Search Results (8,950)

This study presents a comprehensive computational analysis of flow rate efficiency during uranium extraction via the In Situ Recovery method. Using field data from a deposit located in Southern Kazakhstan, a series of mathematical models were developed to evaluate the distribution and balance of leaching solution. A reactive transport model incorporating uranium dissolution kinetics and acid–rock interactions were utilized to assess the accuracy of both traditional and proposed methods. The results reveal a significant spatial imbalance in sulfuric acid distribution, with up to 239.1 tons of acid migrating beyond the block boundaries. To reduce computational demands while maintaining predictive accuracy, two alternative methods, a streamline-based and a trajectory-based approach were proposed and verified. The streamline method showed close agreement with reactive transport modeling and was able to effectively identify the presence of intra-block reagent imbalance. The trajectory-based method provided detailed insight into flow dynamics but tended to overestimate acid overflow outside the block. Both alternative methods outperformed the conventional approach in terms of accuracy by accounting for geological heterogeneity and well spacing. The proposed methods have significantly lower computational costs, as they do not require solving complex systems of partial differential equations involved in reactive transport simulations. The proposed approaches can be used to analyze the efficiency of mineral In Situ Recovery at both the design and operational stages, as well as to determine optimal production regimes for reducing economic expenditures in a timely manner. Full article

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

Background/Objectives: Accurate diagnosis of thyroid cancer is critical but challenging due to overlapping ultrasound (US) features of benign and malignant nodules. This study aimed to evaluate the diagnostic performance of non-invasive and minimally invasive US techniques, including B-mode US, shear wave elastography (SWE), color Doppler, superb microvascular imaging (SMI), and TI-RADS, in patients with suspected thyroid lesions and to assess their reliability and cost effectiveness compared with fine needle aspiration (FNA) biopsy. Methods: A prospective, single-center study (October 2023–February 2025) enrolled 300 patients with suspected thyroid cancer at a Spanish tertiary hospital. Of these, 296 patients with confirmed diagnoses underwent B-mode US, SWE, Doppler, SMI, and TI-RADS scoring, followed by US-guided FNA and Bethesda System cytopathology. Lasso-penalized logistic regression and a bootstrap analysis (1000 replicates) were used to develop diagnostic models. A utility function was used to balance diagnostic reliability and cost. Results: Thyroid cancer was diagnosed in 25 patients (8.3%). Elastography combined with SMI achieved the highest diagnostic performance (Youden index: 0.69; NPV: 97.4%; PPV: 69.1%), outperforming Doppler-only models. Intranodular vascularization was a significant risk factor, while peripheral vascularization was protective. The utility function showed that, when prioritizing cost, elastography plus SMI was cost effective (α < 0.716) compared with FNA. Conclusions: Elastography plus SMI offers a reliable, cost-effective diagnostic rule for thyroid cancer. The utility function aids clinicians in balancing reliability and cost. SMI and generalizability need to be validated in higher prevalence settings. Full article

Improving outdoor thermal comfort is a critical objective in urban design, particularly in densely built urban environments. Elevated semi-open spaces—outdoor areas located beneath raised building structures—have been recognized for enhancing pedestrian comfort by improving airflow and shading. However, previous studies primarily focused on warm or temperate climates, leaving a significant research gap regarding their thermal performance in cold climate zones characterized by extreme seasonal variations. Specifically, few studies have investigated how these spaces perform under conditions typical of northern Chinese cities like Xi’an, which is explicitly classified within the Cold Climate Zone according to China’s national standard GB 50176-2016 and experiences both severe summer heat and cold winter conditions. To address this gap, we conducted field measurements and numerical simulations using the ENVI-met model (v5.0) to systematically evaluate the microclimatic performance of elevated ground-floor spaces in Xi’an. Key microclimatic parameters—including air temperature, mean radiant temperature, relative humidity, and wind velocity—were assessed during representative summer and winter conditions. Our findings indicate that the height of the elevated structure significantly affects outdoor thermal comfort, identifying an optimal elevated height range of 3.6–4.3 m to effectively balance summer cooling and winter sheltering needs. These results provide valuable design guidance for architects and planners aiming to enhance outdoor thermal environments in cold climate regions facing distinct seasonal extremes. Full article

Background/Objectives: The aim of this systematic review was to compare the effectiveness of virtual reality (VR)-based training with conventional exercise programs in improving functional outcomes related to fall risk among older adults with various health conditions. Methods: The review was conducted in accordance with the PRISMA 2020 guidelines and registered in PROSPERO (registration number CRD42022345678). The databases Scopus, PubMed, Web of Science, and EBSCO were searched up to 31 March 2025. Randomized controlled trials (RCTs) were included if they involved participants aged ≥60 years, a VR intervention lasting ≥6 weeks, and a control group performing traditional exercises or receiving usual care. Methodological quality was assessed using the PEDro scale, and a narrative synthesis was performed across four outcome domains: balance, mobility, cognitive function, and fall risk. Results: Seven RCTs were included in the analysis (totaling 664 participants). VR training was found to be at least as effective as conventional exercise in improving balance (e.g., Berg Balance Scale) and mobility (e.g., Timed Up and Go), with some studies showing superior effects of VR. One RCT demonstrated that combining VR with balance exercises (MIX) yielded the greatest improvements in muscle strength and physical performance. Additionally, two studies reported cognitive benefits (e.g., MoCA) and a 42% reduction in fall incidence within six months following VR intervention. The methodological quality of the included studies was moderate to high (PEDro score 5–9/10). Conclusions: VR-based training represents a safe and engaging supplement to geriatric rehabilitation, effectively improving balance, mobility, and, in selected cases, cognitive function, while also reducing fall risk. Full article

Background: Hip fractures represent a major clinical challenge, particularly in elderly and frail patients, where postoperative pain control must balance effective analgesia with motor preservation to facilitate early mobilization. Various regional anesthesia techniques are used in this setting, including the pericapsular nerve group (PENG) block, fascia iliaca compartment block (FICB), femoral nerve block (FNB), and quadratus lumborum block (QLB), yet optimal strategies remain debated. Objectives: To systematically review the efficacy, safety, and clinical applicability of major regional anesthesia techniques for pain management in hip fractures, including considerations of fracture type, surgical approach, and functional outcomes. Methods: A systematic literature search was conducted following PRISMA 2020 guidelines in PubMed, Scopus, Web of Science, and the virtual library of the Hospital Central de la Defensa “Gómez Ulla” up to March 2025. Inclusion criteria were RCTs, systematic reviews, and meta-analyses evaluating regional anesthesia for hip surgery in adults. Risk of bias in RCTs was assessed using RoB 2.0, and certainty of evidence was evaluated using the GRADE approach. Results: Twenty-nine studies were included, comprising RCTs, systematic reviews, and meta-analyses. PENG block demonstrated superior motor preservation and reduced opioid consumption compared to FICB and FNB, particularly in intracapsular fractures and anterior surgical approaches. FICB and combination strategies (PENG+LFCN or sciatic block) may provide broader analgesic coverage in extracapsular fractures or posterior approaches. The overall risk of bias across RCTs was predominantly low, and certainty of evidence ranged from moderate to high for key outcomes. No significant safety concerns were identified across techniques, although reporting of adverse events was inconsistent. Conclusions: PENG block appears to offer a favorable balance of analgesia and motor preservation in hip fracture surgery, particularly for intracapsular fractures. For extracapsular fractures or posterior approaches, combination strategies may enhance analgesic coverage. Selection of block technique should be tailored to fracture type, surgical approach, and patient-specific functional goals. Full article

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article

Background/Objectives: Multifactorial prevention of falls in persons with dementia has minimal or non-significant effects. Personalised prevention is recommended. We have previously shown that gait speed, basic activities of daily living (ADL), and depression (high Cornell scores) were independent predictors of falls in persons with mild and moderate cognitive impairment. This study explored person-specific risks of falls related to physical, mental, and cognitive functions and types of dementia: Alzheimer’s disease (AD), vascular dementia (VD), mixed Alzheimer’s disease/vascular dementia (MixADVD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB). Methods: The study used data from “The Norwegian Registry of Persons Assessed for Cognitive Symptoms” (NorCog). Differences between the dementia groups and predictors of falls, gait speed, ADL, and Cornell scores were analysed. Results: Among study participants, 537/1321 (40.7%) reported a fall in the past year, with significant variations between dementia diagnoses. Fall incidence increased with age, comorbidity/polypharmacy, depression, and MAYO fluctuation score and with reduced physical activity, gait speed, and ADL. Persons with VD and MixADVD had high fall incidences and impaired gait speed and ADL. Training of physical fitness, endurance, muscular strength, coordination, and balance and optimising treatment of comorbidities and medication enhance gait speed. Improving ADL necessitates, in addition, relief of cognitive impairment and fluctuations. Relief of depression and fluctuations by psychological and pharmacological interventions is necessary to reduce the high fall risk in persons with DLB. Conclusions: The fall incidence and fall predictors varied significantly. Personalised interventions presuppose knowledge of each individual’s fall risk factors. Full article

Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address the challenge of scaling up canopy height monitoring by evaluating a recent deep learning model, trained on data from the Amazon and Atlantic Forests, developed to extract canopy height from RGB-NIR Planet NICFI imagery. The research questions are as follows: (i) How are canopy height estimates from the model affected by slope and orientation in natural forests, based on a large and well-balanced experimental design? (ii) How effectively does the model capture the growth trajectories of Pinus and Eucalyptus plantations over an eight-year period following planting? We find that the model closely tracks Pinus growth at the parcel scale, with predictions generally within one standard deviation of UAV-derived heights. For Eucalyptus, while growth is detected, the model consistently underestimates height, by more than 10 m in some cases, until late in the cycle when the canopy becomes less dense. In stable natural forests, the model reveals seasonal artifacts driven by topographic variables (slope × aspect × day of year), for which we propose strategies to reduce their influence. These results highlight the model’s potential as a cost-effective and scalable alternative to field-based and LiDAR methods, enabling broad-scale monitoring of forest regrowth and contributing to innovation in remote sensing for forest dynamics assessment. Full article

Background/Objective: This study explores a novel approach for diagnosing common middle ear pathologies using Pressure-Less Acoustic Immittance (PLAI™), a non-invasive alternative to conventional tympanometry. Methods: A total of 516 ear measurements were collected and stratified into three age groups: 0–3, 3–12, and 12+ years, reflecting key developmental stages. PLAI™-derived acoustic parameters, including resonant frequency, peak admittance, canal volume, and resonance peak frequency boundaries, were analyzed using Random Forest classifiers, with SMOTE addressing class imbalance and SHAP values assessing feature importance. Results: Age-specific models demonstrated superior diagnostic accuracy compared to non-stratified approaches, with macro F1-scores of 0.79, 0.84, and 0.78, respectively. Resonant frequency, ear canal volume, and peak admittance consistently emerged as the most informative features. Notably, age-based stratification significantly reduced false negative rates for conditions such as Otitis Media with Effusion and tympanic membrane retractions, enhancing clinical reliability. These results underscore the relevance of age-aware modeling in pediatric audiology and validate PLAI™ as a promising tool for early, pressure-free middle ear diagnostics. Conclusions: While further validation on larger, balanced cohorts is recommended, this study supports the integration of machine learning and acoustic immittance into more accurate, developmentally informed screening frameworks. Full article

The Red River Jig is a traditional Métis dance practiced among Indigenous and non-Indigenous Peoples. While exercise improves physical health and fitness, the impacts of cultural dances on wholistic health are less clear. This study aimed to investigate the psychosocial (cultural and mental), social, physical function, and physical fitness benefits of a Red River Jig intervention. In partnership with Li Toneur Nimiyitoohk Métis Dance Group, Indigenous and non-Indigenous adults (N = 40, 39 ± 15 years, 32 females) completed an 8-week Red River Jig intervention. Social support, cultural identity, memory, and mental wellbeing questionnaires, seated blood pressure and heart rate, weight, pulse-wave velocity, heart rate variability, baroreceptor sensitivity, jump height, sit-and-reach flexibility, one-leg and tandem balance, and six-minute walk test were assessed pre- and post-intervention. Community, family, and friend support scores, six-minute walk distance (553.0 ± 88.7 m vs. 602.2 ± 138.6 m, p = 0.002), jump, leg power, and systolic blood pressure low-to-high-frequency ratio increased after the intervention. Ethnic identity remained the same while affirmation and belonging declined, leading to declines in overall cultural identity, as learning about Métis culture through the Red River Jig may highlight gaps in cultural knowledge. Seated systolic blood pressure (116.5 ± 7.3 mmHg vs. 112.5 ± 10.7 mmHg, p = 0.01) and lower peripheral pulse-wave velocity (10.0 ± 2.0 m·s⁻¹ vs. 9.4 ± 1.9 m·s⁻¹, p = 0.04) decreased after the intervention. Red River Jig dance training can improve social support, physical function, and physical fitness for Indigenous and non-Indigenous adults. Full article

Background: Persistent Postural-Perceptual Dizziness (PPPD) is a chronic vestibular disorder characterized by dizziness, instability, and visual hypersensitivity. Vestibular Physical Therapy (VPT) is commonly used, but its efficacy remains uncertain due to limited and heterogeneous evidence. Objective: This systematic review and meta-analysis aimed to evaluate the effectiveness of VPT in reducing dizziness and improving balance in individuals with PPPD. Methods: A systematic search of MEDLINE and PEDro was conducted in January 2025. Studies were selected following PRISMA guidelines and included if they assessed VPT interventions in patients diagnosed with PPPD. Risk of bias was assessed using the PEDro scale and the modified Newcastle–Ottawa Scale. The meta-analysis focused on pre- and post-intervention changes in Dizziness Handicap Inventory (DHI) scores using a random-effects model. Results: Six studies met the inclusion criteria. VPT significantly reduced DHI scores (pooled Hedges’ g = 1.60; 95% CI: 0.75–2.45), indicating a moderate to large improvement. Additional outcomes included improvements in postural control (e.g., mini-BESTest and posturography) and psychological well-being (anxiety and depression questionnaires). However, high heterogeneity (I² = 92%) was present across studies. Conclusions: VPT may improve dizziness and balance in PPPD, though evidence is limited. Further high-quality trials with standardized protocols are needed. Full article

Study Design: Prospective observational study. Background: Lumbar spinal stenosis (LSS) impairs balance and gait function, increasing fall risk and limiting quality of life. Although postoperative recovery of balance is clinically important, longitudinal data using multidimensional balance assessments are limited. Methods: A prospective cohort study was conducted in 101 patients (mean age 74.9 ± 6.9 years) undergoing surgery for LSS. The Brief Balance Evaluation Systems Test (Brief BESTest), Oswestry Disability Index (ODI), Modified Falls Efficacy Scale (MFES), Zurich Claudication Questionnaire (ZCQ), and Visual Analog Scales (VAS) for pain/numbness were evaluated preoperatively and at 6 and 12 months postoperatively. Changes over time and correlations between Brief BESTest and PROMs were analyzed. Results: The total Brief BESTest score significantly improved from 13.3 ± 5.3 preoperatively to 16.1 ± 5.1 at 6 months and 16.0 ± 5.1 at 12 months (p < 0.01). Subdomains including Anticipatory Adjustments, Postural Responses, Sensory Orientation, and Stability in Gait improved significantly, while Stability Limits did not. At 12 months postoperatively, ODI decreased by 19.1%, ZCQ symptom and function scores improved by 0.8 and 0.9 points, respectively, and VAS scores improved by 17.1 mm for low back pain, 26.5 mm for lower limb pain, and 19.5 mm for numbness, all showing marked improvements from baseline. MFES also increased significantly postoperatively. The Brief BESTest score correlated significantly with MFES and ZCQ-PFS at baseline, and with ODI, ZCQ, and VAS scores at 12 months. Conclusions: Balance ability in LSS patients improved after surgery, as measured by the Brief BESTest, with clinically meaningful changes maintained for 12 months. Improvements in balance were significantly associated with reductions in pain, disability, and fear of falling, suggesting the Brief BESTest is a comprehensive indicator of postoperative recovery. Full article

Background/Objectives: Diabetic retinopathy is a leading cause of vision impairment worldwide, and the development of reliable automated classification systems is crucial for early diagnosis and clinical decision-making. This study presents a comprehensive comparative evaluation of two state-of-the-art deep learning families for the task of classifying diabetic retinopathy using retinal fundus images. Methods: The models were trained and tested in both binary and multi-class settings. The experimental design involved partitioning the data into training (70%), validation (20%), and testing (10%) sets. Model performance was assessed using standard metrics, including precision, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic curve. Results: In binary classification, the ResNeXt101-64x4d model and RegNetY32GT model demonstrated outstanding performance, each achieving high sensitivity and precision. For multi-class classification, ResNeXt101-32x8d exhibited strong performance in early stages, while RegNetY16GT showed better balance across all stages, particularly in advanced diabetic retinopathy cases. To enhance transparency, SHapley Additive exPlanations were employed to visualize the pixel-level contributions for each model’s predictions. Conclusions: The findings suggest that while ResNeXt models are effective in detecting early signs, RegNet models offer more consistent performance in distinguishing between multiple stages of diabetic retinopathy severity. This dual approach combining quantitative evaluation and model interpretability supports the development of more robust and clinically trustworthy decision support systems for diabetic retinopathy screening. Full article

Background/Objectives: In recent years, lipids have emerged as critical regulators of different disease processes, being involved in cancer pathogenesis, progression, and outcome. Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) has significantly expanded the technology’s reach, enabling spatially resolved profiling of lipids directly from tissue, including formalin-fixed paraffin-embedded (FFPE) specimens. In this context, MALDI matrix selection is crucial for lipid extraction and ionization, influencing key aspects such as molecular coverage and sensitivity, especially in such specimens with already depleted lipid content. Thus, in this work, we aim to explore the feasibility of mapping lipid species in FFPE clinical samples with MALDI-MSI using 6-aza-2-thiothymine (ATT) as a matrix of choice. Methods: To do so, ATT performances were first compared to those two other matrices commonly used for lipidomic analyses, 2′,5′-dihydroxybenzoic acid (DHB) and Norharmane (NOR), on lipid standards. Results: As a proof-of-concept, we then assessed ATT’s performance for the MALDI-MSI analysis of lipids in FFPE brain sections, both in positive and negative ion modes, comparing results with those obtained from other commonly used dual-polarity matrices. In this context, ATT enabled the putative annotation of 98 lipids while maintaining a well-balanced detection of glycerophospholipids (60.2%) and sphingolipids (32.7%) in positive ion mode. It outperformed both DHB and NOR in the identification of glycolipids (3%) and fatty acids (4%). Additionally, ATT exceeded DHB in terms of total lipid count (62 vs. 21) and class diversity and demonstrated performance comparable to NOR in negative ion mode. Moreover, ATT was applied to a FFPE glioblastoma tissue microarray (TMA) evaluating the ability of this matrix to reveal biologically relevant lipid features capable of distinguishing normal brain tissue from glioblastoma regions. Conclusions: Altogether, the results presented in this work suggest that ATT is a suitable matrix for pathology imaging applications, even at higher lateral resolutions of 20 μm, not only for proteomic but also for lipidomic analysis. This could enable the use of the same matrix type for the analysis of both lipids and peptides on the same tissue section, offering a unique strategic advantage for multi-omics studies, while also supporting acquisition in both positive and negative ionization modes. Full article