Search Results (5,296)

Background: Goalkeeper actions directly influence match outcomes and overall team performance. Neuromuscular determinants (e.g., perception–action coupling, reaction speed, rate of force development (RFD), balance, etc.) translate into higher save probability, faster second actions, and more accurate distribution. Objectives: This systematic review analysed neuromuscular performance factors in 11-a-side football goalkeepers and examined how these factors evolve across age and sex groups. Methods: The review adhered to the PRISMA 2020 Statement guidelines. A systematic search was conducted using the PubMed, Web of Science, SPORTDiscus, PsycINFO, Dialnet, LILACS, and Scopus databases. Studies that assessed any aspect of goalkeepers’ neuromuscular performance except for aerobic endurance and VO₂ max (due to the short duration of goalkeeping actions) were included, regardless of the type of observational design. Results: Thirty-five studies were finally included in the synthesis, encompassing neuromuscular performance factors such as agility, speed, anaerobic power, strength, flexibility, and dynamic balance and coordination. The findings underscore the need for neuromuscular training for goalkeepers, particularly agility training. Neuromuscular performance improves with age, especially in linear speed, agility, change-of-direction speed, strength, and power; however, flexibility shows no significant progression. This review identifies key tests for evaluating goalkeepers’ neuromuscular capacities across major performance domains. Conclusions: Although sex differences are apparent, the main limitation is the lack of research on neuromuscular performance in male and female goalkeepers, making it difficult to define indicators for different age and sex categories. Full article

Ensuring food security amidst increasing non-grain utilization of cultivated land is a critical challenge in major grain-producing regions. This study analyzes the spatio-temporal evolution and driving mechanisms of non-grain cultivated land in Henan Province, China, from 2012 to 2023, using spatial autocorrelation, multiple linear regression, geographically and temporally weighted regression model, and cluster analysis. Results show that the non-grain ratio exhibited a fluctuating yet overall increasing trend, from 27.47% in 2012 to 25.91% in 2017 and reaching 30.28% in 2023, with higher values in the northern and southwestern counties of the province. Spatial clustering patterns remained relatively stable, characterized by a “high–high clustering in the southwest and low–low clustering in the north,” which was further substantiated by significant Global Moran’s I values (0.362 in 2012 and 0.307 in 2023). Key drivers included per capita level of agricultural mechanization, labor force per unit of cultivated land area, output value per unit of cultivated land area, and per capita disposable income of rural residents. PCA and K-means clustering identified three zonal types: agricultural production support (45.10% of counties), agricultural production weakening (35.29% of counties), and economically location-guided (19.61% of counties). The findings underscore the need for differentiated policies—such as precision subsidies, land consolidation, and ecological farming practices. This study provides a scientific basis for zonal governance of non-grain cultivated land in grain-producing areas. Full article

Applied muscular strain and hamstring strain capacity have a joint interaction on hamstring strain injury (HSI) with modifiable risk factors frequently assessed. However, to date there is limited observations on the interaction between these factors. The purpose of the present study was to observe if spatiotemporal characteristics, running kinematics and muscle activation were related to modifiable risk factors of HSI. Twenty-two competitive team sport athletes (24.7 ± 4.3 years, 1.82 ± 0.07 m, 84.9 ± 8.5 kg) participated whereby the Bicep femoris long head (BF_LH) fascicle length assessed via ultrasound and isokinetic eccentric hamstring strength was assessed. With running assessment performed at 18 km/h, capturing running kinematics and muscle activation. Multiple linear regressions were used to examine the relationship of running kinematics and muscle activation on the modifiable risk factors of HSI on. The overall model (F_2,19) was statistically significant for both relative eccentric hamstring strength (F = 23.58, p < 0.001) and BF_LH fascicle length (F = 18.87, p < 0.001) highlighting spatiotemporal characteristics, running kinematics and hamstring activation were found to be significantly related to the modifiable risk factors. There is a complex interrelationship between running mechanics and hamstring muscle properties, with the potential of either cause or consequence association. Full article

This study reports the synthesis and comprehensive spectroscopic characterization of 4-indolylcyanamide (4ICA), a novel indole-derived infrared (IR) probe designed for assessing local microenvironments in biological systems. 4ICA was synthesized via a two-step procedure with an overall yield of 43%, and its structure was confirmed using high-resolution mass spectrometry and ¹HNMR. Fourier Transform Infrared (FTIR) spectroscopy revealed that the cyanamide group stretching vibration of 4ICA exhibits exceptional solvent-dependent frequency shifts, significantly greater than those of conventional cyanoindole probes. A strong linear correlation was observed between the vibrational frequency and the combined Kamlet–Taft parameter, underscoring the dominant role of solvent polarizability and hydrogen bond acceptance in modulating its spectroscopic behavior. Quantum chemical calculations employing density functional theory (DFT) with a conductor-like polarizable continuum model (CPCM) provided further insight into the solvatochromic shifts and suppression of Fermi resonance in high-polarity solvents such as DMSO. Additionally, IR pump–probe measurements revealed short vibrational lifetimes (~1.35 ps in DMSO and ~1.13 ps in ethanol), indicative of efficient energy relaxation. With a transition dipole moment nearly twice that of traditional nitrile-based probes, 4ICA demonstrates enhanced sensitivity and signal intensity, establishing its potential as a powerful tool for site-specific environmental mapping in proteins and complex biological assemblies using nonlinear IR techniques. Full article

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring. Full article

Ammonia borane is a promising hydrogen storage material due to its high hydrogen content, but its use as hydrogen carrier under thermal stimuli involves the production of several byproducts, such as borazine, reducing hydrogen purity and the overall efficiency. This work is focused on the use of high-boiling-point amines to modulate ammonia borane decomposition, aiming to enhance hydrogen release and suppress volatile N_xB_y species. Kissinger’s equation kinetics revealed that amines significantly influence the decomposition mechanism, and TGA-IR investigation showed a maximum of 2.4 wt.% of pure hydrogen release in the presence of triphenyl amine. Furthermore, the experimental data herein discussed, together with a computational study of activation energies, allowed us to derive a detailed mechanism that leads to a foundation for further advancement in the exploitation of ammonia borane as a hydrogen carrier, suggesting that the formation of linear species is anchored to amine over the release of borazine and production of poly borazine-like species. Full article

The quality and durability of adhesive joints in wood flooring are determined by both the type of adhesive and the parameters of the bonding process. This study examines the effects of pressing time and seasoning time on the bending strength of adhesive joints in the middle layer of floorboards manufactured using innovative block-bonding technology. Experimental trials were conducted with two adhesive systems—polyvinyl acetate (PVAC) and polyurethane (PUR)—using a full factorial design and statistical evaluation of joint strength in terms of pressing time and seasoning time. For PVAC, an overall tendency toward increased strength with extended pressing time was observed; however, the strongest effects were associated with interactions between pressing and seasoning times, with the most favorable results obtained for short pressing (5 min) combined with extended seasoning (5 h). In the case of PUR, the relationships were non-linear, and the only statistically significant factor was the interaction between pressing and seasoning times, confirming the necessity of joint optimization. The findings demonstrate that simple one-factor analyses are insufficient to explain adhesive performance, as non-linear and interaction effects are critical in defining joint strength. The results provide new insights for optimizing bonding processes in floorboard production, supporting improvements in material efficiency and mechanical reliability of wood flooring. Full article

According to previous research, young children’s numeracy skills may be scaffolded by practicing on the number line. A number line estimation task (NLET) is often conducted with pen and paper, while linear number games are often implemented on a computer or a tablet. If and how the format—physical or digital—influences the accuracy of the estimations is, however, not well-known. If regarding NLET performance as dependent on specific strategies and hypothesizing that these strategies may be affected by the material used, we may also assume that different materials may either support or hinder children’s learning. In this paper, we explore whether training with a physical versus a virtual NLET game will affect children’s strategies when solving NLETs, and if these strategies relate to the accuracy of the estimations. Sixty-two 6-year-old children played an NLET game (virtual or physical) for three sessions, being scaffolded and guided by a researcher. NLET performance was measured by pre- and post-tests, as well as during the intervention. The results show that even if the condition did not significantly affect the children’s overall numeracy skills, the children in the physical condition did express more advanced strategies during the intervention. These strategies, in turn, predicted NLET performance. Full article

As part of non-terrestrial networks (NTN), the Low Earth Orbit (LEO) plays a critical role in supporting high-throughput wireless communication. However, the high-speed mobility of LEO satellites, coupled with the high density of user terminals, makes efficient user assignment crucial in maintaining overall wireless performance. The suboptimal assignment from LEO satellites to user terminals can result in frequent unnecessary handovers, rendering the user terminal unable to receive the entire downlink signal. Consequently, it reduces user rate and user satisfaction metrics. However, finding the optimum user assignment to reduce handover issues is categorized as a non-linear programming problem with a combinatorial number of possible solutions, resulting in excessive computational complexity. Therefore, this study proposes a distributed user assignment for the LEO networks. By utilizing message-passing frameworks that map the optimization problem into a graphical representation, the proposed algorithm splits the optimization problem into a local mapping issue, thereby significantly reducing computational complexity. By exchanging small messages iteratively, the proposed algorithm autonomously determines the near-optimal solution. The extensive simulation results demonstrate that the proposed algorithm significantly outperforms the conventional algorithm in terms of user rate and user satisfaction metric under various wireless parameters. Full article

Intelligent reflecting surface-assisted unmanned aerial vehicles (IRS-UAVs) have been widely applied in various communication scenarios. This paper addressed the uplink communication problem in wireless sensor networks (WSNs) by proposing a novel double IRS-UAVs assisted framework to improve the pairwise sum rate. Specifically, nodes with relatively short signal transmission distances upload signals via a single-reflection link, while nodes with relatively long distances upload signals through a dual-reflection link involving two IRSs. Within each work cycle, the IRS-UAVs followed a fixed service sequence to cyclically assist all sensor node pairs. We designed a joint optimization algorithm that simultaneously optimized the UAV trajectories and IRS phase shifts to maximize the pairwise sum rate while guaranteeing each node’s transmission rate meets a minimum quality of service (QoS) constraint. Specifically, we introduce slack variables to linearize the inherently nonlinear constraints arising from interdependent variables, thereby transforming each subproblem into a more manageable form. These subproblems are then solved iteratively within a coordinated optimization framework: in each iteration, one subproblem is optimized while keeping variables of others fixed, and the solutions are alternately updated to refine the overall performance. The numerical results show that this algorithm can effectively optimize the flight trajectory of the unmanned aircraft and significantly improve the pairwise total rate of the system. Compared with the two traditional schemes, the average optimization rates are 11.91% and 16.36%. Full article

Background: Inflammation of unknown origin (IUO) represents a persistent clinical challenge, often requiring extensive diagnostic efforts despite nonspecific inflammatory findings such as elevated C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). The complexity and heterogeneity of its etiologies—including infections, malignancies, and rheumatologic diseases—make timely and accurate diagnosis essential to avoid unnecessary interventions or treatment delays. Objective: This study aimed to evaluate the potential of machine learning (ML)-based models in distinguishing the major etiologic subgroups of IUO and to explore their value as clinical decision support tools. Methods: We retrospectively analyzed 300 IUO patients hospitalized between January 2023 and December 2024. Four binary one-vs-rest Linear Discriminant Analysis (LDA) models were first developed to independently classify infection, malignancy, rheumatologic disease, and undiagnosed cases using clinical and laboratory parameters. In addition, a multiclass LDA framework was constructed to simultaneously differentiate all four diagnostic groups. Each model was evaluated across 10 independent runs using standard performance metrics, including accuracy, sensitivity, specificity, precision, F1 score, and negative predictive value (NPV). Results: The malignancy model achieved the highest performance, with an accuracy of 91.7% and specificity of 0.96. The infection model demonstrated high specificity (0.88) and NPV (0.86), supporting its role in ruling out infection despite lower sensitivity (0.71). The rheumatologic model showed high sensitivity (0.81) but lower specificity (0.73), reflecting the clinical heterogeneity of autoimmune conditions. The undiagnosed model achieved very high accuracy (96.7%) and specificity (0.98) but limited precision and recall (0.50 each). The multiclass LDA framework reached an overall accuracy of 73.3% (mean 66%) with robust specificity (0.90) and NPV (0.89). Conclusions: ML-based LDA models demonstrated strong potential to support the diagnostic evaluation of IUO. While malignancy and infection could be predicted with high accuracy, rheumatologic diseases required integration of additional serological and clinical data. These models should be viewed not as stand-alone diagnostic tools but as complementary decision-support systems. Prospective multicenter studies are warranted to externally validate and refine these approaches for broader clinical application. Full article

Spatial density, as a key indicator of the quality of the urban residential environment, comprises both physical and perceived dimensions. Physical density refers to objective spatial characteristics (e.g., building density and population density), whereas perceived density denotes residents’ perceptual evaluations (e.g., perceived crowding, visual openness, and overall environmental quality). Clarifying the relationship between physical and perceived density is therefore critical for advancing livability-oriented urban planning and design. This study examines the relationship through an empirical analysis of 50 representative high-density communities in Guangzhou. Using morphological classification, descriptive statistics, and multiple linear regression, the analysis compares objective density indicators with residents’ perceptual evaluations and identifies key environmental factors that shape perceived density. Findings indicate that physical and perceived density are not fully aligned: compact but coherent spatial forms can enhance residents’ perceptual evaluations, whereas overcrowded and deteriorating environments intensify negative perceptions. The identified community typologies—for example, urban villages, traditional walk-up estates, and modern high-rise complexes—exhibit distinct perceptual patterns and influencing factors. These results highlight the need for density regulation to move beyond conventional physical indicators and to incorporate perceptual dimensions into planning frameworks. Overall, the study provides theoretical insights and practical guidance for tailored strategies in the renewal and management of high-density communities. Full article

This paper presents a baseline study on automatic Uzbek text classification. Uzbek is a morphologically rich and low-resource language, which makes reliable preprocessing and evaluation challenging. The approach integrates Term Frequency–Inverse Document Frequency (TF–IDF) representation with three conventional methods: linear regression (LR), k-Nearest Neighbors (k-NN), and cosine similarity (CS, implemented as a 1-NN retrieval model). The objective is to categorize school learning materials by grade level (grades 5–11) to support improved alignment between curricular texts and students’ intellectual development. A balanced dataset of Uzbek school textbooks across different subjects was constructed, preprocessed with standard NLP tools, and converted into TF–IDF vectors. Experimental results on the internal test set of 70 files show that LR achieved 92.9% accuracy (precision = 0.94, recall = 0.93, F1 = 0.93), while CS performed comparably with 91.4% accuracy (precision = 0.92, recall = 0.91, F1 = 0.92). In contrast, k-NN obtained only 28.6% accuracy, confirming its weakness in high-dimensional sparse feature spaces. External evaluation on seven Uzbek literary works further demonstrated that LR and CS yielded consistent and interpretable grade-level mappings, whereas k-NN results were unstable. Overall, the findings establish reliable baselines for Uzbek educational text classification and highlight the potential of extending beyond lexical overlap toward semantically richer models in future work. Full article

Natural Deep Eutectic Solvents (NADES) were synthesized from food-grade components and evaluated as green extractants for the simultaneous recovery and liquid chromatography coupled to quadrupole-Orbitrap mass spectrometry (LC–Q-Orbitrap-MS) analysis of biopesticide residues in a complex matrix like honey. Conventional solid–liquid extraction (SLE) was applied, initially using choline chloride-2,3-butanediol (1:4, molar ratio) as the NADES extractant solvent, before systematically evaluating other NADES formulations. Extraction parameters, such as time (10 min, 20 min, and 30 min), technique (rotary mixing vs. sonication), and NADES composition, namely lactic acid–glucose–water (LGH, 5:1:9, molar ratio), lactic acid–glycerol–water (LGLH, 1:1:3, molar ratio), urea–glycerol–water (UGLH, 1:1:2, molar ratio), and choline chloride–2,3-butanediol (ChClBt, 1:4, molar ratio), were systematically optimized. Rotating agitation for 10 min yielded the highest overall recoveries and was therefore selected as the optimal extraction time. Rotary shaking was chosen over sonication due to its superior performance across both simple and complex matrices. Among the NADES tested, UGLH proved to be the most effective composition for the honey matrix. The analytical method was validated for the honey matrix. Linearity showed excellent performance across the tested concentration range, with R² values above 0.95 for all analytes. Matrix effects were within ±20% for nearly half of the compounds, while a few exhibited moderate matrix enhancement. Recoveries ranged from 50.1% to 120.5% at 500 µg/kg and 1000 µg/kg, demonstrating acceptable extraction performance. Intra-day and inter-day precision showed relative standard deviations (RSDs) below 20% for most analytes. Limits of quantification (LOQs) were established at 500 µg/kg for eight compounds based on recovery and precision criteria. These results confirm the suitability of the proposed NADES-based method for sensitive and reliable analysis of biopesticide residues in honey. When compared to conventional extraction methods, the proposed NADES-based protocol proved to be a greener alternative, achieving the highest AGREEprep score due to its use of non-toxic solvents, lower waste generation, and overall sustainability. Full article

Background: Breastfeeding promotion is a public health priority in China, yet the exclusive breastfeeding rate remains below national targets. Nursing students, as future key promoters, often report insufficient knowledge, but their attitudes are less clear. Objective: This study aimed to assess breastfeeding attitudes and identify their associated factors among Chinese nursing undergraduates, thereby providing an evidence base for the design of effective educational interventions. Design, Setting and Participants: A cross-sectional study was conducted from October 2024 to January 2025 at a medical university in Anhui Province, China, with 753 nursing students participating. Methods: The participants completed the General Information Questionnaire, the Chinese version of the Comprehensive Breastfeeding Knowledge Scale (CBKS), and the Iowa Infant Feeding Attitude Scale (IIFAS). We analyzed the data via Spearman correlation, univariate analysis, and multiple linear regression. Results: The overall IIFAS score for nursing students was 54 (51, 59), with attitude scores showing a significant positive correlation with knowledge (r = 0.462, p < 0.001). Multiple linear regression revealed that breastfeeding attitudes were significantly predicted by CBKS score (β = 2.975), grade (β = 2.887), major (β = 3.235), and breastfeeding intention (β = 8.089, all p < 0.001), as well as by feeding type before six months (β = −1.591, p = 0.020). The overall model accounted for 32.7% of the variance (R² = 0.327, F = 51.666, p < 0.001). Conclusions: This study demonstrates that Chinese nursing undergraduates hold predominantly neutral attitudes toward breastfeeding. These attitudes show significant associations with their knowledge level and personal feeding intention, which underscores the necessity of integrating attitude-focused education into nursing curricula. Full article