Search Results (784)

Introduction/Aims: Quantitative analysis of nerve echogenicity can support the diagnosis of mono- and polyneuropathies, for instance by distinguishing inflammatory-demyelinating from axonal damage. However, echogenicity is mainly assessed qualitatively and examiner-dependently. This study aimed to establish quantitative reference data for grayscale values of peripheral nerves in the upper and lower extremities of healthy children and adolescents to provide a clinical benchmark. Methods: We retrospectively analyzed ultrasound data from 211 healthy children aged two to seventeen years who had undergone standardized examinations of 15 peripheral nerve sites. Grayscale analysis (0–255 levels per pixel) was performed within manually defined regions of interest (ROIs) using ImageJ (version 1.52). Echogenicity values were correlated with age, weight, height, and body mass index (BMI). Results: Echogenicity showed no significant overall association with biometric parameters. Mean grayscale values ranged from 85.23 ± 2.16 for the tibial nerve at the medial malleolus to 134.62 ± 2.69 for the sural nerve. Gain settings below 60 resulted in significantly lower grayscale values, whereas measurements with gain ≥ 60 were stable and comparable. Discussion: We propose reference grayscale ranges for peripheral nerves in healthy children and adolescents as a practical benchmark for clinical use and future studies. Due to technical constraints—particularly retrospective image processing and non-lossless data export—each laboratory should establish its own reference dataset, or multicentric parameters should be established. As our sample consisted predominantly of Caucasian participants, ethnic differences should be considered when applying these values to other populations. Full article

Background: Emergency obstetric situations require rapid clinical decision-making, technical competence, and emotional preparedness to ensure safe and compassionate care for both mother and newborn. However, nursing students often have limited opportunities to experience such high-risk, low-frequency events during clinical placements. Simulation-based education has emerged as an effective strategy to prepare future nurses for caring in emergency contexts, allowing them to develop both technical and non-technical skills in a safe learning environment. This study aimed to evaluate the effects of a high-fidelity obstetric emergency simulation program on nursing students’ knowledge, perceived safety, and learning experience. Methods: A mixed-methods design was employed, combining a quasi-experimental pretest–posttest assessment without a control group and qualitative analysis of open-ended reflections. Eighty-two third-year nursing students participated in two simulation sessions addressing obstetric emergencies such as breech birth, shoulder dystocia, out-of-hospital delivery, eclampsia, postpartum hemorrhage, and maternal cardiac arrest. Data were collected using validated instruments measuring knowledge, perceived safety, and satisfaction and self-confidence in learning, and were analyzed using Wilcoxon signed-rank tests and thematic analysis. Results: Significant improvements were observed in specific knowledge areas related to complex obstetric maneuvers and in their perceived safety when managing emergency situations (p < 0.001, r > 0.40). Participants reported high levels of satisfaction and confidence in learning. Qualitative findings highlighted increased emotional preparedness, improved clinical reasoning, and recognition of the importance of teamwork and reflective debriefing in emergency care contexts. Conclusions: High-fidelity simulation appears to be an effective educational strategy for preparing nursing students to provide safe and confident care in obstetric emergencies. Integrating simulation into nursing curricula can strengthen both technical competence and the emotional readiness required for caring in urgent and high-pressure clinical situations. Full article

Moisture content is a key quality attribute in dried areca nuts, affecting subsequent processing performance and storage stability, yet routine measurement by oven-drying is time-consuming and destructive. This study developed a rapid and non-destructive method for determining moisture content in dried areca nuts by integrating near-infrared spectroscopy with chemometric and machine learning-assisted methodologies. Various spectral preprocessing methods, feature wavelength selection algorithms, and modeling approaches were compared. The results indicated that Multiplicative Scatter Correction (MSC) most effectively eliminated physical scattering interference. The Partial Least Squares Regression (PLSR) model established using full-wavelength spectra demonstrated optimal predictive performance. It achieved a coefficient of determination for the prediction set (Rp²), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) of 0.9639, 0.1960, and 10.3461, respectively, indicating excellent predictive accuracy and robustness. Feature wavelength selection did not enhance model performance in this study, which can be attributed to the broad absorption bands of water in the near-infrared spectrum and its complex interactions with the sample matrix where the full spectrum data retains essential information more comprehensively. This research provides a reliable and practical technical means for moisture management in areca nuts, offering important support for quality assurance and standardized production practices within the areca industry. Full article

Wine sensory analysis traditionally relies on complex terminology. This can be challenging to non-expert consumers, particularly regarding mouthfeel sensations. Despite the importance of the latter in determining wine quality and typicity, they lack standardized classification. In this study, we developed and validated a simplified framework for wine taste evaluation that is accessible to consumers with limited tasting experience. The Delphi technique was applied across multiple rounds with a panel of 18 wine experts, primarily sommeliers with experience of diverse consumer profiles. Through an iterative process, attributes were selected from the existing literature and systematically evaluated for relevance, clarity, and accessibility. The validated framework comprises four dimensions: basic tastes (sweetness, acidity, bitterness, salinity, fruitiness); astringency (hardness, dryness, texture); tactile sensations (tingling, warmth, body); and overall evaluation (complexity, balance, taste persistence, alcohol perception). Each attribute includes accessible descriptions and measurement scales anchored with familiar food references to support comparative cognitive processes. All proposed attributes achieved over 85% expert consensus. This framework provides a practical tool that bridges technical wine terminology and everyday consumer language to facilitate communication between industry professionals and consumers. Furthermore, it enables more reliable sensory evaluations in future research and can potentially be extended to other beverages. Full article

The growing demand for energy-efficient buildings and the urgent need to retrofit aging infrastructure have driven increased interest in advanced diagnostic technologies. Among these, unmanned aerial vehicle (UAV)-based infrared thermography (IRT) has emerged as a promising non-destructive technique for assessing the thermal performance of building envelopes. This review examines recent developments and applications of dynamic infrared thermography (IRT) in the building sector for both qualitative and quantitative thermal assessment, based on previously conducted studies. It highlights the increasing adoption of integrated UAV-based IRT for building inspection and diagnostics, and critically reviews the operational, technical, and methodological advancements in dynamic thermography achieved over the past decade. Furthermore, the review presents a comprehensive framework for operational planning, encompassing environmental conditions, infrared camera configuration, and optimal UAV flight parameters. The key findings identify major challenges associated with dynamic IRT applications, particularly those related to measurement accuracy that currently limit its use for quantitative assessments and synthesize proposed methodologies to address these limitations. The review also highlights the absence of standardized procedures for determining emissivity and reflected apparent temperature in dynamic measurement setups and discusses potential approaches to overcome these gaps. Finally, it outlines priority directions for future research to support the reliable and consistent application of dynamic IRT in quantitative analysis and provides a reference for energy auditors and thermography practitioners to inform the selection of appropriate procedures for accurately quantifying heat loss in building envelopes. Full article

In water-scarce regions such as Xinjiang, China, agricultural development is constrained not only by limited water resources but also by a strong reliance on water-saving irrigation technologies. Drip irrigation is a key measure for improving irrigation efficiency and promoting the sustainable development of water-saving agriculture. However, defects arising during the manufacture of labyrinth Drip emitters—the core components of drip irrigation systems—can undermine system reliability, leading to channel blockage and non-uniform irrigation. To tackle this issue, a defect detection approach is developed by integrating Digital Twin technology with an enhanced YOLOv8 model for online inspection of labyrinth Drip emitters on drip irrigation tape production lines. In parallel, a self-built dataset covering six defect categories is established. Supported by the DT framework, the standard YOLOv8 network is refined to strengthen its capability in identifying complex micro-defects. Specifically, DySnakeConv is introduced to better represent the curved and slender characteristics of labyrinth channels; DySample is incorporated to improve the reconstruction and representation of fine-grained details; an Efficient Multi-Scale Attention module is adopted to capture richer contextual information while suppressing background noise; and Inner-SIoU is applied to optimize the bounding-box regression process. Experimental results show that the model achieves 89.6% precision, 90.9% recall, and 93.9% mAP50. Compared with the baseline YOLOv8, precision, recall, and mAP50 are improved by 7.3, 3.9, and 3.3 percentage points, respectively. Under the same training conditions, the proposed model outperforms YOLOv10 and YOLOv11 in accuracy-related metrics. Specifically, compared with YOLOv11, precision, recall, and mAP50 are improved by 4.8, 5.0, and 2.6 percentage points, respectively; compared with YOLOv10, they are improved by 10.0, 7.7, and 7.3 percentage points, respectively. Meanwhile, the model maintains a lightweight size of 3.7 M parameters and a real-time inference speed of 150.2 FPS, demonstrating a favorable accuracy–efficiency trade-off. By extending manufacturing-level quality control to agricultural applications, the approach helps ensure uniform irrigation and improve water-use efficiency, providing practical technical support for precision agriculture in arid regions. Full article

Amidst escalating global economic instability, urban economic resilience has emerged as a fundamental pillar for sustainable urban development. Using a dataset of 280 prefecture-level cities in China from 2008 to 2021, this study examines the impact of financial agglomeration on urban economic resilience. The entropy weight approach is used to measure urban economic resilience. The main empirical results show that financial agglomeration has a statistically significant positive impact on urban economic resilience, mainly through two mediating channels: the promotion of technical innovation and the optimization of the industrial structure. The beneficial effects of financial agglomeration increase with city size, according to a threshold effect analysis, giving urban sustainable development a stronger boost. Furthermore, compared to resource-based cities, cities in the central and western regions, and cities with low levels of digital finance development, this promotional effect is much more noticeable in non-resource-based cities, cities in the eastern regions, and cities with a high degree of digital finance development. This study underscores the pivotal influence of financial clustering on reinforcing urban economic robustness, offering policy recommendations for fostering sustainable growth and urban development. Full article

This study quantifies a critical winter safety hazard caused by lateral heterogeneity of skid resistance: under non-uniform snow and ice removal, the friction coefficient in edge lanes and near barrier guardrails can be 2–5 times lower than in the central part of the carriageway, creating conditions prone to loss of control during braking and lane changes. Field measurements of friction coefficient and macrotexture were conducted on highways of different technical categories with asphalt concrete and cement concrete pavements in Kazakhstan’s continental climate. Long-term monitoring showed that, over three years of operation, texture peak height decreases by 22–33%, depending on traffic intensity and heavy-vehicle share, leading to a gradual reduction in friction. Predictive assessments of skid-resistance deterioration and braking distance calculations for passenger cars and heavy vehicles under different friction levels were performed. The results support the need for regular texture monitoring, explicit consideration of across-width friction heterogeneity in accident analysis, and targeted improvements in winter maintenance practices, particularly in edge zones adjacent to barriers. Full article

Regional trade liberalization via preferential agreements increasingly shapes economic outcomes in small open economies embedded in overlapping regional frameworks. This study evaluates the short-run economy-wide effects of tariff and non-tariff measure (NTM) reforms under the South Asian Free Trade Area (SAFTA) and the Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) using a Computable General Equilibrium (CGE) model calibrated to the GTAP 10 database. Gravity-based estimates of ad valorem equivalents (AVEs) of NTMs are integrated into the CGE framework, enabling explicit modeling of regulatory barriers alongside tariff reductions. Policy simulations examine scenarios involving a 90 percent tariff cut and a 50 percent NTM reduction, applied individually and jointly, under a short-run closure with fixed factor endowments and a trade balance for Nepal. Results indicate that combined liberalization yields positive macroeconomic adjustments, with real GDP rising by about one percent and exports increasing by over 14 percent, driven primarily by the manufacturing sector, particularly textiles, while agricultural responses vary by exposure to NTMs. These findings provide policy-relevant evidence on the relative effectiveness of tariff and regulatory reforms, informing strategies for deeper regional integration and enhanced competitiveness in small, structurally constrained economies. Full article

(1) Background: Overuse of chemical nitrogen fertilizers drives the need for biological alternatives. Azotobacter chroococcum is a promising free-living nitrogen-fixing bacterium, but its efficiency needs improvement. This study investigated how Fe₃O₄ nanoparticles (Fe₃O₄ NPs) and molybdenum trioxide nanoparticles (n-MoO₃) affect A. chroococcum growth and nitrogen fixation, and tested the modified inoculants on Glycine max (legume) and Nicotiana benthamiana (non-legume); (2) Methods: In vitro tests measured bacterial growth, viable counts (CFU), nitrogenase activity, and nitrogen metabolites (total N, NO₃⁻-N, NH₄⁺-N) under 0–100 ng·mL⁻¹ Fe₃O₄ NPs or n-MoO₃. Pot experiments then tested modified inoculants on Glycine max and N. benthamiana for biomass and N, P, K uptake; (3) Results: Both nanomaterials showed low-dose stimulation and high-dose inhibition. At 10 ng·mL⁻¹, bacterial growth (OD₆₀₀ up ~1.2×) and nitrogenase activity (up >90%) rose significantly (p < 0.05–0.001), along with higher total N, NO₃⁻-N, and NH₄⁺-N. In pots, 10 ng·mL⁻¹ modified inoculant improved all Glycine max traits and nutrient uptake (p < 0.05). For N. benthamiana, biomass peaked at 20 ng·mL⁻¹, while stem and root growth did best at 10 ng·mL⁻¹. At 100 ng·mL⁻¹, effects weakened or vanished. A “metabolic remodeling–rhizosphere transformation–systemic response” mechanism is proposed; (4) Conclusions: Low concentrations (10–20 ng·mL⁻¹) of Fe₃O₄ NPs and n-MoO₃ can effectively boost the nitrogen-fixing function and growth-promoting effect of A. chroococcum inoculant, showing good potential for use on both legume and non-legume crops. This study provides a theoretical basis and technical reference for developing efficient, broad-spectrum nanomaterial-microbe composite inoculants. Full article

Selectively modulating specific brain-rhythm bands with physical stimuli helps both to reveal neural mechanisms and to provide non-pharmacological treatment avenues for brain disorders. This study proposes and implements a multi-channel transcranial magneto-acoustic stimulation driving system based on amplitude-modulated (AM) sine waves (AM-TMAS) intended to supply a reliable hardware platform for noninvasive, focal low-frequency rhythmic electrical stimulation of deep-brain structures. The driving system implements a 64-channel AM module based on an FPGA plus high-speed DACs. Multi-channel precision is achieved via a unified high-speed clock and a global UPDATE trigger. To overcome the large separation between envelope and carrier frequencies, we developed a high-fidelity AM waveform generation method based on DDS + LUT + envelope multiplication. The algorithm first centers the carrier samples to preserve waveform symmetry, then applies LUT-based envelope coefficients and fixed-point envelope multiplication, enabling high-precision AM outputs with carrier frequencies from 100 kHz to 2 MHz and envelope frequencies from 0.1 Hz to 100 kHz. We tested the system’s rhythmic multi-channel AM output performance across frequencies and also measured magneto-acoustic-coupled rhythmic electrical signals produced by the AM-TMAS driving setup. Any single channel reliably produced high-fidelity AM waveforms with a 500 kHz carrier and 8 Hz/40 Hz envelopes; the measured carrier was 499.998 kHz with excellent frequency stability. Both envelope and carrier frequencies are flexibly tunable. At the nominal 500 kHz carrier, envelope fidelity was further quantified: the extracted envelopes achieved NRMSEs of 1.0795% (8 Hz) and 1.9212% (40 Hz), confirming high-fidelity AM synthesis. Under a 0.3 T static magnetic field, the AM-TMAS driving system generated rhythmic electrical responses in physiological saline that carried the expected 40 Hz envelope. The proposed AM-TMAS driver achieves high accuracy in AM waveform generation and robust multi-channel performance, and—when combined with an external static magnetic field—can produce rhythmically modulated magneto-acoustic electrical stimulation. This platform provides a practical technical tool for brain-function research and the development of rhythm-targeted neuromodulation therapies. Full article

Background/Objectives: The success of cardiopulmonary resuscitation relies on both technical and non-technical skills. Cognitive aids, such as checklists, have been shown to enhance technical performance in emergencies. The aim of this study was to evaluate the capabilities of a cognitive aid app (CA-App) in improving non-technical skills. Methods: In this single-centre randomised controlled trial, 62 teams, each consisting of an experienced physician and a specialised nurse, were randomised either to CA-App or control (No-App) groups performing cardiopulmonary resuscitation. The study was registered with the German Clinical Trials Register (DRKS) on 4 November 2025 (DRKS00038336). The primary outcome was the team leader’s performance in non-technical skills, assessed via the validated Team Emergency Assessment Measure (TEAM™) questionnaire by two raters. Secondary analyses examined TEAM™ subdomains (leadership, teamwork, task management) and the correlation between app usage duration and performance. Results: 62 out of 67 teams were finally randomised, with 31 teams in each group. The CA-App group demonstrated a marginally elevated median TEAM™ total score (83.33%) in comparison to the control group (79.33%), although this difference was not statistically significant (p = 0.190). The leadership subgroup score was significantly higher in the app group (p = 0.006). There was no significant correlation between the time spent using the app and improved team performance (r = 0.260, p = 0.166). Conclusions: The CA-App demonstrated potential for improving leadership skills, a critical component of non-technical skills in emergency scenarios. These findings highlight the potential capability of cognitive aids to improve non-technical skills and the need for further research to explore their optimal design and integration into clinical practice to enhance team performance and patient safety. Full article

Background: Breaststroke performance in young swimmers is influenced by a complex interaction of anthropometric, physiological, and technical factors. However, existing studies predominantly focus on pre-selected or elite youth swimmers, limiting insight into performance development in non-elite populations without early selection bias. Purpose: This study aimed to identify key predictors of 50-m breaststroke performance and to examine longitudinal changes in selected characteristics in pre-adolescent, non-elite female swimmers. Methods: Fourteen female swimmers (baseline biological age: 10.52 ± 0.37 years) who entered swimming training without prior anthropometric or physiological selection were followed over three consecutive years. Measurements were collected at six time points and included anthropometric dimensions, body composition, aerobic and anaerobic capacity, respiratory volumes, and 50-m breaststroke performance. This investigation was a prospective longitudinal cohort study. Data were analysed using generalised estimating equations. Results: The correlation-filtered model explained 76% of the variance in 50-m breaststroke time. Chest depth (B = −0.16, p = 0.03), foot length (B = −0.17, p = 0.04), foot width (B = 0.30, p < 0.001), and shoulder width (B = −0.07, p = 0.04) emerged as significant anthropometric predictors. Maximal oxygen uptake also showed a significant association with performance (B = −0.33, p = 0.02). Conclusions: In pre-adolescent, non-elite female swimmers, selected anthropometric characteristics—particularly trunk dimensions and foot morphology—are associated with short-distance breaststroke performance. Aerobic capacity appears to play an indirect, supportive role. These findings highlight the importance of longitudinal monitoring without early selection and support a development-oriented approach to youth swimming training. Full article

Background: Several factors influence reasons for cannabis use in the U.S. This study examines reasons for cannabis use (recreational only, medical only, both) and their frequency of use in association with demographic variables, health-risk behaviors, legal status, and chronic disease. Methods: We performed a cross-sectional analysis of 466,355 adults (aged ≥18) in the 2018–2021 BRFSS surveys in areas that administered the cannabis module. The primary outcome variables were whether cannabis was used in the past 30 days and, if so, reasons for its use and the number of days of use. Regression techniques were used to assess these outcome measures according to selected variables. Results: Approximately 11.5% (SE = 0.1%) used cannabis in the past 30 days. The reasons for use were 36.7% (SE = 0.5%) recreation only, 36.4% (SE = 0.5%) medical and recreation, and 26.9% (SE = 0.4%) medical only. Cannabis use was significantly greater in areas where it was legal for medical and recreational use, but among those who used it, reasons for its use were not significantly associated with legal status. Among those who used cannabis in the past 30 days, using it for recreation only versus medical reasons only was significantly greater in the youngest age group, men, NH Blacks, never married, employed, students, college/technical school graduates, binge drinkers, never smokers, and non-obese and in the years 2020–2021 (vs. 2018–2021). Using it for both medical and recreational reasons versus medical reasons only tended to show similar results. Among those who used cannabis in the past 30 days, the mean number of days of cannabis use was 6.8 (SE = 0.3) days greater for those who used it for medical and recreational reasons vs. recreation only and 5.7 (SE = 0.3) days greater for those who used it for medical reasons only vs. recreation only, after adjusting for several potential confounders. Mean number of days of cannabis use varied significantly across the levels of several variables, including chronic disease status, in the adjusted model. Of those who used cannabis in the past 30 days and had arthritis, asthma, CHD, COPD, depression, diabetes, a heart attack, kidney disease, or cancer, less than half used it for medical purposes only. Conclusions: Cannabis use is more common in areas where it is legal for medical and recreational use, but legal status is not significantly associated with reasons for use. Those who use cannabis for medical purposes use it more often than those who use it for recreation only. Reasons for cannabis use vary by the levels of several variables, including chronic disease status. Less than half of those with a chronic disease use it solely for medical purposes. Full article

The polished stone value (PSV) is a key parameter for assessing the resistance of aggregates to polishing in the laboratory. It is included in technical specifications and serves as both a regulatory and contractual criterion for selecting aggregates for wearing courses. Its determination requires non-negligible amounts of material, long testing durations, and skilled operators. This study aims to develop a predictive modeling approach to estimate the polished stone value (PSV) from the mineralogical and chemical composition of aggregates. A curated database was compiled from the peer-reviewed literature, and compositional data were transformed using Isometric Log-Ratio (ILR) to generate physically interpretable balances and avoid constant-sum artifacts. Machine learning algorithms, including Gradient Boosting, CatBoost, and Multivariate Adaptive Regression Splines (MARS), were trained and evaluated using repeated 10 × 2 K-Fold cross-validation with preprocessing embedded within the loop. CatBoost achieved the highest accuracy, with 90.4% of predictions within ±20% of the measured PSV. Model interpretability using permutation feature importance and SHAP analysis identified meaningful drivers, highlighting the roles of CO₂/SO₃ versus the major-oxide framework, and silica-rich oxides versus CaO/MgO, consistent with petrographic expectations. The proposed workflow provides a practical and interpretable approach for predicting PSV from compositional data. It offers a time- and resource-efficient alternative to conventional laboratory tests, while also providing insight into the material factors that control aggregate polishing resistance. Limitations related to dataset size and inter-source variability are discussed. Full article