Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Reynolds number hydrodynamics of immersed thin and slender bodies

The hydrodynamics of thin (sheet-like) and slender (filamentary) bodies of viscous fluid immersed in a second fluid with a different viscosity is studied. Here we focuses on two examples: the subduction of oceanic lithosphere and the buckling of viscous threads in diverging microchannels, both have a characteristic Reynolds number Re<<1. A hybrid boundary integral & thin sheet method (BITS) is build for the subduction of 2D immersed sheet. After the validation by comparing with the results of full boundary elements method, both instantaneous and time-dependant soloutions are done to analyze the subduction with the BITS method. The scaling analysis of the normalized sinking speed V/V_Stokes as a function of the sheet's 'flexural stiffness' is confirmed by our numerical predictions. For moderate viscosity ratios (≈100), the sheet thins substantially as it sinks, but not enough to lead to the ‘slab breakoff’ that is observed in several subduction zones on Earth. Next, the parallel code BLUE for multi-phases flows is used to simulate the 3-dimensional viscous folding in diverging microchannels. We performed a parameter study comprising five simulations in which the flow rate ratio, the viscosity ratio, the Reynolds number, and the shape of the channel were varied relative to a reference model. The thread becomes unstable to a folding instability due to the longitudinal compressive stress. The initial folding axis can be either parallel or perpendicular to the narrow dimension of the chamber. In the former case, the folding slowly transforms via twisting to perpendicular folding , or may disappear totally.
Computational Tools to Assist in Analyzing Effects of the SERPINA1 Gene Variation on Alpha-1 Antitrypsin (AAT)

In the rapidly advancing field of bioinformatics, the development and application of computational tools to predict the effects of single nucleotide variants (SNVs) are shedding light on the molecular mechanisms underlying disorders. Also, they hold promise for guiding therapeutic interventions and personalized medicine strategies in the future. A comprehensive understanding of the impact of SNVs in the SERPINA1 gene on alpha-1 antitrypsin (AAT) protein structure and function requires integrating bioinformatic approaches. Here, we provide a guide for clinicians to navigate through the field of computational analyses which can be applied to describe a novel genetic variant. Predicting the clinical significance of SERPINA1 variation allows clinicians to tailor treatment options for individuals with alpha-1 antitrypsin deficiency (AATD) and related conditions, ultimately improving the patient’s outcome and quality of life. This paper explores the various bioinformatic methodologies and cutting-edge approaches dedicated to the assessment of molecular variants of genes and their product proteins using SERPINA1 and AAT as an example.
An integrated approach to understanding the depositional environment and organic matter enrichment factor in Carboniferous source rocks, Junggar Basin, NW China

Multiple indicators (trace fossils, trace metals, and δ13Corg) are used to assess the depositional environments and controls on organic carbon accumulation in the Lower Carboniferous Nanmingshui Formation, Junggar Basin, NW China. The majority of trace fossils belong to the Nereites ichnofacies, representing low energy, oxygen‐poor, and relatively quiet bottom water environments. However, the shallow water trace fossil Planolites also appears in the strata, possibly reflecting variations in the bottom water oxygen contents of the turbidite succession. Redox proxies Th/U, V/Sc, and V/(V + Ni) reveal variable redox conditions; most of the samples are in the range from suboxic to anoxic depositional conditions. Ni and Cu show a positive correlation with TOC, but U and V have no correlation with TOC, also suggesting the redox conditions were mainly suboxic. The P/Al and Cu/Al ratios of all samples are higher than those of Post‐Archean Australian Shales (PAAS), and about two‐thirds of the samples' Ba/Al and Ni/Al ratios are close to or higher than PAAS, suggesting the Early Carboniferous Junggar Basin had relatively high palaeo‐productivity levels. δ13Corg values average − 23.75‰, reflecting a mixture of terrestrial organic and marine organic matter. The indexes of Ba/Al, Cu/Al, Ni/Al, and δ13Corg show good positive correlations with TOC, indicating the palaeo‐productivity played an important role for the organic matter accumulation. On the other hand, the TOC correlates inversely with redox proxies (Th/U, V/Sc, and V/(V + Ni)), suggesting the preservation conditions (anoxia) are not the control factor of organic matters accumulation. Using these multiple approaches, our results indicate that productivity was the primary factor controlling organic matter accumulation.
Optimization of the lidar optical design for measurement of the aerosol extinction vertical profile

A lidar for aerosol monitoring with conventional optical design can provide good quality signals from several hundred meters up to tens of kilometres above the ground, but the aerosol load is mainly contained (up to 80%) in the planetary boundary layer that can have a height of only hundreds of meters above the ground level. Therefore, the measurement of the complete aerosol extinction profile is generally a very difficult challenge. In this paper, we proposed an optical design of lidar systems able of producing signals starting from a few tens of meters above the ground. The overlap profiles obtained from an optimized lidar was compared with ray tracing simulations and further conventional lidar apparatuses.
Toward a Sustainable World: Affective Factors Explain How Emotional Salary Influences Different Performance Indicators

The concept of emotional salary refers to the non-monetary rewards granted to workers, focusing on improving interpersonal relationships, strengthening productivity, and enhancing the competitiveness of organizations. The topic of emotional salary is still recent and lacks empirical studies demonstrating its beneficial effects for both workers (e.g., job satisfaction) and organizations (e.g., performance). Therefore, to expand knowledge about the benefits of emotional salary, the present study used the self-determination theory to hypothesize that motivation and satisfaction would serve as affective mechanisms linking emotional salary to workers’ performance. Through a non-experimental correlational study, an online questionnaire was administered to 215 workers from various organizations. The results showed that emotional salary influenced performance (task, contextual, and adaptive) by increasing motivation and job satisfaction. The results also indicated evidence of a serial mediation path between emotional salary, motivation, satisfaction, and then performance. From a management perspective, considering emotional salary as an organizational resource capable of motivating and satisfying workers is a starting point for acknowledging the practical and theoretical importance of this concept, as well as a strategy to contribute to organizational sustainability.
Data-driven fundamental models for pedestrian movements

The focus of the thesis is the utilization of the data collected using state-of-the-art tracking technologies for the characterization and modeling of pedestrian movements. In this context, the main objectives are the development of (i) data-driven definitions of fundamental variables and (ii) data-inspired mathematical formulations of fundamental relationships characterizing pedestrian traffic. The motivation of this research comes from the analysis of a real dataset collected in the train station in Lausanne, Switzerland. To collect the raw data, a large-scale network of smart sensors has been deployed in the station. We consider this case study to illustrate and validate our methodology. The definitions of fundamental traffic variables (speed, density and flow), existing in the literature are extended through a data-driven discretization framework. The framework is based on spatio-temporal Voronoi diagrams, designed using pedestrian trajectory data. The new definitions are (i) independent from an arbitrarily chosen discretization, (ii) appropriate for the multi-directional composition of pedestrian traffic, (iii) able to reflect the heterogeneity of pedestrian population and (iv) applicable to pedestrian trajectories described either analytically or as a sample of points. The performance of the approach and its advantages are illustrated empirically. Our approach outperforms the existing methodologies from the literature, in terms of the smoothness of the results, and in terms of the robustness with respect to the simulation noise and sampling frequency. To represent fundamental relationships of pedestrian traffic, we introduce probabilistic speed-density models. The approach is motivated by the high scatter in the data that we have analyzed. To characterize the observed pattern we relax the homogeneity assumption of the equilibrium relationships, and propose two models. The first model is based on distributional assumptions. The second model is more advanced, and it includes structures that are designed to capture specific aspects of the walking behavior. Various empirical tests validate the specification of both models. Contrasted with existing approaches, they yield a more realistic representation of the empirically observed phenomena. This thesis contributes with respect to the utilization of data potential in modeling of fundamental aspects related to pedestrian traffic. This becomes essential in the context of the growing data revolution and interconnected technologies that can help improve the safety and convenience of pedestrians. The methodological framework is fairly general, and it can be adapted to various pedestrian facilities.
Extending the Applicability of the Generalized Likelihood Function for Zero‐Inflated Data Series

Proper uncertainty estimation for data series with a high proportion of zero and near zero observations has been a challenge in hydrologic studies. This technical note proposes a modification to the Generalized Likelihood function that accounts for zero inflation of the error distribution (ZI‐GL). We compare the performance of the proposed ZI‐GL with the original Generalized Likelihood function using the entire data series (GL) and by simply suppressing zero observations (GLy>0). These approaches were applied to two interception modeling examples characterized by data series with a significant number of zeros. The ZI‐GL produced better uncertainty ranges than the GL as measured by the precision, reliability and volumetric bias metrics. The comparison between ZI‐GL and GLy>0 highlights the need for further improvement in the treatment of residuals from near zero simulations when a linear heteroscedastic error model is considered. Aside from the interception modeling examples illustrated herein, the proposed ZI‐GL may be useful for other hydrologic studies, such as for the modeling of the runoff generation in hillslopes and ephemeral catchments.
Human-centric lighting assessment of balcony design

We curate four human-centric lighting metrics for the design of homes with balconies: residential daylight (RDA, DLA), view quality, balcony gardening potential, and circadian health potential. Balcony gardening potential is a novel metric based on the fraction of global horizontal irradiance received at the balcony. We prepare a parametric simulation space of 31,034 simulations of apartments with balconies in Vancouver and develop a standard method of displaying their results for use in design analysis. Based on an analysis of the results, we propose standard design criteria for typical minimum performance recommendations and a high-quality of daylight design based on the median and third quartile of our results. Correlations between twelve design parameters and our metric outcomes are communicated in order to support intuitive design decisions when simulations are not possible.
E-Learning Using the Flipped Classroom Model-Based Teachmint Application and its Impact on Student Science Learning Outcomes

The lack of teachers' ability to create science learning media that is integrated with technology and information for grade V elementary schools prompted this research. The purpose of this research is to create a valid, practical and effective e-Learning product for the fifth grade science class in elementary schools. The research method used is Research and Development and refers to the ADDIE development model. Data collection techniques are divided into 2, specifically Qualitative and Quantitative data. Qualitative data had been received from suggestions and comments by means of validators and practitioners. The media became validated by 2 material professional lecturers, 2 media professional lecturers, and 1 language professional lecturer, and tested by 2 instructors and 10 students for practicality, and 31 college students for effectiveness. Quantitative information was acquired from the results of validator and practitioner questionnaire exams. Data analysis techniques uses a Likert scale and a paired sample t-test. The results of the validation and testing of science lessons show that the Media Media e-Learning based on the Flipped Classroom Model using the Teachmint application on Ecosystem Theme 5 is said to be very valid with a score of 91, very practical with a value of 92.5 and very effective 92.1. Based on the results of the data acquisition, it shows that e-Learning using the Teachmint application for science learning outcomes is considered very valid, very practical, and very effective in learning science in elementary schools.
Seroprevalence and Risk Factors of Toxoplasmosis Among Pregnant Women Attending Antenatal Clinic in Uyo Nigeria

Background and Objective: Toxoplasmosis is caused by Toxoplasma gondii and can result in foetal and neonatal death or various congenital defects in pregnancy. This study aimed to determine the prevalence and risk factors associated with Toxoplasma gondii infections in pregnant women attending antenatal clinics at the University of Uyo Health Center, Uyo, Nigeria. Materials and Methods: This was a cross-sectional study using 150 pregnant women after obtaining their informed consent. Detection of anti-T. gondii antibodies were conducted using Toxo IgG/IgM Rapid test kits. A structured questionnaire was administered to the participants to collect information on socio-demographic and risk factors associated with the infection. Results: The seroprevalence of T. gondii infection among the pregnant women was 77 (51.3%), which comprised 41.3% positivity for anti-T. gondii IgG (62/150), 6.7% for IgM (10/150) and 3.3% (5/150) for IgG plus IgM. A significant association was observed between seroprevalence and women residing area with an odd ratio of 0.186-1.36. Individuals having a cat as a pet was 55 (36.7%), while individuals with a cat in their neighbourhood were 62 (41.3%) with an odd ratio of 2.63. Pregnant women in their first trimester had the highest anti-T. gondii IgG of 30 (48:4%) and IgM was the lowest (6:60%). Conclusion: The seroprevalence of Toxoplasma gondii was relatively high in this study. Thus, health education and sensitisation on the disease and its transmission to women of childbearing age among pregnant women should be encouraged during antenatal follow-up, to reduce the risk of T. gondii infection during pregnancy.
Determinants of Smoking Cessation Outcomes and Reasons for Relapse in Patients Admitted to a Smoking Cessation Outpatient Clinic in Turkey

The aim of this study was to identify the determinants of smoking cessation outcomes and reasons for relapse following smoking cessation treatment. Using a mixed-method design, 179 patients were recruited from the Smoking Cessation outpatient clinic of Ankara Numune Training and Research Hospital between May 2016 and May 2017. Quantitative data were collected via questionnaires or from patient files and qualitative data were obtained via 5 focus group interviews with 28 patients who relapsed to smoking following treatment. The success rate of the smoking cessation clinic at the end of one year was 26%. The number of applications to the clinic was significantly higher in the group who quit smoking. Treatment success was found to be higher in the group that applied behavioral recommendations. In focus group interviews with patients who relapsed, the most common causes were stressful events, especially workplace problems and serious health problems experienced by relatives. The presence of smokers in the immediate vicinity increased the risk of relapse. It was concluded that not stopping treatment before the recommended period, continuity in follow-up appointments, support of the environment, support of pharmacotherapy with cognitive behavioral therapy and improving patients’ coping skills were important.
Identification and Genome Characterization of a Novel Muscovy Duck-Origin Goose Parvovirus with Three Recombinant Regions between Muscovy Duck Parvovirus and Goose Parvovirus

Muscovy duck-origin goose parvovirus (MDGPV) is a new virus resulting from the natural recombination of Muscovy duck parvovirus (MDPV) and goose parvovirus (GPV). Previously identified MDGPV strains were found to have two recombination regions, one in the P9 promoter to the NS region and one in the VP3 gene, or only one recombination in the VP3 gene. In 2022, a novel strain of MDGPV known as 2022FZ was identified from China’s mainland. Complete genome sequence analysis showed that there were three recombination regions in this strain: one located in the P9 promoter-NS (425–612 nt) region, one in the NS2 (1,483–1,824 nt) region, and one in the VP3 (3,124–4,248 nt) region, respectively. The recombination regions in the P9 promoter-NS, NS2, and VP3 genes were substituted with the relevant GPVs sequences, whereas the MDPV virulent strain served as the skeleton in this instance. In addition, the 2022FZ strain had multiple unique aa mutations in the NS protein and the VP protein. The Muscovy duckling challenge test showed that MDGPV-2022FZ is less pathogenic to Muscovy ducklings than two recombinant or sole recombinant MDGPV strains. For the first time, our study identified a three-region recombinant MDGPV strain and detected the novel recombination event in the NS2 gene. These results contribute to our understanding of the pathogenicity and genetic diversity of duck parvovirus.
Induction of Neuroinflammation and Brain Oxidative Stress by Brain-Derived Extracellular Vesicles from Hypertensive Rats

Neuroinflammation and brain oxidative stress are recognized as significant contributors to hypertension including salt sensitive hypertension. Extracellular vesicles (EVs) play an essential role in intercellular communication in various situations, including physiological and pathological ones. Based on this evidence, we hypothesized that EVs derived from the brains of hypertensive rats with salt sensitivity could trigger neuroinflammation and oxidative stress during hypertension development. To test this hypothesis, we compared the impact of EVs isolated from the brains of hypertensive Dahl Salt-Sensitive rats (DSS) and normotensive Sprague Dawley (SD) rats on inflammatory factors and mitochondrial reactive oxygen species (mtROS) production in primary neuronal cultures and brain cardiovascular relevant regions, including the hypothalamic paraventricular nucleus (PVN) and lamina terminalis (LT). We found that brain-derived DSS-EVs significantly increased the mRNA levels of proinflammatory cytokines (PICs) and chemokines, including TNFα, IL1β, CCL2, CCL5, and CCL12, as well as the transcriptional factor NF-κB in neuronal cultures. DSS-EVs also induced oxidative stress in neuronal cultures, as evidenced by elevated NADPH oxidase subunit CYBA coding gene mRNA levels and persistent mtROS elevation. When DSS-EVs were injected into the brains of normal SD rats, the mRNA levels of PICs, chemokines, and the chronic neuronal activity marker FOSL1 were significantly increased in the PVN and LT. Furthermore, DSS-EVs caused mtROS elevation in brain PVN and LT, particularly in neurons. Our study reveals a novel role for brain-derived EVs from hypertensive rats in triggering neuroinflammation, upregulating chemokine expression, and inducing excessive ROS production. These findings provide insight into the complex interactions between EVs and hypertension-associated processes, offering potential therapeutic targets for hypertension-linked neurological complications.
Reconstructing 3-D Thermohaline Structures for Mesoscale Eddies Using Satellite Observations and Deep Learning

Mesoscale eddies are circular water currents found widely in the ocean and significantly impact the ocean’s circulation, water distribution, and biology. However, our comprehension of eddies’ 3-D structures remains constrained due to the scarcity of in situ data. Therefore, we introduce a novel deep learning (DL) model, 3D-EddyNet, designed for reconstructing the 3-D thermohaline structure of mesoscale eddies. Utilizing multisource satellite data and Argo profiles collected from eddies in the North Pacific Ocean between 2000 and 2015, we optimized the 3D-EddyNet model by adjusting image sizes, introducing a convolutional block attention module (CBAM), and incorporating eddy physical parameters. The results demonstrate remarkable accuracy, with an average root mean square error (RMSE) of 0.32 °C (0.03 psu) for temperature (salinity) within anticyclonic eddies and 0.41 °C (0.04 psu) within cyclonic eddies in the upper 1000 m. We applied 3D-EddyNet to reconstruct 3-D eddy structures in the Kuroshio extension (KE) and the Oyashio current (OC) regions, demonstrating its capability to accurately represent the 3-D thermohaline eddy structures both vertically and horizontally. The consistency in the averaged 3-D eddy structures between our 3D-EddyNet and the ARMOR3D dataset in the KE and OC regions underscores the robust generalizability of our model, indicating the model’s ability to infer 3-D eddy structures when Argo profiles are unavailable. The distinctive advantage offered by 3D-EddyNet enhances our ability to understand mesoscale eddy dynamics, overcoming challenges posed by the limited availability of in situ data.