Search Results (100,332)

Urban playgrounds are vital public spaces that support children’s play, social interaction, and well-being. However, many playgrounds in socially disadvantaged or aging urban areas experience physical deterioration, limited play diversity, and declining use. Although corporate social responsibility (CSR) initiatives have increasingly supported playground regeneration, many projects continue to emphasize short-term physical improvements rather than participatory processes and social value creation. This study conceptualizes CSR-supported, child-participatory playground regeneration as a social value creation process and examines how CSR enables process continuity through a structured six-stage participatory approach spanning planning, design, construction, and post-opening use. Two cases were selected from the “Save the Playground” program in Seoul, Korea: Saerok Children’s Park in a stable residential neighborhood and Mukjeong Children’s Park in a high-mobility, multicultural commercial district. Using a qualitative multiple-case study design, the study triangulates workshop outputs, observational records, facilitator field notes, and official program documents through thematic and cross-case analyses. The findings indicate that CSR support primarily ensured process continuity and facilitated multi-actor coordination across project stages. By securing implementation continuity and stabilizing governance arrangements, CSR support allowed participatory outputs to be iteratively translated into design development and post-opening evaluation. Post-opening outcomes differed by urban context; nevertheless, both cases showed social value creation through strengthened place attachment, responsibility-oriented use, and inclusive mixed-group play. This study advances a cross-case analytical framework linking urban context, participatory mechanisms, and post-opening social value outcomes, contributing to a more context-sensitive understanding of CSR-supported participatory design processes and their implications for sustainable urban public space development. Full article

Background: Inherited kidney diseases represent a genetically and clinically heterogeneous group of disorders affecting both pediatric and adult populations. Advances in next-generation sequencing (NGS) have improved diagnostic precision; however, genotype–phenotype correlations and diagnostic yield vary substantially across disease entities. Methods:We retrospectively evaluated 165 patients referred for genetic testing due to suspected inherited kidney disease. Patients were classified into three clinical groups: polycystic kidney disease, Alport syndrome, and other syndromic patients with inherited kidney diseases. Genetic analysis was performed using NGS with Human Phenotype Ontology–based gene filtering and included evaluation of both single-nucleotide variants and copy number variations. Results: Overall diagnostic yield differed markedly between groups. A molecular diagnosis was achieved in 71.4% of Alport patients, 41.0% of PKD patients, and 70.2% of patients in the Other syndromic group. In the Alport group, variants were identified exclusively in COL4A3, COL4A4, and COL4A5, with pathogenicity and gene involvement correlating with disease severity and the presence of extrarenal manifestations. The PKD group showed predominant involvement of PKD1, followed by PKHD1 and PKD2, while a substantial proportion of patients remained genetically negative, reflecting technical and biological complexity. The Other group exhibited pronounced genetic heterogeneity, with variants distributed across multiple genes involved in tubular, glomerular, metabolic, and ciliopathy-related pathways. Computational assessments demonstrated that several variants of uncertain significance (VUS) were located in functionally critical domains and were predicted to disrupt protein stability, intermolecular interactions, or conserved structural motifs, thereby supporting the biological plausibility of their potential pathogenic impact. Conclusions: Phenotype-driven NGS enables effective molecular diagnosis across diverse inherited kidney diseases while revealing disease-specific differences in diagnostic yield and genotype–phenotype correlations. Systematic inclusion of variants of uncertain significance and careful integration of genetic and clinical data are essential for accurate interpretation and long-term patient management. Collectively, this study enhances understanding of inherited kidney diseases and underscores the value of integrating comprehensive genomic and computational approaches into routine nephrogenetic practice. Full article

Accurate wind flow prediction is essential for various applications, including the placement of wind turbines and a multitude of environmental assessments. Traditionally this can be achieved by using time-consuming computational fluid dynamics (CFD) simulations on reanalysis data. This study explores the performance of an autoencoder (AE) and a variational autoencoder (VAE) in approximating downscaled wind speed and direction using real-world reanalysis data and reference geo- and vegetation data. The AE model was trained for 2000 epochs and demonstrates the ability to replicate wind patterns with a mean absolute error (MAE) of approximately −0.9. However, the AE model exhibited a consistent underestimation of wind speeds and a directional shift of approximately 10 degrees compared to CFD reference simulations. The VAE model produced visually improved results, capturing complex wind flow structures more accurately than the AE model. It mainly achieves better local accuracy and a reduced variance of the results. The overall result suggests that while autoencoders can approximate wind flow patterns, challenges remain in capturing the full variability of wind speeds and directions with sufficient precision. The study highlights the importance of balancing reconstruction accuracy and latent space regularization in VAE models. Future work should focus on optimizing model architecture and training strategies to enhance accuracy, prediction reliability and generalizability across diverse wind conditions and various locations. Full article

Background/Objectives: Daily sleep patterns are associated with cognitive health and Alzheimer’s disease (AD). However, it remains unclear how suboptimal irregular sleep manifests in AD from the preclinical stage to dementia. This study aimed to establish the dose–response association between sleep irregularity and psychometrically defined stage-specific AD as well as executive dysfunction, among adults with subjective cognitive and sleep issues. Methods: Cross-sectional data were obtained from 532 Japanese adults (mean age = 63.9 years) between March 2023 and April 2024. Sleep irregularity was quantified using the Sleep Regularity Index (SRI) with 24/7 accelerometer data. A modified Poisson regression with cubic splines was performed to establish the dose–response association. Results: This study identified novel non-linear associations. The prevalence ratios of cognitive impairment, defined as being in the preclinical and more advanced stages of AD, significantly declined beyond a median SRI of 60. Participants within this SRI range also showed significantly lower prevalence ratios of poorer Trail Making Test B performance. All results were independent of age, sleep duration, and risk of depression. Conclusions: Maintaining balanced-to-regular daily sleep patterns might be optimal for AD progress from its preclinical stages, with a potential benchmark at SRI of 60, especially for those individuals at risk for cognitive decline and sleep disorders. Further research is needed to replicate this benchmark in diverse populations and to evaluate the effect of rigid sleep regularity on cognitive health. Full article

Accurate mapping of grass species in biodiverse ecosystems, such as wetlands, is critical for ecological protection. Rapid advancements in remote sensing have established satellite data as a critical tool for wetland grass species mapping; however, its relatively coarse spatial resolution and susceptibility to cloud contamination limit the distinction of co-occurring species at fine scales. While Unmanned Aerial Vehicle (UAV) remote sensing offers high resolution and operational flexibility, relying on single-source features is often insufficient for fine-scale wetland species mapping due to the spectral similarity of co-occurring species. On the other hand, the fusion of multi-source remote sensing features (i.e., spectral, textural, and geometric features) likely provides a promising solution for achieving accurate, fine-scale grass species mapping in biodiverse ecosystems. In this study, we developed a wetland grass species mapping framework integrating spectral, textural, and geometric features derived from UAV RGB and multispectral imagery. Using a dataset of 95,880 image objects representing 24 wetland grass species classes collected in two years in Dajiu Lake National Wetland Park of China, we evaluated three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—across various feature combinations. We found that while spectral features (i.e., red edge, normalized green–red difference index [NGRDI], and normalized difference vegetation index [NDVI]) (related to leaf pigment concentrations and cellular structures) exhibited the highest importance in wetland grass species mapping, textural (i.e., contrast) and geometric features (i.e., aspect ratio) significantly enhanced classification performance as complementary information, yielding improvements of up to 10.5% in overall accuracy (OA) and 0.103 in Macro-F1 scores. Specifically, the fusion of spectral, textural, and geometric features achieved optimal performance with an OA of 81.9% and a Macro-F1 of 0.807. Furthermore, the XGBoost model outperformed SVM and RF, improving OA by 9.4% and 2.8%, and Macro-F1 by 0.08 and 0.035, respectively. By identifying the optimal feature combination and machine learning algorithm, this study establishes an accurate method for wetland grass species mapping, offering new opportunities for ecological assessment and precision conservation in biodiverse landscapes. Full article

Diet is a key modulator of the gut microbiota, thereby influencing host physiology. Microbial colonization begins early in life, influenced by maternal sources, mode of birth, diet, and environmental exposures, and stabilizes into an adult-like microbiome during early childhood. This maturation yields a microbial ecosystem dominated by Firmicutes and Bacteroidetes that contributes to host physiological homeostasis. Gut microorganisms function as an integrated metabolic system that transforms dietary substrates into bioactive metabolites, including short-chain fatty acids (SCFAs), amino acid-derived compounds, and microbial lipids. These metabolites regulate glucose and lipid metabolism, intestinal barrier integrity, and immune modulation. Although many metabolic functions are conserved, their activity is shaped by diet, microbial cross-feeding, and local intestinal conditions, enabling functional specialization within the gut. Disruption of this system, known as dysbiosis, is associated with alterations in microbial diversity and metabolic output that have been linked to metabolic diseases, including obesity and related disorders. Evidence from experimental models and observational studies suggests that these associations may involve interconnected inflammatory and metabolic mechanisms, such as impaired intestinal barrier function, low-grade inflammation, and altered dietary energy harvest; however, causal relationships in humans remain incompletely understood. Beyond peripheral effects, the gut microbiome influences host metabolism via the gut–brain axis, a bidirectional network that integrates neural, endocrine, immune, and metabolic signaling. Microbiota-derived metabolites and gut hormone modulation contribute to appetite regulation, energy balance, and glucose homeostasis, while central neuroendocrine signaling can reciprocally shape the intestinal microbial niche. Collectively, these findings highlight the gut microbiome as a central regulator of host metabolism, whose disruption may contribute to the development of metabolic disease. Full article

Background: Nigella sativa (black cumin) seeds are renowned for their ethnomedicinal significance and are rich in bioactive phytochemicals, which contribute to food preservation and the prevention of various diseases through their antimicrobial and antioxidant properties. Accordingly, this study aimed to characterize the phytochemical composition of N. sativa seed extracts, isolate thymoquinone, and assess their antibacterial, antibiofilm, antioxidant, anti-inflammatory and antidiabetic activities. Methods: Nigella sativa seed extracts were prepared using solvents of increasing polarity and analyzed for phytochemical content. Metabolite profiling was performed using UHPLC/QTOF-MS. Thymoquinone, the major constituent, was isolated via thin-layer chromatography (TLC), further purified using semi-preparative reverse-phase high-performance liquid chromatography (RP-HPLC), and evaluated in vitro for antibacterial, antibiofilm, antioxidant, anti-inflammatory, and antidiabetic activities. Results: Extraction yields ranged from 5.5% to 8.4% (w/w), with methanol yielding the highest phenol (6.34 ± 0.31 mg GAE/mL) and flavonoid (5.12 ± 0.26 mg QE/mL) contents. UHPLC/QTOF-MS revealed a chemically diverse profile dominated by thymoquinone (58% relative abundance), alongside p-cymene, carvacrol, longifolene, and nigellidine. Thymoquinone (Rf = 0.56) was initially isolated from the methanolic extract with a yield of 270 mg/g and further purified from preparative TLC fractions using semi-preparative RP-HPLC, affording 82 mg of >95% pure compound with a 68.3% recovery, suitable for subsequent biological assays. It inhibited Gram-positive and Gram-negative bacteria, with MICs of 62.5 µg/mL against Staphylococcus aureus, Bacillus subtilis, and Listeria monocytogenes; 125–250 µg/mL against Escherichia coli and Salmonella typhimurium; and 500 µg/mL against Pseudomonas aeruginosa. Thymoquinone reduced biofilm formation (>80% at 25–50 µg/mL; MBIC₅₀ ≈ 5.4–11.6 µg/mL), exhibited antioxidant activity (DPPH IC₅₀ = 52.3 ± 2. 1 µg/mL; ABTS IC₅₀ = 41.6 ± 1.9 µg/mL), stabilized erythrocyte membranes (IC₅₀ ≈ 14.8 µg/mL), and inhibited carbohydrate-hydrolyzing enzymes, with stronger inhibition of α-glucosidase (~92%) than α-amylase (~84%) at 128 µg/mL. Conclusions: Thymoquinone is a major bioactive constituent of N. sativa seeds, exhibiting consistent multi-target in vitro activity. These findings highlight its functional relevance and in vivo investigations to establish therapeutic potential. Full article

The miRNA172–APETALA2 (AP2) regulatory module is a conserved mechanism governing floral development in plants. Disruption of the miR172 target sites in AP2 genes has been shown to be key to the domestication of double flowers in ornamental species. Camellia japonica, a woody ornamental plant with diverse floral forms, serves as an important model for studying double-flower formation. In this study, we characterized two AP2-like transcription factors, CjAP2-1 and CjAP2-2, which possess evolutionarily conserved miR172-binding sites and exhibit broad expression across floral tissues. To investigate the role of the miR172–AP2 module in C. japonica, we identified four members of the miR172 family and demonstrated that miR172 is directly involved in the cleavage of CjAP2-1 and CjAP2-2 transcripts. Through bulked amplicon sequencing of cultivars with diverse floral forms, we uncovered natural variations at the miR172-binding sites of CjAP2-1 and CjAP2-2, which can potentially disrupt miR172-mediated mRNA cleavage. We showed that two dinucleotide mutations (CjAP2-1-mut5 and CjAP2-1-mut9) significantly reduced the miR172-mediated repression of CjAP2-1 transcripts. Functional analysis in Arabidopsis revealed that overexpression of the CjAP2-1-mut5 variant caused significant floral abnormalities, including ectopic formation of reproductive organs, loss of floral determinacy, and fusion of floral organs. Further analysis of downstream genes indicated that key regulators of floral homeotic and meristem activity were markedly altered in the transgenic plants. Our findings demonstrate that perturbations in the miR172–AP2 regulatory relationship underlie the formation of double flowers in C. japonica by altering floral meristem determinacy and organ identity. Full article

Feedback is an integral part of the learning process. However, delivering feedback effectively remains challenging, particularly within massified higher education systems that are characterised by large cohorts and increasingly diverse student populations. The emergence of generative artificial intelligence (GenAI) enables new ways of embedding feedback into educational offerings, some of which may be highly beneficial. In this paper, we introduce Abundant Feedback as a conceptual lens for examining the new capabilities that may be enabled by GenAI. We present a four-dimensional framework identifying the dimensions of GenAI feedback as abundance of Volume, of Availability, of Relevance and of Character. Through a systematic literature search, we describe how these dimensions manifest in recent empirical studies, and identify two educational domains, Computer Programming and Foreign Languages, as early adopters of AI-generated feedback. Beyond merely digitising existing scarce feedback processes, we discuss the emergence of new learner-driven feedback practices that are enabled by abundance, that both stimulate and demand student feedback literacy. Our multi-dimension abundance framework provides a lens, as well as the vocabulary and conceptual tools, to guide the implementation of GenAI feedback in ways that help realise the potential of artificial intelligence to enhance student learning. Full article

Accurate detection of small objects in video analytics is limited by low pixel resolution and insufficient visual cues. While software-based enhancements often fail to recover missing details, Pan–Tilt–Zoom (PTZ) cameras can physically increase spatial resolution through optical zoom. However, mechanical latency and configuration complexity hinder their real-time applicability. We propose ZoomPatch, a real-time video analytics framework tailored for small object detection. ZoomPatch actively schedules PTZ adjustments to capture optically enhanced subframes of regions of interest (ROIs) and fuses inference results back to the global reference frame. Specifically, it introduces a dynamic Cycle Length Proposer to adapt analysis cycles based on scene motion, and a Mixed Integer Linear Programming (MILP)-based Configuration Decider to determine the optimal sequence of pan, tilt, and zoom adjustments under time budget constraints. Simulation-based experimental evaluations across diverse workloads demonstrate that ZoomPatch significantly outperforms fixed-perspective, super-resolution (SR), and greedy baselines. Notably, in the detection task using YOLOv10, ZoomPatch improves the F1-score from 0.33 to 0.47 (a 42% increase) compared to the fixed-perspective baseline. Furthermore, ZoomPatch yields performance gains of 30% and 7% over the SR baseline (0.36) and the greedy baseline (0.44). Full article

Background/Objectives: Mitochondrial DNA (mtDNA) is an important resource for understanding human ancestry, population diversity, and the molecular mechanisms of mitochondrial diseases. However, analyzing mtDNA thoroughly often requires advanced bioinformatics skills and command-line knowledge. To address this challenge, we created Mitochondrial Genome Explorer (MitoGEx), a user-friendly computational pipeline optimized for human mtDNA analysis that combines multiple mtDNA analysis modules within a single graphical user interface. Methods: The platform simplifies key analytical steps, such as quality control, sequence alignment, alignment quality assessment, variant detection, haplogroup classification, and phylogenetic reconstruction. Users can choose between Quick and Advanced modes, which offer default settings or customizable options based on their analysis needs. To demonstrate its effectiveness, we analyzed 15 whole-exome sequencing (WES) samples from Songklanagarind Hospital using MitoGEx. Results: The sequencing data were of high quality, with over 92 percent of bases scoring above a Phred score and consistent GC content across all samples. Variant detection using the GATK mitochondrial pipeline and annotation with ANNOVAR and the MitImpact database revealed multiple high-confidence variants. Haplogroup classification with Haplogrep 3 and phylogenetic analysis with IQ-TREE 2 confirmed diverse maternal lineages within the cohort. Conclusions: Taken together, MitoGEx facilitates mitochondrial genome analysis in a reproducible and accessible manner for both research and clinical bioinformatics applications. The analytical results produced by MitoGEx are concordant with those obtained using standalone bioinformatic tools, demonstrating analytical correctness. By integrating all analysis steps into a single automated workflow, MitoGEx reduces execution time and limits human error inherent to manual, multi-step pipelines. Full article

Lithium-ion batteries are widely used across diverse applications due to their high energy density, long cycle life, and fast charging capabilities. As battery-powered systems become increasingly critical, accurate estimation of the Remaining Useful Life (RUL) is essential for ensuring reliability, safety, and effective maintenance planning. This work investigates Evidential Deep Learning (EDL) for data-driven RUL estimation and introduces a novel risk-aware loss function designed to enhance both predictive accuracy and uncertainty quantification in the End-of-Life (EoL) region, where precise and trustworthy predictions are most needed. Using a publicly available dataset of lithium iron phosphate (LFP) cells, we benchmark the proposed approach against a baseline Conv–LSTM model, Monte Carlo (MC) Dropout, and Deep Ensembles. The results show that integrating the risk-aware loss into the EDL framework substantially improves the calibration of predictive uncertainty while achieving state-of-the-art accuracy near EoL. Unlike MC Dropout and Deep Ensembles, which exhibit increasing or unstable uncertainty as degradation accelerates, the proposed EDL model demonstrates a consistent reduction in uncertainty and significantly higher reliability in late-stage predictions. The findings indicate that the risk-aware evidential framework offers a reliable and computationally efficient solution for battery RUL estimation, enabling more informed decision-making in both safety-critical and consumer-oriented applications. Full article

Machining process route planning (MPRP) is vital for autonomous manufacturing yet remains challenging under complex, multi-dimensional engineering constraints. This paper proposes an attention-augmented deep reinforcement learning (DRL) framework to achieve intelligent process orchestration. First, an Optional Process Attribute Adjacency Graph (OPAAG) is established to formally model the “feature–process–resource–constraint” coupling, enhancing the agent’s perception of manufacturing semantics. The architecture synergistically integrates Graph Attention Networks (GAT) to perceive spatial benchmark dependencies and a Transformer-based encoder to capture sequential resource correlations within variable-length machining chains. Furthermore, a dynamic action masking mechanism is integrated to guarantee a 100% constraint satisfaction rate during both training and inference stages. Experimental evaluations across diverse part geometries demonstrate that the proposed method offers significant advantages in cost optimization, inference efficiency, and topological stability compared to traditional heuristic algorithms and standard DRL models. By effectively distilling the search space and maintaining action feasibility, the framework provides an efficient and robust solution for autonomous process planning in complex industrial scenarios. Full article

This study investigated temporal group-level changes in gut microbiome composition and fecal metabolic markers in Wistar rats following a 10-day administration of morphine. Fecal samples were collected at predefined post-discontinuation time points and analyzed using 16S rRNA gene sequencing and GC×GC-TOF/MS-based metabolomics, with a focus on short-chain fatty acids (SCFAs). Morphine exposure was associated with transient alterations in gut microbiome structure at early post-treatment time points, including changes in alpha diversity and shifts in the relative abundance of major bacterial taxa. Unsupervised multivariate analysis of fecal metabolomic profiles revealed substantial inter-individual variability without persistent global separation between control and morphine-treated groups. Targeted analysis identified transient reductions in the relative signal intensities of selected SCFAs shortly after morphine withdrawal, while no significant differences were observed at later time points. These findings suggest that morphine-associated perturbations of the gut microbiome and fecal metabolome are predominantly time-dependent and tend to diminish during extended post-discontinuation phases. Full article

Chinese font generation is important for digital typography, cultural preservation, and personalized user interfaces. However, existing methods often face challenges in maintaining structural consistency, supporting diverse stylistic variations, and achieving computational efficiency simultaneously, especially in cloud-based environments. A key application is bandwidth-efficient font delivery, where compact structural templates replace large font files for on-demand style customization. To address these issues, this paper proposes Stroke2Font—a hierarchical vector model with AI-driven optimization for dynamic Chinese font generation. The core model decouples structural representation from style rendering through stroke element decomposition and Bézier curve parameterization. To further balance structural fidelity, style diversity, and real-time performance, we introduce a three-module optimization framework: (1) a reinforcement learning policy for dynamic selection of Bézier control parameters to minimize rendering latency; (2) a genetic algorithm for exploring style vector spaces and generating novel font variants; and (3) an adaptive complexity-aware optimization strategy that dynamically configures parameters based on character structural complexity. Experimental results on a dataset of 150 Chinese characters with 1123 stroke trajectories and 5287 feature points demonstrate that the adaptive complexity-aware optimization achieves the highest trajectory similarity of 65.2%, representing a 6.4% relative improvement over baseline (61.3%). The evaluation covers characters ranging from 1 to 18 strokes across 6 stroke types, with standard deviation reduced to ±5.7% (compared to ±6.5% baseline), indicating more consistent performance. Quantitative analysis confirms that the method generalizes effectively across varying character complexity, with the optimization showing stable improvement regardless of stroke count distribution. These results validate that Stroke2Font provides an effective solution for high-quality, efficient, and scalable Chinese font generation in cloud-based applications. Full article