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Comparison of the effects of different expansion agents on alkali-activated rapid repair mortars: workability, mechanical properties, drying shrinkage
BTG3 suppresses oral squamous cell carcinoma progression by inhibiting PI3K/AKT signaling and EMT
Can weight-loss pills replace injectables? What the science says
The FOXE1 rs965513 polymorphism: a pleiotropic risk locus associated with thyroid function, BRAFV600E mutation, and susceptibility to papillary thyroid cancer
Metamorphic evolution of amphibolite from Proto-Tethys South Altyn orogen and its geological significance
Abstract Medium- to low-grade metamorphic (LP–MP) rocks, though major constituents of collisional orogens, remain less studied than high-to ultrahigh-pressure (HP – UHP) counterparts. Their peak conditions and P–T–t evolutions are poorly constrained due to lack of diagnostic assemblages. This study investigates amphibolites enclosed in HP pelitic granulites in Munabulake in South Altyn (SA) HP–UHP belt, unveiling a two-stage metamorphism through inclusion analysis and thermodynamic simulation. The eclogite-facies metamorphism (firstly identified in the westernmost SA) is evidenced by zircon-hosted garnet + omphacite + rutile inclusions and flat heavy rare earth elements (HREEs) patterns without negative Eu anomalies, yielding a peak age of 501.6 ± 2.7 Ma. The amphibolite-facies metamorphism is recorded by titanite-hosted amphibole + plagioclase inclusions, yielding a retrograde age of 437 ± 6.2 Ma and P – T conditions of 3.7–6.1 kbar/640–725 °C. These findings demonstrate that the amphibolite originated from retrograde metamorphism of eclogite. Integrated with previous studies, our results reveal potentially extensive HP–UHP exposures across the SA, with most rocks attaining eclogite-facies conditions at ~ 500 Ma, documenting an entire continental deep subduction of SA during Early Paleozoic. And the heterogeneous spatial-temporal distribution of metamorphic rocks across different grades in SA reflects differential exhumation processes or variable intensities of retrograde overprinting.
Association of aerobic capacity and handgrip strength in obese and non-obese children aged 10–15 years in Riyadh region, KSA–a cross sectional study
Abstract Childhood obesity is associated with impaired physical fitness and increased future cardiometabolic risk. Aerobic capacity and muscular strength are key components of health-related fitness; however, their interrelationship across weight status remains underexplored in Middle Eastern pediatric populations. This study aimed to examine the association between aerobic capacity and muscular strength among obese and non-obese children aged 10–15 years in the Riyadh region, Kingdom of Saudi Arabia. A cross-sectional study was conducted among 200 children (52% boys and 48% girls) selected from screened government and private school students in Riyadh, comprising 100 obese and 100 non-obese participants for balanced group comparison. Obesity was defined using World Health Organization age- and sex-specific BMI percentiles. Aerobic capacity was assessed using the 20-m shuttle run test, with estimated VO2max calculated from performance. Muscular strength was measured using handgrip dynamometry, and relative strength was derived by normalizing grip strength to body weight. Group comparisons were performed using independent t-tests. Associations between aerobic capacity and muscular strength were analyzed using correlation and multivariable linear regression adjusting for age, sex, and BMI. Obese children demonstrated significantly lower shuttle run performance and estimated VO2max compared with non-obese children (p < 0.001). Absolute handgrip strength was higher in obese children (p = 0.01), whereas relative muscular strength was significantly lower (p < 0.001). Aerobic capacity showed moderate to strong positive correlations with both absolute and relative muscular strength in both groups, with stronger associations observed for relative strength. In multivariable regression analyses conducted using separate models to avoid mathematical coupling, relative muscular strength independently predicted VO2max after adjusting for age and sex (β = 0.47, p < 0.001), explaining 52% of variance. In a separate adiposity model, BMI was inversely associated with aerobic capacity (β = −0.51, p < 0.001), explaining 56% of variance. Obese children aged 10–15 years exhibited reduced aerobic capacity and lower relative muscular strength despite higher absolute strength. Both adiposity and functional muscular strength were independently associated with aerobic fitness when modeled separately. The stronger predictive value of relative muscular strength highlights the importance of improving strength relative to body mass, alongside aerobic conditioning, in pediatric obesity prevention and intervention programs.
An attention-based multimodal deep learning framework integrating EEG and ECG for enhanced stress detection
Abstract Chronic stress is an important threat in Public Health, as it negatively impacts both the Body and Mind. Current methods for measuring and identifying stress rely largely on individuals providing subjective assessments or measuring isolated physiological parameters, thereby limiting the accuracy and consistency of these approaches. This study proposes a novel approach to objectively measuring an individual’s level of stress, by combining deep transfer learning methods for detecting psychological stress measured using Electroencephalogram (EEG) and Electrocardiogram (ECG) data. More specifically, this new method uses three pre-trained neural network backbones—VGG16, EfficientNetB0, and ResNeXt50—utilized together, to create a unified system capable of merging information from multiple streams of data in real-time. EEG data is converted to time-frequency maps using wavelet transformations and ECG data uses time-series variability (i.e. patterns of how the heart beats) combined with raw, unfiltered data. An advanced fusion layer uses attention weights to intelligently combine these two data sources, allowing for improved accuracy of stress assessment. Using the WESAD and CASE datasets, both of which were collected from 35 subjects while they were in a neutral (control), tense, and positive state, our method performs at 95.7% accuracy in identifying between these three conditions, which is significantly greater than the accuracy rates of either the EEG-only (82.3%) or ECG-only (85.6%) methods or individual networks. Furthermore, this system is highly flexible and has demonstrated the capability to successfully operate across numerous testing conditions, while additionally demonstrating that EEG signals enhance ECG stress assessment and vice versa. Therefore, this new approach provides a highly reliable way to support medical diagnosis, employee wellness programs, and personalized psychological support.
Rethinking ratio-based normalization towards model-based approaches in heart weight analysis
Abstract Heart weight (HW) is a critical parameter in cardiology and mouse research, commonly normalized to body weight (BW) or tibia length (TL) to account for size differences. Ratio-based normalization, however, assumes strict proportionality between variables, an assumption that is rarely tested and may bias group comparisons. We analysed HW, BW, and TL measurements from over 25,000 C57BL/6N wildtype mice generated by the International Mouse Phenotyping Consortium. Sex- and age-stratified analyses were combined with simulation-based modelling to evaluate empirical scaling relationships and the statistical behaviour of ratio-based normalization. Across all age and sex groups, correlations between HW, BW, and TL were negligible to weak, indicating substantial deviations from proportionality. Simulations demonstrated that ratio-based normalization can generate misleading results, including spurious or reversed group differences, when proportionality assumptions are violated. Ratios were consistent with linear and allometric models only under strictly proportional conditions, characterized by regression lines passing through the origin. Linear models with covariate adjustment and allometric scaling provide more robust and biologically meaningful frameworks for organ weight analysis. Ratio-based normalization should be avoided unless key mathematical assumptions are met.
A TCN-Attention fusion model for fault prediction and remaining useful life estimation of large-scale mining equipment
Abstract Large-scale mining equipment operates under extreme conditions, making accurate fault prediction and remaining useful life (RUL) estimation essential for predictive maintenance strategies. This paper proposes a novel deep learning framework that integrates temporal convolutional networks (TCN) with multi-head attention mechanisms for prognostic applications in mining machinery. The TCN backbone employs dilated causal convolutions to capture long-range temporal dependencies from multivariate sensor data, while a dual-branch attention module adaptively emphasizes informative features along both temporal and channel dimensions. A multi-task learning architecture with uncertainty-based loss weighting enables simultaneous optimization of fault classification and RUL regression objectives. Experimental validation on real-world data collected from haul trucks and hydraulic excavators demonstrates superior performance compared to baseline methods. The proposed model achieves 92.47% accuracy in fault prediction and 98.45 h RMSE in RUL estimation, with an R² coefficient of 0.912. Ablation studies confirm the contribution of each architectural component, while robustness testing reveals graceful degradation under sensor dropout and measurement noise conditions. The framework provides mining enterprises with a practical solution for enhancing operational reliability and maintenance scheduling efficiency.
Geophysical insights into groundwater aquifer characterization in a geologically complex region: a case study from the Meki–Alemtena area, central Ethiopia
Abstract This study aimed to map geological structures and characterize groundwater aquifers in the Meki–Alemtena area of the East Shewa Zone, Ethiopia. Sixteen Vertical Electrical sounding data were collected to reveal five to seven subsurface layers across the profiles, with resistivity values indicating lithologic units such as alluvial deposits, fractured ignimbrite, and rhyolite, forming aquifers with thicknesses of 9 to 76 m. Areas with low resistivity are found to correspond with groundwater-saturated zones, particularly in the northwestern part of the study area. Aquifer characterization using Dar Zarrouk Parameters identifies three primary water-bearing zones: freshwater, brackish, and saline aquifers. High longitudinal conductance (≥ 6 Ω −1 ) and transmissivity values in the northwestern section indicate the highest groundwater potential, while hydraulic conductivity analysis suggests efficient water flow through this zone. Magnetic data further highlight structural influences on groundwater flow, with high magnetic anomalies in the south-central and northwestern areas associated with faulted and fractured zones that act as conduits for groundwater. The integration of these methods offers a comprehensive assessment of groundwater resources, establishing a framework for targeted extraction and sustainable management in the Rift Valley’s water-scarce regions.
Population history and subsistence of farming communities in an agro-pastoral transition zone of northern China: ancient DNA and isotopic evidence from the Erdaojingzi site
Predicting skeletal fluorosis severity using machine learning across diverse fluoride-exposed populations in China
A systematic bias in float pH leads to overestimation of derived pCO2 and underestimation of carbon uptake by the Southern Ocean
Abstract The carbon flux estimated from biogeochemical Argo float data indicates a lower annual carbon uptake by the Southern Ocean compared to fluxes derived from other observations (e.g., ship and aircraft measurements). The root cause of this discrepancy remains controversial, with growing evidence suggesting that potential biases in float-derived p CO 2 may be a plausible explanation. Here, we perform a multi-variable comparison of vertical profiles between float- and ship based-data and reveal consistent discrepancies in pH, p CO 2 and dissolved inorganic carbon, which are not found in other variables such as dissolved oxygen, nitrate and total alkalinity. Our findings are consistent with a previously unrecognized negative bias in float pH driving a positive offset in float-derived p CO 2 . The float-derived surface p CO 2 is, on average, biased high by 15 ± 3 µatm compared to ship data, representing a larger magnitude of bias than previously recognized. Biases exist in both surface and deep waters, including old deep waters containing minimal anthropogenic carbon. A more sophisticated adjustment for float pH, involving multiple cross-reference depths, may be required for accurate estimation of air-sea CO 2 exchange in the Southern Ocean.
The effects of informal digital learning, intrinsic motivation, and grit on online learning effort regulation and English achievement
Ultrashort-pulse laser-modified surface texturing for heat transfer reduction in die-cast aluminum alloys
A GWAS–machine learning framework reveals protein-synthesis pathway signals for yield in Theobroma cacao after population-structure correction
Abstract Improving cacao yield, a key objective in post-domestication crop improvement, remains a primary goal for breeders, but progress is often hindered by the confounding effects of population structure. To overcome this, we analyzed 346 diverse cacao accessions using an ML-based association mapping framework (with and without population structure adjustment) and a phenotype-only ML prediction of yield. By correcting for population structure, our Bootstrap Forest-based GWAS produced SNP-importance rankings whose downstream functional summaries were enriched for ribosome/translation-related terms, and several top-ranked SNPs recurred across multiple yield components (e.g., pod index and seed number) in this panel. In parallel, Neural Networks were utilized to identify cotyledon mass and length as the most powerful predictors for total wet bean mass, providing a phenotype-only prediction example for this panel. Collectively, this study provides an ML-guided, low-density association workflow and a phenotype-only prediction example for this cacao panel, while explicitly outlining limitations related to marker density and phenotype provenance.
Evaluation of groundwater quality in the bouanane plain using the groundwater pollution index, nitrate pollution index, and microbiological indicators
Abstract Groundwater provides a vital component of water supplies in semi-arid environments, wherein its quality directly influences ecosystem stability and human well-being. This investigation presents the first complete evaluation of groundwater quality in the Bouanane basin by implementing an innovative PCA–GIS framework combined with established hydrochemical indices, thereby enhancing the discrimination of pollution sources beyond what conventional methods typically allow. Nine groundwater samples collected in April 2024 were analysed for major ions and microbiological indicators. Water quality was subsequently evaluated using (PIG), (NPI), and a USEPA based Human Health Risk Assessment. PIG values ranged from 1.12 to 3.03 demonstrating that 44% of samples come inside the very highly polluted classification, primarily due to geogenic mineralization associated with carbonate and evaporitic formations. Conversely, NPI values (− 0.98 to − 0.25) indicate negligible nitrate contamination and minimal human influence. Health risk indices for both children and adults remained less than 1, suggesting no significant risk to public health. Although most samples complied with World Health Organization WHO (World Health Statistics, Monitoring Health for the SDGs, Sustainable Development Goals, 2017) drinking water guidelines, Staphylococcus aureus was detected at 22% of sampling locations, underscoring the need for periodic sanitary monitoring. Overall, the findings demonstrate that groundwater chemistry within the basin is predominantly shaped by natural geochemical processes. Furthermore, the integrated PCA–GIS framework proved to be a robust and efficient tool for groundwater quality diagnosis. This pioneering investigation establishes an essential scientific baseline for the Bouanane basin and provides a foundational reference for evidence-based water resource management amid rising climatic and anthropogenic pressures.
Mechanical, thermal, structure and radiation shielding efficiency of natural kaolinite-based composites reinforced with heavy metal oxides
Abstract In this work, a low-cost, natural kaolinite-based matrix reinforced with gypsum and ground marble waste was prepared with the aim of developing materials suitable for radiation shielding applications. The prepared matrix was reinforced with a fixed 30 wt% of various metal oxides, including bismuth oxide (Bi 2 O 3 ), tungsten oxide (WO 3 ), copper oxide (CuO), iron oxide (Fe 2 O 3 ), and titanium oxide (TiO 2 ). The prepared clay composites were characterized using X-ray diffraction (XRD) to determine the crystalline phases, Fourier transform infrared (FTIR) spectroscopy to study the chemical bonds, and Scanning electron microscope (SEM) to evaluate the surface morphology and the distribution of the additives within the matrix. The mechanical and thermal properties of the composites were also evaluated, along with their efficiency in attenuating gamma rays at different energies. The results showed that reinforcing the natural matrix with different oxides led to a significant improvement in density and thermal stability, as well as a marked increase in radiation attenuation coefficients compared to the unreinforced matrix. The shielding efficiencies for a 3 cm thickness were 35.14, 38.16, 39.67, 40.67, 43.33 and 45.51% for Reference, C-Ti, C-Fe, C-Cu, C-W and C-Bi, respectively. The clay sample reinforced with CuO exhibited the highest mechanical strength, while the clay sample reinforced with Bi 2 O 3 exhibited the highest shielding efficiency due to the high density. These results confirm the potential use of the proposed composites as environmentally friendly and low-cost building materials for radiation shielding applications.