Yoxas/statistical_literacy huggingface dataset:
In the pedagogy of classical vocal singing, it can be difficult to determine the human voice fach, especially for the voice of aspiring vocalists. Hence, an objective metric-based system for the determination of the human voice is needed. In the present study, we investigated the anthropological and aerodynamic parameters for 60 professional singers with a professionally confirmed singing range. Amongst the 60 included professional singers, there were ten participants each for sopranos, mezzo-sopranos, altos (female vocal fach), and tenors, baritones, basses (male vocal fach). Airflow measurements were recorded using spirometry whilst anthropological measurements were taken using CT scans. Appropriate statistical analyses were done using the Mann-Whitney U test and Kruskal Wallis H test with post-hoc tests and Bonferroni correction. p < 0.05 was considered statistically significant. Soprano singers, who have the highest pitch, were found to be the shortest and least heavy, whilst basses, who have the lowest pitch, were found to be tallest and heaviest amongst the study participants. Furthermore, sopranos had the smallest lung volumes while the basses had the largest lung volumes (raw spirometry measures). However, when normalized ratios were considered, no differences were observed. Finally, laryngeal size showed sexual dimorphism due to developmental changes. A mix of anthropological and aerodynamic measurements may be useful to assist singers and vocal pedagogues to assess and determine voice types before the beginning of their vocal studies.
In the post-COVID-19 era, the content of work and the necessary skills are rapidly changing due to the digital transformation of the way people work. Entrepreneurial adaptability and digital capability are the most necessary competencies for exploring opportunities and quickly turning them into a professional career amid a crisis. Financial literacy is also essential for expanding skills in economic and social life. The purpose of this study is to verify the influence of university students' financial literacy and digital capability on entrepreneurial intention and the mediating effect of entrepreneurship. To this end, a survey was conducted on university students in Busan and Gyeongnam, and a sample of 162 respondents was verified using SPSS 28.0. As a result of the study, it was found that financial literacy had a partially positive effect on entrepreneurship and entrepreneurial intention. Digital capability was found to have a positive effect on entrepreneurship and entrepreneurial intention. It was found that entrepreneurship had a partially positive effect on entrepreneurial intention. It was found that entrepreneurship had a partially positive mediating effect between financial literacy and entrepreneurial intention. It was found that entrepreneurship had a positive mediating effect between digital capability and entrepreneurial intention. As a result of this study, it was confirmed that financial literacy, digital capability, and entrepreneurship are very important competencies for university students to adapt to new trends and promote start-ups in a rapidly changing job environment after COVID-19, suggesting the need for further education.
In the post-epidemic era, public panic has emerged as a highly significant secondary disaster, necessitating an urgent enhancement of emergency management capabilities by governments at all levels. In order to ensure a robust assessment of the government's ability to manage public panic, it is crucial to effectively address the influence of uncertain and ambiguous factors associated with such scenarios. This paper proposes a governmental public panic emergency management capability assessment method based on fuzzy Petri nets. By analyzing the factors influencing public panic across the four evolutionary stages, namely gestation, outbreak, diffusion, and fading, we establish a hierarchical evaluation index system for assessing emergency management capabilities. Additionally, we develop a range of multi-scenario emergency management strategies. To address the challenges posed by uncertainty, randomness, fuzziness, and insufficient statistical data within the assessment index system, we introduce fuzzy Petri nets and fuzzy reasoning rules to evaluate the emergency management capability of the assessment system and derive the optimal emergency management strategy. According to example simulations, the effectiveness and practicality of models and rules constructed using fuzzy Petri nets are demonstrated, highlighting their superiority over traditional assessment methods. This comprehensive approach equips the government with a versatile toolkit for effectively managing public panic emergencies.
In the present day, we need outstanding scientists, engineers, mathematicians, and medical science researchers more than ever to solve the world's most pressing issues, such as climate change, water contamination, and cyber security. Naturally, we ask the question: What does it take to develop eminence in science, technology, engineering, mathematics, and medical science (STEMM)? To answer this question, we interviewed two relevant groups of experts: 14 talent development researchers and 14 STEMM experts. The interview questions were developed based on the theoretical framework of the Actiotope Model of Giftedness and the related educational and learning capital approach that differentiates five types of exogenous resources (educational capital) and five types of endogenous resources (learning capital) that feed into talent development toward eminence. The results show that all types of capital were regarded as important by the experts for developing eminence in STEMM. However, there were also differences. We describe the educational and learning capital that talent development researchers and STEMM experts considered to be important for talent development in STEMM, as well as the similarities and differences between the two groups.
In the present descriptive study, we aimed to examine the relationship between the levels of religious attitude, compassion and spiritual care perception among nursing students. Data were obtained from 135 nursing students using the Socio-Demographic Diagnosis Form, the Ok-Religious Attitude Scale, the Compassion Scale, and the Spiritual Care-Giving Scale. The data were analyzed using the Kruskal-Wallis H test, the Spearman correlation test, and the Mann-Whitney U test. A positive correlation was found between the scores of the Ok-religious attitude scale and cognition sub-dimension, emotion sub-dimension, behavior sub-dimension, god sub-dimension and compassion scale and spiritual care-giving scale scores. Furthermore, we observed that some socio-demographic, and nursing profession and spiritual care characteristics of the students affected their religious attitudes, compassion and spiritual care perception. The sub-dimension scores of compassion and awareness of sharing among female students and the mindfulness scores of students with nuclear families were high. Our data suggest that the course contents for spiritual care, which is an integral part of holistic nursing care, should be included in the nursing curriculum. Moreover, we recommend that efforts be made to maintain a high level of compassion while considering the socio-demographics of the students and their opinions on the nursing profession. Finally, the data presented here has the potential to serve as a valuable resource for the development of novel approaches to the provision of spiritual care within nursing education programs.
In the present era of information technology, people tend to seek out news to enhance their current knowledge and awareness and to gain literacy. The reliance on seeking out news and relevant information has become very necessary to accomplish personal and organizational objectives. The present study has undertaken an inquiry to investigate the impact of social media news overload on news avoidance and news filtering with the mediating and moderating mechanisms of the need for news and media literacy, respectively. For this purpose, data were obtained from 358 Chinese social media users through the aid of survey forms. The data obtained were then analyzed through Smart-PLS software. The statistical technique used for analysis is structural equation modeling (SEM) to determine the validity of the proposed hypotheses. The results of the study indicated that social media news overload has a significant effect on news avoidance, the need for news, and news filtering behavior. It was also observed that the need for news had a significant impact on news avoidance. In addition to this, it was also revealed that the need for news significantly mediated the relationship between social media news overload and news avoidance; however, it did not mediate the relationship between social media news overload and news filtering. Lastly, it was identified that media literacy significantly moderated the relationship between the need for news and news avoidance and it did not moderate the relationship between the need for news and news filtering behavior. This study has made important theoretical contributions by advancing the current literature in terms of the empirical evidence that indicates a significant relationship between social media news overload, news avoidance, and news filtering. Practically, this study contributed by emphasizing the need to encourage and train people to use strategies to seek relevant news in a vast repository of information available through information technology.
In the present study, we analyzed a large corpus of English-language online media articles covering genome-wide association studies (GWAS), exemplifying the use of computational methods to study science communication in biological sciences. We analyzed trends in media coverage, readability, themes, and mentions of ethical and social issues, in over 5,000 websites published from 2005 to 2018 from 3,555 GWAS publications on 1,943 different traits, identified via GWAS Catalog using a text-mining approach to inform the discussion about genetic literacy and media coverage. We found that 22.9% of GWAS papers received media attention but most were described in language too complex to be understood by the public. Ethical issues are rarely mentioned and mentions of translation are increasing over time. We predicted media attention based on year of publication, number of genetic associations identified, study sample size, and journal impact factor, using a regression model (r2 = 38.7%). We found that chronotype, educational attainment, alcohol and coffee consumption, sexual orientation, tanning, and hair color received substantially more attention than predicted by the regression model. We also evaluated the prevalence of the clickbait "one gene, one disease" headlines (e.g., "Scientists Say They've Found Gene That Causes Breast Cancer") and found that it is declining. In sum, online media coverage of GWAS should be more accessible, introduce more modern genetics terms, and when appropriate, ELSI should be mentioned. Science communication research can benefit from big data and text-mining techniques which allow us to study trends and changes in coverage trends across thousands of media outlets. Results can be explored interactively in a website we have built for this manuscript: https://jjmorosoli.shinyapps.io/newas/.
In the previous study the work experience on organization of teaching Robotics to secondary school students at school lessons and in study groups was introduced. This study which was conducted within 2019 and 2021 covered the period of distant learning caused by COVID-19 pandemic and even post-pandemic period, when a part of school students continued learning online. The study deals with the problem of developing school students' computational thinking in online learning. We consider computational thinking as a set of cognitive skills of solving educational and cognitive problems. The research questions raised were aimed at solving the problem of the influence of Educational Robotics on developing computational thinking. During the research we have found out that due to the adaptability of robots, Educational Robotics, the development of individual learning programs, and the arrangement of collaborative online learning are instruments and a solution to the problem of developing computational thinking. The main components of computational thinking, which were studied within those 3 years, are the following: algorithmic thinking, ability to program, and efficiency in team work. The influence of the learning strategy we chose enabled us to determine the level of computational thinking and its dependence on learning Robotics. We used statistical criteria in order to summarize the results of our research. The statistics provided suggests progress in the indicator tracked. Based on the experimental data received we approximated reliability (R2) and relevant exponential equation (trend lines). The research we carried out also has led to the general conclusion that Educational Robotics helps to create synergistic learning environment for stimulating students' motivation, collaboration, self-efficacy and creativity.
In the realm of computer vision, the integration of advanced techniques into the pre-processing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmentation of objects in RGB-D imagery. Traditional computer vision systems face limitations in simultaneously capturing precise object boundaries and achieving high-precision object detection on depth maps, as they are mainly proposed for RGB cameras. To address this challenge, FusionVision adopts an integrated approach by merging state-of-the-art object detection techniques, with advanced instance segmentation methods. The integration of these components enables a holistic (unified analysis of information obtained from both color RGB and depth D channels) interpretation of RGB-D data, facilitating the extraction of comprehensive and accurate object information in order to improve post-processes such as object 6D pose estimation, Simultanious Localization and Mapping (SLAM) operations, accurate 3D dataset extraction, etc. The proposed FusionVision pipeline employs YOLO for identifying objects within the RGB image domain. Subsequently, FastSAM, an innovative semantic segmentation model, is applied to delineate object boundaries, yielding refined segmentation masks. The synergy between these components and their integration into 3D scene understanding ensures a cohesive fusion of object detection and segmentation, enhancing overall precision in 3D object segmentation.
In the realm of structural and bonding investigations within chemical systems, elucidating global minimum energy configurations stands as a paramount goal. As the systems increase in size and complexity, this pursuit becomes progressively challenging. Herein, we introduce Bonobo optimizer (BO), a metaheuristic algorithm inspired by the social and reproductive behaviors of bonobos, to the domain of chemical problem solving. Focusing on small carbon clusters, this study systematically evaluates BO's performance, showcasing its robustness and efficiency. Parametric studies highlight the algorithm's adaptability, consistently converging to global minimum structures. Rigorous statistical validation supports the results, and a comparative analysis against established global optimization algorithms underlines BO's superior efficiency. This exploration extends the applicability of BO to the optimization of atomic clusters, providing a promising avenue for future advancements in computational chemistry.
In the school years 2019/20 and 2020/21, children were physically, psychologically, and socially stressed by school closures caused by the SARS-CoV-2 pandemic. To ensure attendance with optimal infection protection, PCR pool testing was conducted during the 2021/22 school year at Bavarian elementary schools and schools for pupils with special needs for timely detection of SARS-CoV-2 infection. This study analyzes the results of PCR pool testing over time stratified by region, school type, and age of children. The data were obtained from classes in elementary and special needs schools, involving pupils aged 6 to 11 years, who participated in the Bavaria-wide PCR pool testing from 09/20/21 to 04/08/22. Samples were collected twice weekly, consisting of PCR pool samples and individual PCR samples, which were only evaluated in case of a positive pool test. A class was considered positive if at least one individual sample from that class was positive within a calendar week (CW). A school (class) was considered to be infection-prone if three or more classes in that school (students in that class) were positive within a CW. The data included 2,430 elementary schools (339 special needs schools) with 23,021 (2,711) classes and 456,478 (29,200) children. A total of 1,157,617 pools (of which 3.37% were positive) and 724,438 individual samples (6.76% positive) were analyzed. Larger schools exhibited higher PR compared to smaller schools. From January 2022, the Omicron variant led to a massive increase in PR across Bavaria. The incidence rates per 100,000 person-weeks within the individual school samples were significantly lower than the concurrently reported age-specific and general infection incidences in the overall Bavarian population. PCR pool testing revealed relatively few positive pools, with an average of four children per one hundred pools testing positive. Schools and classes were rarely considered infection-prone, even during periods of high incidences outside of schools. The combination of PCR pool testing and hygiene measures allowed for a largely safe in-person education for pupils in primary and special needs schools in the school year 2021/22.
In the student-centered modern teaching environment of higher education, the affective domain was of great value to the overall development of medical students and the sustainable development of medical education. However, in the teaching practice of the medical specialty in Our country, there are still a lot of phenomena that pay attention to knowledge teaching but neglect affective education. Compared with affective domain, teachers tended to focus on the learning of specialized knowledge and skills. This paper investigated the attitudes and evaluations of teachers and students of medical school on affective education, analyzed the current situation and problems of teaching in the affective educauion of medical professions, and explored the path of combining the affective field and medical profession from the perspective of medical teachers. A questionnaire survey was conducted among medical teachers and students across the country. Using the self-appointed teacher scale and the student scale to obtain their ratings of all dimensions of teaching in the affective field through a free online tool called "WenJuanXing". Descriptive statistics and multiple regression analysis methods were used to analyze the data to explore the obstacles in affective education and the factors affecting the outcome of affective education. A total of 523 medical teachers and 3268 medical students were surveyed, according to the results of questionnaire data analysis, there are differences in the needs of senior and lower grade students in various dimensions of affective education, and the current Chinese medical teachers carrying out affective education are facing unclear positioning and interpretation of affective education goals, lack of affective experience in teaching methods, lack of affective education evaluation norms, lack of continuity and progressivity of affective cultivation, and school organizational mechanisms need to be improved. This paper had some suggestions aimed at the above problem. Firstly, It is necessary to strengthen the construction of the organizational mechanism of medical universities, provide them with guarantees and training according to the characteristics of teachers of different teaching ages, and comprehensively improve teachers' affective literacy from the inside out. Secondly, teachers should design clear progressive goals and content systems of affective education, constructing an evaluation system of affective education in the experiential teaching method.
In the Taihang Mountain areas, known as the "esophageal cancer zone" in China, the incidence of esophageal cancer (ESCA) ranks the first in the country and shows a familial and regional clustering trend. Taihang Mountain areas are located in a mountainous area, with inconvenient transportation, limited living conditions, unbalanced diet, and poor nutrition. Ninety percent of the pathological types of ESCA in Taihang Mountain areas are squamous cell carcinoma, among which the risk factors have not been well understood. These areas are usually remote villages and mountains with low population mobility, large family members, similar environmental factors, and a clear and stable genetic background. Therefore, according to the current situation, second-generation sequencing and multigroup analysis technology are used to analyze the familial ESCA patients; disease-related genetic variation are located; and then disease-related susceptibility genes associated with ESCA are screened and analyzed. Health education, tobacco control, endoscopic screening, and other health management projects for suspected and high-risk patients in areas with a high incidence of ESCA can be carried out for screening and early diagnosis, and the incidence of ESCA in Taihang Mountain areas can be reduced. A comprehensive continuous care pattern based on traditional medical nursing to track, monitor, evaluate, and intervene with patients diagnosed with ESCA to facilitate them with medications guidance, dietary guidance, and timely health problem-solving is established. Furthermore, statistical analysis of epidemiology, gene sequencing, and family genetics information can be performed on patients with ESCA in the Taihang Mountains areas to clarify the relationship between genetic phenotype and genotype during the occurrence of ESCA.
In the transition from academic to clinical learning, the development of clinical reasoning skills and teamwork is essential, but not easily achieved by didactic teaching only. Case-based learning (CBL) was designed to stimulate discussions of genuine clinical cases and diagnoses but in our initial format (CBL'10) remained predominantly tutor-driven rather than student-directed. However, interactive teaching methods stimulate deep learning and consolidate taught material, and we therefore introduced a more collaborative CBL (cCBL), featuring a structured format with discussions in small breakout groups. This aimed to increase student participation and improve learning outcomes. A survey with open and closed questions was distributed among 149 students and 36 tutors that had participated in sessions of both CBL formats. A statistical analysis compared exam scores of topics taught via CBL'10 and cCBL. Students and tutors both evaluated the switch to cCBL positively, reporting that it increased student participation and enhanced consolidation and integration of the wider subject area. They also reported that the cCBL sessions increased constructive discussion and stimulated deep learning. Moreover, tutors found the more structured cCBL sessions easier to facilitate. Analysis of exam results showed that summative assessment scores of subjects switched to cCBL significantly increased compared to previous years, whereas scores of subjects that remained taught as CBL'10 did not change. Compared to our initial, tutor-led CBL format, cCBL resulted in improved educational outcomes, leading to increased participation, confidence, discussion and higher exam scores.