in most of the basic approaches with homogenization theory, each microstructure is analyzed and optimized separately under periodic boundaries, so connectivity with adjacent microstructures is not considered (or guaranteed). Consequently, unrealistic discontinuous structures are often obtained, resulting in poor mechanical performance and manufacturability. To address this problem, we propose a method to introduce an "enlarged connective domain" around the boundary when dividing the macrostructure into multiple domains with different microstructures in advance. The microstructure of the enlarged connective domain is formed by combining the unit cells of adjacent microstructures, which allows micro-scale connectivity to be reflected in the macrostructure. Therefore, this method improves the mechanical connectivity between microstructures through the original optimization process of maximizing the performance of the entire macrostructure. This paper demonstrates that the proposed method improves connectivity and mechanical performance by performing optimization analyses for two- and three-dimensional problems. In addition, we focus on the width (volume ratio) of the connective domain as a design parameter that specifies the degree to which connectivity is considered and discuss how to set the parameter appropriately. [ABSTRACT FROM AUTHOR]

Heavy rains usually hit Laos countrywide and cause serious floods, influencing local agriculture, households, and the economy. Therefore, it is crucial to monitor the flooding in Laos to better understand the flood patterns and characteristics. This paper aims to analyze the influence of the flooding in Laos with multi-source data, e.g., Synthetic Aperture Radar (SAR), optical multi-spectral images, and geographic information system data. First, the flood areas in Laos from 2018 to 2022 are detected using a decision fusion method. Based on the flood areas and the global Land Use/Land Cover (LULC) product, the macro scale global impact of the flood is analyzed. Second, taking the Vientiane Capital as a case study area, a flood forecasting method is applied to estimate the risk of flooding. Finally, optical images before and after the flood event are extracted for a close-up comparison at the micro scale. Based on the above multi-scale analysis, floods in Laos are found to be predominantly concentrated in the flat areas near the Mekong River, with a decreasing trend over time, which could be helpful for flood management and mitigation strategies in Laos. The validation results exhibited notable average indices across a five-year period, with mIoU (0.7782), F1 score (0.7255), and overall accuracy (0.9854), respectively. [ABSTRACT FROM AUTHOR]

The short-term forecast of photovoltaic (PV) power is crucial for the security and economics of power system operations. However, the fluctuation characteristics of the PV power, which are closely related to the meteorological factors, introduce inaccuracies in its forecast. Towards this end, the paper studies the effects of clustering analysis at long time scale and data reconstruction technique at short time scale on capturing PV power fluctuation characteristics. A short-term PV power forecasts method based on multi-scale fluctuation characteristics extraction (MFCE), which employs a path analysis to identify the relevance of meteorological factors with PV power at long time scale and a phase space reconstruction to analyze PV power fluctuation characteristics at short time scale, is proposed in this paper. The proposed MFCE methodology deploys a widely-used extreme gradient boosting (XGBoost) model to output the forecasting results. Both the effectiveness and accuracy of the proposed methodology are verified by using the real data under the conditions of sunny and cloudy days of four seasons compared to traditional methodologies. • The multi-scale fluctuation characteristics are analyzed for PV power forecast. • Identify dominant meteorological factors affecting PV power by path analysis. • The hour-level fluctuation is reduced by using path analysis-based clustering. • The phase space reconstruction is used to reduce the minute-level fluctuation. [ABSTRACT FROM AUTHOR]

The Hayabusa2 mission provided a unique data set of asteroid Ryugu that covers a wide range of spatial scale from the orbiter remote sensing instruments to the returned samples. The MASCOT lander that was delivered onto the surface of Ryugu aimed to provide context for these data sets by producing in situ data collected by a camera (MasCam), a radiometer (MARA), a magnetometer (MasMag) and a spectrometer (MicrOmega). In this work, we evaluate the success of MASCOT as an integrated lander to bridge the gap between orbiter and returned sample analysis. We find that MASCOT's measurements and derivatives thereof, including the rock morphology, colour in the visible wavelengths, possible meteorite analogue, density, and porosity of the rock at the landing site are in good agreement with those of the orbiter and the returned samples. However, it also provides information on the spatial scale (sub-millimetres to centimetres) at which some physical properties such as the thermal inertia and reflectance undergo scale-dependent changes. Some of the in situ observations such as the presence of clast/inclusions in rocks and the absence of fine particles at the landing site was uniquely identified by MASCOT. Thus, we conclude that the delivery of an in situ instrument like MASCOT provides a valuable data set that complements and provides context for remote sensing and returned sample analyses. [ABSTRACT FROM AUTHOR]

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug–disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net. [ABSTRACT FROM AUTHOR]

This study presents the influence of phase change materials (PCMs) on the cyclic behaviour of mortars. A multi-scale analysis comprising mechanical and thermal tests was used to explain the phenomena involved. Mechanical and thermal cyclic tests were performed on four configurations of mortars containing 0%, 5%, 8%, and 10% of PCMs. It was revealed that mechanical cyclic loading did not significantly affect the resistance of mortars contrary to thermal solicitations. Indeed, it was shown that the incorporation of PCMs improved the thermal capacity of the mortars. However, severe degradation of the heat capacity and enthalpy was noticed after the temperature cycles. The physicochemical characterisation tests confirmed a moderate structural degradation, which primarily occurred in the initial cycles. Regarding the thermal cycles, scanning electron microscopy (SEM) and calorimetry characterisations corroborated the leakage of PCMs into the cement matrix, which was responsible for the degradation of latent heat. Finally, it was observed that the PCM–mortars resisted mechanical cyclic solicitations, at least up to 1000 cycles; however, they were still sensitive to repeated variations in temperature. [ABSTRACT FROM AUTHOR]

Deep learning techniques excel at capturing and understanding the symmetry inherent in data patterns and non-linear properties of photovoltaic (PV) power, therefore they achieve excellent performance on short-term PV power forecasting. In order to produce more precise and detailed forecasting results, this research suggests a novel Autoformer model with De-Stationary Attention and Multi-Scale framework (ADAMS) for short-term PV power forecasting. In this approach, the multi-scale framework is applied to the Autoformer model to capture the inter-dependencies and specificities of each scale. Furthermore, the de-stationary attention is incorporated into an auto-correlation mechanism for more efficient non-stationary information extraction. Based on the operational data from a 1058.4 kW PV facility in Central Australia, the ADAMS model and the other six baseline models are compared with 5 min and 1 h temporal resolution PV power data predictions. The results show in terms of four performance measurements, the proposed method can handle the task of projecting short-term PV output more effectively than other methods. Taking the result of predicting the PV energy in the next 24 h based on the 1 h resolution data as an example, MSE is 0.280, MAE is 0.302, RMSE is 0.529, and adjusted R-squared is 0.824. [ABSTRACT FROM AUTHOR]

An automatic temporal video segmentation framework is introduced in this article. The proposed cut detection technique performs a high-level feature extraction on the video frames, by applying a multi-scale image analysis approach combining nonlinear partial differential equations (PDE) to convolutional neural networks (CNN). A nonlinear second-order hyperbolic PDE model is proposed and its well-posedness is then investigated rigorously here. Its weak and unique solution is determined numerically applying a finite difference method-based numerical approximation algorithm that quickly converges to it. A scale-space representation is then created using that iterative discretization scheme. A CNN-based feature extraction is performed at each scale and the feature vectors obtained at multiple scales are concatenated into a final frame descriptor. The feature vector distance values between any two successive frames are then determined and the video transitions are identified next, by applying an automatic clustering scheme on these values. The proposed PDE model, its mathematical investigation and discretization, and the multi-scale analysis based on it represent the major contributions of this work. Some temporal segmentation experiments and method comparisons that illustrate the effectiveness of the proposed framework are finally described in this research paper. [ABSTRACT FROM AUTHOR]

An overview of the edge detection techniques based on partial differential equations (PDE) is presented in this work. Nonlinear anisotropic diffusion-based boundary extraction approaches, like the influential Perona-Malik model and some improved variants of it, are described first. Anisotropic diffusion-based detection schemes using the mean curvature motion and nonlinear PDE-based approaches combining anisotropic diffusion to the bilateral filter, are then disscused here. Some nonlinear reaction-diffusion based edge detection methods are described next. Variational edge detection solutions using the total variation (TV) regularization or combining the anisotropic diffusion to the TV-based models are then presented. Directional diffusion-based image edge extraction algorithms, are also disscused. Our own contributions in this computer vision domain are finally described. [ABSTRACT FROM AUTHOR]

Image aesthetics assessment (IAA) is inherently subjective, with generic image aesthetics assessment (GIAA) often overlooking individual user preferences. Personalized aesthetic assessment (PIAA) addresses this by accounting for user-specific inconsistencies. However, the limited annotated data from individual users and the scarcity of user-jointly annotated image data pose challenges. To cope with these problems, we propose a multi-scale regional attribute-weighting via meta-learning for PIAA, namely MRAM, which aims to simulate how the brain computes aesthetic values from visual stimuli. The human brain is to predict the subjective aesthetic value of the individual to the image by combining the features of the visual scene segmentation through the weighting method. MRAM introduces a multi-scale attention module to capture visual scene regions segmented at different scales, adjusting feature selection based on user attention. Additionally, a regional attribute-weighting module (RAM) is proposed to weigh these features using aesthetic attribute representations. In the meta-training phase, MRAM learns aesthetic prior knowledge from a diverse set of PIAA tasks. Transitioning to the personalization phase, MRAM is fine-tuned with only a small number of specific user samples, enabling it to rapidly adapt to the unique aesthetic preferences of individuals. Experimental results demonstrate the superior performance of the proposed MRAM compared to state-of-the-art PIAA methods. [ABSTRACT FROM AUTHOR]

• A coupled carrier dynamics model is adopted on the thermophotovoltaic system. • The influences of doping- and temperature-dependent material properties is considered in detail. • The multiscale influence is analyzed from macroscopic to microscopic. Thermophotovoltaic (TPV) cells offer a novel approach to enhancing sustainable energy applications, both in terms of thermal energy storage extraction and high-temperature heat recovery. Focusing on radiation-to-electric conversion, a coupled carrier dynamics model is employed to analyze the effects of doping- and temperature-dependent properties on the multiscale performance of GaSb TPV systems. First, the effects of changed bandgap, absorption coefficient, and carrier mobility on cell performances are assessed under varying doping levels and temperatures. The effect of carrier mobility counteracts that of the bandgap and absorption coefficient. Ignoring doping dependence results in an overestimation of output power, while disregarding temperature dependence leads to an opposite outcome. Additionally, system performance is evaluated as various parameters change, such as emitter and cell characteristics. Influences of condition-dependent properties are discussed, showing that as emitter temperature increases, both cell and system efficiencies are significantly affected (33.05% and 17.57%, respectively), with a maximum discrepancy of 8%. The impact of these properties can reach up to 29% with changes in emitter emissivity, and the optimized bandwidth of ideal spectral selectivity decreases by 50 nm. When considering condition-dependent properties, system efficiency becomes more sensitive than cell efficiency to emitter characteristics. Furthermore, peaks in efficiency of 39.2% and 40.6% are observed with variations in cell thickness and doping concentration. [ABSTRACT FROM AUTHOR]

As an inherent attribute of asphalt pavement, texture durability is the essential reason for the change in skid resistance. However, different scale texture deterioration behavior remains unclear by tire wearing at present. To explore the influence of the durability of multi-scaled textures on skid resistance, nine pavement specimens were tested using the Harbin polishing and dynamic friction integrated experimental machine (HPFM). The pavement anti-skid texture tester (PATT-II) was used for high-precision texture scanning and reconstruction. The results show that the texture richness and skid resistance of the three types of pavements in 300 min polishing are ranked as Open-graded Friction Coarse (OGFC) > Stone Mastic Asphalt (SMA) > Asphalt Concrete (AC), while SMA presents the best durability, determined by the material skeleton structure of asphalt mixture. The macro-texture deterioration occurs slightly at first and then tends to stabilize in 300 min polishing. The micro-texture deterioration behavior is the same as the μ HPFM , increasing rapidly at the beginning, then slowly decreasing, and finally remaining constant. This deterioration is caused by the asphalt damage and continuously wearing aggregate surface. The 2D-PSD deterioration behavior is more significant with a smaller texture scale in 300min polishing, and the most considerable contribution wavelength in the microscopic scale range is 0.15 mm–0.3 mm, which is determined by the adhesion characteristics and molecular mechanism between a mineral mixture and asphalt material. The influence of pavement texture on the dynamic friction coefficient is significantly different under different water film thicknesses, the micro-texture and macro-texture play the main roles in boundary and mixed lubrication, respectively. • The effect mechanism of vehicle tire wear on the durability of multi-scale pavement texture and skid resistance deterioration behavior is proposed. • The mapping relationship between the change of texture wavelength caused by polishing and multi-factor coupling pavement skid resistance is analyzed. • The contribution characteristics of particle size aggregates to pavement texture and skid resistance durability are proposed. [ABSTRACT FROM AUTHOR]

• The multi-scale analysis method is introduced to analyze the scale effect of landslide factors systematically. • The changing dynamic characteristics of landslide susceptibility distribution with scale are clarified. • The optimal analysis scale is identified to balance multiple factors' effects, enhancing the accuracy of LSM. • Identify and quantify the main controlling factors of different types of landslides. Landslides are common and extremely destructive geological hazards in mountainous areas, posing a serious threat to life, property, and socioeconomic development. Landslide susceptibility mapping is a key link in landslide risk management, but existing studies are mostly limited to single landslide types and single scale analysis, which makes it difficult to fully reflect the complexity and diversity of landslide occurrence. This paper takes Zhenxiong County, Yunnan Province as the study area, and constructs a multi-type and multi-scale landslide susceptibility mapping method by integrating information value (IV) model and principal component analysis (PCA) model. It reveals the scale effect of landslide driving force and the impact of this scale effect on the accuracy of susceptibility mapping, and identifies the main controlling factors of landslides at different scales. The results of the study showed that: (1) The influencing factors of the three landslide types have significant scale effects. Natural factors such as topography and geology play a controlling role at the buffer zone scale of 800–1000 m, while inducing factors such as human activities and rainfall mainly play a role at the buffer zone scale of 100–600 m. (2) The scale effect of influencing factors significantly affects the accuracy of landslide susceptibility mapping. The AUC value of soil landslide shows a trend of first increasing and then decreasing with the increase of scale, and the optimal AUC value is 0.815 at the 600-meter scale; the AUC value of rock landslide shows a trend of fluctuating and then increasing with the increase of scale, and the AUC value at the 1000-meter scale reaches the highest value of 0.877; the AUC value of ground collapse fluctuates with the expansion of scale, and the AUC value at the 800-meter scale is 0.883, which is the highest value among all scales. (3) Different types of landslides are controlled by different dominant factors. Slope height, rainfall and engineering geological rock formations are the main controlling factors for soil landslides; topographic factors such as slope height and DEM are the main controlling factors for rock landslides; DEM and engineering geological rock formations are the main controlling factors for ground collapse. The multi-type and multi-scale landslide susceptibility mapping method proposed in this paper can effectively identify the main controlling factors and scale effects of different types of landslides, improve the accuracy and reliability of landslide susceptibility mapping, and provide a scientific basis and decision-making support for landslide disaster prevention and control in Zhenxiong County. [ABSTRACT FROM AUTHOR]

Agricultural performance assessment spans various spatial scales, from single plots to entire irrigation systems. A multi-scale analysis is thus crucial for informed decision-making. The Gezira irrigation sScheme in Sudan is a longstanding large-scale irrigation system experiencing severe water management challenges, manifested by low land productivity, water productivity, and irrigation efficiency. Recognizing the interdependence of decision-making, this research focuses on variations in water and land productivity at different spatial scales within the Gezira irrigation scheme. As one of the world's largest gravity-irrigated systems, covering 2.1 million feddan (1 feddan = 0.42 ha ∼ 1 acre), the scheme serves as an interesting case to study head-tail performance variations across four spatial scales: tertiary, secondary, and major units, and the whole scheme. This study is centered on the winter season wheat cultivation 2022–2023, and employs the FAO's Water Productivity Open Access Portal (WaPOR v2.1) datasets, with 100 m resolution, for computing land and water productivity. Ground-observed yield data from nine tertiary units (nimras) in the Wadelbur irrigation division were used to validate WaPOR. The results showed a systematic underestimation of WaPOR derived land productivity by about 40 % compared to the ground dataset. The head-tail analysis of land and water productivity reveal contrasting results at different scales. At tertiary and secondary scales, no correlation exists between distance from offtake and productivity. At the major unit (irrigation division), a moderate correlation is observed: 0.7 for land productivity, and 0.6 for water productivity. At the scheme scale, the correlation factors are somewhat lower for land productivity (0.4) and the same for water productivity (0.6). At the largest scale, the productivity appears to increase from head to tail divisions, suggesting potential overirrigation and waterlogging in the head parts leading to reduced productivity. Another possibility is the presence of better agricultural practices in the tail areas compared to the head. • WaPOR captures average yield, but under/over estimates high/low yield respectively. • Distance from offtake and productivity have a moderate correlation in larger scales. • Distance from offtakes has no correlation with productivity in smaller scales. • In irrigation divisions of Gezira scheme tail-end users have higher water productivity. [ABSTRACT FROM AUTHOR]

To investigate the effect of different texture wavelength deterioration on pavement skid resistance, a tire-pavement tribology model was developed through theoretical derivation and numerical simulation, and five asphalt mixture specimens were used for full life-cycle performance analysis. The results show that simulation model can efficiently calculate stress contribution of each asperity on 3D texture, resulting in simulation outcomes closely matched those of the laboratory tests. Two-dimensional (2D) pavement profile and 2D-PSD show apparent consistency in initial and subsequent stages, it correlates with the asphalt film shedding and aggregate crushing during polishing. Intrinsic factors mainly determine skid resistance durability, which is closely related to the percentage of different particle size aggregates and skeletal structure of the asphalt mixture. [ABSTRACT FROM AUTHOR]

Metropolitan areas face the challenge of balancing increasing social needs against limited natural resources. The multi-scale evaluation of ecosystem services (ES) supply, demand and flows is crucial but under explored. Four prominent ES, food provision (FP), carbon sequestration (CS), soil conservation (SC), and outdoor recreation (OR) in Wuhan metropolitan area (WMA) were selected as cases. ES supply and demand were assessed by InVEST model, geographic information system (GIS) spatial analysis, and statistical methods. A linear programming model was employed to reveal the characteristics of ES flows. Results showed the following: (1) the total ES supply capacity can meet the demand of the study area. At different scales, the supply patterns were fundamentally consistent, except for FP, which was significantly higher in Wuhan's suburban areas. In contrast, the other three ES exhibited high supply in the northeast and south WMA, encompassing the Dabie and Mufu Mountains. The high demand for all ES radiated from the city center, except for SC. (2) A significant scale effect of ES mismatch was found, and ecological deficit patches decreased with increasing research scale. The Mismatch was evident on finer scales but lessened at coarser ones. (3) ES flows, consisting of FP, CS, and OR, formed a flow pattern from the suburbs to the city center. Coarse-scale management integrating ecological flows may better maintain ES balance than fine-scale measures. These findings provide insights for multi-scale management strategies that not only contribute to localized ecological restoration, but also optimize strategies for ecological corridors and compensation mechanisms. [Display omitted] • Assess the Ecosystem services (ES) supply and demand at different scales. • Mismatch of ES supply and demand has spatial scale effects. • Simulate the dynamic process of ES flows using linear programming model. • Policy implications of multi-scale ES management. [ABSTRACT FROM AUTHOR]

• A three-layer depth-dominated wetland flow is studied under the effects of ecological-degradation and free-surface-wind. • The maximum value of mean concentration decreases due to the increase of ecological-degradation reaction rates and wind intensity. • Environmental dispersivity is derived using multi-scale homogenization technique. • The maximum critical length and time duration is greatly change for the wind force and its direction. A three-layer depth-dominated wetland flow with ecological-degradation and free-surface-wind effect is studied to comprehend the contaminant transport in wetlands with aquatic vegetation that find wide application in agricultural water pollution management, and wastewater treatment. The present paper attempts to highlight the effect of wind on depth-dominated velocity and environmental dispersion in terms of longitudinal mean concentration. Also, the longitudinal mean concentration is analysed taking the effect of different ecological-degradation reaction rates for each layer. It is observed that the peak level of mean concentration decreases with the increase of both ecological-degradation reaction rate and wind intensity. The velocity distribution and environmental dispersion are derived analytically from the basic momentum and mass transport equations. Results indicate that the velocity and dispersion coefficient are greatly influenced by the wind force, in terms of its magnitude as well as the direction. To measure the environmental dispersivity, a Mei's multi-scale analysis is performed. For the typical contaminant Pb , the critical length and time duration of the region influenced by the contaminant transport are analytically derived and its dependence on different wind intensities is explored. Further, the present paper presents the comparison of the critical length and time of the pollutant affected area between the three-layer and two-layer wetland flow. [ABSTRACT FROM AUTHOR]

Vegetation patterns with a variety of structures is amazing phenomena in arid or semi-arid areas, which can identify the evolution law of vegetation and are typical signals of ecosystem functions. Many achievements have been made in this respect, yet the mechanisms of uptake–diffusion feedback on the pattern structures of vegetation is not fully understood. To well reveal the influences of parameters perturbation on the pattern formation of vegetation, we give a comprehensive analysis on a vegetation–water model in the forms of reaction–diffusion equation which is posed by Zelnik et al. (Proc Natl Acad Sci 112:12,327–12,331, 2015). We obtain the exact parameters range for stationary patterns and show the dynamical behaviors near the bifurcation point based on nonlinear analysis. It is found that the model has the properties of spot, labyrinth and gap patterns. Moreover, water diffusion rate prohibits the growth of vegetation while shading parameter promotes the increase of vegetation biomass. Our results show that gradual transitions from uniform state to gap pattern can occur for suitable value of parameters which may induce the emergence of desertification. [ABSTRACT FROM AUTHOR]

Coherent change detection is a technology that utilizes phase information in complex-valued synthetic aperture radar images. It is mostly used to detect subtle changes that cannot be detected by amplitude images on the ground, and it also has excellent detection performance when it comes to low-coherence typical changed areas. However, due to its high sensitivity to changes, this technology will falsely detect areas of natural change such as vegetation disturbance, river flow, and low signal-to-noise ratio areas (e.g., uninteresting areas) as changes, resulting in false-alarm interference areas. In order to tackle this problem, this paper studies a coherent change detection method based on multi-scale analysis to extract typical changed areas in complicated scenes. The method uses an equal variance coherence estimator to calculate the coherence value, separates the interference areas and the typical changed areas using a multi-scale method, and then extracts a binary image of the typical changed areas through noise filtering and threshold segmentation. The method in this paper is experimentally verified with publicly available Airbus spaceborne SAR data, and ESAR airborne data, which is provided by the ESA. The experimental results are visualized and quantitatively evaluated. Through the results, by calculating the probability of correct classification and false-positive and other performance parameters, as well as drawing the receiver operating characteristic curve and the kappa coefficient curve of different threshold values, we find that the method has the capability to suppress the interference areas and the high detection performance of the typical changed areas. The experimental data are complicated scenes that include various types of ground object changes. The results show that the method is effective and universal and can provide reference value for the application of coherent change detection. [ABSTRACT FROM AUTHOR]

The interface in a composite plays an important role in enhancing compatibility of the fibers and the matrix. This paper models the AFP process at meso- and micro-scale using finite element and molecular dynamics methods. Furthermore, the molecular model of the interface is established using molecular dynamics method. Interface adsorbability and resin fluidity along the interface are, respectively, evaluated under different processing parameters using adsorption energy and diffusion coefficient. Finally, the use of gray correlation analysis method reveals the correlation between processing parameters and interface mechanical parameters. The results are verified by measurements of interlaminar shear strength and flexural strength. [ABSTRACT FROM AUTHOR]

Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated ( 0.72 ± 0.014 ) to Sleep Efficiency (SE) and DS/DI positively correlated ( 0.85 ± 0.007 ) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects' awareness. [ABSTRACT FROM AUTHOR]

In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network model to incorporate the external influence of financial markets such as stock market and exchange market in a multiscale setting into the modeling of crude oil market risk movement. We propose a multivariate empirical model decomposition to analyze the finer details of interdependence among risk movement of different markets across different time horizons or scales. We also introduce the convolutional neural network to construct a new nonlinear ensemble algorithm to reduce the estimation bias and improve the forecasting accuracy. We used the major crude oil price data, stock market index, and the euro/United States dollar exchange rate data to evaluate the performance of the multivariate empirical model decomposition convolutional neural network model. The combination of both the multivariate empirical model decomposition and the convolutional neural network model in this paper has produced the risk forecasts with significantly improved risk forecasting accuracy. [ABSTRACT FROM AUTHOR]

Many spatial analysis methods suffer from the scaling issue identified as part of the Modifiable Areal Unit Problem (MAUP). This article introduces the Pyramid Model (PM), a hierarchical data framework integrating space and spatial scale in a 3D environment to support multi-scale analysis. The utility of the PM is tested in examining quadrat density and kernel density, which are commonly used measures of point patterns. The two metrics computed from a simulated point set with varying scaling parameters (i.e. quadrats and bandwidths) are represented in the PM. The PM permits examination of the variation of the density metrics computed at all different scales. 3D visualization techniques (e.g. volume display, isosurfaces, and slicing) allow users to observe nested relations between spatial patterns at different scales and understand the scaling issue and MAUP in spatial analysis. A tool with interactive controls is developed to support visual exploration of the internal patterns in the PM. In addition to the point pattern measures, the PM has potential in analyzing other spatial indices, such as spatial autocorrelation indicators, coefficients of regression analysis and accuracy measures of spatial models. The implementation of the PM further advances the development of a multi-scale framework for spatio-temporal analysis. [ABSTRACT FROM AUTHOR]

Pavement surface texture plays a leading role in the tire-road interaction. To increase the understanding of the pavement texture evolution, two typical asphalt wearing courses, Asphalt Concrete (AC) and Stone Mastic Asphalt (SMA), were polished using the real car tires of Harbin accelerated polishing machine (HAPM). After each polishing cycle, the surfaces were reconstructed using X-ray Computed Tomography (CT) technology. Then, the texture evolution was analysed using material area ratio and two-dimensional power spectral density (2D-PSD). In addition, the critical texture wavelength related to British Pendulum Number (BPN) was determined through Pearson analysis. The results show that the texture roughness changes asynchronously at all observed scales (0.1∼80 mm) under the polishing effects. During the short-term polishing, the surface becomes rougher due to bare aggregates and extended gaps caused by the removal of surfacing mortar. With the increasing polishing cycles, the roughness at short wavelengths under 0.6 mm significantly decreases. Meanwhile, the wear rate in the initial stage is higher than that in the subsequent polishing period. The mixture SMA-13 is more wear-resistant than AC-13 during the polishing. It is also found that the texture with a wavelength under 5 mm is well correlated with BPN. [ABSTRACT FROM AUTHOR]

In this paper, three-dimensional structure feature points of the asphalt mixture were obtained based on the industrial CT. The surface morphology of the asphalt mixture was then extracted through image morphology processing and evolutionary matching algorithms. A series of accelerated polishing tests were utilized to simulate the long-term polishing effect of the tire load on the asphalt pavement. Combining with digital image analysis, the full-life morphology evolution trend of the asphalt pavement was analyzed, and the full-life surface morphology datasets of the asphalt pavement were constructed. By comparing the power spectrum density of the asphalt pavement morphology at different wear stages, a segmented evaluation method based on the spectral fractal method was proposed. Combining the spectral fractal parameters and statistical geometric parameters, a multi-scale asphalt pavement surface morphology feature evaluation index was proposed. This provides a more intricate and detailed analysis of the surface structure of asphalt mixtures. • The full-life morphology evolution trend of the asphalt pavement was analyzed. • A segmented evaluation method based on the spectral fractal method was proposed. • A multi-scale asphalt pavement surface morphology feature evaluation index was proposed. [ABSTRACT FROM AUTHOR]

Conventional macro-micro experiments have not fully elucidated the thermal self-healing mechanism of virgin, aged, and recycled asphalt at the molecular level at different temperatures. Molecular dynamics and macro-micro experiments were used to investigate the multi-scale mechanism of thermal self-healing of nano-cracks in three asphalt types. The molecular characteristics (concentration, energy changes, and molecular diffusion properties) of the three asphalt types were simulated during self-healing using molecular dynamics simulations Macro-micro experimental tests were conducted to validate the simulation results. The self-healing process was analyzed at multiple scales, and correlation analysis between the self-healing parameters and the macroscopic healing characteristics was performed at nano- and microscopic scales. The results showed that the relative concentration increased significantly from 0 mol/L to 0.6 mol/L in the 60–80 Å range during the long-distance self-healing phase (0–40 ps), promoting the rapid reorganization of molecules around cracks and the formation of self-healing networks. The van der Waals forces were the dominant forces affecting self-healing, and the transition from repulsive to attractive forces promoted the self-healing of cracks. This study clarifies the thermal self-healing mechanism of asphalt cracks at multiple scales, ensuring the sustainable use of asphalt materials. • Distance self-healing phase plays a key role in microcrack self-healing. • High sensitivity of functional groups to the self-healing behaviour of asphalt cracks • Van der Waals force shift from repulsive to attractive promotes self-healing of cracks. • "Channels" between asphaltene components facilitate molecular healing behaviour. [ABSTRACT FROM AUTHOR]

During terahertz (THz) non-destructive testing (NDT), multiple echoes from the sample interface reflection signals are mixed with the detection signals, resulting in signal distortion and affecting the accuracy of the THz NDT results. Combined with the frequency property of multiple echoes, an improved wavelet multi-scale analysis is put forth in this paper to correct multiple echoes, allowing the maximum retention of detailed signal information in contrast with the existing echo correction methods. The results showed that the improved wavelet multi-scale analysis enhanced the continuity and smoothness of the image at least twice in testing adhesive layer thickness, prevented missing judgments and misjudgments in identifying characteristic defects, and ensured accurate detection results. Hence, it is of great significance for evaluating the THz NDT results. [ABSTRACT FROM AUTHOR]

In order to analyze the effects of freeze-thaw environment, multi-scale analysis (macro to micro) was applied to study the sustainable service performance for porous asphalt pavement in Sponge City (PASC). Based on the molecular simulation theory, the asphalt-water (ice) interaction was studied at the micro in the paper. Combined with the results of temperature melting and contact angle in macro experiments, the sustainable service performance for PASC under freeze-thaw environment was explained in multi-scale. The results show that the effects of freeze-thaw environment on PASC's sustainable service performance are multi-scale. It is a process from macro to micro, including the fluctuation of interface transition zone, the change of contact angle, the melting of pore ice, the service performance of porous asphalt pavement and the sustainability of Sponge City. Additionally, the aging effect reduces the recovery ability of the service performance for PASC. The critical temperature (0°C) is more sensitive to the interaction between aged asphalt and water (ice). The research results reveal the multi-scale correlation of sustainable service performance for PASC, which has certain theoretical significance and practical value for the application and exploration of Sponge City in freeze-thaw region. [ABSTRACT FROM AUTHOR]

Background: Compact bone mainly consists of cylindrical osteon structures. In microgravity, the change in the mechanical microenvironment of osteocytes might be the root cause of astronauts' bone loss during space flights. Methods: A multi-scale three-dimensional (3D) fluid–solid coupling finite element model of osteons with a two-stage pore structure was developed using COMSOL software based on the natural structure of osteocytes. Gradients in gravitational fields of − 1, 0, 1, 2.5, and 3.7 g were used to investigate the changes in the mechanical microenvironment on osteocyte structure. The difference in arteriole pulsating pressure and static compression stress caused by each gravity gradient was investigated. Results: The mechanical response of osteocytes increased with the value of g, compared with the Earth's gravitational field. For instance, the fluid pressure of osteocytes and the von Mises stress of bone matrix near lacunae decreased by 31.3% and 99.9%, respectively, in microgravity. Under static loading, only about 16.7% of osteocytes in microgravity and 58.3% of osteocytes in the Earth's gravitational field could reach the fluid shear stress threshold of biological reactions in cell culture experiments. Compared with the Earth's gravitational field, the pressure gradient inside osteocytes severely decreased in microgravity. Conclusion: The mechanical microenvironment of osteocytes in microgravity might cause significant changes in the mechanical microenvironment of osteocytes, which may lead to disuse osteoporosis in astronauts. [ABSTRACT FROM AUTHOR]

Although seabird ecologists intuitively understand the importance of geographical features to nest site selection, this work is the first attempt to systematically characterize these features, and establish their relative importance to different seabird species. Keywords: biodiversity; geodiversity; geomorphology; multi-scale analysis; nesting selection; seabirds EN biodiversity geodiversity geomorphology multi-scale analysis nesting selection seabirds 1 4 4 01/05/22 20220101 NES 220101 The importance of habitats to animal, and, in particular, bird conservation, has long been recognized (Martin et al. 2007, Yorio 2009, Oppel 2018). Gannets have predominantly open nests regardless of land mass or slope type; Fulmars are the most complete generalists, exploiting all nest site, slope, and land mass types. In contrast, the equally vulnerable Guillemot and Razorbill achieve the same result using either of two alternative strategies: enclosed nests on small islands in the absence of vertical cliffs, or open nests on vertical cliff faces for all land mass types. [Extracted from the article]

Enhancing biodiversity‐based ecosystem services can generate win–win opportunities for conservation and agricultural production. Pollination and pest control are two essential agricultural services provided by mobile organisms, many depending on native vegetation networks beyond the farm scale. Many studies have evaluated the effects of landscape changes on such services at small scales. However, several landscape management policies (e.g. selection of conservation sites) and associated funding allocation occur at much larger spatial scales (e.g. state or regional level). Therefore, it is essential to understand whether the links between landscape, ecosystem services and crop yields are robust across broad and heterogeneous regional conditions.Here, we used data from 610 Brazilian municipalities within the Atlantic Forest region (~50 Mha) and show that forest is a crucial factor affecting coffee yields, regardless of regional variations in soil, climate and management practices. We found forest cover surrounding coffee fields was better at predicting coffee yields than forest cover at the municipality level. Moreover, the positive effect of forest cover on coffee yields was stronger for Coffea canephora, the species with higher pollinator dependence, than for Coffea arabica. Overall, coffee yields were highest when they were near to forest fragments, mostly in landscapes with intermediate to high forest cover (>20%), above the biodiversity extinction threshold.Coffee cover was the most relevant management practice associated with coffee yield prediction. An increase in crop area was associated with a higher yield, but mostly in high forest covers municipalities. Other localized management practices like irrigation, pesticide use, organic manure and honeybee density had little importance in predicting coffee yields than landscape structure parameters. Neither the climatic or topographic variables were as relevant as forest cover at predicting coffee yields.Synthesis and application. Our work provides evidence that landscape relationships with ecosystem service provision are consistent across regions with different agricultural practices and environmental conditions. These results provide a way in which landscape management can articulate small landscape management with regional conservation goals. Policies directed towards increasing landscape interspersion of coffee fields with forest remnants favour spillover process, and can thus benefit the provision of biodiversity‐based ecosystem services, increasing agricultural productivity. Such interventions can generate win–win situations favouring biodiversity conservation and increased crop yields across large regions. [ABSTRACT FROM AUTHOR]