Search Results (9,447)

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

Family empowerment is a key component of effective family-centered practices in healthcare, mental health, and educational services. The Family Empowerment Scale (FES) is the most commonly used instrument to evaluate empowerment in families raising children with emotional, behavioral, or developmental disorders. Despite its importance, the FES for diverse populations, especially Latinx parents, has rarely been evaluated using innovative psychometric approaches. In this study, we evaluated key dimensions and psychometric evidence of the Family Empowerment Scale (FES) for 96 Latinx parents of children with intellectual and developmental disabilities (IDD) in the United States using an exploratory graph analysis (EGA). The EGA identified a five-dimensional structure, and EGA models outperformed the original CFA 3-factor models for both parents of children with autism and other disabilities. This study identified distinct, meaningful dimensions of empowerment that reflect both shared and unique empowerment experiences across two Latinx parent groups. These insights can inform the design of culturally responsive interventions, instruments, and policies that more precisely capture and boost empowerment in Latinx families. This study contributes to closing a gap in the literature by elevating the voices and experiences of Latinx families by laying the groundwork for more equitable support systems in special education and disability services. Full article

Skeleton-based action recognition is an important subject in deep learning. Graph Convolutional Networks (GCNs) have demonstrated strong performance by modeling the human skeleton as a natural topological graph, representing the connections between joints. However, most existing methods rely on non-adaptive topologies or insufficiently expressive representations. To address these limitations, we propose a Multi-view Topology Refinement Graph Convolutional Network (MTR-GCN), which is efficient, lightweight, and delivers high performance. Specifically: (1) We propose a new spatial topology modeling approach that incorporates two views. A dynamic view fuses joint information from dual streams in a pairwise manner, while a static view encodes the shortest static paths between joints, preserving the original connectivity relationships. (2) We propose a new MultiScale Temporal Convolutional Network (MSTC), which is efficient and lightweight. (3) Furthermore, we introduce a new temporal topology strategy by modeling temporal frames as a graph, which strengthens the extraction of temporal features. By modeling the human skeleton as both a spatial and a temporal graph, we reveal a topological symmetry between space and time within the unified spatio-temporal framework. The proposed model achieves state-of-the-art performance on several benchmark datasets, including NTU RGB + D (XSub: 92.8%, XView: 96.8%), NTU RGB + D 120 (XSub: 89.6%, XSet: 90.8%), and NW-UCLA (95.7%), demonstrating the effectiveness of our GCN module, TCN module, and overall architecture. Full article

Artistic style transfer aims to transfer the style of an artwork to a photograph while maintaining its original overall content. Although current style-transfer methods have achieved promising results when processing photorealistic images, they often struggle with brushstroke preservation in artworks, especially in styles such as oil painting and pointillism. In such cases, the extracted style and content features tend to include redundant information, leading to issues such as blurred edges and a loss of fine details in the transferred images. To address this problem, this paper proposes a multi-scale general style-transfer network based on diffusion models. The proposed network consists of a coarse style-transfer module and a refined style-transfer module. First, the coarse style-transfer module is designed to perform mainstream style-transfer tasks more efficiently by operating on downsampled images, enabling faster processing with satisfactory results. Next, to further enhance edge fidelity, a refined style-transfer module is introduced. This module utilizes a segmentation component to generate a mask of the main subject in the image and performs edge-aware refinement. This enhances the fusion between the subject’s edges and the target style while preserving more detailed features. To improve overall image quality and better integrate the style along the content boundaries, the output from the coarse module is upsampled by a factor of two and combined with the subject mask. With the assistance of ControlNet and Stable Diffusion, the model performs content-aware edge redrawing to enhance the overall visual quality of the stylized image. Compared with state-of-the-art style-transfer methods, the proposed model preserves more edge details and achieves more natural fusion between style and content. Full article

Background: Accurate estimation of the postmortem interval (PMI) is critical in forensic death investigations. Traditional signs of death—algor mortis, livor mortis, and rigor mortis—are generally reliable only within the first two to three days after death, with their accuracy decreasing as decomposition progresses. This paper presents a systematic review conducted in accordance with PRISMA guidelines, aiming to evaluate and compare current methods for estimating the PMI. Specifically, the study identifies both traditional and modern techniques, analyzes their advantages, limitations, and applicable timeframes, critically synthesizes the literature, and highlights the importance of combining multiple approaches to improve accuracy. Methods: A systematic search was conducted in the PubMed, Scopus, and Web of Science databases, following the PRISMA guidelines. The review included original articles and reviews that evaluated PMI estimation methods (through thanatological signs, entomology, microbial succession, molecular, imaging, and omics approaches). Extracted data included study design, methodology, PMI range, and accuracy information. Out of the 1245 identified records, 50 studies met the inclusion criteria for qualitative synthesis. Results: Emerging methods, such as molecular markers, microbial succession, omics technologies, and advanced imaging show improved accuracy across extended postmortem intervals. RNA degradation methods demonstrated higher accuracy within the first 72 h, while entomology and microbial analysis are more applicable during intermediate and late decomposition stages. Although no single method is universally reliable, combining traditional and modern approaches tailored to case-specific factors improves overall PMI estimation accuracy. Conclusions: This study supports the use of an integrative, multidisciplinary, and evidence-based approach to improve time-since-death estimation. Such a strategy enhances forensic outcomes by enabling more precise PMI estimates in complex or delayed cases, increasing legal reliability, and supporting court-admissible expert testimony based on validated, multi-method protocols. Full article

Unsupervised feature selection aims to characterize the cluster structure of original features and select the optimal subset without label guidance. However, existing methods overlook fuzzy information in the data, failing to model cluster structures between data effectively, and rely on squared error for data reconstruction, exacerbating noise impact. Therefore, a robust unsupervised feature selection algorithm based on fuzzy anchor graphs (FWFGFS) is proposed. To address the inaccuracies in neighbor assignments, a fuzzy anchor graph learning mechanism is designed. This mechanism models the association between nodes and clusters using fuzzy membership distributions, effectively capturing potential fuzzy neighborhood relationships between nodes and avoiding rigid assignments to specific clusters. This soft cluster assignment mechanism improves clustering accuracy and the robustness of the graph structure while maintaining low computational costs. Additionally, to mitigate the interference of noise in the feature selection process, an adaptive fuzzy weighting mechanism is presented. This mechanism assigns different weights to features based on their contribution to the error, thereby reducing errors caused by redundant features and noise. Orthogonal tri-factorization is applied to the low-dimensional representation matrix. This guarantees that each center represents only one class of features, resulting in more independent cluster centers. Experimental results on 12 public datasets show that FWFGFS improves the average clustering accuracy by 5.68% to 13.79% compared with the state-of-the-art methods. Full article

The modified Rankin Scale (mRS) is a widely used outcome measure for assessing disability in stroke care; however, its administration is often affected by subjectivity and variability, leading to poor inter-rater reliability and inconsistent scoring. Originally designed for hospital discharge evaluations, the mRS has evolved into an outcome tool for disability assessment and clinical decision-making. Inconsistencies persist due to a lack of standardization and cognitive biases during its use. This paper presents design principles for creating a standardized clinical assessment tool (CAT) for the mRS, grounded in human–computer interaction (HCI) and cognitive engineering principles. Design principles were informed in part by an anonymous online survey conducted with clinicians across Canada to gain insights into current administration practices, opinions, and challenges of the mRS. The proposed design principles aim to reduce cognitive load, improve inter-rater reliability, and streamline the administration process of the mRS. By focusing on usability and standardization, the design principles seek to enhance scoring consistency and improve the overall reliability of clinical outcomes in stroke care and research. Developing a standardized CAT for the mRS represents a significant step toward improving the accuracy and consistency of stroke disability assessments. Future work will focus on real-world validation with healthcare stakeholders and exploring self-completed mRS assessments to further refine the tool. Full article

Causation is fundamentally important to science, and yet our understanding of causation is spread out across disparate fields, with different measures of causation having been proposed in philosophy, statistics, psychology, and other areas. Here we examined over a dozen popular measures of causation, all independently developed and widely used, originating in different fields. We identify a high degree of consilience, in that measures are often very similar, or indeed often rediscovered. This is because, we show, measures of causation are based on a small set of related “causal primitives”: sufficiency and necessity, or alternatively, determinism and degeneracy. These primitives are ways of capturing the degree of uncertainty inherent in causal relationships. In turn, these results help us understand the phenomenon whereby macroscales can reduce the uncertainty in causal relationships, leading to stronger causes at macroscales—an outcome known as “causal emergence”. First identified using the effective information and later the integrated information in model systems, causal emergence has been found in real data across the sciences since. But is it simply a quirk of these original measures? Using a simple model system, we demonstrate how the consilience of causation guarantees that causal emergence is commonly found in causal measures, identifying instances across all measures analyzed. This finding sets the mathematical understanding of emergence on firmer ground, opening the door for the detection of natural scales of causal interaction in complex systems, as well as assisting with scientific modeling and experimental interventions. Full article

With the expansion of muskmelon cultivation, manual pollination is increasingly inadequate for sustaining industry development. Therefore, the development of automatic pollination robots holds significant importance in improving pollination efficiency and reducing labor dependency. Accurate flower detection and localization is a key technology for enabling automated pollination robots. In this study, the YOLO-MDL model was developed as an enhancement of YOLOv7 to achieve real-time detection and localization of muskmelon flowers. This approach adds a Coordinate Attention (CA) module to focus on relevant channel information and a Contextual Transformer (CoT) module to leverage contextual relationships among input tokens, enhancing the model’s visual representation. The pollination robot converts the 2D coordinates into 3D coordinates using a depth camera and conducts experiments on real-time detection and localization of muskmelon flowers in a greenhouse. The YOLO-MDL model was deployed in ROS to control a robotic arm for automatic pollination, verifying the accuracy of flower detection and measurement localization errors. The results indicate that the YOLO-MDL model enhances AP and F1 scores by 3.3% and 1.8%, respectively, compared to the original model. It achieves AP and F1 scores of 91.2% and 85.1%, demonstrating a clear advantage in accuracy over other models. In the localization experiments, smaller errors were revealed in all three directions. The RMSE values were 0.36 mm for the X-axis, 1.26 mm for the Y-axis, and 3.87 mm for the Z-axis. The YOLO-MDL model proposed in this study demonstrates strong performance in detecting and localizing muskmelon flowers. Based on this model, the robot can execute more precise automatic pollination and provide technical support for the future deployment of automatic pollination robots in muskmelon cultivation. Full article

This study employs k-means clustering to group 30 Chinese provinces into four CO₂ emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While Shanghai, Jiangxi, and Hebei retained their original classifications, provinces such as Beijing, Fujian, Tianjin, and Anhui transitioned from higher to lower emission patterns, indicating notable reversals in emission trajectories. To identify the determinants of these transitions, GDP growth rate, population growth rate, and energy investment are incorporated as time varying covariates. The empirical findings demonstrate that GDP growth substantially increases interpattern mobility, thereby weakening state persistence, whereas population growth and energy investment tend to reinforce emission pattern stability. These results imply that policy responses must be tailored to regional dynamics. In rapidly growing regions, fiscal incentives and technological upgrading may facilitate downward transitions in emission states, whereas in provinces where emissions remain persistent due to demographic or investment related rigidity, structural adjustments and long term mitigation frameworks are essential. The study underscores the importance of integrating economic, demographic, and investment characteristics into carbon reduction strategies through a region specific and data informed approach. Full article

A facile and versatile strategy to obtain NPs@ZnAlO nanocomposite materials, comprising controlled-size nanoparticles (NPs) within a ZnAlO matrix is reported. The mono-(Au, Ni) and bimetallic (Ni-Au) NPs serving as an active phase were prepared by the polyol-alkaline method, while the ZnAlO support was obtained via the thermal decomposition of its corresponding layered double hydroxide (LDH) precursors. X-ray diffraction (XRD) patterns confirmed the successful fabrication of the nanocomposites, including the synthesis of the metallic NPs, the formation of LDH-like structure, and the subsequent transformation to ZnO phase upon LDH calcination. The obtained nanostructures confirmed the nanoplate-like morphology inherited from the original LDH precursors, which tended to aggregate after the addition of gold NPs. According to the UV-Vis spectroscopy, loading NPs onto the ZnAlO support enhanced the light absorption and reduced the band gap energy. ATR-DRIFT spectroscopy, H₂-TPR measurements, and XPS analysis provided information about the functional groups, surface composition, and reducibility of the materials. The catalytic performance of the developed nanostructures was evaluated by the photodegradation of bisphenol A (BPA), under simulated solar irradiation. The conversion of BPA over the bimetallic Ni-Au@ZnAlO reached up to 95% after 180 min of irradiation, exceeding the monometallic Ni@ZnAlO and Au@ZnAlO catalysts. Its enhanced activity was correlated with good dispersion of the bimetals, narrower band gap, and efficient charge carrier separation of the photo-induced e⁻/h⁺ pairs. Full article

By 2050, it is estimated that approximately 200 million people will be displaced due to the impacts of climate change. Vulnerability to climate change is shaped not only by environmental factors but fundamentally by systemic power relations and structural conditions present at both the places of origin and destination. In Latin America, climate-displaced persons predominantly settle in marginalised neighbourhoods, where widely accepted informality facilitates their rapid arrival but obstructs genuine progress and full integration as urban citizens. This paper critically examines the prevailing myths that justify the persistence of informality, revealing the socioeconomic challenges faced by climate migrants in the region. These four dominant myths are (1) Latin America’s inherently low productivity levels; (2) concessions by the ruling class enabling excluded groups to merely survive; (3) the perceived privilege of marginalised neighbourhoods to generate income outside formal legal frameworks, which supports their social capital; and (4) the limited benefits associated with formalisation. Debunking these myths is essential for developing effective public policies aimed at reducing informality and promoting inclusive urban integration, ultimately benefiting both climate migrants and host communities. Full article

Internal crack defects in high-strength low-alloy (HSLA) steels during continuous casting pose significant challenges to downstream processing and product reliability. However, due to the inherent class imbalance in industrial defect datasets, conventional machine learning models often suffer from poor sensitivity to minority class instances. This study proposes a predictive framework that integrates conditional tabular generative adversarial network (CTGAN) for synthetic minority sample generation and CatBoost for classification. A dataset of 733 process records was collected from a continuous caster, and 25 informative features were selected using mutual information. CTGAN was employed to augment the minority class (crack) samples, achieving a balanced training set. Feature distribution analysis and principal component visualization indicated that the synthetic data effectively preserved the statistical structure of the original minority class. Compared with the other machine learning methods, including KNN, SVM, and MLP, CatBoost achieved the highest metrics, with an accuracy of 0.9239, precision of 0.9041, recall of 0.9018, and F1-score of 0.9022. Results show that CTGAN-based augmentation improves classification performance across all models. These findings highlight the effectiveness of GAN-based augmentation for imbalanced industrial data and validate the CTGAN–CatBoost model as a robust solution for online defect prediction in steel manufacturing. Full article

Soil nematodes are essential components of the soil food web and are widely recognised as key bioindicators of soil health because of their sensitivity to environmental factors and disturbance. In agriculture, many studies have documented the effects of fertilisation on nematode communities and explored their role in nutrient cycling. Despite this, a key gap in knowledge still exists regarding how fertilisation-induced changes in nematode communities modify their role in nutrient cycling. We reviewed the literature on the mechanisms by which nematodes contribute to nutrient cycling and on how organic, inorganic, and recycling-derived fertilisers (RDFs) impact nematode communities. The literature revealed that the type of organic matter and its C:N ratio are key factors shaping nematode communities in organically fertilised soils. In contrast, soil acidification and ammonium suppression have a greater influence in inorganically fertilised soils. The key sources of variability across studies include differences in the amount of fertiliser applied, the duration of the fertiliser use, management practices, and context-specific factors, all of which led to differences in how nematode communities respond to both fertilisation regimes. The influence of RDFs on nematode communities is largely determined by the fertiliser’s origin and its chemical composition. While fertilisation-induced changes in nematode communities affect their role in nutrient cycling, oversimplifying experiments makes it difficult to understand nematodes’ functions in these processes. The challenges and knowledge gaps for further research to understand the effects of fertilisation on soil nematodes and their impact on nutrient cycling have been highlighted in this review to inform sustainable agricultural practices. Full article

Background: Postoperative recurrence of sacrococcygeal chordomas presents significant clinical challenges due to unusual recurrence patterns. This study aimed to characterize these patterns of recurrence to inform improved adjuvant radiotherapy planning. Methods: We retrospectively analyzed 31 patients with recurrent sacrococcygeal chordoma following surgery, assessing recurrence locations considering initial tumor extent, resection levels, and postoperative anatomical changes on MRI. In 18 patients, pre- and postoperative imaging enabled the spatial mapping of early recurrence origins relative to the initial tumor volume using isotropic expansions. The median initial gross tumor volume was 113 mL. Results: Recurrences were mostly multifocal and predominantly involved soft tissues (e.g., mesorectal/perirectal space (80.6%), piriformis and gluteal muscles (80.6% and 67.7%, respectively) and osseous structures, particularly the sacrum (87.1%)). The median time to recurrence was 15 months. The initial surgery was R0 in 17 patients (55%). The highest infiltrated sacral vertebra was S1 in 3%, S2 in 10%, S3 in 35%, S4 in 23%, S5 in 10%, and coccygeal in 19%. Anatomical changes post-resection, including rectal herniation into gluteal and subcutaneous tissues, significantly affected radiotherapy planning. Expansion of the initial tumor volume by 2 cm failed to encompass all recurrence origins in 72% of cases. A 5 cm expansion was required to achieve full coverage in 56% of patients, though 22% of recurrences still lay beyond this margin and the remaining were covered only partially. Conclusions: Recurrent sacrococcygeal chordomas exhibit complex, soft-tissue-dominant patterns and are influenced by significant anatomical displacement post-surgery. Standard target volume expansions are often insufficient to cover the predominantly multifocal recurrences. Full article