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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]

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]

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]

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]

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]

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]

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.

Timely and effective environmental sustainability assessment is critical for promoting sustainable development in the transnational area of Changbai Mountain (TACM). However, due to the limitations of the existing environmental degradation index, the environmental sustainability in this transnational area was still not comprehensively evaluated. In this study, the main objectives were to construct a comprehensive environmental sustainability index (CESI) by integrating four environmental elements of ecological, soil, water as well as air, and evaluate the environmental sustainability of TACM from 2000 to 2020. The results indicated that the environmental sustainability of the TACM exhibited a significantly degraded trend. The area of degraded environmental sustainability (73147.16 km2) accounted for 44.35 % of the total area of TACM. In the three subregions, the environmental sustainability showed decreasing trends in the subregions of China and the Democratic People’s Republic of Korea (DPRK), whereas showed an upward trend in Russia. In particular, the DPRK’s side exhibited the most significant degradation, where the area of reduced environmental sustainability (40405.60 km2) was 1.25 times and 129.98 times larger than that on the Chinese and Russian sides, respectively. Forest loss and cropland expansion were the primary reasons for the degradation of environmental sustainability in the TACM. The major findings of this study could contribute to our understanding of environmental sustainability and will be helpful to promote the sustainable development in this transnational area as well as Northeast Asia.

A meticulous investigation into turbulent boundary layers over an isothermally heated flat plate with zero pressure gradient has been conducted. Eight distinct turbulence models, including algebraic yPlus, standard k-ω, standard k-ε, length-velocity, Spalart-Allmaras, low Reynolds number k-ε, shear stress transport, and v2-f turbulence models, are carefully chosen for numerical simulation alongside thermal energy and Reynolds-Averaged Navier-Stokes equations. A comparative analysis has determined that the Spalart-Allmaras model exhibits remarkable agreement with the results from direct numerical simulation, making it a reliable tool for predicting turbulent heat transfer and fluid flow, particularly at higher Prandtl and Reynolds numbers. Subsequently, a multi-scale investigation employs a comprehensive four-layer structure scheme and encompasses various momentum thickness Reynolds numbers of 1432, 2522, and 4000, and Prandtl numbers of 0.71, 2, and 5. The subsequent investigation reveals the governing non-dimensional numbers' substantial impact on the distribution and magnitude of mean thermal and flow characteristics. Notably, the scaling of mean thermal and momentum fields discloses the existence of a meso or intermediate layer characterized by a logarithmic nature unique to itself. The multi-scaling analysis of the flow field demonstrates greater conformity with the selected scaling variables primarily relying on the Reynolds number. Furthermore, the scaling of the energy field yields compelling outcomes within the inner and intermediate layers. However, according to the four-layer theory, minor discrepancies are observed in the outer layer when using the current scaling.

Recently, human action recognition has gained widespread use in fields such as human–robot interaction, healthcare, and sports. With the popularity of wearable devices, we can easily access sensor data of human actions for human action recognition. However, extracting spatio-temporal motion patterns from sensor data and capturing fine-grained action processes remain a challenge. To address this problem, we proposed a novel hierarchical multi-scale adaptive Conv-LSTM network structure called HMA Conv-LSTM. The spatial information of sensor signals is extracted by hierarchical multi-scale convolution with finer-grained features, and the multi-channel features are fused by adaptive channel feature fusion to retain important information and improve the efficiency of the model. The dynamic channel-selection-LSTM based on the attention mechanism captures the temporal context information and long-term dependence of the sensor signals. Experimental results show that the proposed model achieves Macro F1-scores of 0.68, 0.91, 0.53, and 0.96 on four public datasets: Opportunity, PAMAP2, USC-HAD, and Skoda, respectively. Our model demonstrates competitive performance when compared to several state-of-the-art approaches. [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]

Advancement in weaving techniques has made woven composites used in the diverse fields of aerospace, automobile, and many more. In the past few decades, textile composites have been considered the best materials, where most of the studies are focused on plain woven composites. The designing of these structures made up of plain-woven composites associated with discontinuities in the form of cut-outs may lead to the failure of the structure. The collapse in the structure will be due to stress concentration or popularly known as stress raisers. Therefore, this research proposes a numerical study based on finite element analysis (FEA) using a multi-scale modeling approach for a three-dimensional (3D) plain-woven composite 3D shell with varied cut-out shapes. The analysis is performed to comprehend the result of fiber orientation on the mechanical behavior of 3D woven composites having circular and elliptical cut-outs. This research proposes a ply stacking sequence that significantly reduces stress concentration primarily at interruptions in the structure. It is concluded that combining different ply layers having fiber orientation of 4 5 ∘ in a multilayer composite 3D shell reduces stress concentration around the periphery of circular or elliptical cut-outs. [ABSTRACT FROM AUTHOR]

Abstract 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. Graphical Abstract

Epithelial-to-mesenchymal transition (EMT) plays an important role in many biological processes during development and cancer. The advent of single-cell transcriptome sequencing techniques allows the dissection of dynamical details underlying EMT with unprecedented resolution. Despite several single-cell data analysis on EMT, how cell communicates and regulates dynamics along the EMT trajectory remains elusive. Using single-cell transcriptomic datasets, here we infer the cell-cell communications and the multilayer gene-gene regulation networks to analyze and visualize the complex cellular crosstalk and the underlying gene regulatory dynamics along EMT. Combining with trajectory analysis, our approach reveals the existence of multiple intermediate cell states (ICSs) with hybrid epithelial and mesenchymal features. Analyses on the time-series datasets from cancer cell lines with different inducing factors show that the induced EMTs are context-specific: the EMT induced by transforming growth factor B1 (TGFB1) is synchronous, whereas the EMTs induced by epidermal growth factor and tumor necrosis factor are asynchronous, and the responses of TGF-β pathway in terms of gene expression regulations are heterogeneous under different treatments or among various cell states. Meanwhile, network topology analysis suggests that the ICSs during EMT serve as the signaling in cellular communication under different conditions. Interestingly, our analysis of a mouse skin squamous cell carcinoma dataset also suggests regardless of the significant discrepancy in concrete genes between in vitro and in vivo EMT systems, the ICSs play dominant role in the TGF-β signaling crosstalk. Overall, our approach reveals the multiscale mechanisms coupling cell-cell communications and gene-gene regulations responsible for complex cell-state transitions.

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.

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.

Congestive heart failure (CHF) and arrhythmia (ARR) are common heart diseases that affect a growing population of patients worldwide. In this work, we employ multi-scale analysis (MSA) to estimate generalized Hurst exponent (GHE) from electrocardiogram (ECG) records under CHF, ARR, and normal sinus rhythm (NSR). As a result, fractal correlations in short and long fluctuations of CHF, ARR, and NSR are measured. Then, a set of six statistical tests are applied to GHE estimates to check how they are different at each time scale between two different ECG conditions. Particularly, the goal is verify if two different ECG conditions can be statistically differentiated by short or by long fluctuations. The battery of statistical tests includes Kolmogorov–Smirnov, Kruskal–Wallis, Wilcoxon rank sum, Student t-test, Ansari–Bradley, and F-test. The results from MSA show evidence that CHF, ARR, and NSR all exhibit multi-fractal properties. Besides, the results from statistical tests revealed that long fluctuations statistically differentiate CHF and ARR, ARR and NSR, and CHF and NSR. Therefore, long fluctuations account most for the characterization of CHF, ARR, and NSR. Our findings are helpful to better understand the mechanics of heart disease and normal heart beats, and also promising to eventually designing computer-aided diagnosis systems for CHF and ARR classification.

Allee effect, as one of the research hotspots in ecology and conservation biology, extremely easily affects the distribution of population in space, especially for the low-density population. In the ecosystem, the spatial distribution of predator is more vulnerable to Allee effect since the density of predator is always smaller than that of prey, but the relevant attention paid to it is few. In this paper, we establish and analyze a spatial predator–prey system incorporating Allee effect on predator population. Theoretically, we use multi-scale analysis to derive the amplitude equation and obtain the different Turing patterns under different intensities of Allee effect. Numerically, we verify the theoretical results. We also find that the growth rate of predator is significantly inhibited and the time of reaching equilibrium tends to be slower with the increase of Allee effect intensity within certain limits. More interestingly, it is discovered that the predator under Allee effect in a certain extent is more stable although Allee effect on predator may lead to more complex dynamic behaviors. Our results help better understand species invasion and nature conservation in ecology.

As a deployable mechanism and structural integrated component, the shape memory polymer composites (SMPCs) will inevitably be damaged during folding and storage. However, damage effects on the shape memory behaviour of the SMPCs lack thoroughly investigations. This article aims to research the entire shape memory process of the SMPCs with the consideration of progressive damage. Firstly, uniaxial tensile tests and DMA tests with three-point bending mode are conducted to obtain basic mechanical properties and shape memory capabilities of the SMPCs. A 5 cm-long SMPCs thin-wall tube is subsequently applied in radial flattening and shape recovery experiments. The thin-wall tube's damage state and shape recovery process are investigated and evaluated in detail. The Von-Mises and 3-D Hashin's failure criteria are applied on damage evaluation of pure polymer matrix and yarns, respectively. As a result, a homogenized stiffness degradation model was developed through multi-scale analysis method. The homogenized progressive damage model is combined with the shape memory constitutive equation based on phase transition theory to form a simplified algorithm to accurately describe the mechanical behaviour of the SMPCs in the shape memory process. The algorithm is implemented by user subroutine UMAT of finite element analysis packages Abaqus. It is verified that the proposed algorithm could accurately describe the mechanical behaviour for the SMPCs in entire shape memory process. [ABSTRACT FROM AUTHOR]

To address the inverse problem of thermal growth oxide (TGO) thickness in thermal barrier coatings (TBCs), a novel multi-scale analysis (MSA) method based on terahertz time-domain spectroscopy (THz-TDS) is introduced. The proposed method involves a MSA technique based on four wavelet basis functions (db4, sym3, haar, coif3). Informative feature parameters characterizing the TGO thickness were extracted by performing continuous wavelet transform (CWT) and max-pooling operations on representative wavelet coefficients. Subsequently, multi-linear regression and machine learning regression models were employed to predict and assess the wavelet feature parameters. Experimental results revealed a discernible trend in the wavelet feature parameters obtained through CWT and max-pooling in the MSA, wherein the visual representation of TGO thickness initially increases and then gradually decreases. Significant variations in these feature parameters with changes in both thickness and scale enabled the effective inversion of TGO thickness. Building upon this, multi-linear regression and machine learning regression prediction were performed using multi-scale data based on four wavelet basis functions. Partial-scale data were selected for multi-linear regression, while full-scale data were selected for machine learning regression. Both methods demonstrated high accuracy prediction performance. In particular, the haar wavelet basis function exhibited excellent predictive performance, as evidenced by regression coefficients of 0.9763 and 0.9840, further confirming the validity of MSA. Hence, this study effectively presents a feasible method for the inversion problem of TGO thickness, and the analysis confirms the promising application potential of terahertz time-domain spectroscopy's multi-scale analysis in the field of TBCs evaluation. These findings provide valuable insights for further reference. [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 persistent high deforestation rate and fragmentation of the Amazon forests are the main threats to their biodiversity. To anticipate and mitigate these threats, it is important to understand and predict how species respond to the rapidly changing landscape. The short-eared dog Atelocynus microtis is the only Amazon-endemic canid and one of the most understudied wild dogs worldwide. We investigated short-eared dog habitat associations on two spatial scales. First, we used the largest record database ever compiled for short-eared dogs in combination with species distribution models to map species habitat suitability, estimate its distribution range and predict shifts in species distribution in response to predicted deforestation across the entire Amazon (regional scale). Second, we used systematic camera trap surveys and occupancy models to investigate how forest cover and forest fragmentation affect the space use of this species in the Southern Brazilian Amazon (local scale). Species distribution models suggested that the short-eared dog potentially occurs over an extensive and continuous area, through most of the Amazon region south of the Amazon River. However, approximately 30% of the short-eared dog's current distribution is expected to be lost or suffer sharp declines in habitat suitability by 2027 (within three generations) due to forest loss. This proportion might reach 40% of the species distribution in unprotected areas and exceed 60% in some interfluves (i.e. portions of land separated by large rivers) of the Amazon basin. Our local-scale analysis indicated that the presence of forest positively affected short-eared dog space use, while the density of forest edges had a negative effect. Beyond shedding light on the ecology of the short-eared dog and refining its distribution range, our results stress that forest loss poses a serious threat to the conservation of the species in a short time frame. Hence, we propose a re-assessment of the short-eared dog's current IUCN Red List status (Near Threatened) based on findings presented here. Our study exemplifies how data can be integrated across sources and modelling procedures to improve our knowledge of relatively understudied species.

Forward-looking sonar (FLS) images present various challenges in interpretation, recognition, and segmentation due to limitations like low resolution, speckle noise, and low contrast, making them more complex than optical images. Existing methods often focus solely on denoising or enhancement, neglecting the potential benefits of utilizing multi-scale features to create an integrated image processing approach. This paper introduces the Laplacian pyramid-based multi-scale denoising and enhancement (LPMsDE) method tailored for FLS images. The proposed method begins by presenting a novel multiplicative speckle noise model, grounded in the Gaussian distribution, specifically designed for FLS images. Next, the Laplacian pyramid decomposition is utilized to estimate noise variance, with an modified adaptive local filter. Lastly, a combination of the Laplacian pyramid framework, the enhanced adaptive local filter, and Contrast-Limited Histogram Equalization (CLHE) is employed to denoise and enhance images at different resolution levels. Through comprehensive experiments conducted on both simulated and real sonar images, the effectiveness of the LPMsDE method is demonstrated. It surpasses other denoising and enhancement techniques, as evidenced by superior scores in Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Contrast-to-Noise Ratio (CNR), Equivalent Number of Looks (ENL), Natural Image Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE).

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]

The trade-offs and synergies reveal the profit and loss relationship between ecosystem services, which is of great significance to the sustainable development of natural resources. The ecosystem services in Jiangxi Province, such as net primary productivity (NPP), soil conservation (SC) and water yield (WY) during 2000–2020, were estimated in this study. The correlation coefficient was adopted to analyze the trade-offs and synergies between the three ecosystem services by static space correlation and dynamic space correlation from such perspectives as Watershed, county and grid. Moreover, the influence of the three ecosystem services and the relations between them were explored from four aspects: landform, NDVI, accumulated temperature and precipitation. The results showed that the ecological environment quality in Jiangxi Province was improved and that the distribution of ecosystem services had significant regional characteristics. In the static analysis, ecosystem services at all scales were remarkably synergistic, and synergies weakened rapidly and even turned into trade-offs as the scale decreased. In the dynamic analysis, ecosystem services at all scales were mainly synergistic; the proportion of significant samples was much lower than that in the static analysis, the degree of trade-offs/synergies decreased with the decrease in scale, and the decrease was smaller than that in the static analysis. The major constraints for SC were landform and NDVI. The main constraint for WY was precipitation, and that for NPP was NDVI. Affected by various factors, NPP and SC were stably synergistic, NPP and WY were in a stable trade-off relationship, and the relationship between SC and WY was unstable. The trade-offs and synergies changed with factors and zoning. [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 Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present a computational method, BASS, that enables multi-scale and multi-sample analysis for single-cell resolution spatial transcriptomics. BASS performs cell type clustering at the single-cell scale and spatial domain detection at the tissue regional scale, with the two tasks carried out simultaneously within a Bayesian hierarchical modeling framework. We illustrate the benefits of BASS through comprehensive simulations and applications to three datasets. The substantial power gain brought by BASS allows us to reveal accurate transcriptomic and cellular landscape in both cortex and hypothalamus.

Wetlands serve as a critical habitat for many plants and animals. However, with urban expansion and other processes, the wetlands in China are lost at an alarming rate in recent decades. This study quantifies the pace of wetland loss in the past two decades and investigates the hidden mechanisms of wetland loss via a proposed innovative grid-based geographically weighted regression computing unified device architecture (Grid-GWR-CUDA) method. Also assessed in this study is the relative importance of four natural factors (precipitation, digital elevation model (DEM), slope, evaporation), and four human related factors (distance to city, distance to roads, population, and gross domestic product (GDP)) in wetland loss at both the national and regional levels. The importance of some critical factors is compared across different regions. The results indicated that GDP, population size and DEM are the three most significant factors impacting on the rate of wetland loss at the national level. They are still the most influential factors for most regions at the sub-national level while precipitation is an important factor only in the eastern regions. Therefore, the main causes of wetland loss in China are attributed to human socioeconomic activities. Compared with the traditional geographically weighted regression (GWR) method, the proposed Grid-GWR-CUDA method is innovative enough to handle large data volumes efficiently while retaining all data samples at a fine (e.g., 30 m) spatial resolution because the computational efficiency is significantly improved via graphics processing unit (GPU) acceleration. It improves the accuracy of regression by several folds over traditional ordinary least square and GWR. It has the potential to predict future wetland loss if supplied with the latest land cover data.

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]