Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the Relationship between Participation in Sports and Harmful Alcohol Drinking in Early Adolescence: Two-Year Prospective Analysis

Alcohol drinking is an important health-related problem and one of the major risk factors for a wide array of non-communicable diseases, while there is a lack of studies investigating environment-specific associations between sports participation and alcohol drinking in adolescence. This study prospectively investigated the relationship between sports factors (i.e., participation in sports and competitive achievement), with the prevalence of harmful alcohol drinking (HD), and HD initiation in 14-to-16 years old adolescents from Bosnia and Herzegovina (n = 641, 337 females, 43% living in rural community). Participants were tested over 4-time points divided by approximately 6 months, from the beginning of high school to the end of the second grade. Variables included gender, factors related to sport participation, a community of residence (urban or rural), and outcome: alcohol consumption was assessed by the AUDIT questionnaire. Results evidenced that the prevalence of HD increased over the study period from 6 to 19%, with no significant differences between urban and rural youth. Logistic regression for HD as criterion evidenced adolescents who participated in sports and then quit as being at particular risk for drinking alcohol at the study baseline. Sports factors were not correlated with HD initiation in the period between 14 and 16 years of age. It seems that the problem of alcohol drinking should be preventively targeted in all youth, irrespective of living environment. Although sports participation was not evidenced as being a factor of influence on HD initiation, results highlight the necessity of developing targeted preventive campaigns against alcohol drinking for adolescents who quit sports.
Evidence of Global Convergence: Perspectives for Economic and Territory Planning in Times of the COVID-19 Pandemic

Governments and international organizations have implemented efforts to promote the convergence of socioeconomic indicators between countries. The structural funds adopted by the European Union institutions are examples of policy instruments implemented to promote convergence in the GDP (gross domestic product) among the member states. Nonetheless, these policy measures are dependent on several internal and external factors, making these efforts vulnerable to exogenous shocks such as those associated with the global financial crisis and the COVID-19 pandemic. From this perspective, this research aims to analyze the convergence trends over the last few years and assess the respective implications of the pandemic on this framework. For that, statistical information from the World Bank for the GDP per capita was considered for the period 2006–2021 for all countries and organized for each group of levels of income and each world region. These data were analyzed through panel data approaches, considering the developments in convergence theory. The results show that the signs of convergence are different for each level of income and each region, highlighting the idea of clubs of convergence. On the other hand, the pandemic disturbed the trends of convergence verified worldwide, but nonetheless, it seems to be on a smaller scale than the global financial crisis. In any case, these findings should be confirmed in future research with more recent data.
Transdermal Flunixin Meglumine as a Pain Relief in Donkeys: A Pharmacokinetics Pilot Study

Recent approval of transdermal flunixin meglumine (FM) (Banamine®) in cattle has opened the door for the drug’s potential application in other species. Transdermal FM could provide a safe and effective form of pain relief in donkeys. In order to evaluate the pharmacokinetics and effects of FM on anti-inflammatory biomarkers in donkeys, a three-way crossover study design was employed. In total, 6 healthy donkeys were administered transdermal (TD) FM at a dosage of 3.3 mg/kg, and oral (PO) and intravenous (IV) doses of 1.1 mg/kg body weight. Blood samples were collected over 96 h to determine the concentration of flunixin, 5OH flunixin, and eicosanoids (TXB2 and PGF2 alpha) using LC-MS/MS. The results indicated that both flunixin and 5OH flunixin were detectable in blood samples collected during TD. The elimination of the drug was slower following the TD route compared to PO and IV. TD administration significantly decreased TXB2 levels in non-stimulated serum from 1 to 96 h post-administration, while IV and PO resulted in TXB2 reduction for 1 to 8 h. A significant reduction in PGF2 alpha was observed in PO and IV 1 h after administration, while TD resulted in a gradual decline from 4 to 72 h. The study concluded that the off-label use of transdermal FM at 3.3 mg/kg could be effective in controlling inflammation in donkeys.
Multi-Supervised Feature Fusion Attention Network for Clouds and Shadows Detection

Cloud and cloud shadow detection are essential in remote sensing imagery applications. Few semantic segmentation models were designed specifically for clouds and their shadows. Based on the visual and distribution characteristics of clouds and their shadows in remote sensing imagery, this paper provides a multi-supervised feature fusion attention network. We design a multi-scale feature fusion block (FFB) for the problems caused by the complex distribution and irregular boundaries of clouds and shadows. The block consists of a fusion convolution block (FCB), a channel attention block (CAB), and a spatial attention block (SPA). By multi-scale convolution, FCB reduces excessive semantic differences between shallow and deep feature maps. CAB focuses on global and local features through multi-scale channel attention. Meanwhile, it fuses deep and shallow feature maps with non-linear weighting to optimize fusion performance. SPA focuses on task-relevant areas through spatial attention. With the three blocks above, FCB alleviates the difficulties of fusing multi-scale features. Additionally, it makes the network resistant to background interference while optimizing boundary detection. Our proposed model designs a class feature attention block (CFAB) to increase the robustness of cloud detection. The network achieves good performance on the self-made cloud and shadow dataset. This dataset is taken from Google Earth and contains remote sensing imagery from several satellites. The proposed model achieved a mean intersection over union (MIoU) of 94.10% on our dataset, which is 0.44% higher than the other models. Moreover, it shows high generalization capability due to its superior prediction results on HRC_WHU and SPARCS datasets.
Mortality Risk Prediction in Abdominal Septic Shock Treated with Polymyxin-B Hemoperfusion: A Retrospective Cohort Study

Endotoxin, a component of the cell membrane of gram-negative bacteria, is a trigger for dysregulated inflammatory response in sepsis. Extracorporeal purification of endotoxin, through adsorption with polymyxin B, has been studied as a therapeutic option for sepsis. Previous studies suggest that it could be effective in patients with high endotoxin levels or patients with septic shock of moderate severity. Here, we perform a retrospective, single-centre cohort study of 93 patients suffering from abdominal septic shock treated with polymyxin-B hemoperfusion (PMX-HP) between 2015 and 2020. We compared deceased and surviving patients one month after the intervention using X2 and Mann-Whitney U tests. We assessed the data before and after PMX-HP with a Wilcoxon single-rank test and a multivariate logistic regression analysis. There was a significant reduction of SOFA score in the survivors. The expected mortality using APACHE-II was 59.62%, whereas in our sample, the rate was 40.9%. We found significant differences between expected mortality and real mortality only for the group of patients with an SOFA score between 8 and 13. In conclusion, in patients with abdominal septic shock, the addition of PMX-HP to the standard therapy resulted in lower mortality than expected in the subgroup of patients with intermediate severity of illness.
Maternal and Cord Blood Serum Metabolite Associations with Childhood Adiposity and Body Composition Outcomes

Maternal metabolites influence the size of newborns independently of maternal body mass index (BMI) and glycemia, highlighting the importance of maternal metabolism on offspring outcomes. This study examined associations of maternal metabolites during pregnancy with childhood adiposity, and cord blood metabolites with childhood adiposity using phenotype and metabolomic data from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and the HAPO Follow-Up Study. The maternal metabolites analyses included 2324 mother–offspring pairs, while the cord blood metabolites analyses included 937 offspring. Multiple logistic and linear regression were used to examine associations between primary predictors, maternal or cord blood metabolites, and childhood adiposity outcomes. Multiple maternal fasting and 1 hr metabolites were significantly associated with childhood adiposity outcomes in Model 1 but were no longer significant after adjusting for maternal BMI and/or maternal glycemia. In the fully adjusted model, fasting lactose levels were negatively associated with child BMI z-scores and waist circumference, while fasting urea levels were positively associated with waist circumference. One-hour methionine was positively associated with fat-free mass. There were no significant associations between cord blood metabolites and childhood adiposity outcomes. Few metabolites were associated with childhood adiposity outcomes after adjusting for maternal BMI and glucose, suggesting that maternal BMI accounts for the association between maternal metabolites and childhood adiposity.
Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE

In this paper, the feasibility of satellite remote sensing in detecting and predicting locations of buried objects in the archaeological site of Saruq Al-Hadid, United Arab Emirates (UAE) was investigated. Satellite-borne synthetic aperture radar (SAR) is proposed as the main technology for this initial investigation. In fact, SAR is the only satellite-based technology able to detect buried artefacts from space, and it is expected that fine-resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (>1 m) that might be buried in the subsurface (<2 m) under optimum conditions, i.e., dry and bare soil. SAR data were complemented with very high-resolution Worldview-3 multispectral images (0.31 m panchromatic, 1.24 m VNIR) to obtain a visual assessment of the study area and its land cover features. An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. Results from SAR feature extraction and geospatial analyses showed detection of the areas on the site that were already under excavation and predicted new, hitherto unexplored archaeological areas. The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. The validated results can provide guidance for future on-site archaeological work. The pilot process developed in this work can therefore be applied to similar arid environments for the detection of archaeological features and guidance of on-site investigations.
Deciphering the Color Origin of Pink Conch Pearl Using Nondestructive Spectroscopies and DFT Calculations

Conch pearl is a calcareous concretion produced by the Queen conch mollusk and is of extremely high economic value. Its attractive pink color has remained a mystery for hundreds of years. This difficulty limits the development of cultivation and preservation technology of conch pearl. Due to its super-high value, it is generally impossible to use destructive chemical analysis methods. Here, various nondestructive spectroscopic analysis methods and theoretical calculations were used to reveal the color origin of pink conch pearl. The experimental Raman and UV-Vis spectroscopies were used to characterize the color and pigments. We further discuss the pigments in conch pearl by comparing the Raman spectral characteristics and pigments of the red coral. Based on the characteristics of the experimental spectra, we speculate that the carotenoids color the conch pearl. The theoretical Raman and UV-Vis spectra of all-trans polyene and carotenoids were simulated using DFT calculations. According to the results, we proposed that the pigments in pink conch pearl are polyenes containing 11 shortened C–C bonds with multiplicities > 1.5, which may be demethylated carotenoids.
Experimental and Numerical Study on the Mechanical Performance of Ultra-High-Performance Concrete T-Section Beams

Aiming to investigate the mechanical performance of UHPC T-section beams, five specimens are fabricated and tested, considering the variable steel fiber volume fraction (SFVF). The code of the Association Francaise de Génie Civil (AFGC) is evaluated by test data. Additionally, based on Abaqus (2020), refined finite element analysis (FEA) models of specimens are established and validated by experimental data. Moreover, the parametric sensitivity analysis is carried out, which aims to further investigate the effect of shear span ratio, longitude reinforcement ratio, and stirrup ratio on the bending-shear behavior of T-section beams. The test results indicated that the ultimate load of the specimen improves with the increase of SFVF, and the use of steel fibers can greatly improve the shear capacity instead of the bending capacity. Furthermore, SFVF can change the failure mode; the specimens fail in shear failure when SFVF < 2%, while they fail in bending failure when SFVF ≥ 2%. From the evaluation of codes, the AFGC code is conservative in the prediction of ultimate capacity, which can guide the design of UHPC structures well. Additionally, from the parametric analysis of FEM, the failure mode transformed from shear failure to bending failure as the shear span ratio increased, particularly in specimens with SFVF ≥ 2.5%. Moreover, the stirrup ratio ρsv has a significant effect on the shear performance of structures with SFVF ≤ 1%, while it has less effect with SFVF ≥ 2%.
Hyperspectral Infrared Observations of Arctic Snow, Sea Ice, and Non-Frozen Ocean from the RV Polarstern during the MOSAiC Expedition October 2019 to September 2020

This study highlights hyperspectral infrared observations from the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) collected as part of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) deployment on the icebreaker RV Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The ARM M-AERI directly measures the infrared radiance emission spectrum between 520 cm−1 and 3000 cm−1 (19.2–3.3 μm) at 0.5 cm−1 spectral resolution. These ship-based observations provide a valuable set of radiance data for the modeling of snow/ice infrared emission as well as validation data for the assessment of satellite soundings. Remote sensing using hyperspectral infrared observations provides valuable information on sea surface properties (skin temperature and infrared emissivity), near-surface air temperature, and temperature lapse rate in the lowest kilometer. Comparison of the M-AERI observations with those from the DOE ARM meteorological tower and downlooking infrared thermometer are generally in good agreement with some notable differences. Operational satellite soundings from the NOAA-20 satellite were also assessed using ARM radiosondes launched from the RV Polarstern and measurements of the infrared snow surface emission from the M-AERI showing reasonable agreement.
The Effect of Sodium Bicarbonate Injection on the Physico-Chemical Quality of Post-Harvest Trout

The muscle hardness of fish is an important parameter associated with meat quality, and the post-mortem decrease in the pH of fish tissue pH affects its physical properties. We hypothesized that maintaining a high pH in fish tissue after death would prevent protein denaturation and consequent textural deterioration. This study aimed to determine the effectiveness of sodium bicarbonate (SBC) injections in preventing tissue softening caused by low pH after death in trout. We injected varying molar concentrations of SBC in rainbow trout (Oncorhynchus mykiss; 0 M, 0.5 M, 0.75 M, and 1 M) after harvest, and the product quality was assessed at 0, 24, 48, 72, and 96 h of ice storage. Quality was evaluated using proximate analyses for color, pH, water holding capacity (WHC), texture profile, and rigor index. The 0 M group had the lowest pH, and the 0.75 M group had the highest pH at all time points during storage. We observed improved tissue texture during storage in fish treated with 0.75 and 1 M SBC. The texture profile analysis showed higher hardness, frangibility, and stickiness in the tail than in the other regions. These varying results can be explained by significant differences between parts of the fish and sampling point selection. We also observed the highest pH and WHC values in the groups injected with 0.75 and 1 M SBC during storage.
On the Monotonicity of Certain Functionals in the Theory of Analytic Functions

Let f(z) be a function regular for | z | < R, and denote by L(r) the length of the curve Γ onto which the circle | z | = r is mapped by f(z), i. e. If Dr is the image of | z | ≤ r on the Riemann surface of f(z) then its area S(r) is given by
Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review

We conducted this Systematic Review to create an overview of the currently existing Artificial Intelligence (AI) methods for Magnetic Resonance Diffusion-Weighted Imaging (DWI)/Fluid-Attenuated Inversion Recovery (FLAIR)—mismatch assessment and to determine how well DWI/FLAIR mismatch algorithms perform compared to domain experts. We searched PubMed Medline, Ovid Embase, Scopus, Web of Science, Cochrane, and IEEE Xplore literature databases for relevant studies published between 1 January 2017 and 20 November 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed the included studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Five studies fit the scope of this review. The area under the curve ranged from 0.74 to 0.90. The sensitivity and specificity ranged from 0.70 to 0.85 and 0.74 to 0.84, respectively. Negative predictive value, positive predictive value, and accuracy ranged from 0.55 to 0.82, 0.74 to 0.91, and 0.73 to 0.83, respectively. In a binary classification of ±4.5 h from stroke onset, the surveyed AI methods performed equivalent to or even better than domain experts. However, using the relation between time since stroke onset (TSS) and increasing visibility of FLAIR hyperintensity lesions is not recommended for the determination of TSS within the first 4.5 h. An AI algorithm on DWI/FLAIR mismatch assessment focused on treatment eligibility, outcome prediction, and consideration of patient-specific data could potentially increase the proportion of stroke patients with unknown onset who could be treated with thrombolysis.
Spherical Subspace Potential Functional Theory

The recently introduced version of the density functional theory that employs a set of spherically symmetric densities instead of the density has a ‘set-representability problem’. It is not known if a density exists for a given set of the spherically symmetric densities. This problem can be eliminated if potentials are applied instead of densities as basic variables. Now, the spherical subspace potential functional theory is established.
Effect of Irrigation Water Salinity on Soil Characteristics and Microbial Communities in Cotton Fields in Southern Xinjiang, China

Irrigation with saline water is a possible solution to alleviate freshwater shortages. The long-term use of saline water for irrigation requires consideration of the influence of salt on the environmental conditions of the soil. The objective of this field study was to determine the effects of three continuous years of saline water irrigation on physiochemical properties and microbial communities in drip-irrigated cotton fields. The three total dissolved solid (TDS) levels of irrigation water treatments were (i) 1 g L−1 (fresh water, FWI), (ii) 3 g L−1 (brackish water, BWI), and (iii) 7 g L−1 (salt water, SWI). After three years, the electrical conductivity (EC), sodium adsorption ratio (SAR), and contents of K+, Na+, Mg2+, Cl−, and SO42− in the SWI treatment were significantly higher than those in the FWI and BWI treatments, but there were no significant differences in EC and K+ between the FWI and BWI treatments. BWI treatment significantly increased microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), urease, and sucrase contents. The diversity and abundance of bacteria and fungi were not affected by saline water irrigation, but the microbial community structure was altered. Saline water irrigation resulted in an elevation in the bacterial abundance of the phylum Chloroflexi and a decline in Proteobacteria and Actinobacteria. For fungi, the abundance of the phylum Ascomycota in the BWI treatment was greater than that in the FWI and SWI treatments. Linear discriminant analysis effect size (NMDS) results indicated clear variation in the microbiota profiles between the FWI, BWI, and SWI treatments for bacteria. Regarding the fungal microbiota profiles, the BWI and SWI treatments had similar microbiota profiles but were different from the FWI treatment. The number of bacterial biomarkers gradually increased with increasing total dissolved solids of irrigation water, while the number of fungal biomarkers gradually decreased. Additionally, cotton yield was significantly and positively correlated with the observed species of fungi, while it was significantly and negatively correlated with EC. Redundancy analysis (RDA) showed that bacterial community structure was regulated by SAR and fungal community structure was regulated by soil salinity and bulk density (BD). Future research will need to look into how the structure of the microbial community and the associated functional microorganisms are gradually changing with increased irrigation frequency under saline irrigation, as well as explore and screen for advantageous functional microorganisms.
Conservative error measures for classical and quantum metrology

The classical and quantum Cram\'er-Rao bounds have become standard measures of parameter-estimation uncertainty for a variety of sensing and imaging applications in recent years, but their assumption of unbiased estimators potentially undermines their significance as fundamental limits. In this note we advocate a Bayesian approach with Van Trees inequalities and worst-case priors to overcome the problem. Applications to superlocalization and gravitational-wave parameter estimation are discussed.