Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of maternal environmental tobacco smoke exposure on periodontal health and mother-infant bonding in relation to salivary cotinine level

Background: Environmental tobacco smoking is produced by active smokers burning the tip of a cigarette and breathed by nonsmokers and measured by cotinine level. It has the potential to raise the risk of periodontal disease. One of the most frequent chronic diseases in adults is periodontal disease. The lower maternal-fetal attachment has been found to predict smoking status in previous studies, but no research has examined whether maternal-fetal attachment predicts environmental tobacco smoking. This study assessed the effects of maternal environmental tobacco smoke exposure on periodontal health and mother-infant bonding concerning salivary cotinine levels. Materials and methods: This is a comparative cross-sectional study comparing environmental tobacco smoke on exposed and non-exposed mothers aged between 20-35 years with their infants aged up to one year who attended primary health care centers in rural areas of AL-Karkh sector/Baghdad. Along with the essential socio-demographic data, a secondhand smoke exposure scale and postpartum bonding questionnaire were employed. Collection of unstimulated saliva from mothers was done according to Navazesh and Kumer in 2008. After that, the clinical Assessment of gingival bleeding and periodontal pockets was performed by using Community Periodontal Index according to the world health organization in 1997. Results: Out of 150 subjects,67(44.66%) were exposed to environmental tobacco whereas the non-exposed mothers were composed of 83 (55.33%). The highest mean number of CPI0(healthy gingiva) and CPI1(gingival bleeding) were among the non-exposed mothers while the highest mean number of CPI2(dental calculus), CPI3 (shallow pocket 4-5mm) and CPI4(deep pocket 6mm or more) were among the exposed mothers. The mean value of cotinine level among the non-exposed mothers was lower than exposed mothers with significant results. A higher salivary cotinine level was linked to a lower maternal-fetal bonding score. Conclusions: Mother’s exposure to environmental tobacco smoke significantly negatively impacts periodontal disease. Furthermore, mothers who have a stronger sense of attachment and affiliation to their fetus have lower salivary cotinine concentrations than mothers who have a less sense of fetal attachment.
Serum Irisin Levels Are Inversely Correlated With the Severity of Coronary Artery Disease Confirmed by Coronary Angiography: A Comparative Cross-Sectional Study

Introduction Irisin, a newly discovered myokine, has been reported for its role in coronary artery disease (CAD), which is a major cause of mortality worldwide. Atherosclerosis is the primary cause of CAD. Irisin has been reported to reduce atherosclerosis by improving endothelial function and inhibiting inflammation via iNOS/NF-κB pathways. We sought to investigate the relationship between serum irisin levels and the severity of CAD that is confirmed with coronary angiography. Methods A comparative cross-sectional study was designed between the Chemical Pathology and Cardiology departments at KEMU/Mayo Hospital in Lahore, Pakistan. Patients were divided into group A with mild CAD (<50% stenosis) and group B with moderate-severe CAD (>50% stenosis). Serum was collected from venous blood, and irisin levels were analyzed by ELISA. Inclusion criteria: patients with stable CAD. Exclusion criteria: History of coronary artery bypass grafting (CABG), acute coronary syndrome (ACS), active or chronic infection, hepatic or renal dysfunction. Results The mean + SD age (years) of patients in group B (57.0±9.5) was significantly higher than group A (50.0±13.7). Irisin levels (μg/ml) were significantly higher in group A (15.3±4.6) than in group B (9.3±2.4). Irisin levels were significantly negatively correlated with the severity of CAD (% stenosis). Conclusion Serum irisin levels are low in patients with moderate to severe CAD, and they are negatively correlated with the severity of CAD (% stenosis).
Multiple restarts mixed Gibbs sampling detector for large-scale antenna systems

This work proposes a low-complexity detector for medium- and high-order modulation large-scale multiple-input multiple-output (LS-MIMO) systems based on the set of Markov chain Monte-Carlo techniques. Such efficient signal detection algorithm is based on the mixed Gibbs sampling with multiple restarts (MGS-MR) strategy with sample-averaged approach during the coordinate updating process, named averaged MGS (aMGS). The proposed strategy applies multiple samples average procedure to restrict the range of the random solution, which comes from the mixture proposed by the original MGS. Numerical simulation results considering higher-order M -QAM demonstrated that the proposed detection method can substantially improve the convergence of the MGS-MR algorithm, while no extra computational complexity is required. The proposed aMGS-based detector suitable for medium- and high-order modulation LS-MIMO further exhibits improved performance when the system loading is high, i.e. when ( K / N ) ≥ 0.75. In addition, the proposed numerical simulation analyses have shown that the optimal value of the mixing ratio parameter can vary regarding system and channel configuration scenarios, resulting somewhat different from the 1/2 K value disseminated in the literature.
Supersymmetric sigma models and their indices

Supersymmetric indices for sigma models are known to compute topological invariants of the target space on which the sigma model is built. In the case where the target space is a K3 surface, the worldsheet of the sigma model enjoys an N=4 superconformal symmetry. A supersymmetric index known as the elliptic genus can be constructed for this theory and decomposed into a sum of massless and massive characters of the N=4 superconformal algebra governing the symmetries. This index exhibits a phenomenon known as Mathieu moonshine, in which the coefficients of the massive characters in that decomposition are dimensions of representations of the sporadic group Mathieu 24. In this thesis, motivated by this moonshine phenomenon for theories with N=4 superconformal symmetries, we consider sigma models which exhibit a larger N=4 superconformal symmetry on the worldsheet, and discuss two supersymmetric indices which could be applied to such sigma models in search of a new moonshine. We discuss the states which contribute to these indices and calculate one of them for some specific theories.
Menstrual Cycle Length and Dysmenorrhea in Female Adolescents Aged 9-18 Years

Abstract Objective: To determine the relationship between menstrual cycle length and dysmenorrhea in female adolescents aged 9-18 years in Sang Timur Tomang School West Jakarta. Methods: This study used a cross-sectional study approach to conduct observational analytic research. The study samples were female students aged 9-18 years in Sang Timur Tomang School. Study data were obtained through the Verbal Multidimensional Scoring System (VMSS) questionnaire in Indonesian. The Chi-square test of independence was used to determine the relationship between menstrual cycle length and dysmenorrhea. Results: From a total of 137 respondents, 38% have abnormal menstrual cycle length. Eighty-two-point-seven percent of respondents who have abnormal menstrual cycle length experienced dysmenorrhea, and 17,3% of participants who have abnormal menstrual cycle length did not experience dysmenorrhea. Chi-square test results showed a significant relationship between menstrual cycle length and dysmenorrhea in female adolescents aged 9-18 years in Sang Timur Tomang School West Jakarta (p = 0,005). Conclusion: There is a significant relationship between menstrual cycle length and dysmenorrhea in female adolescents aged 9-18 years in Sang Timur Tomang School West Jakarta. Keywords: female adolescents, dysmenorrhea, menstrual cycle, menstrual cycle length
A Leading but Simple Classification Method for Remote Sensing Images

Recently, researchers have proposed a lot of deep convolutional neural network (CNN) approaches with obvious flaws to tackle the difficult semantic classification (SC) task of remote sensing images (RSI). In this paper, the author proposes a simple method that aims to provide a leading but efficient solution by using a lightweight EfficientNet-B0. First, this paper concluded the drawbacks with an analysis of mathematical theory and then proposed a qualitative conclusion on the previous methods’ theoretical performance based on theoretical derivation and experiments. Following that, the paper designs a novel method named LS-EfficientNet, consisting only of a single CNN and a concise training algorithm called SC-CNN. Far different from previous complex and hardware-extensive ones, the proposed method mainly focuses on tackling the long-neglected problems, including overfitting, data distribution shift by DA, improper use of training tricks, and other incorrect operations on a pre-trained CNN. Compared to previous studies, the proposed method is easy to reproduce because all the models, training tricks, and hyperparameter settings are open-sourced. Extensive experiments on two benchmark datasets show that the proposed method can easily surpass all the previous state-of-the-art ones, with an outstanding accuracy lead of 0.5% to 1.2% and a remarkable parameter decrease of 78% if compared to the best prior one in 2022. In addition, ablation test results also prove that the proposed effective combination of training tricks, including OLS and CutMix, can clearly boost a CNN's performance for RSI-SC, with an increase in accuracy of 1.0%. All the results reveal that a single lightweight CNN can well tackle the routine task of classifying RSI.
Digital Transformation and Corporate Sustainability: The Moderating Effect of Ambidextrous Innovation

Digital transformation (DT) has become the new normal. Research has focused on the effect of the overall level of DT in enterprises. However, the effects of DT across different dimensions remain unclear. This study divided DT into technology- (TDT) and market-based digital transformation (MDT). It examined the effects on corporate sustainability and how ambidextrous innovation affects the relationship between both types and corporate sustainability. This study used the two-way fixed-effects model and the two-stage least squares method to study A-share listed companies in China from 2013 to 2021. The results showed that both TDT and MDT had positive effects on corporate sustainability. The higher the levels of exploratory and exploitative innovation in enterprises, the stronger the contribution of both types of DT to corporate sustainability. The findings validate the research on DT in line with the resource-based view, enrich the literature on and expand the boundary conditions of DT applications across various dimensions, and offer useful insights for practitioners.
Analisis Kemampuan Pemahaman Konsep Matematika Siswa Pada Materi Sistem Persamaan Linear Dua Variabel

Penelitian ini bertujuan untuk mendeskripsikan kemampuan pemahaman konsep siswa pada materi sistem persamaan linear dua variabel dan untuk mengetahui tingkat kemampuan pemahaman konsep siswa. Pendekatan dalam penelitian yang digunakan dalam penelitian ini adalah kualitatif. Subyek dalam penelitian ini adalah siswa kelas VIII MTs PP Saifullah. Pengambilan data dalam penelitian ini diperoleh dari pemberian tes soal kemampuan pemahaman konsep siswa agar dapat mengetahui tingkat kemampuan pemahaman konsep matematika siswa yang dialami siswa tersebut. Hasil penelitian menunjukkan bahwa ada 15 orang siswa yang memiliki kemampuan pemahaman konsep yang dikategori rendah, ada 12 orang siswa yang memiliki kemampuan pemahaman konsep yang dikategori sedang, da nada 3 orang siswa yang memiliki kemampuan pemahaman konsep yang dikategori tinggi. Hal ini menunjukkan bahwa siswa tersebut memilki kemampuan pemahaman konsep yang berbeda dan termasuk dalam kata rendah.
Lattice models for Josephson junctions and graphene superlattices

In this thesis we study quantum transport phenomena on the nanometer scale, in two classes of materials: topological insulators with induced superconductivity and graphene superlattices. Both topics are motivated by recent experimental developments: the first topic arose from the search for Majorana fermions in a quantum spin Hall insulator, the second topic arose from the search for massive Dirac fermions in the Kekule band structure of graphene on a copper substrate. We focus on lattice models, solving them both numerically and analytically.
Self-efficacy and Job Stress among Tobacco Farmers

Farmers have heavy work demands, so farmers are at risk of injury and work-related illnesses such as mental stress disorder. This study analyzed the relationship between self-efficacy and work stress in tobacco farmers. The research design used is the analytic correlation with a cross-sectional approach. The sampling technique uses probability sampling with cluster sampling. The sample for this research is tobacco farmers who are members of the farmers' group, with a sample size of 100 respondents. The instrument used in this study was the GSE (General Self-efficacy) Questionnaire to measure self-efficacy in tobacco farmers. The DASS 42 Questionnaire (Depression, Anxiety, Stress, Scale) was used to measure work stress—data analysis using the Pearson test correlation test. The average tobacco farmer has high Self-Efficacy and low work stress. The results of the Pearson test, namely the p-value of 0.001 with a correlation value of -0.419, show a relationship between self-efficacy and work stress with a moderate correlation level with a negative correlation direction. Nurses are expected to carry out counseling activities for tobacco farmers to prevent work stress and increase self-efficacy in tobacco farmers.
Robust Tracking Control for Tail-Sitters in Flight Mode Transitions

Tail-sitters can fly as rotary-wing or fixed-wing aircraft and the transition between the two flight modes is critical. This paper develops a robust nonlinear control method to achieve the trajectory tracking control in flight mode transitions. The coordinate system does not need to be switched neither the controller structures or parameters. Theoretical analysis and simulation studies are conducted to verify the effectiveness of the developed control approach.
UNHEARD SENTIMENTS OF MAMANWA (INDIGENOUS) LEARNERS IN THE MAINSTREAM EDUCATION: A BASIS FOR EDUCATIONAL POLICY

This article aimed to investigate the unheard sentiments among Mamanwa learners in mainstream education to create a suitable educational policy for indigenous learners in the Philippines. The study utilized purposive (non-probability) sampling comprising fifteen participants residing in Panaon Area, Southern Leyte, Philippines. Since the study involved the indigenous people community, free prior informed consent (FPIC) was secured from authorities. This study focused on the lived experiences of Mamanwa learners, particularly the barriers, challenges, and coping mechanisms for handling the struggles they faced in mainstream education. A face-to-face semi-structured interview was employed to gather meaningful responses analyzed through Colaizzi's strategy. The findings revealed that the Mamanwa learners' social isolation, the inadequacy of learning materials, lack of access to educational services, and limited support resources and educational funds from the government have contributed to their ongoing challenge to completion in mainstream education. These factors have all affected their personal and social well-being. Conclusively, despite the barriers and challenges, however, the Mamanwa learners are becoming resilient and diversely unique individuals who need to be understood, accepted, and embraced just like any other member of society. Furthermore, the fueling mechanisms of every indigenous group to cross mainstream education are in the hopes of building growth, promoting connections through intense exposure in the community, and as individuals of disadvantaged groups with a social change mindset aimed to improve their social status.
Hybrid uncertainty model for multi-state systems and linear programming-based approximations for reliability assessment

Abstract This article studies reliability assessment for Multi-State Systems (MSSs) with components states that are uncertain in both probability and performance realizations. First, we propose a model of (discrete) Hybrid Uncertainty Variable (HUV) for modeling the hybrid uncertainty of the MSS, in which both state performance levels and associated probability level are described by uncertain values. The HUV can be regarded as a generalization of random variable whose realizations and corresponding probabilities are both uncertain values. Especially the uncertain probabilities are controlled by the probability law. Leveraging the HUV-based hybrid uncertainty model, the primitive probability law is considered throughout the whole process from modeling component state probabilities, through the resulting system state probabilities, to the final reliability computations. Therefore, the information loss is reduced to a minimum. Furthermore, we develop a framework for assessing the reliability of the MSS with hybrid uncertainty. In particular, due to hybrid uncertainty considered together with the primitive probability law constraints, the reliability bound computations essentially require solving a pair of multi-linear optimization problems, which in general are non-convex and non-concave and therefore belong to a class of difficult optimization problems. Therefore, we develop a linear programming‐based cut-generation approach for solving the reliability bound assessment problem which achieves a computationally attractive approximation. Finally, the effectiveness of our approaches is validated in the case study with the comparisons to the published results.
Synthesis of Ni-Cu-CNF Composite Materials via Carbon Erosion of Ni-Cu Bulk Alloys Prepared by Mechanochemical Alloying

The unique physical and chemical properties of composite materials based on carbon nanofibers (CNFs) makes them attractive to scientists and manufacturers. One promising method to produce CNFs is catalytic chemical vapor deposition (CCVD). In the present work, a method based on carbon erosion (CE) of bulk microdispersed Ni-Cu alloys has been proposed to prepare efficient catalysts for the synthesis of CNF-based composites. The initial Ni-Cu alloys were obtained by mechanochemical alloying (MCA) of metallic powders in a planetary mill. The effect of MCA duration on the phase composition of Ni-Cu samples was studied by X-ray diffraction analysis and temperature-programmed reduction in hydrogen. It has been also revealed that, during such stages as heating, reduction, and short-term exposure to the reaction mixture (C2H4/H2/Ar) at 550 °C, the formation of a Ni-based solid solution from the initial Ni-Cu alloys takes place. The early stages of the CE process were monitored by transmission electron microscopy combined with energy-dispersive X-Ray analysis. It was found that the composition of the catalytic particles is identical to that of the initial alloy. The morphological and structural features of the prepared Ni-Cu-CNF composites were studied by scanning and transmission electron microscopies. The textural characteristics of the composites were found to be dependent on the reaction time.
Coagulopathy with COVID19 and Tyrosine Kinase Receptor Tie 2: Review Article

Background: The newest coronavirus-caused SARS outbreak was first reported in December 2019 (COVID-19). The Director-General of the World Health Organization declared a global pandemic of COVID-19 three months after the first cases were identified. It is still not apparent how exactly COVID-19 causes coagulopathy, they may be similar to those which cause septic coagulopathy/disseminated intravascular coagulation (DIC) when bacteria are present. Endothelial cells have an abundance of the receptor Tyrosine Kinase Tie2. Additionally, it is expressed by a certain subpopulation of macrophages and has been shown to facilitate angiogenesis. Objective: Review of coagulopathy with COVID19 and Tyrosine Kinase Receptor Tie2. Methods: We scoured scholarly papers and databases including PubMed, Google Scholar, and Science Direct for information on COVID19 and Tyrosine Kinase Receptor. Between March 2016 and February 2023, however, only the latest or most comprehensive study was considered. The authors also assessed the usefulness of references taken from similar books. Documents written in languages other than English have been overlooked because of a lack of funding to translate them. Unpublished articles, oral talks, conference abstracts, and dissertations were all generally agreed upon not to constitute valid scientific investigation. Conclusion: Extreme cases of COVID-19 are characterized by microvascular thrombosis and accompanying endothelial dysfunction. Signaling through the Tie2 receptor helps keep the endothelial surface anticoagulant in a constitutive state. Angiopoietin-2 (Angpt-2) is a prothrombotic phenotypic switch induced by the Tie2 antagonist produced from endothelial cells during inflammation.
The Challenge of Atmospheric Data Assimilation on Mars

Data assimilation is carried out for the Martian atmosphere with the Mars Climate Sounder (MCS) retrievals of temperature, dust, and ice. It is performed for the period Ls = 180° to Ls = 320° of Mars Year 29 with the Local Ensemble Transform Kalman Filter scheme and the Laboratoire de Météorologie Dynamique (LMD) Mars Global Climate Model (GCM). In order to deal with the forcings of aerosols (dust and water ice) on atmospheric temperatures, a framework is given for multivariate analysis. It consists of assimilating a GCM variable with the help of another GCM variable that can be more easily related to an observation. Despite encouraging results with this method, data assimilation is found to be intrinsically different for Mars and more challenging, due to the Martian atmosphere being less chaotic and exhibiting more global features than on Earth. This is reflected in the three main issues met when achieving various data assimilation experiments: (1) temperature assimilation strongly forces the GCM away from its free‐running state, due to the difficulty of assimilating global atmospheric thermal tides; (2) because of model bias, assimilation of airborne dust is not able to reproduce the vertical diurnal variations of dust observed by MCS, and not present in the GCM; and (3) water ice clouds are nearly impossible to assimilate due to the difficulty to assimilate temperature to a sufficient precision. Overall, further improvements of Martian data assimilation would require an assimilation that goes beyond the local scale and more realism of the GCM, especially for aerosols and thermal tides.
Development of methods to improve the accuracy and efficiency of unsteady incompressible flow simulation

The runtime of unsteady incompressible flows were reduced through different techniques in the formulation and solution of the governing equations. The implicit Runge-Kutta based IRKSIMPLER algorithm was developed and compared to the Crank-Nicholson based SIMPLER and the explicit RK-SIMPLER algorithms. The IRK-SIMPLER algorithm was tested for steady and unsteady problems, both on structured Cartesian and unstructured vertex-centered median-dual grids, and proved to be an accurate and robust algorithm that also required less runtime. Further, a second order unstructured Flux Corrected Method improved the accuracy of flux calculation with minimal/negligible increase in runtime. The Flux Corrected Method provided a small stencil scheme that required little additional runtime compared to the commonly used Power Law scheme. Wind turbine cases were simulated with momentum source modeling. Both steady rotor models and three variations of the unsteady momentum source model were tested. The power predicted by the simulations matched experiments well, and for the cases presented, the simulations required 447 times less runtime than traditional methods.