Search

Atlantic salmon aquaculture companies in British Columbia (BC) must report fish health events to Fisheries and Oceans Canada (DFO) as part of their licensing conditions. Our study aimed to summarize these fish health events reported by Atlantic salmon sites in BC to identify spatial and spatio‐temporal clusters. We conducted descriptive, retrospective global, and local cluster analyses using Moran's I and scan statistics. Between 2016 and 2022, 265 fish health events were reported. The annual incidence ranged from 5.60 (95% CI: 3.90–7.80) to 6.86 (95% CI: 4.70–9.60) health events per 100 active site‐months. The most common events were yellow mouth (60.75%; 161/265) and salmonid rickettsial septicaemia (SRS) (15.47%; 41/265). The Moran's I index was positive and significant for yellow mouth, SRS, and overall fish health events at different distance bands. Most of the spatial and spatio‐temporal clusters were identified in the west‐central and southwestern parts of Vancouver Island. Our study hypothesizes that management practices, environmental conditions, and water quality parameters may have influenced the increased reporting of fish health events in these regions. Overall, the study highlights the potential of publicly available data for practical risk mapping in understanding the patterns of farmed Atlantic salmon diseases in BC. [ABSTRACT FROM AUTHOR]

While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets. In this work, we depart from the conventional approach of relying on pre-trained 2D diffusion models for testtime optimization of 3D textures. Instead, we focus on the fundamental problem of learning in the UV texture space itself. For the first time, we train a large diffusion model capable of directly generating high-resolution texture maps in a feed-forward manner. To facilitate efficient learning in high-resolution UV spaces, we propose a scalable network architecture that interleaves convolutions on UV maps with attention layers on point clouds. Leveraging this architectural design, we train a 700 million parameter diffusion model that can generate UV texture maps guided by text prompts and single-view images. Once trained, our model naturally supports various extended applications, including text-guided texture inpainting, sparse-view texture completion, and text-driven texture synthesis. The code is available at https://github.com/CVMI-Lab/TEXGen. [ABSTRACT FROM AUTHOR]

Higher-order patterns reveal sequential multistep state transitions, which are usually superior to origin-destination analyses that depict only firstorder geospatial movement patterns. Conventional methods for higher-order movement modeling first construct a directed acyclic graph (DAG) of movements and then extract higher-order patterns from the DAG. However, DAG-based methods rely heavily on identifying movement keypoints, which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban environments. To overcome these limitations, we propose HoLens, a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment. HoLens mainly makes twofold contributions: First, we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity, contextual information, and temporal variability. Second, we developed an interactive visual analytics interface comprising well-established visualization techniques, including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions. Two real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using HoLens. We also demonstrate the feasibility, usability, and effectiveness of our approach through expert interviews with three domain experts. [ABSTRACT FROM AUTHOR]

Maps are fundamental medium to visualize and represent the real word in a simple and philosophical way. The emergence of the big data tide has made a proportion of maps generated from multiple sources, significantly enriching the dimensions and perspectives for understanding the characteristics of the real world. However, a majority of these map datasets remain undiscovered, unacquired and ineffectively used, which arises from the lack of numerous well-labelled benchmark datasets, which are of significance to implement the deep learning techniques into identifying complicated map content. To address this issue, we develop a large-scale benchmark dataset involving well-labelled datasets to employ the state-of-the-art machine intelligence technologies for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. Furthermore, these well-labelled datasets would facilitate map feature detection, map pattern recognition and map content retrieval. We hope our efforts would provide well-labelled data resources for advancing the ability to recognize and discover valuable map content. [ABSTRACT FROM AUTHOR]

Dense reconstruction have been studied for decades in the fields of computer vision and robotics, in which LiDAR and camera are widely used. However, vision-based methods are sensitive to illumination variation and lack direct depth, and LiDAR-based methods are limited by sparse LiDAR measurement and lacking color and texture information. In this paper, we propose a novel 3D reconstruction algorithm based on LiDAR and a monocular camera, which realizes dense reconstruction. In the algorithm, a LiDAR odometry is used to get accurate poses and poses calculated by the odometry module are used in the calculation of depth maps and fusion of depth maps, and then mesh and texture mapping are implemented. In addition, a semantic segmentation network and a depth completion network are used to obtain dense and accurate depth maps. The concept of symmetry is utilized to generate 3D models of objects or scenes; that is, the reconstruction and camera imaging of these objects or scenes are symmetrical. Experimental results on public dataset show that the proposed algorithm achieves higher accuracy, efficiency and completeness than existing methods. [ABSTRACT FROM AUTHOR]

To address the challenge of rapid geometric model development in the digital twin industry, this paper presents a comprehensive pipeline for constructing 3D models from images using monocular vision imaging principles. Firstly, a structure-from-motion (SFM) algorithm generates a 3D point cloud from photographs. The feature detection methods scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE are compared across six datasets, with SIFT proving the most effective (matching rate higher than 0.12). Using K-nearest-neighbor matching and random sample consensus (RANSAC), refined feature point matching and 3D spatial representation are achieved via antipodal geometry. Then, the Poisson surface reconstruction algorithm converts the point cloud into a mesh model. Additionally, texture images are enhanced by leveraging a visual geometry group (VGG) network-based deep learning approach. Content images from a dataset provide geometric contours via higher-level VGG layers, while textures from style images are extracted using the lower-level layers. These are fused to create texture-transferred images, where the image quality assessment (IQA) metrics SSIM and PSNR are used to evaluate texture-enhanced images. Finally, texture mapping integrates the enhanced textures with the mesh model, improving the scene representation with enhanced texture. The method presented in this paper surpassed a LiDAR-based reconstruction approach by 20 % in terms of point cloud density and number of model facets, while the hardware cost was only 1 % of that associated with LiDAR. [ABSTRACT FROM AUTHOR]

Benthic species assemblages are groups of species that co-occur on the seafloor. Linking assemblages to physical environmental features allows for understanding and predicting their spatial distribution. Species identity and abundance are commonly quantified using a taxonomic approach to assess benthic diversity, yet functional traits that describe the behavior, life history, and morphology of a species may be equally or more important. Here, we investigate the biodiversity of five benthic species assemblages in relation to their habitat and environmental conditions in an Ecologically and Biologically Significant Area (EBSA) along Canada's east coast, using both a taxonomic approach and biological traits analysis. Random Forest regression was applied to map spatial patterns of functional and taxonomic diversity metrics, including richness, Shannon index, and Rao's quadratic entropy. We evaluate discrepancies between related taxonomic and trait measures, and the community-weighted mean of trait data was calculated to characterize each assemblage. Taxonomic and functional richness - representing the number of species and the species community volume in the trait space, respectively - showed similar spatial patterns. However, when considering diversity, which also accounts for the relative abundance and differences among species or traits, these patterns diverged. Taxonomically different assemblages exhibited similar trait compositions for two assemblages, indicating potential trait equivalencies, while one assemblage exhibited traits potentially indicating sensitivity to human activity. The taxonomic and functional metrics of richness and diversity were low close to the coast, which could be indicative of disturbance. Consideration of functional metrics can support spatial planning and prioritization for management and conservation efforts by assessing the sensitivity of traits to different stressors. [ABSTRACT FROM AUTHOR]

We introduce a novel framework for surface cutting and flattening, aiming to align the boundary of planar parameterization with a target shape. Diverging from traditional methods focused on minimizing distortion, we intend to also achieve shape similarity between the parameterized mesh and a specific planar target, which is important in some applications of art design and texture mapping. However, with existing methods commonly limited to ellipsoidal surfaces, it still remains a challenge to solve this problem on general surfaces. Our framework models the general case as a joint optimization of cuts and parameterization, guided by a novel metric assessing shape similarity. To circumvent the common issue of local minima, we introduce an extra global seam updating strategy which is guided by the target shape. Experimental results show that our framework not only aligns with previous approaches on ellipsoidal surfaces but also achieves satisfactory results on more complex ones. [ABSTRACT FROM AUTHOR]

Existing facial image shadow removal methods predominantly rely on pre‐extracted facial features. However, these methods often fail to capitalize on the full potential of these features, resorting to simplified utilization. Furthermore, they tend to overlook the importance of low‐frequency information during the extraction of prior features, which can be easily compromised by noises. In our work, we propose a frequency‐aware shadow removal network (FSRNet) for facial image shadow removal, which utilizes the skin color and texture information in the face to help recover illumination in shadow regions. Our FSRNet uses a frequency‐domain image decomposition network to extract the low‐frequency skin color map and high‐frequency texture map from the face images, and applies a color‐texture guided shadow removal network to produce final shadow removal result. Concretely, the designed fourier sparse attention block (FSABlock) can transform images from the spatial domain to the frequency domain and help the network focus on the key information. We also introduce a skin color fusion module (CFModule) and a texture fusion module (TFModule) to enhance the understanding and utilization of color and texture features, promoting high‐quality result without color distortion and detail blurring. Extensive experiments demonstrate the superiority of the proposed method. The code is available at https://github.com/laoxie521/FSRNet. [ABSTRACT FROM AUTHOR]

Restoring the appearance of the model is a crucial step for achieving realistic 3D reconstruction. High‐fidelity textures can also conceal some geometric defects. Since the estimated camera parameters and reconstructed geometry usually contain errors, subsequent texture mapping often suffers from undesirable visual artifacts such as blurring, ghosting, and visual seams. In particular, significant misalignment between the reconstructed model and the registered images will lead to texturing the mesh with inconsistent image regions. However, eliminating various artifacts to generate high‐quality textures remains a challenge. In this paper, we address this issue by designing a texture optimization method to generate seamless and aligned textures for 3D reconstruction. The main idea is to detect misalignment regions between images and geometry and exclude them from texture mapping. To handle the texture holes caused by these excluded regions, a cross‐patch texture hole‐filling method is proposed, which can also synthesize plausible textures for invisible faces. Moreover, for better stitching of the textures from different views, an improved camera pose optimization is present by introducing color adjustment and boundary point sampling. Experimental results show that the proposed method can eliminate the artifacts caused by inaccurate input data robustly and produce high‐quality texture results compared with state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

We investigate spatiotemporal pattern formation in cancer growth using discrete time and space variables. We first introduce the coupled map lattices (CMLs) model and provide a dynamical analysis of its fixed points along with stability results. We then offer parameter criteria for flip, Neimark–Sacker, and Turing bifurcations. In the presence of spatial diffusion, we find that stable homogeneous solutions can experience Turing instability under certain conditions. Numerical simulations reveal a variety of spatiotemporal patterns, including patches, spirals, and numerous other regular and irregular patterns. Compared to previous literature, our discrete model captures more complex and richer nonlinear dynamical behaviors, providing new insights into the formation of complex patterns in spatially extended discrete tumor models. These findings demonstrate the model's ability to capture complex dynamics and offer valuable insights for understanding and treating cancer growth, highlighting its potential applications in biomedical research. [ABSTRACT FROM AUTHOR]

Texture editing is a crucial task in 3D modelling that allows users to automatically manipulate the surface materials of 3D models. However, the inherent complexity of 3D models and the ambiguous text description lead to the challenge of this task. To tackle this challenge, we propose ITEM3D, a Texture Editing Model designed for automatic 3D object editing according to the text Instructions. Leveraging the diffusion models and the differentiable rendering, ITEM3D takes the rendered images as the bridge between text and 3D representation and further optimizes the disentangled texture and environment map. Previous methods adopted the absolute editing direction, namely score distillation sampling (SDS) as the optimization objective, which unfortunately results in noisy appearances and text inconsistencies. To solve the problem caused by the ambiguous text, we introduce a relative editing direction, an optimization objective defined by the noise difference between the source and target texts, to release the semantic ambiguity between the texts and images. Additionally, we gradually adjust the direction during optimization to further address the unexpected deviation in the texture domain. Qualitative and quantitative experiments show that our ITEM3D outperforms the state‐of‐the‐art methods on various 3D objects. We also perform text‐guided relighting to show explicit control over lighting. Our project page: https://shengqiliu1.github.io/ITEM3D/. [ABSTRACT FROM AUTHOR]

Remote sensing allows us to conduct large-scale scientific studies that require extensive mapping and the amalgamation of numerous images. However, owing to variations in radiation, atmospheric conditions, sensor perspectives, and land cover, significant color discrepancies often arise between different images, necessitating color consistency adjustments for effective image mosaicking and applications. Existing methods for color consistency adjustment in remote sensing images struggle with complex one-to-many nonlinear color-mapping relationships, often resulting in texture distortions. To address these challenges, this study proposes a convolutional neural network-based color consistency method for remote sensing cartography that considers both global and local color mapping and texture mapping constrained by the source domain. This method effectively handles complex color-mapping relationships while minimizing texture distortions in the target image. Comparative experiments on remote sensing images from different times, sensors, and resolutions demonstrated that our method achieved superior color consistency, preserved fine texture details, and provided visually appealing outcomes, assisting in generating large-area data products. [ABSTRACT FROM AUTHOR]

The texture mapping technique based on irregular surfaces is widely used in many fields such as film and television, industry and games, etc. In order to adapt to the rapid development in the field of computer graphics and further enhance the uniformity and effectiveness of the texture mapping effect, a triangular mesh simplified texture mapping technique based on the optimized spring-fingertip is proposed. Firstly, a complex two-dimensional graph is established through the spring-fingertip model, which is parameterized and normalized to reduce the deformation of the texture; subsequently, the concept of triangular mesh simplification is introduced to optimize the model timeliness, which replaces the traditional way of folding the edges; finally, the weight of each edge is further analyzed through local curvature calculation. In order to verify the effectiveness of the texture mapping method, simulation and analysis experiments are done, and the experimental results show that the accuracy of the model reaches 99.25%, which is an average improvement of 7.19% relative to the remaining four models. Therefore, the texture mapping model based on optimized triangular mesh effectively improves the realism of the mapping effect and reduces the computational burden of the model. [ABSTRACT FROM AUTHOR]

Mapping skills between different codes to represent numerical information, such as number symbols (i.e., verbal number words and written digits) and non‐symbolic quantities, are important in the development of the concept of number. The aim of the current study is to investigate children's mapping skills by incorporating another numerical code that emerges at early stages in development, finger patterns. Specifically, the study investigates (i) the order in which mapping skills develop and the association with young children's understanding of cardinality; and (ii) whether finger patterns are processed similarly to symbolic codes or rather as non‐symbolic quantities. Preschool children (3‐year‐olds, N = 113, Mage = 40.8 months, SDage = 3.6 months; 4‐year‐olds, N = 103, Mage = 52.9 months, SDage = 3.4 months) both cardinality knowers and subset‐knowers, were presented with twelve tasks that assessed the mappings between number words, Arabic digits, finger patterns, and quantities. The results showed that children's ability to map symbolic numbers precedes the understanding that such symbols reflect quantities, and that children recognize finger patterns above their cardinality knowledge, suggesting that finger patterns are symbolic in essence. Research Highlights: Children are more accurate in mapping between finger patterns and symbols (number words and Arabic digits) than in mapping finger patterns and quantities, indicating that fingers are processed holistically as symbolic codes.Children can map finger patterns to symbols above their corresponding cardinality level even in subset‐knowers.Finger patterns may play a role in the process by which children learn to map symbols to quantities.Fingers patterns' use in the classroom context may be an adequate instructional and diagnostic tool. [ABSTRACT FROM AUTHOR]

We present an automated framework founded on texture transfer, facilitating the substitution of textures in garment images with specified ones for applications in garment design and online presentation. In contrast to previous methodologies, our approach achieves seamless texture transfer from a single image while preserving fold variations and shadow intricacies. Given a garment image and a texture image, we initially extract pixel-aligned features from the garment image and construct a parametric model of the garment through spatial sampling. The mesh structure is subsequently validated employing the Marching Cube algorithm. We then enhance the model quality through mesh optimization using a variational approach. Finally, we optimize parallax mapping to execute the texture transfer from the source texture image. Experimental results convincingly demonstrate the effectiveness of our method in achieving texture transfer in garment images while maintaining the fidelity of folds and shadows. [ABSTRACT FROM AUTHOR]

Industrialization and urbanization have intensified land‐use pressures on agroecosystems. Monitoring cultivated land use intensity (CLUI) is crucial for implementing sustainable agriculture. However, current agroecosystem management in Shandong Province lacks high‐resolution CLUI information. To address this gap, this study measured and analyzed CLUI at a 1‐km scale in Shandong Province from 2018 to 2022, using self‐produced crop maps and the human appropriation of net primary production (HANPP) framework. The spatial autocorrelation model was used to analyze the spatiotemporal pattern and aggregation characteristics of cultivated land use intensity. The influencing factors of CLUI were analyzed using the propensity score matching method, which helps reduce the interference of confounding factors. The results are as follows: (1) The wheat‐maize planting pattern in Shandong Province has remained relatively stable, with a notable trend toward intensified cultivation in the western region. (2) CLUI exhibited notable spatial and temporal heterogeneity, with low and medium values predominantly located in the western region. CLUI increased from 1.13 to 1.24, exceeding the global average of 0.84. (3) CLUI showed significant spatial aggregation characteristics. In 2018, 2020, and 2022, the western region was mainly characterized by high‐high and high‐low types. In 2019 and 2021, it was mainly characterized by the low‐low type, with less prevalence of low‐high type. The center of gravity of high‐high and low‐high types shifted southwest, whereas that of high‐low and low‐low types shifted northeast. (4) Chemical fertilizers, pesticides, and plastic mulch exhibited significant positive correlations with CLUI, whereas temperature and precipitation showed significant negative correlations. Favorable natural conditions can mitigate human interference, leading to lower CLUI. [ABSTRACT FROM AUTHOR]

Summer learning loss is a perennial concern for educators and parents alike. However, researchers have recently questioned whether summer learning loss is just a statistical artifact driven by how achievement is measured across the school year. In this study, we empirically investigated a plausible critique of summer learning loss research, namely that students do not put forth their best effort on the fall test compared with the spring test. While we cannot conclude based on our findings that students do in fact lose ground during the summer, we did not find evidence that seasonal differences in test effort are a main driver of summer learning patterns estimated with MAP Growth assessments. [ABSTRACT FROM AUTHOR]

The application of realistic 3D models in railway engineering survey and design is becoming increasingly widespread, but the distortion phenomenon and texture mapping in the model visualization process are more prominent due to the limitations of oblique photogrammetry field environment, which seriously restricts the actual effect and application scope of realistic 3D models. By converting the real-world 3D model from the commonly used OSGB format to an easily editable OBJ format, and creating a new PBR (Physically Based Rendering) material within its data framework, the overall realism of the model is improved. Finally, visualization rendering is completed within OSG (Open Scene Graph). During the process, a method was proposed to optimize the parameter settings of PBR materials by integrating real-life photos and classifying them according to the characteristics of ground objects to further improve the texture effect of the real scene 3D model. The lighting model within the OSG engine was analyzed, the lighting model application was selected to match the actual situation of railway engineering and the working conditions of the day, and the constraints of the oblique photography working environment on the quality of the final visualization results of the real scene 3D model was overcame without changing the existing data structure. The method has been validated on real-life 3D models of different parts of existing railway engineering, and the results show that the technology can effectively improve the visualization effect and expression realism of real-life 3D models, and has a certain promotion and application value. [ABSTRACT FROM AUTHOR]

The study of evolutionary rates and patterns is the key to understand how natural selection shaped the current and past diversity of phenotypes. Phylogenetic comparative methods offer an array of solutions to undertake this challenging task, and help understanding phenotypic variation in full in most circumstances. However, complex, three-dimensional structures such as the skull and the brain serve disparate goals, and different portions of these phenotypes often fulfil different functions, making it hard to understand which parts truly were recruited by natural selection. In the recent past, we developed tools apt to chart evolutionary rate and patterns directly on three-dimensional shapes, according to their magnitude and direction. Here, we present further developments of these tools, which now allow to restitute the mapping of rates and patterns with full biological realism. The tools are condensed in a new R software package. Evolutionary rates embody the velocity of evolution. Parcellating different velocities across the phenotype is difficult. RRmorph resolves this conundrum by charting evolutionary patterns on 3D shapes, according to their magnitude and direction. [ABSTRACT FROM AUTHOR]

As artificial intelligence technology progresses, deep learning models are increasingly utilized for machine fault classification. However, a significant drawback of current state-of-the-art models is their high computational complexity, rendering them unsuitable for deployment in portable devices. This paper presents a compact fault diagnosis model that integrates a self-attention SqueezeNet architecture with a hybrid texture representation technique utilizing empirical mode decomposition (EMD) and a gammatone spectrogram (GS) filter. In the model, the dominant signal is first isolated from the audio fault signals by discarding lower intrinsic mode functions (IMFs) from EMD, and subsequently, the dominant signals are transformed into 2D texture maps using the GS filter. These generated texture maps feed as input into the modified self-attention SqueezeNet classifier, featuring reduced model width and depth, for training and validation. Different attention modules were tested in the paper, including the self-attention, channel attention, spatial attention, and convolutional block attention module (CBAM). The models were tested on the MIMII and ToyADMOS datasets. The experimental results demonstrated that the self-attention mechanism with SqueezeNet achieved an accuracy of 97% on the previously unseen MIMII and ToyADMOS datasets. Furthermore, the proposed model outperformed the SqueezeNet attention model with other attention mechanisms and state-of-the-art deep architectures, exhibiting a higher precision, recall, and F1-score. Lastly, t-SNE is applied to visualize the features of the self-attention SqueezeNet for different fault classes of both MIMII and ToyADMOS. [ABSTRACT FROM AUTHOR]

How complex 3D tissue shape emerges during animal development remains an important open question in biology and biophysics. Here, we discover a mechanism for 3D epithelial shape change based on active, in-plane cellular events that is analogous to inanimate "shape programmable" materials, which undergo blueprinted 3D shape transformations from in-plane gradients of spontaneous strains. We study eversion of the Drosophila wing disc pouch, when the epithelium transforms from a dome into a curved fold, quantifying 3D tissue shape changes and mapping spatial patterns of cellular behaviors on the evolving geometry using cellular topology. Using a physical model inspired by shape programming, we find that active cell rearrangements are the major contributor to pouch eversion and validate this conclusion using a knockdown of MyoVI, which reduces rearrangements and disrupts morphogenesis. This work shows that shape programming is a mechanism for animal tissue morphogenesis and suggests that patterns in nature could present design strategies for shape-programmable materials. [ABSTRACT FROM AUTHOR]

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning. [ABSTRACT FROM AUTHOR]

3D urban building modeling is a vital foundational step for building Digital Twins and Smart Cities. In response to existing challenges, such as high time costs, complex production processes, and low consistency with real‐world textures in large‐scale 3D urban building modeling methods, this research proposes a reconstructing 3D urban building models (3DUBM) approach that integrates geospatial data and street view. The approach achieves an enhanced generation of large‐scale 3DUBMs. Based on open geospatial data and street‐view imagery (SVI), the approach was tested in modeling experiments conducted in Shanghai, Hongkong, and Nanjing. Furthermore, a dataset covering unique blocks of 30 cities in China was constructed to demonstrate the approach's characteristics of large coverage, high time efficiency, high model quality and low economic cost. The accuracy of texture mapping from SVI to 3DUBM reached 85%. This achievement has significant economic value in bridging the gap in the production of large‐scale and low‐cost 3DUBM data, promoting the construction of Digital Twins, Smart Cities, and Real‐world 3D modeling. [ABSTRACT FROM AUTHOR]

• Bilateral somatosensory evoked spikes (SESs) (previously reported only in children) were detected in spontaneous MEG recordings of 10/30 adult subjects. • The first peak of SESs was considered to be identical to the somatosensory evoked field (SEF) M2 in latency, equivalent current dipole (ECD) position, and ECD orientation. • M2 amplitude of SEFs was significantly greater in subjects with SESs. Somatosensory evoked spikes (SESs) have been reported only in children aged under 14 years and are considered as an age-dependent phenomenon. However, we detected SESs in adult patients with epilepsy using magnetoencephalography (MEG). The present study investigated whether MEG can detect SESs in normal adults. Spontaneous MEG was recorded during measurement of somatosensory evoked fields (SEFs) for bilateral electrical median nerve stimuli in 30 healthy adults. Bilateral SESs were observed in 10 adults but none in the other 20 subjects. SESs consisted of one or two peaks, and the first peak latency corresponded to that of the second peak (M2) of SEFs. The first SES peak was identical to the M2 in isofield map pattern, as well as location and orientation of the equivalent current dipole (ECD). M2 ECD strength in the 10 subjects with SESs was larger (p <0.0001) than in 20 without SESs. All-or-nothing detection of bilateral SESs by MEG in normal adults must depend on the signal-to-noise issue of symmetrical SEFs and background brain activity. Our results further confirm the higher sensitivity of MEG compared to scalp EEG for the detection of focal cortical sources tangential to the scalp such as SESs. [ABSTRACT FROM AUTHOR]

The semiconductor industry is the core industry of the information age. As a key link in the semiconductor industry, wafer fabrication plays a key role in its development. In the testing stage of the wafer, each die of the wafer is detected and marked, and a wafer map with a certain spatial pattern can be formed. The analysis and classification of these spatial patterns can identify the cause of wafer defects, thereby improving production yield. However, as wafer size increases, line widths become smaller, etc., the probability of a mixed defect mode wafer pattern increases. Moreover, the mixed defect mode wafer map is more difficult to identify and classify than the single defect mode wafer map. Therefore, this paper proposes an improved deep convolutional neural network (DCNN) structure model for the recognition and classification of mixed defect pattern wafer maps. From the perspective of increasing the width of the DCNN, the improved network structure can avoid problems such as over-fitting and limited extraction of features due to the continuous deepening of the DCNN. The network is called Multi-Path DCNN (MP-DCNN) structure. The experimental results show that the proposed Multi-Path DCNN structure has better performance and higher classification accuracy than existing methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Graphics is the property of Journal of Graphics 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.)

The categories and styles of garment are constantly diversifying. For designers, it is a pressing issue to evaluate how different fabrics will look on them in a timely manner for users. In this paper, we present a novel garment texture transfer framework from a single person image. Based on the garment model constructed in the person image, we render the texture to the model surface using parallax mapping. To determine the relative positions of the garments when exporting the images, we calculate the contour center moments of the garment mask and the center of mass coordinates of the 3D model and use their consistency to perform position calibration. Finally, we align the rendered garment image with the figure image to obtain the final transfer effect. Experiments demonstrated that our method is robust to different character pose with different garments and background. Qualitative experimental results show that our method accurately and realistically relocates the texture of the garment in the image of the person while preserving the original folds of the garment. Quantitative comparisons with other methods show that our method is optimal in several metrics. [ABSTRACT FROM AUTHOR]

Collective spin arrangements manifest diverse spin textures, encompassing ferromagnetism, antiferromagnetism, and chiral vortices. However, mapping these spin textures in ultrathin magnetic multilayers has remained elusive. A reconfigurable chiral spintronic terahertz emission method is introduced through investigations on a model system of synthetic antiferromagnet and the spin information of individual ferromagnet (FM) layers is extracted. Upon femtosecond photoexcitation of the synthetic antiferromagnet (FM1/Ru/FM2), the ferromagnets generate a pair of linearly polarized ultrafast spin currents, which, after relaxation at the FM/Ru interface, emit corresponding THz pulses. The Ruderman–Kittel–Kasuya–Yosida interactions between the two FMs give rise to magnetic‐field‐controlled spin textures causing a spin relaxation imbalance at the interfaces and induce a phase shift between the orthogonal components of the emitted terahertz fields, consequently reconfiguring the polarization of the emitted terahertz field from linear to circular. This approach offers a novel means of investigating electronic and magnetic states in ultrathin spintronic heterostructures, low‐dimensional quantum materials, topological insulators, and Weyl semimetals. Furthermore, it enables the exploration of spin textures, including skyrmions, merons, and solitons. [ABSTRACT FROM AUTHOR]

Despite the growing recognition of the importance of street vendors and informal markets for urban life and the economy globally, research attention has tended to focus on essential products such as food or clothes and paid less attention to cultural products, such as antiques where potential gender, and other socio-demographic, differences in vending and buying behaviours may systematically differ. To explore these issues, this study employed spatial–temporal behaviour mapping (STBM) and field observations (n = 8587) at Beijing's Panjiayuan antiques "ghost market" a term reflecting its previously illegal/underground and mobile nature. We monitored four representative sites within the market, four times a day for six consecutive days. The data included five categories of behaviour, four age groups, and two genders. Data were recorded and analysed using ArcGIS. Results showed that, unlike common marketplaces, the antique market is primarily frequented by male vendors and buyers, replicating patterns seen in ancient Chinese paintings of men being involved in the trading of "non-essentials". Nonetheless, we found differing gender balances depending on the time and day of the week, different age groups, and spatial settings in different market areas. We summarise the nuanced relationships between the emerging gender spatio-temporal behaviour patterns and three key factors, spatial characteristics, physical activities and the social division of labour, in the Chinese context. The study reveals how the varied spatial–temporal patterns of a large antique "ghost market" raise long-established issues of equity and inclusivity and provide empirical insights that could inform decision-making and urban planning, such as better use of dynamic lighting provision to encourage a more gender-balanced experience. [ABSTRACT FROM AUTHOR]

Defect inspection of existing buildings is receiving increasing attention for digitalization transfer in the construction industry. The development of drone technology and artificial intelligence has provided powerful tools for defect inspection of buildings. However, integrating defect inspection information detected from UAV images into semantically rich building information modeling (BIM) is still challenging work due to the low defect detection accuracy and the coordinate difference between UAV images and BIM models. In this paper, a deep learning-based method coupled with transfer learning is used to detect defects accurately; and a texture mapping-based defect parameter extraction method is proposed to achieve the mapping from the image U-V coordinate system to the BIM project coordinate system. The defects are projected onto the surface of the BIM model to enrich a surface defect-extended BIM (SDE-BIM). The proposed method was validated in a defect information modeling experiment involving the No. 36 teaching building of Nantong University. The results demonstrate that the methods are widely applicable to various building inspection tasks. [ABSTRACT FROM AUTHOR]

When it became clear that cause-related marketing (CaRM) is a primary driver of a company's marketing strategy, CaRM as a research domain developed rapidly. Despite the fact that CaRM has received a lot of attention, only a few authors have done bibliometric analysis on it. As a result, an additional bibliometric analysis of CaRM is required to bring together the additional contributions, developments, and current research lines. Thus, this research aims to use bibliometric analysis to better understand the CaRM concept in marketing literature. In this study, 443 articles from Scopus, a widely used electronic database, are retrieved for the years 2000 to 2020. The authors used VOSviewer to perform citation analysis, co-citation analysis, keyword analysis, and bibliographic coupling. Our findings revealed that the field's development is dominated by authors and institutes from the United States and Europe. The presence of Asian countries indicates that the topic of research is becoming increasingly important. The data support the claim that the majority of current ideas in the field are derived from publications published in prestigious journals. Bibliographic coupling resulted in the identification of five clusters: (1) mechanism of CaRM, (2) attributes of CaRM, (3) CaRM and consumer behavior, (4) role of different factors in the effectiveness of CaRM, (5) cause and brand in CaRM. The findings of this study will be relevant not only to scholars working in the CaRM field but also to practitioners and policymakers who want to improve their understanding of CaRM. [ABSTRACT FROM AUTHOR]

Multidimensional sensory data prediction and resource scheduling are paramount challenges in the design of smart cities. This paper delves into the utilization of multi-objective evolutionary algorithms to enhance the accuracy and efficiency of target detection through optimized YOLO_v3 network models. By integrating the YOLO_v3 model with the K-means++ algorithm for Anchor_Box generation, the novel approach exhibits superior adaptability and flexibility, particularly in handling variable-sized feature pattern mappings. This adaptability better caters to the detection of targets of diverse sizes, thus elevating the performance and precision of target detection algorithms. To further scrutinize the YOLO-v3 joint algorithm's performance in urban traffic detection, P-R curves were plotted for various loss types on the NEU-DET dataset. Comparative analysis of these curves highlights the optimized algorithms' superiority in detecting various types of losses in urban model completeness. Additionally, practical application analysis revealed that the optimized monitoring results outperform the detection time of the original YOLO-v3_means++ network model on FP_GA. Notably, post-processing with C-FENCE can reduce average single-frame image detection time to 2.01 seconds, while convolutional degree-level fusion with the BN layer cuts it down to 2.25 seconds. In summary, the FP_GA-based YOLO-v3_means++ network algorithm offers superior detection capabilities, and the multi-objective evolutionary algorithm's optimization of the YOLO-v3 model enhances target detection performance and precision. [ABSTRACT FROM AUTHOR]

In the dynamic world of textile design, where digital tools have revolutionized the process of creating patterns, ARAHNE software programs, ArahPaint, ArahWeave, ArahDrape and ArahView 3D, are recognized as leading CAD/CAM software tailored for professionals. ArahPaint is a software designed for instant drawing in seamless repeats, enabling users to easily view and generate repetitions of their pattern. Specifically optimized for designing and weaving Dobby and Jacquard woven fabrics, ArahWeave software exemplifies how digital technology is reshaping the creative process in the textile industry. The advanced simulation tools of ArahWeave facilitate a highly realistic preview of how seamless pattern will appear when woven into fabric. Users can adjust structural parameters including thread patterns, yarn characteristics, colour schemes, fabric density, and weaves to achieve their desired aesthetic and structural appearance in woven fabric and present a 3D model simulation of their fabric in ArahView 3D. ArahDrape is a texture mapping software designed to help weavers, designers, and retailers enhance the presentation of their fabrics. Like mastering any other skill, using ARAHNE software programs requires dedicating time to learn and practice creating various textile patterns suitable for a range of textile applications. Additionally, users' artistic skills empower them to enhance both the quality and uniqueness of their textile designs. This paper explores the advanced shading techniques of Jacquard woven fabrics, simulated in ArahWeave software. The aim is specifically focusing on the process from inspiration to creating seamless patterns suitable for various Jacquard fabric types, providing solutions to users who wish to transcend the confines of traditional textile design. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of accurately analyzing the reliability of aviation power systems (APS) using traditional models by introducing the Aviation Power System Reliability Probability Network Model (APS-RPNM). The model directly transforms the system architecture into an equivalent probability network, aiming to develop a precise reliability model that captures system functions and fault logic. By classifying APS components into five distinct structural patterns and mapping them to corresponding nodes in the APS-RPNM, the model is successfully constructed. Specifically, None-Input-to-Multiple-Output components are transformed into two-state nodes, while Multiple-Input-to-None-Output, Single-Input-to-Multiple-Output, and Multiple-Input-to-Single-Output components are mapped to three-state nodes. For Multiple-Input-to-Multiple-Output components, a novel approach employing multiple two-state sub-nodes is adopted to capture their complex functional logic. A case study comparing the performance of the APS-RPNM with the traditional minimal path set method in reliability analysis was conducted. The results demonstrate that the APS-RPNM not only simplifies the model construction process and eliminates errors stemming from subjective engineering judgments but also enables the efficient computation of power supply reliability for all load points in a single inference by integrating all of the components. This significantly improves computational efficiency and system dependency analysis capabilities, highlighting the APS-RPNM's tremendous potential in optimizing the reliability design of APS. [ABSTRACT FROM AUTHOR]

The spatial distribution of extreme precipitation significantly affects flow‐producing processes and flooding. Previous studies on heavy rainfall have mainly emphasized the temporal distribution characteristics, with little emphasis on rainfall spatial patterns. We objectively classified 3‐h scale extreme precipitation spatial patterns (EPSPs) in Guangdong Province, China. We calculated the importance of the influencing factors on EPSPs, analysed weather backgrounds corresponding to various EPSPs, and explained the causes of extreme precipitation. We found that the incidence of most EPSPs increased significantly over this 40‐year period, and this increase has been particularly pronounced since the beginning of the 21st century. The Pacific Decadal Oscillation (PDO) was found to be the main factor influencing the extreme precipitation events (EPEs) in western Guangdong, and the weakening of the PDO contributed to the occurrence of EPEs in these areas. Urbanization was the main factor contributing to the increase in EPEs in the southern and coastal areas of Guangdong. The EPSPs in central Guangdong were caused by a southwest jet stream and topographic uplift. Extreme precipitation in southern and coastal Guangdong was mainly triggered by the convergent shear of southwesterly winds. The EPSPs over western Guangdong were caused by the low vortex in western Guangdong and the influx of large amounts of water vapour from the southern ocean. This study has provided new ideas for the study of the formation process and mechanism of localized heavy precipitation as well as an important reference for the simulation of runoff in coastal areas. [ABSTRACT FROM AUTHOR]

This work will first focus on the various technical aspects of the design of photorealistic 3D renderings of architectural objects. Special attention will be given to the role of new technologies, such as photogrammetry or texture mapping, in the preservation and enhancement of the architectural heritage. The problem of restoring the visual appearance of 3D architectural heritage objects through texture mapping will also be addressed. Hence, an algorithm that achieve a piecewise texture mapping by minimizing length distortion based on artificial neural networks (ANN) and the theory of elasticity was presented. To begin, a surface triangulation S (comprising triangular facets and a boundary) is created. The obtained mesh is then partitioned into disjoint pieces, with each interior vertex belonging to a single piece. Each subsurface, corresponding to one of the obtained pieces, is then mapped independently from the others by imposing local length-preservation constraints and barycentric coordinates of the interior mesh vertices. The proposed method aims to achieve uniform and seamless texturing, allowing for photorealistic representation of 3D models of heritage buildings and cities. This work provides an effective mathematical framework to obtain geometrically precise texture generating process on surfaces of arbitrary architectural topology.

AbstractIn the present work, air-water two-phase flow pattern and pressure drop in vertical upward annular channel with different diameters are experimentally studied. Two concentric annular test sections with the same hydraulic diameter of 4.6 mm are used in the experiments. The outer diameter and inner diameter corresponding to the two test sections are 18.60 and 12.30, and 86.75 and 80.45, respectively. Bubble flow, cap-slug flow, cap-churn flow, and annular flow were found in both the test sections and the flow pattern map is developed. It is found that the superficial gas velocity has an important effect on the transition of the flow patterns. Pressure drop correlations are reviewed and a total of 12 correlations are collected, including 4 homogeneous flow model (HFM)-based correlations and 8 separated flow model-based correlations. The existing correlations for estimating pressure drop were tested against the measured data. The comparisons show that no correlation studied here provides satisfactory agreement with the whole database. The Chisholm correlation provides the best accuracy with 20.9% for the small test section and 21.5% for large test section. It is also found that the existing models based on separated flow method predict frictional pressure drop with higher accuracy than those based on HFM. The result of this study highlights the requirement of a new method for calculating the frictional pressure drop in annular channels. [ABSTRACT FROM AUTHOR]

In order to improve the modeling efficiency and ensure the authenticity of the model, this paper proposes a 3D facial animation generation method based on front and side images. First, the feature point information reflecting the shape characteristics of the face is extracted from the front and side photos of a given face, and then these feature point information is used to modify the general face model to obtain a specific face model. Then, the face texture information in the front and side photos is used to texture map the specific face model, and finally a realistic virtual 3D face animation is obtained. In the aspect of feature extraction, this paper uses an improved color Snake model to accurately locate contour lines. The experimental results show that the system still has good processing performance, and the average processing time is less than 0.1s. On the set V, the light source angle is greater than 78 °, the illumination is seriously uneven, and the recognition rate is very low, which indicates that the texture features are not well extracted. But even in this case, the recognition rate after light compensation has doubled, which proves that the two-dimensional image can realize the three-dimensional facial animation generation technology. [ABSTRACT FROM AUTHOR]

Creating fast parallel iterative statistical algorithms based on the use of graphics accelerators is an important and urgent task of great scientific and practical importance. An algorithm based on the method of maximizing the maximum likelihood expectation (maximum likelihood expectation maximization—MLEM) is considered. The MLEM is a numerical method for determining maximum likelihood estimates and, since its first application in the field of image reconstruction in 1982, remains one of the most popular statistical image reconstruction methods and is the foundation for many other approaches. A new version of the MLEM parallel algorithm is proposed that provides global convergence of the iterative algorithm. To parallelize the algorithm, we use the texture mapping method using the OpenGL graphics library. The parallel algorithm is described in as much detail as possible. Examples of several reconstructions of images of aluminum casting products are given. The obtained result can be used for nondestructive testing of various industrial products, including testing of foundry products. [ABSTRACT FROM AUTHOR]

Hybrid zones have been viewed as an opportunity to see speciation in action. When hybrid zones are replicated, it is assumed that if the same genetic incompatibilities are maintaining reproductive isolation across all instances of secondary contact, those incompatibilities should be identifiable by consistent patterns in the genome. In contrast, changes in allele frequencies due to genetic drift should be idiosyncratic for each hybrid zone. To test this assumption, we simulated 20 replicates of each of 12 hybrid zone scenarios with varied genetic incompatibilities, rates of migration, selection and different starting population size ratios of parental species. We found remarkable variability in the outcomes of hybridisation in replicate hybrid zones, particularly with Bateson–Dobzhansky–Muller incompatibilities and strong selection. We found substantial differences among replicates in the overall genomic composition of individuals, including admixture proportions, inter‐specific ancestry complement and number of ancestry junctions. Additionally, we found substantial variation in genomic clines among replicates at focal loci, regardless of locus‐specific selection. We conclude that processes other than selection are responsible for some consistent outcomes of hybridisation, whereas selection on incompatibilities can lead to genomically widespread and highly variable outcomes. We highlight the challenge of mapping between pattern and process in hybrid zones and call attention to how selection against incompatibilities will commonly lead to variable outcomes. We hope that this study informs future research on replicate hybrid zones and encourages further development of statistical techniques, theoretical models and exploration of additional axes of variation to understand reproductive isolation. [ABSTRACT FROM AUTHOR]

In response to the growing societal awareness of the critical role of healthy soils, there has been an increasing demand for accurate and high-resolution soil information to inform national policies and support sustainable land management decisions. Despite advancements in digital soil mapping and initiatives like GlobalSoilMap, quantifying soil variability and its uncertainty across space, depth and time remains a challenge. Therefore, maps of key soil properties are often still missing on a national scale, which is also the case in the Netherlands. To meet this challenge and fill this data gap, we introduce BIS-4D, a high-resolution soil modeling and mapping platform for the Netherlands. BIS-4D delivers maps of soil texture (clay, silt and sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity and their uncertainties at 25 m resolution between 0 and 2 m depth in 3D space. Additionally, it provides maps of soil organic matter and its uncertainty in 3D space and time between 1953 and 2023 at the same resolution and depth range. The statistical model uses machine learning informed by soil observations amounting to between 3815 and 855 950, depending on the soil property, and 366 environmental covariates. We assess the accuracy of mean and median predictions using design-based statistical inference of a probability sample and location-grouped 10-fold cross validation (CV) and prediction uncertainty using the prediction interval coverage probability. We found that the accuracy of clay, sand and pH maps was the highest, with the model efficiency coefficient (MEC) ranging between 0.6 and 0.92 depending on depth. Silt, bulk density, soil organic matter, total nitrogen and cation exchange capacity (MEC of 0.27 to 0.78), and especially oxalate-extractable phosphorus (MEC of -0.11 to 0.38) were more difficult to predict. One of the main limitations of BIS-4D is that prediction maps cannot be used to quantify the uncertainty in spatial aggregates. We provide an example of good practice to help users decide whether BIS-4D is suitable for their intended purpose. An overview of all maps and their uncertainties can be found in the Supplement. Openly available code and input data enhance reproducibility and help with future updates. BIS-4D prediction maps can be readily downloaded at 10.4121/0c934ac6-2e95-4422-8360-d3a802766c71. BIS-4D fills the previous data gap of the national-scale GlobalSoilMap product in the Netherlands and will hopefully facilitate the inclusion of soil spatial variability as a routine and integral part of decision support systems. [ABSTRACT FROM AUTHOR]

This study aims to analyze two-phase flow patterns of water and air in a vertical column using experimental tests, and investigate the effect of pulsed gas flow on the characteristics of the Taylor bubble under different inlet conditions. First, three patterns of bubble, slug, and churn were observed with above hundred tests and a flow patterns map was drawn. Then, the effect of the pulsed gas flow on the length of the Taylor bubble was investigated in 150 different experiments. The ranges of superficial velocity of liquid and gas phases were 0.12–0.28 m/s and 0.05–0.25 m/s, respectively. The frequency of pulsed gas feed was 0.25–4 Hz. The results of this study showed that the length of the Taylor bubble in pulsed gas feed decreases with increasing the frequency. In addition, at a fixed gas frequency, the bubble length increases with increasing gas velocity. Present study prove that the pulsed gas flow technique can reduce the length of Taylor bubbles, which will be useful for future industrial applications in two-phase gas-liquid columns. [ABSTRACT FROM AUTHOR]

This paper proposes a fractional structure and texture aware Retinex (FSTAR) model for image decomposition with application to low-light enhancement. First, a novel structure aware measure called Maximum Fractional Difference (MFD) is introduced, which is the maximum of fractional differences of the input image in eight symmetric directions. Then fractional structure and texture aware maps are built based on MFD and Huber function from robust statistics, which are used as weighted matrices in the regularization terms of reflectance and illumination components. To ensure the intrinsic physical constraints of Retinex on two components, two extra penalty terms are incorporated into the objective function of FSTAR to penalize deviations of the estimated components from the corresponding constraints. The FSTAR is solved via an alternating iterative algorithm; i.e., the objective function is minimized with respect to one variable with another variable fixed, and this process is done alternately until termination condition is met. Qualitative and quantitative evaluations of FSTAR on three low-light image datasets, compared to ten state-of-the-art methods, show its superior performance in Retinex decomposition and low-light image enhancement. • Present texture aware measure based on fractional directional difference for texture extraction. • Propose fractional structure-texture aware Retinex model with constraints on two components. • Model is faithful to intrinsic physical meaning of Retinex in theory and easy to solve numerically. • Model has outstanding performance in terms of image decomposition and low-light enhancement. [ABSTRACT FROM AUTHOR]