Search Results (6,358)

Most existing reactive agility assessments rely on screen-based or light-based stimuli that are spatially separated from the movement execution plane, thereby limiting ecological validity. The purpose of this study was to develop and validate a novel projection-based, ground level reactive agility test (RAT) designed to better reflect the perceptual motor demands of soccer. A total of 57 male soccer players (24 professional and 33 amateur) participated in the study. The system projects sport-specific visual stimuli onto the ground and uses a three-dimensional depth camera to track foot–stimulus interactions in real time. Two reactive agility protocols—a randomized simple reaction test and a randomized selective reaction test—were implemented. Construct validity was examined by comparing reactive agility and planned change-of-direction (PCOD) performance between professional and amateur players, as well as by analyzing relationships between PCOD and RAT outcomes. Professional players demonstrated significantly faster performance than amateurs across all tests (p < 0.01), with larger between-group differences observed in reactive agility compared with PCOD measures. Correlations between PCOD and reactive agility outcomes were low to moderate (r = 0.34–0.61), indicating that reactive agility captures performance components beyond planned movement ability. The reactive agility protocols showed excellent test–retest reliability (ICC = 0.92–0.99) with low measurement error (CV = 0.96–3.47%). In conclusion, the proposed projection-based, ground-level RAT provides a valid and reliable assessment of reactive agility in soccer. By integrating sport-specific stimuli and movement execution within the same spatial plane, the system enhances ecological validity and offers a scalable framework for both performance assessment and perceptual cognitive training in open-skill sports. Full article

Large language models (LLMs) have shown strong potential for automated code generation in software development, yet their effectiveness in embedded systems programming—requiring understanding of software logic and hardware constraints—has not been well studied. Existing evaluation frameworks do not comprehensively cover practical microcontroller development scenarios in real-world Internet of Things (IoT) projects. This study systematically evaluates 27 state-of-the-art LLMs across eight embedded systems scenarios of increasing complexity, from basic sensor reading to complete cloud database integration with visualization dashboards. Using ESP32 microcontrollers with environmental and motion sensors, we employed the Analytic Hierarchy Process with four weighted criteria: functional, instructions, output and creativity, evaluated independently by two expert reviewers. Top-performing models were Claude Sonnet 4.5, Claude Opus 4.1, and Gemini 2.5 Pro, with scores from 0.984 to 0.910. Performance degraded with complexity: 19–23 models generated compilable code for simple applications, but only 3–5 produced functional solutions for complex scenarios involving Grafana and cloud databases. The most frequent failure was hallucinated non-existent libraries or incorrect API usage, with functional capability as the primary barrier and instruction-following quality the key differentiator among competent models. These findings provide empirical guidance for embedded developers on LLM selection and identify limitations of zero-shot prompting for hardware-dependent IoT development. Full article

The Philippines, which is rich in natural resources, has significant biomass potential. Among the country’s renewable energy sources, biomass is currently the slowest-growing in terms of power generation. Various types of biomass resources with full or partial use in Laguna Province include bagasse, sweet sorghum, coconut, rice husk, corn cobs, and municipal solid waste. Additionally, the adoption and implementation of HRESs (hybrid renewable energy systems) are mainly achieved through large-scale projects. This paper intentionally showcases highly optimized hybrid configurations for off-grid microgrids to promote rural electrification in Laguna, with a focus on various technoeconomic parameters, specifically the minimization of net present costs and the levelized cost of electricity across all simulations. Each off-grid scenario was compared with scenarios featuring hybrid renewable energy systems incorporating a biomass generator. Laguna, one of the few provinces in the Philippines with all forms of renewable energy systems present, each with high renewable energy potential and renewable fraction values, was selected as the primary study site in this paper. After optimizing and analyzing technoeconomic parameters such as the net present cost and the levelized cost of electricity, a hybrid biomass-solar-wind energy system is proposed to power off-grid areas in Laguna, thereby supporting rural electrification and decarbonization goals. Scenario simulations and comparisons using hybrid optimization demonstrate that adding battery backup systems improves both economic and environmental performance. This paper highlights two key benefits of including a biomass generator: (1) a 17.0% reduction in long-term carbon emissions for the entire system and (2) approximately 9.4% savings in operation and maintenance costs after seven years. The optimization results support the goal of providing Laguna with power through off-grid, decentralized, community-based hybrid renewable energy systems. Full article

Background: Supplier selection in shipbuilding is a high-stakes decision problem due to stringent quality requirements, compressed construction schedules, and elevated project risks. This study develops a systematic decision-support framework for selecting shipbuilding material suppliers while enhancing supply-chain resilience. Methods: A hybrid multi-criteria decision-making framework integrating the Best Worst Method (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed. BWM is used to derive consistent criteria weights with fewer pairwise comparisons, while TOPSIS ranks supplier alternatives based on their distances from ideal and negative-ideal solutions. Results: Quality emerges as the most influential criterion (weight = 0.460), followed by risk-related factors, underscoring the importance of compliance, reliability, and risk mitigation in shipbuilding procurement. The TOPSIS results indicate that Supplier 3 achieves the highest closeness coefficient (Ci = 0.592), followed by Supplier 4, Supplier 2, and Supplier 1, with strong consistency observed in expert judgments. Conclusions: The proposed BWM–TOPSIS framework is rigorous, transparent, and replicable, supporting a Quality–Risk-Oriented multi-sourcing strategy to enhance supply continuity and operational resilience. Full article

This systematic review synthesizes passive and passive-first cooling strategies for greenhouses in hot–arid climates, organizing evidence across four domains: Airflow & Ventilation, Shading & Radiative Control, Thermal Storage & Ground Coupling, and Structural Design & Geometry. Drawing on the project corpus, we analyze 10–13 distinct techniques including ridge and side natural ventilation, windcatchers and solar chimneys, external shade nets, NIR-selective and transparent radiative-cooling films, and dynamic PV shading; earth-to-air heat exchangers (EAHE/GAHT), rock-bed sensible storage, phase-change materials (PCMs), and sunken or buried envelopes; as well as roof slope and shape, span number, and orientation. Across studies, cooling outcomes are reported as peak or daytime indoor air temperature reductions, defined relative either to outdoor conditions or to a control greenhouse, with the reference frame and temporal aggregation specified in the synthesis. Typical outcomes include ≈3–7 °C daytime reduction for optimized ventilation, ≈2–4 °C for shading and spectral covers while preserving PAR, ≈5–7 °C intake cooling for EAHE with winter pre-heating, and up to ≈14 °C peak attenuation for rock-bed storage under favorable conditions. Structural choices consistently amplify these effects by sustaining pressure head and limiting thermal heterogeneity. Performance is strongly context-dependent—governed by wind regime, diurnal amplitude, dust and UV exposure, and crop-specific light and temperature thresholds—and the most robust results arise from stacked, site-specific designs that combine skin-level radiative rejection, buoyancy-supportive geometry, and ground or latent buffering with minimal active backup. Smart controllers that modulate vents, shading, and targeted fogging or fans based on VPD or temperature differentials improve stability and reduce water and energy use by engaging actuation only when passive capacity is exceeded. We recommend standardized composite metrics encompassing temperature moderation, humidity stability, PAR availability, and water and energy use per unit yield to enable fair cross-study comparison, multi-season validation, and policy adoption. Collectively, the synthesized techniques provide a practical palette for improved greenhouse climate management under hot and arid conditions. Full article

Background/Objectives: Worldwide, Type 1 diabetes mellitus (T1DM) in youth represents a growing public health concern, and Italy is among the countries with the highest incidence in the pediatric population. The transition from pediatric to adult care is a vulnerable period associated with increased risks of acute complications and long-term morbidity. This scoping review aimed to map the available Italian evidence on healthcare transition in adolescents and young adults (AYAs) with T1DM, addressing five key areas: characteristics of the transition process and involved populations, emotional and psychological experiences, the role of technology, existing transitional care models and related outcomes, and assessment criteria and tools for transition readiness. Methods: This review followed the JBI methodology and included studies focused on Italian AYAs (aged 10–24 years) with T1DM. Study selection was documented using the PRISMA flow chart. Results: Twenty studies were included. The evidence revealed a heterogeneous and inconsistently implemented transition landscape. Several structured transition projects were identified, differing in multidisciplinary team composition, organization, and outcome evaluation. Emotional distress, fear of separation from pediatric providers, and variable satisfaction with transition experiences were commonly reported. Adoption of technologies increased over time and was associated with improved clinical outcomes, although overall uptake remained suboptimal. Importantly, no Italian-validated tools for assessing transition readiness were identified. Conclusions: Transitional care for Italian AYAs with T1DM is increasingly recognized but remains insufficiently standardized and evaluated. Future research should prioritize multicenter studies, stratified analyses, and the development of culturally validated readiness assessment tools to support effective and individualized transitions. Full article

This paper exposes the latent but potent role of seemingly hidden spatial autocorrelation (SA) in all geographic theories, highlighting that it is everywhere, matters, and is a fundamental property of geotagged phenomena. This narrative examines and extends the literature about the inescapable nature of the SA paradigm and the near-universal mixing of positive and negative SA. This study summary transcends the widespread but often implicit treatment of SA within geographic theories that their assumptions help achieve when they embed spatial processes, shape geospatial expectations, and define independent areal units so that these theory-delineating constraints largely absorb SA, reducing residual spatial dependence/correlation and improving conjectural validity, masking its presence for decades if not centuries. This paper explores selected prominent human geography theories (spatial optimization, agricultural location, gravity-model-based spatial interaction, central place systems), cultural and humanistic geography, geohumanities abstractions, physical geography theories (plate tectonics, climatology, uniformitarianism, soil formation), cartographic theories (geometric projections, semiotic/communication, cognitive/perceptual, geographic information systems anchored spatial analysis), and basic geospatial data gathering methodologies (qualitative and quantitative spatial sampling). It demonstrates that across the discipline of geography, exposing masquerading SA deepens theoretical coherence and strengthens methodological integrity, encouraging integrated spatial reasoning that bridges interpretive and analytical traditions. This article concludes by providing exemplifications of bringing scholastically unrealized SA in geographic theories out of obscurity, together with certain salient benefits from doing so, affirming the magnitude of fulfilling its major objective: SA is poised for discovery in all geospatial theories, from those for human and humanistic geography, through physical geography, to those for cartography as well as methodologies concerning all georeferenced data collection missions. Full article

Over the last three decades, architecture has undergone a sustained digital transformation that has progressively displaced the ontology of the geometric generator, understood here as the primary artefact through which form is produced, controlled, and legitimized. This paper argues that, within one extended digital epoch, three successive regimes have reconfigured architectural agency. First, a digital model regime, in which computer-generated 3D models become the main generators of geometry. Second, a parametric code regime, in which scripted relations and numerical parameters supersede the individual model as the core design object, defining a space of possibilities rather than a single instance. Third, an emerging latent regime, in which diffusion and transformer systems produce high plausibility synthetic images as image-first generators and subsequently impose a post hoc image-to-geometry translation requirement. To make this shifting paradigm comparable across time, the paper uses the blob as a stable morphological reference and develops a comparative reading of four blobs, Kiesler’s Endless House, Greg Lynn’s Embryological House, Marc Fornes’ Vaulted Willow, and an author-generated GenAI blob curated from a traceable AI image archive, to show how the geometric generator migrates from object, to model, to code, to latent image-space. As a pre-digital hinge case, Kiesler is selected not only for anticipating blob-like continuity, but for clarifying a recurrent disciplinary tension, “ form first generators” that precede tectonic and programmatic rationalization. The central hypothesis is that GenAI introduces an ontological shift not primarily at the level of style, but at the level of architectural judgement and evidentiary legitimacy. The project can begin with a predictive image that is visually convincing yet tectonically underdetermined. To name this condition, the paper proposes the plausibility gap, the mismatch between visual plausibility and tectonic intelligibility, as an operational criterion for evaluating image-first workflows, and for specifying the verification tasks required to stabilize them as architecture. Selection establishes evidentiary legitimacy, while a friction map and Gap Index externalize the translation pressure required to turn predictive imagery into accountable geometry, making the plausibility gap operational rather than merely asserted. The paper concludes by outlining implications for authorship, pedagogy, and disciplinary judgement in emerging multi-agent design ecologies. Full article

Under rural revitalization and low-carbon development, the sustainable transformation of vernacular architecture has become an important research focus. Taking the Linpan as the research object, this study proposes an integrated design methodology that combines typological translation with ecological material logic for contemporary architectural design. The methodology decodes the Linpan spatial prototype—characterized by the “house–forest–field–water” structure—by abstracting key spatial relationships and translating them into contemporary architectural formal strategies, while incorporating locally grounded ecological materials to coordinate environmental performance and cultural continuity. The proposed approach is validated through the Daoming Zhuli project in Chengdu, where typological translation generates courtyard-centered layouts, semi-open transitional spaces, and bamboo-based envelope systems adapted to a humid subtropical climate. A scenario-based material comparison indicates that the use of local materials can significantly reduce embodied carbon emissions while reinforcing regional identity. In addition, comparative analyses of other vernacular settlements, including Huizhou ancient villages, Fujian Tulou, and Ait Benhaddou, are conducted to examine the methodological transferability across different climatic, spatial, and cultural contexts. This study contributes a design-oriented framework linking spatial typology and material selection, providing guidance for the sustainable renewal of Linpan and references for the contemporary adaptation of vernacular architecture in international contexts. Full article

Accurate prediction and optimized control of shield tunneling attitude are critical for ensuring tunneling quality and construction safety. In karst and other highly heterogeneous strata, complex geological conditions and construction parameters exhibit significant nonlinear coupling, greatly increasing the difficulty of attitude regulation. To address this challenge, this study proposes a machine learning-based approach for the automatic control of shield tunneling attitude. First, a Tree-structured Parzen Estimator-optimized Light Gradient Boosting Machine predictive model is employed to construct a nonlinear mapping model between construction parameters and shield tunneling attitude. Subsequently, the SHapley Additive exPlanations (SHAP) interpretability model is introduced to identify the core tunneling factors influencing attitude stability. On this basis, the developed predictive model is integrated into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework as a fitness function to achieve multi-objective optimization of key construction parameters. Using field data from shield tunneling construction in the karst strata of Shenzhen Metro Line 16, the proposed model achieved prediction accuracies of R² = 0.959 for pitch and R² = 0.936 for roll, outperforming XGBoost, Random Forest, Long Short-Term Memory, and Transformer baselines. SHAP analysis identified the partitioned propulsion thrust, partitioned chamber pressure, cutterhead rotational speed, and advance rate as key parameters influencing attitude. Further, MOEA/D optimization generated a Pareto set of construction parameters, from which the optimal solution was selected using the ideal point method, resulting in reductions of 26.45% and 39.47% in pitch and roll deviations, respectively. These findings demonstrate the feasibility and effectiveness of the proposed method in achieving high-precision prediction and intelligent optimization control of shield tunneling attitude under complex geological conditions, providing a reliable technical pathway for metro and tunnel construction projects. Full article

Green hydrogen production is a fundamental component of the sustainable energy transition; however, the success of such projects largely depends on the strategic selection of reliable and sustainable equipment suppliers. Supplier selection plays a critical role in aligning operational performance with long-term objectives, including technological competitiveness, environmental sustainability, and societal acceptance. Nevertheless, conventional multi-criteria decision-making (MCDM) approaches remain insufficient in adequately capturing the uncertainty, subjectivity, and group decision-making dynamics inherent in real-world supplier evaluation processes. To address this gap, this study proposes a group decision-making supported Spherical Fuzzy TOPSIS (SF-TOPSIS) framework for selecting sustainable green hydrogen production equipment suppliers. Within the model, ten evaluation criteria covering technical, economic, environmental, and social dimensions are defined to ensure alignment between supplier selection decisions and the strategic orientation of the business unit. The empirical findings, based on aggregated global fuzzy weights and relative closeness values, indicate that technical criteria such as electrolyzer efficiency and technical competence (C1), hydrogen safety (C2), and system robustness (C3) are decisive in the evaluation process. Moreover, the social criterion representing local supplier contribution and societal acceptance (C9) has been identified as playing a critical role, highlighting the increasing importance of social legitimacy and regional integration in sustainable hydrogen investments. These findings are derived directly from the model’s quantitative outputs, without relying on prior assumptions, reflecting the strategic significance of the criteria for operational reliability and long-term sustainability. The primary methodological contribution of this study lies in the development of a spherical fuzzy group decision-making framework capable of addressing multidimensional uncertainties across technical, economic, environmental, and social dimensions. This framework provides decision-makers with a reliable, systematic ranking tool for selecting sustainable hydrogen production equipment suppliers under complex uncertainty. From a practical perspective, the proposed model enables stakeholders to quantitatively assess trade-offs between technological performance and socio-economic impacts and serves as a guiding tool for strategic decision-making. Full article

The dried leaves of Turpinia arguta (Lindl.) Seem, a traditional Chinese medicinal herb, have been used for the treatment of tonsillitis, sore throat, throat arthralgia, and novel coronavirus pneumonia. This plant possesses significant medicinal, economic, and ecological values. Assessing its distribution patterns and its response to global climate change is critical for the conservation and sustainable use of its resources. This study used GIS technology and ENMTools v1.3 to select 247 distribution records of T. arguta and employed the kuenm R package (running on R v4.4.3, package version 2.0.1) to optimize the MaxEnt model parameters. Based on current and future climate data, this study predicted the current and future potential suitable areas of T. arguta in China during the periods of the 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100) under three SSP emission scenarios (SSP126, SSP245, and SSP585). Additionally, it identified the key environmental variables driving its distribution patterns and conducted a quality suitability regionalization analysis using sample chemical content data. The results show that under current climatic conditions, the highly suitable areas for T. arguta are mainly distributed across five provinces: Jiangxi, Guangdong, Guangxi, Fujian, and Hunan. The distribution of T. arguta is primarily influenced by precipitation and temperature. The suitable ranges of key environmental variables are as follows: average temperature in September > 26 °C (optimal range: 28–32 °C), precipitation in April 175–250 mm, precipitation in September 100–160 mm, annual mean temperature 20–30 °C (optimal range > 22.5 °C), and annual precipitation 1500–2000 mm (peak value: 1750 mm). Quality analysis reveals a positive correlation between ligustroflavone content and the mean diurnal temperature range, as well as between rhoifolin content and soil sand content. Compared with current suitable areas, the total suitable areas of T. arguta are projected to contract by varying degrees across all scenarios in the future. This study will provide a robust scientific basis for guiding the sustainable development/utilization of its resources and optimizing artificial cultivation practices. Full article

Accurate detection of road surface defects and their integration into geospatial representations are key requirements for scalable UAV-based inspection and maintenance systems.This work presents a lightweight processing pipeline that converts image-based pavement defect segmentations into compact geospatial vector representations suitable for integration with GIS-driven inspection workflows. In addition, we introduce and publicly release a UAV-based road defect dataset with pixel-level annotations, specifically designed for crack-like pavement damage. A deep convolutional neural network is trained to perform semantic segmentation of pavement defects using images derived from the publicly available RDD2022 dataset. Segmentation performance is evaluated across a range of probability thresholds using standard pixel-wise metrics, and a validation-selected operating point is used to generate binary defect masks. These masks are subsequently processed to identify individual defect instances and extract vector polygons that preserve the underlying geometry of crack-like structures. For illustrative geospatial integration, predicted defects are projected into geographic coordinates and exported in standard GIS formats. By transforming dense segmentation outputs into compact georeferenced polygons, the proposed framework bridges deep learning-based perception and GIS-based infrastructure assessment, enabling instance-level geometric analysis and providing a practical representation for UAV-based road inspection scenarios. Full article

Overhead transmission lines are crucial components of power grid construction, and their carbon emissions significantly impact the low-carbon construction of the power grid. This study adopts a cradle-to-gate life cycle assessment (LCA) method, defining the system boundary as the material production, transportation, and construction phases. Using the carbon-accounting software eFootprint and the emission factor method, we calculate and analyze the carbon emissions of a 500 kV double-circuit overhead transmission line project in Shantou, Guangdong Province, and systematically examine the emission characteristics from material production through construction. Results show that the material production phase dominates the carbon emissions of the project, accounting for 99.82% of the total emissions. Among them, conductors (49.41%) and tower materials (37.28%) are the core sources of carbon emissions, with a combined contribution of 86.69%. The findings highlight conductors and towers as key targets for emission reduction through strategies such as optimized material selection, adoption of high-strength lightweight alternatives, and modular construction techniques. However, this analysis has limitations: it is confined to a single subtropical coastal project, relies on industry-average emission factors from the CLCD database (with inherent methodological uncertainties), excludes operational and end-of-life phases, and should not be generalized without regional validation. While the study identifies key emission hotspots and potential mitigation levers, quantitative low-carbon design guidance requires project-specific data and full life-cycle assessment. Full article

Effective clash management is critical to the success of complex construction projects, yet BIM coordinators face severe information overload when modern detection tools generate thousands or even millions of collision reports, making interdisciplinary coordination increasingly difficult. This article presents a conceptual framework for using AI for collision triage in a Building Information Modeling (BIM) environment. Previous approaches have focused mainly on collision detection itself and simple, rule-based prioritization, rarely exploiting the potential of Artificial Intelligence (AI) methods for post-processing of results, which constitutes the main innovation of this work. The proposed framework describes a modular system in which collision detection results and data from BIM models, schedules (4D), and cost estimates (5D) are processed by a set of AI components, offering adaptive, data-driven decision support unlike static rule-based methods. These include: a classifier that filters out irrelevant collisions (noise), algorithms that group recurring collisions into single design problems, a model that assesses the significance of collisions by determining a composite ‘AI Triage Score’ indicator, and a module that assigns responsibility to the appropriate trades and process participants. The framework leverages supervised machine learning methods (gradient boosting algorithms, selected for their effectiveness with tabular data) for noise filtering, density-based clustering (HDBSCAN, chosen for its ability to detect clusters of varying densities without predefined cluster count) for clash aggregation, and multi-criteria scoring models for priority assessment. The article also discusses a potential way to integrate the framework into the existing BIM workflow and possible scenarios for its validation based on case studies and expert evaluation. The proposed conceptual framework represents a step towards moving from manual, intuitive collision triage to a data- and AI-based approach, which can contribute to increased coordination efficiency, reduced risk of errors, and better use of design resources. As a conceptual study, the framework provides a foundation for future empirical validation and its limitations include dependency on historical training data availability and the need for calibration to project-specific contexts. Full article