Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 Vaccine Hesitancy among Parents of Children under Five Years in the United States

On 17 June 2022, the U.S. FDA authorized the Pfizer-BioNTech and Moderna COVID-19 (SARS-CoV-2) vaccines for emergency use (EUA) in children ages 6 months–4 years. Seroprevalence has increased during the current Omicron variant wave for children under 5 years, and the burden of hospitalization for this age group is similar or exceeds other pediatric vaccine-preventable diseases. Research following the October 2021 EUA for vaccines for children 5–11 indicates a high prevalence of parental vaccine hesitancy and low uptake, underscoring the urgency of understanding attitudes and beliefs driving parental COVID-19 vaccine rejection and acceptance for younger children. One month prior to FDA approval, in the present study 411 U.S. female guardians of children 1–4 years from diverse racial/ethnic, economic, and geographic backgrounds participated in a mixed method online survey assessing determinants of COVID-19 pediatric vaccine hesitancy. Only 31.3% of parents intended to vaccinate their child, 22.6% were unsure, and 46.2% intended not to vaccinate. Logistic regression indicated significant barriers to vaccination uptake including concerns about immediate and long-term vaccination side effects for young children, the rushed nature of FDA approval and distrust in government and pharmaceutical companies, lack of community and family support for pediatric vaccination, conflicting media messaging, and lower socioeconomic status. Vaccine-resistant and unsure parents were also more likely to believe that children were not susceptible to infection and that the vaccine no longer worked against new variants. Findings underscore the need for improved public health messaging and transparency regarding vaccine development and approval, the importance of community outreach, and increased pediatrician attention to parental concerns to better improve COVID-19 vaccine uptake for young children.
Prevalence and Associated Risk Factors of Urinary Schistosomiasis among Primary School Pupils in the Jidawa and Zobiya Communities of Jigawa State, Nigeria

Background: Urogenital schistosomiasis (UgS) is a parasitic disease caused by Schistosoma haematobium and can lead to chronic ill-health. Nigeria is endemic for schistosomiasis, but epidemiology of UgS has not been studied in most states. This study was conceived with the aim to contribute towards an accurate national picture of UgS in Nigeria. The prevalence of UgS and the associated risk factors were for the first time investigated among primary school pupils in Jidawa and Zobiya communities of the Dutse Local Government Area (LGAs) of Jigawa State, Nigeria. Method: Focus group discussions with teachers and parents were conducted. After obtaining written consent from parents, questionnaires were administered to pupils to obtain socio-demographic data and information on water contact activities. Urine samples (279) were collected and processed by the urine filtration technique to evaluate haematuria and the presence of S. haematobium eggs. Results: Prevalences of 65.7% (90/137) and 69.0% (98/142) were recorded in the Jidawa and Zobiya communities, respectively. In both communities, there was a significant association between gender and UgS: 63.3% of the infected pupils were males as compared to 36.7% females (χ2 = 5.42, p = 0.020). Grade 5 students had a significantly higher prevalence (χ2 = 17.919, p = 0.001) (80.0%) compared to those in grades 2, 3, 4, and 6 (63.8%, 66.7%, 61.5%, and 64.6%, respectively). Water contact activities showed that pupils involved in fishing, irrigation, and swimming were at greater risk of becoming infected in Jidawa and Zobiya, with odds ratios (risk factors) of 5.4 (0.994–28.862) and 4.1 (1.709–9.862), respectively (p = 0.05). Conclusion: Both the Jidawa and Zobiya communities of the Dutse LGAs of Jigawa State are hyperendemic for UgS. In collaboration with the State Ministry of Health, mass administration of praziquantel was carried out in the Jidawa and Zobiya communities after this study.
Impact of Ceramic Material and Preparation Design on Marginal Fit of Endocrown Restorations

Background: The aim of this study is to investigate the impact of ceramic material and preparation design on the marginal fit of endocrown restorations. Methods: Forty endocrown restorations were CAD/CAM-fabricated for forty extracted maxillary first premolar teeth. Samples were divided into two groups (n = 20) according to the ceramic materials used: Celtra Duo and Vita Enamic. Each group was divided into two subgroups (n = 10) according to the preparation design: with no intraradicular extension and with 3 mm intraradicular extension. The marginal gap was examined using a digital microscope. Results: Celtra Duo without intraradicular extension recorded the least mean marginal gap (7.74 ± 1.55 µm), while Group Celtra Duo with 3 mm intraradicular extension recorded the highest mean marginal gap (29.54 ± 6.32 µm). Group Vita Enamic recorded a lesser marginal gap (18.03 ± 12.11 µm) than group CD (Celtra Duo) (18.64 ± 12.05 µm). There is a statistically non-significant difference between the two groups of materials (p = 0.873). There is a statistically significant difference between the two tested preparation designs (p < 0.001). Conclusion: All groups recorded a marginal gap within clinically accepted values. Material selection may influence the fitting of restorations. Intraradicular extension for endocrown restorations adversely affects the marginal fit, however, the marginal gap is still within the clinically accepted range.
Dielectric Permittivity Measurement Using Open-Ended Coaxial Probe—Modeling and Simulation Based on the Simple Capacitive-Load Model

The study aim was to validate that dielectric permittivity measurement using the open-ended coaxial probe can be reliably modeled using electromagnetic modeling and simulations, followed by the postprocessing calculations based on the simple capacitive-load model. Saline solutions with various NaCl concentrations were used as materials under test (MUTs) to investigate how ionic conductivity affects the model validity. Two different solvers and simulation methods were used: FEKO for the frequency domain and CST for the time domain. Furthermore, we performed physical experiments with the same probe and MUTs, again implementing the capacitive-load model on the measurement data to observe the model validity. Relative error of the capacitive-load model with respect to the reference permittivity values, both in measurements and simulations, was within 10% for all cases except for the measured εr′ of 1M solution at the lowest frequencies. The model yielded average relative errors well below 1% for the physiological saline, which is relevant for biological materials. The error increased for higher concentrations and for the lowest simulated frequencies but was within the declared measurement accuracy of the probe itself. This makes the simple capacitive-load model valid for all analyzed concentrations in the microwave frequency range from 0.5 to 18 GHz.
Automated Wheat Diseases Classification Framework Using Advanced Machine Learning Technique

Around the world, agriculture is one of the important sectors of human life in terms of food, business, and employment opportunities. In the farming field, wheat is the most farmed crop but every year, its ultimate production is badly influenced by various diseases. On the other hand, early and precise recognition of wheat plant diseases can decrease damage, resulting in a greater yield. Researchers have used conventional and Machine Learning (ML)-based techniques for crop disease recognition and classification. However, these techniques are inaccurate and time-consuming due to the unavailability of quality data, inefficient preprocessing techniques, and the existing selection criteria of an efficient model. Therefore, a smart and intelligent system is needed which can accurately identify crop diseases. In this paper, we proposed an efficient ML-based framework for various kinds of wheat disease recognition and classification to automatically identify the brown- and yellow-rusted diseases in wheat crops. Our method consists of multiple steps. Firstly, the dataset is collected from different fields in Pakistan with consideration of the illumination and orientation parameters of the capturing device. Secondly, to accurately preprocess the data, specific segmentation and resizing methods are used to make differences between healthy and affected areas. In the end, ML models are trained on the preprocessed data. Furthermore, for comparative analysis of models, various performance metrics including overall accuracy, precision, recall, and F1-score are calculated. As a result, it has been observed that the proposed framework has achieved 99.8% highest accuracy over the existing ML techniques.
Wind speed prediction over Malaysia using various machine learning models: potential renewable energy source

Modeling wind speed has a signiﬁcant impact on wind energy systems and has attracted attention from numerous researchers. The prediction of wind speed is considered a challenging task because of its natural nonlinear and random characteristics. Therefore, machine learning models have gained popularity in this field. In this paper, three machine learning approaches – Gaussian process regression (GPR), bagged regression trees (BTs) and support vector regression (SVR) – were applied for prediction of the weekly wind speed (maximum, mean, minimum) of the target station using other stations, which were specified as reference stations. Daily wind speed data, gathered via the Malaysian Meteorological Department at 14 measuring stations in Malaysia covering the period between 2000 and 2019, were used. The results showed that the average weekly wind speed had superior performance to the maximum and minimum wind speed prediction. In general, the GPR model could effectively predict the weekly wind speed of the target station using the measured data of other stations. Errors found in this model were within acceptable limits. The findings of this model were compared with the measured data, and only Kota Kinabalu station showed an unacceptable range of prediction. To investigate the prediction performance of the proposed model, two models were used as the comparison models: the BTs model and SVR model. Although the comparison of GPR with the BTs model at Kuching station showed slightly better performance for the BTs model in maximum and minimum wind speed prediction, the prediction outcomes of the other 13 stations showed better performance for the proposed GPR model. Moreover, the proposed model generated smaller prediction errors than the SVR model at all stations.
Health Problems of Students during Distance Learning in Central and Eastern Europe: A Cross-Sectional Study of Poland and Ukraine

The COVID-19 pandemic has significantly disrupted the functioning of society. Issues of deteriorating health were among the main problems resulting from restrictions such as self-isolation, social distancing, and remote education. The aim of this research was to attempt to probe the psychophysical condition of students after more than a year of remote education. The survey method (CAPI) was used to collect the data on a representative sample of 1000 students in Poland and 1022 in Ukraine. The research sample was selected in a randomly stratified manner, taking into account such characteristics as: gender, age, and place of residence. The results of the research showed that 44% of Polish and 50% of Ukrainian students experienced health problems. The burden of remote education mainly contributed to the reduction of physical fitness. Young Poles more often paid attention to the deterioration of mental well-being, and Ukrainians to the deterioration of their physical condition. Based on the conducted analyses, it was also established that health problems appeared more often among the females, students with lower social support and with lower trust levels. The main risk factors were Internet addiction, secondary effects of the pandemic, and negative remote education experiences. Research has shown that remote education and problems that arise in students’ households during the pandemic may have significantly contributed to the deterioration of their psychophysical condition.
Neural dynamics of social emotion processing in alexithymia

The Toronto Alexithymia Scale-20 (TAS-20) has been widely used to assess alexithymia. The Bermond-Vorst Alexithymia Questionnaire (BVAQ) assesses two additional features of alexithymia, the affective factors emotionalizing and fantasizing not included in the TAS-20. However, there is currently no Chinese version of the BVAQ. Here, data from 439 college students (293 females; age: 20.25±1.88) were used to evaluate the psychometric properties for a Chinese BVAQ translation. Exploratory Structural Equation Modeling (ESEM) and Confirmatory Factor Analysis (CFA) provided satisfactory validity and acceptable reliability for a six-factor first-order solution of a 35-item Chinese BVAQ. This adaptation retained the original five BVAQ factors (identifying, analyzing, verbalizing, emotionalizing and fantasizing) and further specified the factor identifying (successful and unsuccessful identifying feelings). We also found a two-factor second-order model of cognitive and affective components. Higher correlations with the TAS-20 were observed for identifying, analyzing and verbalizing feelings (0.34~0.61), relative to fantasizing and emotionalizing (0.02~-0.05). These results support the construct validity of the adaptation. In sum, this work provides a reliable and valid Chinese adaptation of the BVAQ.
Understanding the international student’s experiences in a multilingual university context from a sociolinguistic perspective

This paper takes a sociolinguistic perspective in understanding the international student’s experiences in a multilingual university context. Drawing on a narrative inquiry approach, the present qualitative case study investigates a Brazilian Chinese international student’s lived experiences of studying in a Hong Kong university, with particular attention to his identity construction in both in-class and out-of-class contexts and the role of language in shaping his dynamic identity construction. The findings suggest that the international student’s language (in)competence appears to be closely intertwined with his identity construction in situated contexts, intersecting with his inclusion/exclusion and empowerment/marginalisation. The findings also reveal that multiple language ideologies mediate the impact of the international student’s language (in)competence on his identity construction in various situated contexts. Taken together, the findings call attention to the complex and dynamic interaction between identity construction, language (in)competence and language ideology in advancing our understanding of the international student’s experiences in a multilingual university context.
TG-SMR: A Text Summarization Algorithm Based on Topic and Graph Models

Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights. The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods. Finally, through experiments on the DUC2004 and DUC2006 datasets, our proposed improved graph model algorithm TG-SMR (Topic Graph-Summarizer) is compared to other text summarization systems. The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores. It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.
Alternative Counter-News Use and Fake News Recall During the COVID-19 Crisis

Abstract The outbreak of the COVID-19 pandemic was accompanied by a massive flood of “fake news”, conspiracy theories, and distorted information more generally. Alternative news media have been accused of contributing to this pollution the information sphere. In this article, we argue that audiences with a counter-hegemonic, ideologically biased worldview are particularly likely to use alternative counter-news, because these outlets validate their worldview ― and worldview validation being a relevant psychological need in times of crises. We suggest that by turning to alternative counter-news, audiences increase their exposure to fake news and end up being less well informed about important events. Results of a random-quota survey in Germany (N = 967) confirmed that those with higher levels of conspiracy mentality and lower media trust were more likely to use alternative counter-news. Alternative counter-news users recalled more fake news than non-users and alternative counter-news use partially mediated the relationship between counter-hegemonic attitudes and fake news recall. Thus, although not all content in alternative counter-news is fake news, these outlets do attract a specific counter-hegemonic audience and they do contribute to the “pollution” of the information ecosystem.
Masked Face Recognition Using MobileNet V2 with Transfer Learning

Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks. © 2023 CRL Publishing. All rights reserved.
Does the implementation of IFRS improve transparency regarding the company's financial conditions?: evidence from an emerging market

PurposeThis study aims to examine whether there are differences between financial statements prepared in accordance with International Financial Reporting Standards (IFRS) and financial statements prepared in accordance with local accounting standards in terms of its ability to present the financial conditions of companies listed on the Saudi Stock Exchange as one of the emerging markets.Design/methodology/approachData on study variables were obtained from the published financial statements of 67 of listed companies in the Saudi Stock Exchange during the period 2014–2019. The study addressed the research hypotheses by using Altman Z-score model. Both the T-test and Wilcoxon rank test were used to investigate the significance of differences between the values of Z-score and the individual variables included in the model in the pre- and post-IFRS mandatory adoption periods.FindingsThe results revealed a decrease in the values of Z-score as well as the values of the individual variables included in the model in the period following the adoption of IFRS than it was before the adoption of IFRS, which indicates the ability of IFRS to show the financial conditions of companies more transparently than local accounting standards. However, the results of the T-test and Wilcoxon test showed that these decreases were not statistically significant.Research limitations/implicationsThis study has some limitations, including the small sample size as a result of the small size of the Saudi Stock Exchange, As well as the reliance of this study only on the Altman model with its five variables in assessing financial conditions without examining the impact of other factors that may affect the financial conditions of companies.Practical implicationsFinancial conditions of the companies have important implications for multiple parties such as management, government, investors and others as an early warning sign that enables them to take the necessary measures early before the actual bankruptcy occurs and what results in costs.Originality/valueAlthough assessing financial conditions of the companies is one of the basic uses of accounting information, this topic has not received sufficient attention as a means to test the benefits of adopting IFRS, especially in emerging markets such as Saudi Stock Exchange. This is the first study to examine the impact of adopting IFRS on the transparency of financial reporting in assessing financial conditions in Saudi Arabia.
Identification of the causative mutation in the ITGB2 gene in a LAD1 patient by whole exome sequencing

Leukocyte adhesion deficiency type 1 (LAD1) is a rare congenital immunodeficiency disease. The cause of disease is determined to be the mutations in the ITGB2 gene that codes for CD18, the beta chain of beta-2 integrins, leads to decreased expression or functioning of CD18. This deficiency leads to severe impairment of leukocyte adhesion to the vascular wall and leukocyte migration to sites of infection and inflammation. LAD1 has also been associated with inhibition of interleukin-23 and interleukin-17 resulting in a hyperinflammatory and chronic inflammation. Patients with LAD1 typically present in early infancy with recurrent, life threatening infections that are frequently fatal before 2 years of age without hematopoietic stem cell transplant (HSCT). However, LAD1 is difficult to diagnose and many LAD1 patients die at a young age despite intensive antibiotic therapy. Accurate diagnosis requires detailed clinical information (delayed umbilical cord loss, severe periodontitis, delayed wound healing and sores, skin abscesses, and recurrent infection), and confirmation the absence of integrins by flow cytometric analysis. A better understanding of the molecular characteristics of this disease is needed to raise awareness and definitive diagnosis infants with LAD1. To definitive diagnosis, whole exome sequencing and Sanger sequencing were performed in an eighteen-month-old boy with severe leukocytosis, recurrent infections, delayed wound healing, and hepatosplenomegaly associated with an acquired cytomegalovirus infection. Two variants: One previously reported mutation (c.533C>T, p.Pro178Leu) and one novel variant (c.59-1G>A), in the ITGB2 gene were detected. These results can be used for definitive genetic diagnosis, genetic counseling, as well as a prenatal diagnosis in LAD1 patients.