Search Results (153)

Selenium (Se) is a beneficial element involved in plant growth, metabolism, stress adaptation, and crop quality improvement, but its effects are strongly influenced by chemical form, application dose, plant species, growth stage, and environmental conditions. To integrate mechanistic understanding with global research trends, this study combines a concise mini-review with a bibliometric analysis of Se research in plants from 2000 to 2025. The mini-review summarizes Se speciation and bioavailability in the soil–plant–microbe system, root uptake and long-distance transport, metabolic assimilation and detoxification, physiological regulation, stress tolerance, biofortification, and nano-Se applications. Bibliographic data were retrieved from the Web of Science Core Collection and analyzed using CiteSpace, VOSviewer, and Scimago Graphica. A total of 3451 valid publications were identified, showing a sustained increase in annual output, especially after 2018. The field has expanded from early studies on Se speciation, uptake, assimilation, and antioxidant responses toward broader themes involving crop biofortification, molecular regulation, stress physiology, foliar application, nano-Se applications, green synthesis, and phytoremediation. Overall, plant Se research has evolved into an interdisciplinary field linking mechanistic studies with safe agricultural application. Future work should emphasize standardized experimental frameworks, causal mechanism validation, precise biofortification, field-based evaluation, and safety assessment of emerging Se-based technologies. Full article

Although exercise interventions have been shown to alleviate core symptoms of Autism Spectrum Disorder (ASD), the neural mechanisms underlying these improvements, particularly those involving the White Matter Network (WMN), remain poorly understood. This study investigated the effects of a Mini-Basketball Training Program (MBTP) on core symptoms and WMN in children with ASD. This study adopted a two-site cluster-Randomized Controlled Trial (cRCT) design. Participants from two special education centers in China were randomly assigned to either an intervention group (MBTP) or a control group (CON). The participants underwent a 12-week MBTP. Core symptom assessments and a Diffusion Tensor Imaging (DTI) scan were conducted before and after the intervention. The individual WMNs were constructed using Deterministic Fiber Tracking (DFT). Graph theoretical analysis was applied to examine changes in WMN topological properties after MBTP. The MBTP significantly improved core symptoms in children with ASD, alongside the decreased normalized clustering coefficient (Gamma, γ), characteristic path length (Lambda, λ), small-world attributes (Sigma, σ), and increased global efficiency (E_glob). The nodal clustering coefficient (NCC) increased in the left cuneus (CUN.L) and left cerebellum 9 (CRBL9.L). Notably, the increased NCC in CRBL9.L was significantly correlated with improvements in core symptoms following the MBTP. The improvement in core symptoms in children with ASD following exercise intervention is associated with the remodeling of the WMN, highlighting the cerebellum as a key node in this neural mechanism. Full article

Manual compliance auditing in cloud environments consumes up to 40% of IT security budgets annually, yet existing approaches verify control presence rather than effectiveness, leaving institutions vulnerable to adversarial evasion. This paper presents an AI-augmented hybrid ML–LLM compliance auditing system evaluated on Rwanda’s National Cyber Security Authority (NCSA) Minimum Cybersecurity Standards (169 controls across 14 families). The system combines leakage-free XGBoost multi-label classification with GPT-4o-mini semantic log analysis, grounded in a formal effectiveness model. Key findings: (1) XGBoost v2 achieves 85.45% macro-F1 on leakage-free synthetic data (Wilson 95% CI = [84.9%, 86.0%]); an initial 86.3% data-leakage rate artificially inflated prior results to 99.99% and was identified and corrected in this revision; (2) GPT-4o-mini achieves 92.3% macro accuracy across four log types (n = 628, 37.5% real enterprise data, Wilson CI = [89.9%, 94.3%]); (3) adversarial validation across five MITRE ATT&CK scenarios yields 92.8% macro detection with 0.0% false-positive rate on real SSH/PAM compliant logs (n = 75); (4) a cross-dataset generalization analysis confirms 87.6% F1 on real SSH logs but identifies a 37.8-percentage-point out-of-vocabulary gap for Windows and HTTP log types, motivating the hybrid architecture; (5) the combined hybrid system (XGBoost for in-vocabulary logs, GPT-4o-mini for out-of-vocabulary) achieves 85.1% F1 with 6.4% false-positive rate on 180 real-world logs. The system runs at 2.0 CPU cores, 2.66 GB RAM, on $50/month cloud hosting (Apple M1 Pro baseline; storage and maintenance excluded), producing audit reports in 2–5 s depending on log volume and policy document size, demonstrating that effectiveness-based compliance auditing is accessible without enterprise-grade infrastructure. Full article

Shock buffet is one of the critical issues affecting the aerodynamic performance, flight quality, and flight safety of large aircraft. To overcome the limitations of traditional experimental measurement methods, such as insufficient capability in capturing flow features and high cost, an integrated experimental system tailored for extreme cryogenic and high-Reynolds-number conditions is developed based on the conventional tuft technique. This system comprises “preparation of low-flow-disturbance fluorescent mini-tufts, high-efficiency large-area tuft taping, automatic generation of digital streamline, and flow topology analysis”. Furthermore, a technique for assessing the transonic shock buffet onset using dynamic flow visualization with fluorescent mini-tufts is proposed. This paper takes a typical supercritical airfoil as the research object. First, through high-precision numerical simulations, it reveals that low-energy, unstable boundary-layer separation is the core driving force for the development and maintenance of shock buffet, and that flow separation characteristics serve as an important basis for determining the shock buffet onset. Subsequently, experimental validation is conducted in a 0.3 m high-Reynolds-number transonic wind tunnel. Using a dual-excitation-band composite light source, simultaneous measurements of pressure-sensitive paint (PSP) and fluorescent mini-tuft patterns are realized. The experimental results show that under extreme conditions, characterized by a wide total temperature range of 110 K to 280 K and strong scouring at Mach numbers from 0.6 to 0.9, the fluorescent mini-tufts (approximately 0.05 mm in diameter) exhibit excellent flow-following capability without any detachment. The digitized flow patterns of the fluorescent mini-tufts, obtained via computer image recognition algorithms, clearly reveal the location and area of boundary-layer separation. The trends show good agreement with the cryogenic PSP results, providing an important reference for determining the shock buffet onset. Full article

Real-time tracking of high-speed targets in autonomous systems requires detection and decision-making pipelines that can operate within sub-millisecond time budgets. Single Photon Avalanche Diode (SPAD) sensors are well suited for this task, offering 10 kHz Time-of-Flight (ToF) measurements with picosecond timing precision. However, processing such high-frequency time-series data with conventional deep learning models introduces computational bottlenecks that are difficult to handle on resource-constrained embedded hardware. This paper presents an ultra-lightweight, dual-head architecture built on the MiniRocket transformation algorithm, where a single shared feature extractor simultaneously feeds two independent decision pathways: one for multi-class target classification and one for 3-parameter kinematic regression covering velocity, pitch, and yaw. As a single-pixel sensor, the device provides only 1D range information; lateral 3D spatial localization is outside the scope of this work. To the best of the authors’ knowledge, this is the first application of MiniRocket to continuous kinematic estimation from high-frequency sensor data. Since collecting labeled physical flight data at these speeds is largely infeasible, a physics-based ray-casting simulation was developed to generate a 55,440-sample dataset across four 3D CAD target models under varying speed (100–450 m/s), orientation, and noise conditions. The proposed architecture achieves 98.6% classification accuracy and a velocity Mean Absolute Error (MAE) of 0.26 m/s, with orientation estimation yielding a pitch MAE of 3.47° and a yaw MAE of 2.46°—values consistent across all five cross-validation folds, indicating that the orientation performance floor is governed by the sensor’s physical angular resolution rather than by model capacity. With approximately 27,000 trainable parameters, the system completes full dual-task inference in 0.56 ms on a 16-core CPU (1785 Frames Per Second-FPS), satisfying the 1 ms real-time constraint of a 10 kHz sensor without GPU acceleration. It should be noted that the single-pixel SPAD architecture provides only 1D range-along-beam information; full 3D spatial localization is physically not extractable from a single sensor and is not addressed in this study. Full article

Background/Objectives: Liuwei Dihuang (LWDH) is a classical plant-derived herbal formula widely used for cognitive decline. This study aimed to evaluate its efficacy and safety in cognitive disorders and to explore its potential pharmacological mechanisms using network pharmacology. Methods: We searched 11 databases through November 2024 for randomized controlled trials comparing LWDH plus conventional therapy with conventional therapy alone in cognitive disorders. Meta-analysis was performed for clinical outcomes, and herb–compound–target and disease-target datasets were integrated to identify core molecular modules. Results: Twelve randomized controlled trials involving 1137 participants were included. Adjunctive LWDH was associated with improvements in Mini-Mental State Examination scores (MD = 2.34, 95% CI 0.88–3.79), activities of daily living, and quality of life. However, substantial heterogeneity and methodological limitations, including unclear randomization and blinding, were observed across studies, indicating a potential risk of bias. Fewer adverse events were reported in the LWDH plus conventional treatment group, although reporting quality was limited. The overall risk of bias was judged as “some concerns”. Network pharmacology analysis identified a broad set of overlapping genes between LWDH-associated targets and cognitive disorder-related genes, which were further refined through filtering procedures. Subsequent analyses suggested associations with pathways related to neurodegeneration, apoptosis, and central nervous system function; however, these findings are exploratory and based on in silico predictions. Conclusions: LWDH may be associated with potential adjunctive benefits in cognitive disorders. However, given the methodological limitations and clinical heterogeneity of the included studies, the findings should be interpreted with caution. The proposed pharmacological mechanisms are exploratory and require further validation. Well-designed randomized controlled trials are needed to establish more robust evidence. Full article

Background: Language impairment is a core feature of Major Neurocognitive Disorder (MND), yet the domain-specific relationship between language functioning and everyday functional status remains insufficiently characterized. Methods: We conducted a retrospective observational study in 125 older adults diagnosed with MND according to DSM-5 criteria with mild-to-moderate cognitive impairment measured with Mini-Mental State Examination (MMSE). Language performance was assessed using semantic, phonemic verbal fluency and confrontation naming. Functional status was evaluated using basic (BADL) and instrumental activities of daily living (IADL). Ordinal logistic regression models examined associations between language domains and functional outcomes, adjusting for global cognitive status (MMSE), demographic variables, multimorbidity, and depressive symptoms. Model fit was evaluated using the Akaike Information Criterion. Results: Semantic fluency emerged as the best-performing predictor of BADL across all hierarchical models, remaining statistically significant after full adjustment for MMSE and clinical covariates (β ≈ 0.60, p < 0.05). Phonemic fluency showed the most robust association with IADL, with a stable effect across models, reaching a trend toward statistical significance in the fully adjusted analyses (β ≈ 0.22–0.27, p = 0.069). Naming ability did not influence functional outcomes. All observed associations persisted after controlling for MMSE, demographic variables, multimorbidity, and depressive symptoms. Conclusions: Language abilities showed differential associations across language domains with functional status in this sample of patients with MND. Semantic fluency was associated with basic self-care, while phonemic fluency showed a trend toward association with instrumental daily activities. These relationships remained observable after adjustment for global cognitive impairment, suggesting verbal fluency as a potentially sensitive marker of functional vulnerability. Full article

As China’s urbanization transitions from incremental expansion to stock-based renewal, industrial land redevelopment has become a key pathway for promoting high-quality urban development. However, existing studies mostly assess redevelopment potential from a single dimension and lack a systematic framework integrating ecological function (E), spatial structure (S), economic conditions (C), and building foundations (B). Taking the built-up area of Xi’an as a case study, this study adopts a functional–structural optimization perspective and constructs a four-dimensional ESCB assessment framework based on 13 indicators covering ecological function, spatial structure, economic conditions, and building foundations. GIS-based spatial quantification, MiniBatchKMeans clustering, and the XGBoost algorithm were employed to identify the redevelopment potential of industrial land, while SHAP analysis was used to interpret indicator contributions and determine the core influencing factors. The results show that industrial land in the study area can be classified into four types: vitality–density dominant, transport–scale coordinated, scale–facility lagging, and topography–vegetation sensitive, with significant differences in spatial distribution and indicator characteristics. The interpretable machine learning model further identifies road network density, block-level economic vitality, and land-use suitability as the three principal drivers of redevelopment potential, among which road network density plays the most critical role. By integrating clustering analysis with interpretable machine learning, the ESCB framework effectively reveals the synergies and trade-offs among multidimensional indicators and provides differentiated and precise support for industrial land redevelopment strategies. Full article

Urban regeneration has traditionally focused on large-scale developments that aim at increasing the livability and vitality of disadvantaged areas. Alternative views of urban regeneration have emerged to challenge such a structural approach. These novel ideas reflect contextual changes in progressive and innovative Western countries that embrace the culture of experimentation, prefer sharing to ownership, and emphasize participation and inclusion as fundamental aspects of public governance. This article elaborates the idea of lean urban regeneration in the progressive welfare society context, with a special view of citizen and stakeholder involvement through inclusion, sharing, and co-creation. Empirical research utilizes mini cases of the largest cities in the growth triangle of Finland. This article identifies the manifestations of lean urban regeneration and discusses its preconditions and ability to tackle urban development challenges. The results emphasize the framing nature of inclusion, the underutilization of sharing, and the key role of co-creation in lean urban regeneration. A particular potential of lean interventions is based on co-creation as the core of multimodal or hybrid regenerative projects that are firmly anchored on economic inclusion. By utilizing the input of residents, entrepreneurs, and other local stakeholders, it is possible to open up a path to integrated high-leverage activities with a potential to alleviate structural urban problems. Full article

Retrieving farmland soil water content with both high accuracy and physical interpretability remains a significant challenge, particularly for winter wheat. To bridge the gap between purely empirical data-driven approaches and mechanistic scattering models, this study proposed a novel hybrid framework that integrates an improved water cloud model (IWCM) with machine learning algorithms. Multi-modal unmanned aerial vehicle (UAV) experiments were conducted during the heading stage of winter wheat over two consecutive years (2024–2025) using a synchronized system equipped with a miniature synthetic aperture radar (MiniSAR) and a multi-spectral sensor. The core innovation of the proposed framework lies in the IWCM, which explicitly decouples vegetation and soil scattering contributions by incorporating fractional vegetation cover, thereby deriving physically meaningful soil backscatter coefficients from complex microwave signals. Unlike traditional methods that treat remote sensing variables as black box inputs, our approach employed these physics-derived features to guide data-driven modeling. Four feature input schemes including spectral reflectance, vegetation indices, MiniSAR polarimetric parameters, and their multi-source fusion were systematically evaluated using back propagation neural network (BPNN) and random forest (RF) regressors. The results demonstrated that the proposed framework significantly enhances retrieval performance. Notably, the RF model driven by spectral band reflectance within this physically constrained architecture achieved optimal accuracy, with a coefficient of determination (R²) of 0.865, a mean absolute error (MAE) of 0.0152, and a root mean square error (RMSE) of 0.0197. Compared to purely empirical approaches, the IWCM significantly improved the physical interpretability of microwave polarimetric characteristics, enabling the multi-source data fusion to better represent the interactions among vegetation, soil, and microwave scattering. This study demonstrated that integrating mechanistic models with multi-source UAV remote sensing data not only improves soil water content retrieval accuracy in winter wheat fields but also provides a valuable reference for developing operationally applicable and physically interpretable farmland soil water content monitoring systems. Full article

In a rapidly changing era, education systems must empower learners as community innovators through Place-Based Education (PBE). While School–University partnerships are global drivers of reform, the specific administrative mechanisms required to support and scale these innovations within decentralized policy frameworks, such as Thailand’s Education Sandbox, remain underexplored. This Research and Development (R&D) study, integrated with a Design Thinking framework, investigated school-led administrative innovations across four diverse jurisdictions in the Songkhla Education Sandbox over 12 months. The study synthesized a collaborative administrative framework structured around four core pillars: Strategic Mentoring and Thinking Partnership, Place-Based Educational Ecosystems, Adaptive Governance and Resource Autonomy, and Collective Synergy and Iterative Development. Empirical findings indicate that this framework supported the development of “Innovative Co-creator” characteristics among students, generating high-value outcomes such as “Songkhla Mini Mango Coffee” and social innovations from water hyacinth. The study concludes that educational transformation thrives when administrative structures shift from compliance-driven mandates to flexible, context-responsive partnerships. By integrating university-led coaching with community assets, the framework offers a promising, contextually adaptable model for enhancing student learning outcomes while preserving local socio-cultural identity. This systematic approach supports the continuity of educational reform across diverse regional contexts. Full article

Mini-LED local dimming systems commonly suffer from luminance discontinuity, halo artifacts, and temporal instability in dynamic scenes. Traditional heuristic-based methods and standard convolutional neural networks often fail to capture long-range spatial dependencies and struggle to balance spatial smoothness, content fidelity, and real-time performance under hardware constraints. To address these challenges, this paper proposes SwinLightNet, an efficient adaptive backlight optimization network tailored for Mini-LED displays. Built upon a Swin Transformer framework tailored for Mini-LED backlight optimization, SwinLightNet integrates five hardware-aware design strategies: (i) a lightweight Swin variant (window size = 8, MLP ratio = 2.0) for efficient global context modeling; (ii) CNN encoder–decoder integration for multi-scale feature extraction; (iii) a partition-level alignment module ensuring spatial consistency; (iv) a backlight constraint module enforcing local luminance consistency and contrast preservation; (v) a change-aware temporal decision framework stabilizing dynamic sequences. These components synergistically resolve core limitations: global modeling suppresses halo artifacts while preserving content fidelity; alignment and constraint modules eliminate luminance discontinuity without compromising contrast; and the temporal framework guarantees flicker-free output under motion. Evaluated on DIV2K (static images) and a custom 2K-resolution video dataset (dynamic scenes), SwinLightNet demonstrates robust reconstruction quality while maintaining only 1.18 million parameters and 0.088 GFLOPs (Computational Cost). The results confirm SwinLightNet’s effectiveness in holistically addressing spatial, temporal, and hardware constraints, demonstrating strong potential for practical deployment in resource-constrained Mini-LED backlight control systems. Full article

Occupant presence is a primary driver of Heating, Ventilation, and Air Conditioning (HVAC) and lighting energy consumption in office environments. Existing occupancy-sensing solutions often rely on privacy-sensitive modalities or require costly infrastructure, limiting their applicability in Small and Medium Offices (SMOs). To address these limitations, this study proposes a lightweight CSI-based occupancy-sensing framework based on a dual-core ESP32-S3 architecture, enabling concurrent CSI processing, environmental sensing, and cloud communication. A multi-stage signal preprocessing pipeline compresses raw CSI streams into a compact $56\times 8$ statistical feature matrix, achieving 98.86% classification accuracy for multi-level occupancy estimation. Compared with image-based baselines such as DenseNet121, the proposed approach reduces input data size to 24 kB and model parameters to 138 K, yielding over 129× reduction in transmission volume without sacrificing performance. These results demonstrate that the proposed framework provides a practical, privacy-preserving, and edge-deployable solution for occupancy-aware energy management in SMOs. Full article

Introduction: Large language models (LLMs) have proven effective in many different fields, including the allocation of scarce resources. Triage within emergency departments (ED) is a core process that ensures the sickest patients are seen in a timely manner. Relatively little research has examined the use of existing LLMs in the triage process. Methods: 12 widely available LLMs were provided with real-world patient triage vital sign data from an academic trauma center in a major metropolitan area. The LLMs were asked to assign a triage score to each patient based on this information alone. The deviation between each LLM triage score and the real-world triage score for each patient was calculated, and the absolute value of the deviation was calculated and then averaged across the entire dataset per LLM. The average absolute value of deviation (AAVD) could then be used to compare LLMs against each other. All LLMs were blinded to the real-world triage score and received no additional training or instruction. Results: The models with the highest concordance with real-world triage scores were Claude Sonnet 4.5 (AAVD: 0.37; 62.37% concordance), ChatGPT-5 Instant (AAVD: 0.39; 62.89% concordance), and Claude Opus 4.1 (AAVD: 0.40; 62.37% concordance). The least accurate models were Gemini 2.5 Flash (AAVD: 0.42; 43.81% concordance), ChatGPT-4o Mini (AAVD: 0.49; 45.36% concordance), and ChatGPT-o3 (AAVD: 0.48; 48.45% concordance). Conclusions: This study analyzes the ability of LLMs to triage emergency department patients based primarily on vital sign data. Certain LLMs demonstrated moderate concordance with real-world triage scores. LLMs may be able to synthesize objective vital sign data and provide a triage recommendation. Further study could involve clinical validation against patient outcomes. Full article

This study aimed to develop an automated irrigation system for substrate-grown strawberry plants and to evaluate whether irrigation and biostimulation levels influence yield and fruit quality. The system comprised two Arduino Pro Mini devices equipped with LoRa transceivers, substrate moisture sensors, and servomotors for valve control. Six biostimulants were assessed [control (without biostimulation), microalga Spirulina platensis (SP), mycorrhiza Scutellospora heterogama (SH), a mycorrhizal community (SJ CS), SP + SH, and SP + SJ CS] under four irrigation levels [reference tension of 5 kPa (moderate water deficit), 10% above the reference tension (severe water deficit), 10% below the reference tension (mild water deficit), and standard irrigation without restriction] defined by substrate water tension. Data were collected in real time and analyzed using the InfluxDB (version 3 Core) and Grafana (version 12.3.2) platforms. The automated system-controlled valve activation was based on moisture sensor readings, enabling the establishment of irrigation levels supported by energy-efficient technologies. Under standard irrigation, fruits exhibited lower acidity and improved flavor compared to those from plants under water deficit. Plants subjected to mild water deficit or standard irrigation achieved higher yields than those exposed to moderate or severe deficit. Fruits produced by plants treated with S. heterogama showed higher phytochemical concentrations. Overall, the findings support the use of automated irrigation and biostimulation as sustainable management strategies to enhance water use efficiency, productivity, and fruit quality in soilless strawberry cultivation. Full article