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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]

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles. [ABSTRACT FROM AUTHOR]

The current goals for implementing the hydrogen economy have highlighted a need to further optimize water‐splitting technologies for clean hydrogen production. Proton exchange membrane water electrolysis (PEMWE) is a leading technology, but further optimizations of anode materials including the porous transport layer (PTL) and the adjacent catalyst layer (CL) are required to increase overall cell performance and reduce cost. This literature review describes advances in PTL development and characterization, highlighting early PTL characterization work and most common methods including capillary flow porometry and mercury intrusion porometry, optical imaging, neutron and x‐ray radiography, and x‐ray computed tomography. The article also discusses PTL protective coatings and their characterizations, focusing on platinum group metal (PGM)‐based coatings, alternative non‐PGM‐based coatings, post‐treated PTLs, and investigations into thin PGM‐based coatings. Furthermore, it highlights the integration of the PTL and the adjacent CL along with associated characterization challenges. Lastly, this review discusses future developments in the characterization needed to improve PEMWE's performance and long‐term durability are discussed. [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]

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

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]

The Direct FE2 method is a recently developed concurrent multi-scale simulation approach with a monolithic solution scheme, where the macroscopic and microscopic models are solved in a unit iteration process. The conventional Direct FE2 using quadrilateral element has some limitations in determining the failure state of a macro-element. In this work, we develop a new Direct FE2 method based on the Single-integration Point triangle element (Direct SP-FE2) and apply it to simulate the micro-crack-induced fracture problems for the first time. The cohesive element is inserted into a representative volume element (RVE), so that the failure process of a macro-element can be determined by the micro-crack propagation on RVE. Accordingly, the relationship between macro-deformations and microstructures can be established directly, thus avoiding complex derivation and utilization of a multi-scale damage constitutive model. In Direct SP-FE2, the failure state of a macro-element can be directly predicted by the RVE at the single-integration point inside, avoiding the uncertainty associated with predictions by multiple RVEs. As a result, compared to the conventional Direct FE2 with a quadrilateral element, the Direct SP-FE2 shows advantages in capturing complex geometric boundaries and predicting the damage evolution process and failure state more accurately. Various numerical examples are conducted to comprehensively validate the effectiveness of the present method in describing the influence of micro-cracks on macro-structure deformations. For the macro-damage behaviors, the simulation results by the Direct SP-FE2 show good agreement with the direct numerical simulation (DNS) results of the full FE model at a significantly lower computational time. With the developed Direct SP-FE2, a variety of complex micro-crack fracture problems in composite materials can be successfully modeled by manipulating the interior RVE structure. [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]

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

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

The Arctic rapidly transforms due to global warming and increased human activities, triggering complex changes at unprecedented speeds that challenge conventional institutional responses. We analyse these changes through the lenses of social, political, and environmental boundaries and investigate their impacts on both inhabitants' livelihoods and the region's political framework. Employing an interdisciplinary approach, we highlight the complexities of understanding the interplay among global, regional, and local dynamics in an era where human and non-human aspects are entwined. Our analysis concentrates on three areas: definition of the Arctic; legal disputes concerning the waters around the Svalbard Archipelago; evolving natural hazards and societal risk perceptions in Longyearbyen. Through these examples, we underscore the intricate nature of social, political, and ecological changes and how they challenge current boundary-making processes. By combining knowledge from different systems and scales, our research reveals the interplay between policy-driven science, science-influenced policy, and performative behaviors in reshaping today's Arctic borders and boundaries. We particularly emphasize how climate change is challenging borders and advocate for a departure from static definitions, towards the formulation of environmentally conscious, socially just, and politically viable policies, acknowledging the new biophysical realities of the Anthropocene. [ABSTRACT FROM AUTHOR]

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.

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.

Abstract The current goals for implementing the hydrogen economy have highlighted a need to further optimize water‐splitting technologies for clean hydrogen production. Proton exchange membrane water electrolysis (PEMWE) is a leading technology, but further optimizations of anode materials including the porous transport layer (PTL) and the adjacent catalyst layer (CL) are required to increase overall cell performance and reduce cost. This literature review describes advances in PTL development and characterization, highlighting early PTL characterization work and most common methods including capillary flow porometry and mercury intrusion porometry, optical imaging, neutron and x‐ray radiography, and x‐ray computed tomography. The article also discusses PTL protective coatings and their characterizations, focusing on platinum group metal (PGM)‐based coatings, alternative non‐PGM‐based coatings, post‐treated PTLs, and investigations into thin PGM‐based coatings. Furthermore, it highlights the integration of the PTL and the adjacent CL along with associated characterization challenges. Lastly, this review discusses future developments in the characterization needed to improve PEMWE's performance and long‐term durability are discussed.

Understanding thermal shock behaviors and related damage mechanism of needled C/C–SiC composites is of much significance to their engineering application. In this study, a multi-scale framework is developed to characterize the degeneration of mechanical properties and damage accumulation in the needled composites comprehensively across different scales under cyclic thermal shock. Thermal shock temperature and thermal shock cycles are involved in the simulation, and the outcomes are verified with experiments performed in an inert atmosphere. The results show that the strength of C/C–SiC composites decreases continuously as the test temperature and thermal shock cycles increase. Meanwhile, the fracture of the material mainly occurs in the short-fiber felt. The damage initiates in the short-fiber felt near the contact area of the non-woven (NW) fiber tow and the needle-punched (NP) fiber tow when the test temperature is low (about 900 °C), and gets severe as temperature increases. With the increase of thermal shock cycles at 1700 °C, the fracture of the composites is more significant, and spreads from inside to the outer surface. Based on the multi-scale simulation and the microstructure of the composites after TSR tests, the primary damage mechanisms in the short-fiber felt are identified as ceramic-matrix damage and interface debonding.

To enhance the thermal stability of L-PBF Al-Si-Mg alloy, Fe-based metallic glass powder was mixed with Al-Si-Mg-Zr powder to fabricate a novel alloy via L-PBF. The study systematically investigated the effects of process parameters on processability and the impact of stress-relief annealing (300[Formula: see text]C–2h) on the microstructure and mechanical properties. Results showed that metallic glass addition improved surface quality. Plastic deformation was analysed using finite element and molecular dynamics methods. The as-built alloy had a cellular substructure with [Formula: see text]Zr, Si, AlFeMgSi, and AlFeSi phases. Si nanoparticles and stacking faults were observed in α-Al cells. The as-built samples had yield strength (YS) of 249±4 MPa, ultimate tensile strength (UTS) of 434±6 MPa, and 6.3±0.3% elongation. Post-annealing, the alloy retained high strength and plasticity with YS of 255±3 MPa, UTS of 449±15 MPa, and 7.2±0.3% elongation, outperforming similar L-PBF Al-Si and Al-Si-Mg alloys.

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]

In this paper, we establish quantitative Green's function estimates for some higher-dimensional lattice quasi-periodic (QP) Schrödinger operators. The resonances in the estimates can be described via a pair of symmetric zeros of certain functions and the estimates apply to the sub-exponential-type non-resonance conditions. As the application of quantitative Green's function estimates, we prove both the arithmetic version of Anderson localization and the finite volume version of ( (1 2 −) )-Hölder continuity of the integrated density of states (IDS) for such QP Schrödinger operators. This gives an affirmative answer to Bourgain's problem in Bourgain (2000). [ABSTRACT FROM AUTHOR]

Freeze–thaw (F–T) cycle-induced mechanical deterioration of mudstone layers in slopes is a critical challenge for the stability of slopes located in cold-regions open pit mines. Indoor F–T cycle experiments were initially conducted on mudstone samples obtained from the North Slope of the Fushun West Open Pit Mine. Furthermore, a constitutive model for F–T damage was developed using Weibull distribution fracture mechanics. After that, a Voronoi tessellation-generating program and a program for inserting cohesive elements between tessellations were developed to establish a multi-scale thermo-mechanical coupling analysis platform in ABAQUS software. Finally, the damage constitutive model was integrated into Voronoi tessellations to simulate the mineral matrix. The research findings revealed that F–T cycles induced substantial damage to mudstone. Moreover, with an increase in the number of F–T cycles, the damage factors associated with each parameter significantly increased. Multi-scale analysis results indicated that for higher numbers of F–T cycles, samples may not necessarily develop cracks along initial flaws, but could instead be cracked in the matrix phase due to F–T damage. The F–T cycle exhibited a destructive effect on the stability of slopes in cold regions. Therefore, it is essential to consider the effect of F–T damage in the stability analysis and protection design of open-pit mine slopes in cold regions. [ABSTRACT FROM AUTHOR]

Additive manufacturing processes have attracted broad attention in the last decades since the related freedom of design allows the manufacturing of parts with unique microstructures and unprecedented complexity in shape. Focusing on the properties of additively manufactured parts, major efforts are made to elaborate process-microstructure relationships. For instance, the inevitable thermal cycling within the process plays a significant role in microstructural evolution. Various driving forces contribute to the final grain size, boundary character, residual stress state, etc. In the present study, the properties of commercially pure iron processed on three different routes, i.e., hot rolling as a reference, electron powder bed fusion, and laser powder bed fusion, using different raw materials as well as process conditions, are compared. The manufacturing of the specimens led to five distinct microstructures, which differ significantly in terms of microstructural features and mechanical responses. Using optical and electron microscopy as well as transmission electron microscopy, the built specimens were explored in various states of a tensile test in order to reveal the microstructural evolution in the course of quasistatic loading. The grain size is found to be most influential in enhancing the material's strength. Furthermore, substructures, i.e., low-angle grain boundaries, within the grains play an important role in terms of the homogeneity of strain distribution. On the contrary, high-angle grain boundaries are found to be regions of strain localization. In summary, a holistic macro-meso-micro-nano investigation is performed to evaluate the behavior of these specific microstructures. [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]

Abstract In view of the serious color and definition distortion in the process of the traditional image fusion, this study proposes a Haar-like multi-scale analysis model, in which Haar wavelet has been modified and used for the medical image fusion to obtain even better results. Firstly, when the improved Haar wavelet basis function is translated, inner product and down-sampled with each band of the original image, the band is decomposed into four sub-images containing one low-frequency subdomain and three high-frequency subdomains. Secondly, the different fusion rules are applied in the low-frequency domain and the high-frequency domains to get the low-frequency sub-image and the high-frequency sub-images in each band. The four new sub-frequency domains are inverse-decomposed to reconstruct each new band. The study configures and synthesizes these new bands to produce a fusion image. Lastly, the two groups of the medical images are used for experimental simulation. The Experimental results are analyzed and compared with those of other fusion methods. It can be found the fusion method proposed in the study obtain the superior effects in the spatial definition and the color depth feature, especially in color criteria such as OP, SpD, CR and SSIM, comparing with the other methods.

Infrared and visible image fusion (IVIF) aims to generate a fused image with both salient target and rich textures from two different complementary modality images. To better integrate valuable edge information into the fused image, we first propose a novel two-stream network based on Auto-Encoder (AE) framework, which extracts deep hierarchical detail information at coarse scale from base stream by multi-level wavelet decomposition progressively and incorporates them into detail stream for information compensation. The aggregation of edge information ranging from coarse to fine facilitates a more comprehensive representation of contours and textures. Then, we propose a new feature fusion strategy, termed as Structural Feature Map Decomposition (SFMD). The first step is to decompose local patches of feature map with each modality into three independent components by Structural Patch Decomposition (SPD). In the second step, appropriate fusion rules are carefully designed for each component and the fused patch can be derived by inverse SPD. Our extensive experiments on several benchmark datasets show that our method outperforms seven compared state-of-the-art methods, especially in human visual perception.

Abstract 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.

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles.

In view of the serious color and definition distortion in the process of the traditional image fusion, this study proposes a Haar-like multi-scale analysis model, in which Haar wavelet has been modified and used for the medical image fusion to obtain even better results. Firstly, when the improved Haar wavelet basis function is translated, inner product and down-sampled with each band of the original image, the band is decomposed into four sub-images containing one low-frequency subdomain and three high-frequency subdomains. Secondly, the different fusion rules are applied in the low-frequency domain and the high-frequency domains to get the low-frequency sub-image and the high-frequency sub-images in each band. The four new sub-frequency domains are inverse-decomposed to reconstruct each new band. The study configures and synthesizes these new bands to produce a fusion image. Lastly, the two groups of the medical images are used for experimental simulation. The Experimental results are analyzed and compared with those of other fusion methods. It can be found the fusion method proposed in the study obtain the superior effects in the spatial definition and the color depth feature, especially in color criteria such as OP, SpD, CR and SSIM, comparing with the other methods. [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]

Complex systems often produce multifractal signals defined by stationary increments that exhibit power-law scaling properties. The Legendre transform of the domain-dependent scaling function that defines the power law is known as the multifractal spectrum. The multifractal spectrum can also be defined by a power-series expansion of the scaling function and in practice the first two leading coefficients of that series are estimated from the discrete wavelet transform of the signal. To quantify, validate, and compare simulations of complex systems with data collected empirically from the actual system, practitioners require methods for approximating the variance associated with estimates of these coefficients. In this work, we generalize a previously developed semi-parametric statistical model for the values extracted from a discrete multi-scale wavelet transform to include both within-scale and between-scale covariance dependencies. We employ multiplicative cascades to simulate multifractals with known parameters to illustrate the necessity for this generalization and to test the precision of our improved model. The combined within- and between-scale model of covariance results in a more accurate estimate of the expected variance of the coefficients extracted from an empirical data set. [ABSTRACT FROM AUTHOR]

Studying spatiotemporal water quality characteristics and their correlation with land use/land cover (LULC) patterns is essential for discerning the origins of various pollution sources and for informing strategic land use planning, which, in turn, requires a comprehensive analysis of spatiotemporal water quality data to comprehend how surface water quality evolves across different time and space dimensions. In this study, we compared catchment, riparian, and reach scale models to assess the effect of LULC on WQ. Using various multivariate techniques, a 14-year dataset of 20 WQ variables from 20 monitoring stations (67,200 observations) is studied along the Middle Ganga Basin (MGB). Based on the similarity and dissimilarity of WQPs, the K-means clustering algorithm classified the 20 monitoring stations into four clusters. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs > 0.70, the variables EC, pH, Temp, TDS, NO2 + NO3, P-Tot, BOD, COD, and DO have been identified as dominant pollution sources. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the wet season but not during the dry season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land, and built-up area degrade WQ. Besides that, the findings suggest that the relationship between WQPs and LULC differs at different scales. The stacked ensemble regression (SER) model is applied to understand the model's predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than the watershed and reach scales. [ABSTRACT FROM AUTHOR]

Currently, research on the mechanical properties of corroded steel wire is primarily conducted through corrosion testing, with a lack of in-depth theoretical investigations into the underlying mechanisms. Addressing this research gap, this study employs molecular dynamics (MD) and finite element theory to explore the corrosion process, micro- and macro-mechanical properties of corroded steel wire, and the fracture mechanisms after corrosion. The study achieves a coupling of micro- and macro-scale analyses. Firstly, a molecular dynamics model is established based on the chemical composition of high-strength steel wire, and a novel mixed potential function derived from quantum mechanics is employed to analyze the fracture mechanisms of corroded steel wire. Secondly, a simplified model of randomly corroded pits in steel wire is developed using Python in Abaqus, based on experimental measurements. Finally, the macroscopic fracture process and fracture strength of corroded steel wire are analyzed based on the results from molecular dynamics simulations. These results are then compared to experimental data to validate the accuracy of the theoretical analysis.

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.

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.

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 R2 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 (R2 = 0.43); at intermediate extents, landscape structure was related to the species richness of large trees (R2 = 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.

High-resolution biological tissue slice images provide opportunities for more precise observation and analysis of histopathological features. These images typically consist of enclosed contours, and Bendlets transform is an effective means to approximate such structures. In this study, we leverage Bendlet transform to incorporate multi-level image data, thereby establishing a multi-scale pyramid feature set. This approach aids in achieving a sparser representation of image textures and structures. We apply Bendlets transform to extract multi-frequency wavelet subband features and formulate structured dictionaries for both high and low resolutions based on these features. After creating these dictionaries, we compute sparse representation coefficients using the low-resolution dictionary and subsequently integrate them with the high-resolution dictionary to generate high-resolution subbands. Ultimately, through the process of inverse wavelet transformation, we completed the reconstruction of high-resolution images. This method not only significantly enhances the restoration of image details and clarity but also effectively preserves the overall structural characteristics of the original images.