Search Results (343)

Floor plans are a central representational component of architectural design, operating in close relation to sections, elevations, and three-dimensional reasoning to support the production and understanding of architectural space. In this context, we address the bounded computational task of completing incomplete floor plan representations as a form of early-stage design assistance, rather than treating the floor plan as an isolated architectural object. Within this workflow, being able to automatically complete a floor plan from an unfinished draft is highly valuable because it allows architects to generate preliminary schemes more quickly, streamline early discussions, and reduce the repetitive workload involved in revisions. To meet this need, we present FP-MAE, a self-supervised learning framework designed for floor plan completion. This study proposes three core contributions: (1) We developed FloorplanNet, a dedicated dataset that includes 8000 floorplans consisting of both schematic line drawings and color-coded plans, providing diverse yet consistent examples of residential layouts. (2) On top of this dataset, FP-MAE applies the Masked Autoencoder (MAE) strategy. By deliberately masking sections of a plan and using a lightweight Vision Transformer (ViT) to reconstruct the missing regions, the model learns to capture the global structural patterns of floor plans from limited local information. (3) We evaluated FP-MAE across multiple masking scenarios and compared its performance with state-of-the-art baselines. Beyond controlled experiments, we also tested the model on real sketches produced during the early stages of design projects, which demonstrated its robustness under practical conditions. The results show that FP-MAE can produce complete plans that are both accurate and functionally coherent, even when starting from highly incomplete inputs. FP-MAE is a practical and scalable solution for automated floor plan generation. It can be integrated into design software as a supportive tool to speed up concept development and option exploration, and it also points toward broader opportunities for applying AI in architectural automation. While the current framework operates on two-dimensional plan representations, future extensions may integrate multi-view information such as sections or three-dimensional models to better reflect the relational nature of architectural design representations. Full article

Against the backdrop of China’s “Dual Carbon” strategy (peak carbon emissions and carbon neutrality), timber forests serve the dual function of wood supply and carbon sink enhancement. In this study, we employed the Kuenm package in R to optimize Maximum Entropy model (MaxEnt) parameters. Based on the distribution data of six timber tree species in Sichuan Province and 43 environmental factors, we utilized the MaxEnt outputs and ArcGIS 10.8 software to map the geographic distribution of the suitable habitats for these species from the present day into the future (2061–2080) under different climate scenarios (SSP126 and SSP585). Furthermore, we analyzed the migration trend of their future distribution centers. The model optimization significantly improved both fit and predictive performance, with AUC values ranging from 0.8552 to 0.9637 and TSS values ranging from 0.6289 to 0.84, indicating high predictive capability and stability of the model. Analysis of environmental factors, including altitude, precipitation, and temperature, revealed that altitude plays a dominant role in species distribution. Future climate scenario simulations indicated that climate change will significantly alter the distribution of suitable habitats for these timber tree species. The suitable areas for some species contracted, with changes being particularly pronounced under the SSP585 scenario, in which the high-suitability area for Phoebe zhennan is projected to increase from 12,788 km² to 20,004 km², whereas the high-suitability area for Eucalyptus robusta is expected to contract from 8706 km² to 7715 km². The migration distances of suitable habitats for timber tree species in Sichuan range from 5 km to 101 km southwestward under different climate scenarios, and these shifts are statistically significant (p < 0.01), with shifts in elevation and precipitation patterns, reflecting species-specific responses to climate change. This study aims to predict future suitable habitats of timber tree species in Sichuan, providing scientific support for forestry planning, forest quality improvement, and climate risk mitigation. Full article

Instruction Set Architecture (ISA) extensions, particularly scalar cryptography extensions (Zk), combine the performance advantages of hardware with the adaptability of software, enabling the direct and efficient execution of cryptographic functions within the processor pipeline. This integration eliminates the need to communicate with external cores, substantially reducing latency, power consumption, and hardware overhead, making it especially suitable for embedded systems with constrained resources. However, current scalar cryptography extension implementations remain vulnerable to physical threats, notably power side-channel attacks (PSCAs). These attacks allow adversaries to extract confidential information, such as secret keys, by analyzing the power consumption patterns of the hardware during operation. This paper presents an optimized and secure implementation of the RISC-V scalar Advanced Encryption Standard (AES) extension (Zkne/Zknd) using Domain-Oriented Masking (DOM) to mitigate first-order PSCAs. Our approach features optimized assembly implementations for partial rounds and key scheduling alongside pipeline-aware microarchitecture optimizations. We evaluated the security and performance of the proposed design using the Xilinx Artix7 FPGA platform. The results indicate that our design is side-channel-resistant while adding a very low area overhead of 0.39% to the full 32-bit CV32E40S RISC-V processor. Moreover, the performance overhead is zero when the extension-related instructions are properly scheduled. Full article

Tai Chi has evolved into a widely used mind–body practice increasingly incorporated into complementary therapy, rehabilitation, and public health. This study provides an updated global bibliometric overview, with VOSviewer mapping publication performance, co-authorship networks, and keyword-based thematic clusters. Articles and reviews with Tai Chi–related terms in the title were retrieved from Scopus, with no restrictions on language or publication year. A total of 2253 publications from 1978 to 2025 were analyzed, revealing steady growth, concentrated largely in the past decade. China led the publication output, while the United States had the highest number of citations, forming a dual-core pattern. The field is largely driven by a small group of authors and regional clusters, and its visibility in mainstream medical journals remains limited. Nine software-generated keyword clusters were manually synthesized into five themes: motor function (balance and fall prevention), musculoskeletal conditions (osteoarthritis, rheumatoid arthritis, fibromyalgia), chronic disease management (cardiovascular disease, stroke, COPD), psychological health (quality of life, depression, anxiety, mindfulness), and cognitive aging (dementia, mild cognitive impairment). Future progress requires greater methodological rigor, including mechanistic inquiry, long-term study designs, and community- or population-level applications, along with stronger international collaboration and deeper integration into clinical and public health practice. Full article

Background: Ankle sprains are one of the most frequent ligament injuries in elite sports. Despite their high incidence, current rehabilitation approaches are often based on time-based criteria and neglect the physiological status of the injured tissues. Infrared thermography (IRT) is a non-invasive tool useful for detecting temperature asymmetries related to inflammation and tissue dysfunction. This study aimed to analyze the temporal evolution of ankle temperature asymmetry during return-to-play (RTP). Methods: A retrospective observational study of 26 ankle injuries analyzed with thermography that met the inclusion criteria. Thermograms were processed with a software to calculate temperature asymmetry in the ankle region of interest (ankleROI). Statistical analyses included paired and one-sample t-tests, as well as linear regression models, to assess temporal changes throughout the RTP process. Results: A significant hyperthermic response was observed immediately after injury (Δ = +0.594 °C; p < 0.001, Cohen’s d = 0.918). The first significant asymmetry reduction occurred between 21.5 and 28.5 days post-injury (Δ = −0.488 °C; p = 0.004), with a consistent weekly decrease of −0.109 °C (95% CI [−0.143, −0.078]). These findings indicate a progressive decrease in decrement on thermal asymmetry over approximately four weeks of RTP. Conclusions: IRT demonstrates potential as a physiological monitoring tool during the RTP process after ankle sprains. The observed pattern of temperature recovery provides objective reference thresholds that could complement existing functional and clinical criteria. Full article

Background/Objectives: Hemophagocytic Lymphohistiocytosis (HLH) shares many clinical features with sepsis. To improve HLH diagnosis and its differential diagnosis with sepsis, serum proteomic analyses of healthy donors, HLH and septic patients were performed. Methods: Twenty-four individuals were enrolled in a label-free MS/MS spectrometry analysis. STRING was conducted to study the protein–protein interactions overrepresented within the proteins of each comparison. To integrate the functions of the proteins with their respective regulation patterns, Ingenuity Pathway Analysis software was used. Validation of selected proteins was carried out by ELISA. Results: Proteomic results revealed 537 differentially expressed proteins (DEPs) between HLH and sepsis, 471 DEPs between HLH and healthy donors, and 37 DEPs between sepsis and healthy donors. These results were subjected to functional analysis, which showed that apart from inflammation and lipid metabolism, the proteostasis network was deeply impaired in the HLH condition. Considering this information, protein fold changes and the functions of six proteins were validated by ELISA. Conclusions: sCD300a, sCD300b and sCD25 could be specific serum biomarkers for HLH diagnosis, and SAA-1 and LRG1 might be useful biomarkers for differential diagnosis between sepsis and HLH. PSMB1, a non-catalytic subunit of the 20S proteasome, showed promising results for HLH-specific and differential diagnosis. Its elevation in HLH patients may reflect an intensified demand for protein turnover, possibly driven by a higher activation of the immunoproteasome. These insights contribute to expanding our understanding of HLH pathophysiology regarding new pathways and highlight innovative therapeutic interventions, such as Bortezomib and other next-generation inhibitors, designed to modulate immunoproteasome activity. Full article

The purpose of this study is to enhance the performance of the mesohabitat model MesoHABSIM by lowering the necessary hydraulic modelling effort. This proof-of-concept study tests an application of the MesoHydraulics model to simulate the hydraulic characteristics of hydromorphological units (HMUs) occurring in a regulated river at different low discharges. In this quantitative approach, hydraulic patterns are transferred from a source site, where depth and velocity distributions were derived from field measurements and a 2D hydrodynamic model, to a target site, where only a single field hydrometric survey was conducted. Instead of modelling changes in individual hydraulic measurement values to estimate hydraulic responses to discharge, the model relies on statistical distributions of these values within HMUs. We were testing whether changes in the distribution of HMU’s and their hydraulics can be transferred between morphologically comparable river sections to serve as a sufficient hydraulic input for mesoscale habitat modelling. The hydrodynamic component of the River2D software (V.0.95a), routinely used in MesoHABSIM, served as a baseline for testing the MesoHydraulic model’s performance and for producing source data for deriving distribution functions. The test was conducted using data from two one-kilometre sites on the upper Oder River (Poland). The model transfers the HMU area distributions, along with corresponding depth and velocity frequency distributions, for a number of flows from one site (the source) to another (the target). The hydraulics at both sites were surveyed under single-discharge conditions. For the source site, the hydrodynamic model was applied to classify the HMU mosaic at three additional discharge stages. At the target reach, the HMU mapping was conducted based on survey data, and statistical frequency functions were used to model distributions of hydraulic patterns at discharges modelled for the source. The hydraulic model’s performance was evaluated at the target reach by comparing simulated hydraulics and HMU patterns with those modelled using River2D. Finally, both models were used to calculate habitat availability for the fish communities, and dissimilarities were observed. The resulting hydraulic distributions were similar, with an average affinity index of 90%. Higher affinity indices were reached at flows close to the measured value, with increasing model disagreement toward flow extremes, most notably for Run and Backwater units. Regardless, habitat models for the fish community were also highly correlated with R² = 0.98 for amounts of suitable habitat and almost identical habitat distribution among the species. Yet, the MesoHydraulics-based model slightly, but consistently, overestimated habitat availability. While the model was tested in a large and regulated river system, its accuracy may vary depending on the natural river morphology. Further research should evaluate modelling uncertainties and their applicability in less-modified water bodies. Full article

Docosahexaenoic acid (DHA) is a long-chain omega-3 fatty acid essential for neurodevelopment, immune regulation, and key physiological functions during early life. In low- and middle-income countries (LMICs), limited access to DHA-rich foods contributes to disparities in intake and health outcomes. This narrative review describes the current evidence on dietary patterns of DHA intake and supplementation from pregnancy through childhood in LMICs and highlights the implications of these patterns for child health. The review is based on a systematic search conducted in PubMed using Medical Subject Heading (MeSH) terms related to DHA, dietary patterns, health outcomes, and LMICs. Studies published between 2014 and 2025 were screened using Covidence software. Eligible studies included observational, interventional, and review designs that reported DHA through dietary intake, supplementation, or measurement in biological samples during pregnancy, lactation, infancy, or childhood. Data extraction followed the PICOS (Population, Intervention, Comparison, Outcome, Study Design) framework. A total of 76 studies were included. Across LMICs, DHA intake was consistently insufficient among pregnant and lactating women, infants, and children. Reported dietary sources were generally low in DHA content. Intake or supplementation was associated with neurodevelopment, immune response, pregnancy outcomes, and cardiometabolic health, although findings were sometimes mixed or modified by gene–environment interactions. Results varied by study design, contextual factors, income level, and geographic access. Large gaps remain in nationally representative intake data. Despite its physiological relevance, DHA intake remains inadequate in LMICs during early life. This review underscores the importance of improving DHA intake in vulnerable populations and identifies evidence gaps to guide future research and inform context-specific nutrition strategies. Full article

Autonomous mowers’ navigation pattern plays a crucial role in turfgrass quality, influencing both esthetic and functional performance. However, despite extensive research on mowing efficiency, the effects of different navigation patterns on turfgrass damage and visual quality remain inadequately investigated. This study aimed to evaluate the impact of three different autonomous mower navigation patterns (random, vertical, and chessboard) on operational performance and the effect of trampling activity on turfgrass. Each pattern was tested in terms of data on the number of passages, distance traveled (m), number of intersections and the percentage of area mowed using a remote sensing system and an updated custom-built software. Green coverage percentage was assessed weekly using image analysis (Canopeo app) to evaluate the turfgrass green coverage. The green coverage percentage, together with the number of passages, is analyzed and correlated. The random pattern generated the highest number of passages and intersections, leading to lower average green coverage (64%) compared with the chessboard (80%) and vertical (81%) patterns. Data of the green coverage percentage in the function of the average number of passages recorded using the custom-built software for each pattern fit the asymptotic regression model. The effective number of passages to reach 60% green cover (EP60) was 56.26, 87.30, and 155.32 for random, vertical, and chessboard, respectively. The model could be integrated into DSS, useful for the end user in turf management in order to maintain a high quality. Future studies should extend this approach to other species and environmental conditions, integrating the effective dose (in terms of passages) method for smart mowing management. Full article

This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts was generated using a custom Python algorithm that simulates realistic garment-like shapes within a fixed fabric size. Each layout was characterized by five geometric descriptors: number of pieces (NP), average piece area (APA), average aspect ratio (AAR), average compactness (AC), and average convexity (CVX). The relationship between these descriptors and NE was modeled using a Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS). Various membership function (MF) structures were examined, and the configuration 3-3-2-2-2 was identified as optimal, yielding a mean relative error of −0.1%, with high coefficient of determination (R² > 0.98). The model was validated through comparison between predicted NE values and results obtained from an actual nesting process performed with Deepnest.io, demonstrating strong agreement. The proposed method enables efficient estimation of NE directly from CAD-based parameters, without requiring computationally intensive nesting simulations. This approach provides a valuable decision-support tool for fabric and apparel designers, facilitating rapid assessment of material utilization and supporting design optimization toward reduced fabric waste. Full article

Background: Sucrose metabolism plays a crucial role in plant responses to abiotic stresses such as drought and high temperatures, significantly influencing plant growth and yield formation. In higher plants, the second step in sucrose bioconversion involves sucrose phosphate phosphatase (SPP) hydrolyzing sucrose-6-phosphate to form sucrose. This study determined the number of SPP gene family members in upland cotton (Gossypium hirsutum), systematically analyzed their fundamental characteristics, physicochemical properties, phylogenetic relationships, chromosomal localization, and expression patterns across different tissues and under various abiotic stresses. Methods: The SPP gene family in hirsutum was identified using Hidden Markov Models (HMMER) and the NCBI Conserved Domain Database (NCBI CDD), and its physico-chemical properties were analyzed via the SOPMA online analysis website. Phylogenetic relationships were determined using MEGA 12.0 software. Promoter regions were analyzed with PlantCARE, sequence patterns were identified via MEME, and transcriptome data were downloaded from the CottonMD database. Results: This study identified four members of the hirsutum SPP gene family, with amino acid lengths ranging from 335 to 1015, molecular weights between 38.38 and 113.28 kDa, and theoretical isoelectric points (pI) between 5.39 and 6.33. These genes are localized across four chromosomes. The SPP gene family in hirsutum exhibits closer phylo-genetic relationships with SPP genes in Arabidopsis thaliana and Chenopodium quinoa. Their promoter regions are rich in cis-elements associated with multiple abiotic stress resistance functions, and their expression patterns vary across different tissues and under different abiotic stress conditions. Conclusions: The GhSPP gene may play an important role in the growth and development of upland cotton and its responses to salt stress and drought. Therefore, it could be considered as a candidate gene for future functional analysis of cotton resistance to salt and drought stress. Full article

Cine (dynamic) MRI is a non-invasive MRI technique that captures moving images and can be valuable in evaluating inflammatory bowel disease (IBD). This sequence shows emerging potential in providing functional data to assess bowel motility patterns, to aid in the differentiation between predominantly inflammatory (showing reduced peristalsis) and fibrotic strictures (rigid, non-motile segments) and detecting functional obstructions in Crohn’s disease (CD). Unlike static MRI, cine MRI enables clinicians to observe peristaltic movements, aiding in disease characterization and treatment monitoring. Its non-invasive nature and lack of ionizing radiation make it especially useful for repeated assessments in CD. Studies indicate it improves diagnostic accuracy when used with conventional MRI sequences, providing a complementary, functional dimension to the comprehensive management of this chronic condition. While the functional assessment offered by cine MRI presents a significant advantage over conventional static imaging, its clinical translation is currently challenged by high technical variability. Specifically, there is a distinct lack of standardized acquisition protocols (such as field strength, sequence parameters), post-processing software, and universally validated quantitative motility metrics (such as motility index). Therefore, a primary objective of this review is not only to summarize the evolving diagnostic and monitoring applications of cine MRI but also to critically address the methodological inconsistencies and reproducibility hurdles that must be overcome before this technique can be fully integrated into clinical guidelines for precision management of CD. Full article

This study presents a novel low-cost workflow integrating smartphone-based photogrammetry, Building Information Modeling (BIM), infrared thermography, and real-time interactive visualization to create digital twins for comprehensive energy assessment of existing buildings. Unlike conventional approaches requiring expensive laser scanning equipment and specialized software, this methodology democratizes advanced building diagnostics through accessible technologies and academic licenses. The research aims to develop and validate a replicable workflow that enables architects, engineers, and educators to conduct detailed energy assessments without high-end equipment, while establishing technical criteria for accurate geometric reconstruction, thermal data integration, and interactive visualization. The workflow combines terrestrial photogrammetry using smartphone cameras for 3D reconstruction, BIM modeling in Autodesk Revit for semantic building representation, infrared thermography for thermal performance documentation, and Unreal Engine for immersive real-time visualization. The approach is validated through application to the historic control tower of the former Rabassa aerodrome at the University of Alicante, documenting data capture protocols, processing workflows, and integration criteria to ensure methodological replicability. Results demonstrate that functional digital twins can be generated using consumer-grade devices (high-end smartphones) and academically licensed software, achieving geometric accuracy sufficient for energy assessment purposes. The integrated platform enables systematic identification of thermal anomalies, heat loss patterns, and envelope deficiencies through intuitive three-dimensional interfaces, providing a robust foundation for evidence-based energy assessment and renovation planning. The validated workflow offers a viable, economical, and scalable solution for building energy analysis, particularly valuable in resource-constrained academic and professional contexts, advancing both scientific understanding of accessible digital twin methodologies and practical applications in building energy assessment. Full article

Protein function emerges from dynamic conformational changes, yet structure prediction methods provide only static snapshots. While AlphaFold3 (AF3) predicts protein structures, the potential for extracting dynamic information from its ensemble predictions has remained underexplored. Here, we demonstrate that AF3 structural ensembles contain substantial dynamic information that correlates remarkably well with molecular dynamics simulations (MD). We developed ChronoSort, a novel algorithm that organizes static structure predictions into temporally coherent trajectories by minimizing structural differences between neighboring frames. Through systematic analysis of four diverse protein targets, we show that root-mean-square fluctuations derived from AF3 ensembles can correlate strongly with those from MD (r = 0.53 to 0.84). Principal component analysis reveals that AF3 predictions capture the same collective motion patterns observed in molecular dynamics trajectories, with eigenvector similarities significantly exceeding random distributions. ChronoSort trajectories exhibit structural evolution profiles comparable to MD. These findings suggest that modern AI-based structure prediction tools encode conformational flexibility information that can be systematically extracted without expensive MD. We provide ChronoSort as open-source software to enable broad community adoption. This work offers a novel approach to extracting functional insights from structure prediction tools in minutes, with significant implications for synthetic biology, protein engineering, drug discovery, and structure–function studies. Full article

Spatial metabolomics is a rapidly advancing field offering powerful insights into metabolic heterogeneity in biological tissues. However, its widespread adoption is hindered by fragmented tools and the lack of comprehensive, open-source GUI software covering the full analytical workflow (quality control, preprocessing, identification, pattern, and differential analysis). To address this, we developed SMAnalyst, an open-source, integrated web-based platform. SMAnalyst consolidates core functionalities, including multi-dimensional data quality assessment (background consistency, intensity, missing values), a comprehensive metabolite annotation scoring system (mass accuracy, isotopic similarity, adduct evidence), and dual-dimension spatial pattern discovery (metabolite co-expression and pixel clustering). It also offers flexible differential analysis (cluster- or user-defined regions). With its intuitive GUI and modular workflow, SMAnalyst significantly lowers the analysis barrier, by providing a unified solution that eliminates the need for tool switching and advanced computational skills. Tested with a mouse brain dataset, SMAnalyst efficiently handles large-scale data (e.g., >14,000 pixels, >3000 ion peaks), effectively filling a critical gap in integrated analytical solutions for spatial metabolomics. Full article