Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Predictors of COVID-19 Mortality Using Machine Learning

(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality in patients with pre-existing health conditions and to examine the association between COVID-19 mortality and other morbidities. (2) Methods: De-identified data from 113,882, including 14,877 COVID-19 patients, were collected from the UK Biobank. Different types of data, such as disease history and lifestyle factors, from the COVID-19 patients, were input into the following three machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting classifier (XGB) and Support Vector Machine (SVM). The Area under the Curve (AUC) was used to measure the experiment result as a performance metric. (3) Results: Data from 14,876 COVID-19 patients were input into the machine learning model for risk-level mortality prediction, with the predicted risk level ranging from 0 to 1. Of the three models used in the experiment, the RF model achieved the best result, with an AUC value of 0.86 (95% CI 0.84–0.88). (4) Conclusions: A risk-level prediction model for COVID-19 mortality was developed. Age, lifestyle, illness, income, and family disease history were identified as important predictors of COVID-19 mortality. The identified factors were related to COVID-19 mortality.
Inequality in labour market opportunities for people with disabilities: Evidence for six Latin American countries

In Latin America, approximately 70 million individuals live with a disability. Although global evidence suggests that people with disabilities are one of the poorest groups and present lower employment rates, the evidence for Latin America is still weak. This article aims to contribute to the literature by estimating and analysing the levels of employment opportunity for persons with disabilities in six countries in Latin America (Chile, Bolivia, Mexico, Peru, Colombia, and Costa Rica). Using household survey data, we measure inequality of opportunities using the Paes de Barros approach and compare the probability distributions of being employed for people with disabilities according to different individual characteristics. This research makes several contributions to the literature. First, it analyses and compares the characteristics of persons with disabilities in six countries of the region. Second, it is the first paper in the region that computes and compares the levels of employment opportunities for persons with disabilities, using the Human Opportunity Index. Third, it analyses which are the main aspects contributing to the levels of employment opportunities for persons with disabilities in each of the countries. The main results of the study reveal that people with disabilities face high levels of inequality of employment opportunity compared with people without disabilities in the six countries. Peru shows the lowest disadvantage, with higher coverage of opportunities for people with disabilities. Colombia and Costa Rica were the countries where this group presents the largest disadvantages to be employed. In addition, women with disabilities and people with disabilities living in rural areas have a lower probability of being employed compared with people without disabilities. These findings reveal that policies in the region aiming to include this group in the labour market have not been effective, and there is a necessity to guarantee the proper labour inclusion of this group.
Large-Scale Thermochemistry Calculations for Combustion Models

Accurate thermochemical properties for chemical species are of vital importance in combustion chemistry research. Group additivity approaches are widely used to generate thermochemistry data used in chemical kinetic models, but this approach has limited accuracy. Following on previous studies by the combustion community, we performed electronic structure calculations to obtain reliable thermochemistry data for a large set of molecules taken from a well-established chemical kinetic model. The developed database consists of 1340 species that contain up to 18 and 5 carbon and oxygen atoms, respectively. The M06-2X/aug-cc-pVTZ level of theory was used for the geometry optimizations, vibrational frequency calculations, and dihedral angle scans. The potential energy of the different species was further refined with different composite methods, and the G3 method with the atomization reaction approach was selected to calculate the enthalpy of formation at 0 K. This information was then used in statistical thermodynamics calculations to obtain standard enthalpies of formation and entropy, as well as heat capacities at different temperatures. Our thermochemistry data exhibits good agreement with existing values in the literature, verifying the accuracy of our approach. Comparisons against group additivity (GA) method are also presented. The database of thermochemistry quantities developed in this study can be used to improve the training GA or machine learning models. The impact of the developed dataset is illustrated by examining the variation in ignition delay times using the updated thermochemistry values.
The Effect of Deepfake Video on News Credibility and Corrective Influence of Cost-Based Knowledge about Deepfakes

Abstract With rapid technical advancements, deepfakes—i.e., hyper-realistic fake videos using face swaps—have become more widespread and easier to create, challenging the old notion of “seeing is believing. ” Despite raised concerns over potential impacts of deepfakes on people’s credibility toward audio-visual evidence in journalism, systematic investigation of the topic has been lacking. This study conducted an experiment (N = 230) that tested (1) how a news article using deepfake video (vs. real video) affects news credibility and viral behavioral intentions and (2) whether, based on signaling theory, obtaining knowledge about the low cost of producing deepfakes reduces the impact of deepfake news. Results show that people whose pre-existing attitudes toward controversial issues (abortion, marijuana legalization) are congruent with the advocated position of a news article are more likely to believe and be willing to share deepfake news as much as real video news. In addition, educating participants about the low cost of producing deepfakes was effective in reducing the credibility and viral behavioral intention of deepfake news for those who have congruent issue attitudes. This study provides evidence for differing levels of susceptibility for deepfake news and the importance of media literacy education regarding deepfakes that would prevent biased reasoning.
Arab Male Students’ Preferences for Oral Corrective Feedback: A Case Study

ABUKHADRAH, QUTAIBA A. , Ph.D., March 2012, Curriculum and Instruction Arab Male Students’ Preferences for Oral Corrective Feedback: A Case Study Director of Dissertation: John E. Henning This case study investigated a sample of Arab male students’ preferences for oral error correction while they studied in an advanced ESL program at a Midwestern university. Twenty students and ten teachers were purposively selected to take part in this study. The major goal of the study was to identify students’ preferences regarding error types and corrective feedback strategies. The study used information gathered by means of qualitative research methods utilizing interviews, observations and the use of a focus group interview for students. The findings show that both students and teachers have positive attitudes towards error correction indicating that corrective feedback can have a positive influence on L2 learning. Most of the students reported that errors relating to grammar, pronunciation and vocabulary should receive more attention than any other error types. As for errors which relate to discourse organization, social interaction, pragmatics and communication, some students believe that they are less important than grammar, pronunciation and vocabulary. In contrast, the results of teachers’ interviews revealed different patterns of error types’ preferences from those of students’. The majority of teachers believed that semantic and grammatical errors should receive more attention than other types of errors. Teachers’ views also differed from students’ preferences in terms of correcting errors
Undergraduate Students’ Perspective on Factors Affecting their Academic Performance: A Qualitative Exploration

This research aimed at discovering the factors which affect the academic performance of undergraduate college/university students. Qualitative research design was implied and semi-structured interview was used to collect data. Sample included 15 (N=15, females=10, males=5) students studying in undergraduate programs at different public sector colleges/universities. Thematic analysis was used to find out the factors which affect academic performance of undergraduate college/university students. Three major themes clusters emerged naming positive factors, negative factors and suggestive factors. Both positive and negative factors contained sub themes like psychological factors (e.g. determination, casual behavior) social/external factors (e.g., social support, excessive use of social media) and academic factors (conceptual study, language difficulties) while suggestive themes contained sub themes like steps by students and steps by teachers/institutes. Results have implications for students, teachers and policy makers. Findings may guide both students and institutes in working to improve the academic performance of undergraduate college/university students.
An attitude measurement method of industrial robots based on the inertial technology

Abstract The attitude control error of the robot end-effector directly affects the manufacturing accuracy. The study aims to develop a real-time measurement method of the industrial robot end-effector attitude in the field environment for improving the control accuracy of robot attitude. In this paper, an attitude measurement method of robot end-effector based on the inertial technology was proposed. First, an inertial measurement system was designed, and the measurement parameters and installation errors were calibrated. Then the inertia measurement principle of robot end-effector attitude was explored, and the robot end-effector attitude measurement was realized with the fourth-order Runge−Kutta algorithm. In addition, the influence of the data processing algorithm and sampling frequency on the attitude accuracy was analyzed. Finally, a test platform was built to experimentally explore the proposed inertial measurement method. The inertial measured data were compared with the data obtained with the laser tracker. The measurement accuracy of the inertial measurement method reached 0.15°, which met the accuracy requirements of real-time measurements of robot end-effector attitude in the manufacturing field. The method proposed in this paper is convenient and can realize the real-time attitude measurement of industrial robot. The measurement results can compensate the attitude control error of the robot end-effector and improve the attitude control accuracy of the robot.
CFTR Modulator Therapies: Potential Impact on Airway Infections in Cystic Fibrosis

Cystic Fibrosis (CF) is an autosomal recessive disease caused by mutations in the gene encoding for the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) protein, expressed on the apical surface of epithelial cells. CFTR absence/dysfunction results in ion imbalance and airway surface dehydration that severely compromise the CF airway microenvironment, increasing infection susceptibility. Recently, novel therapies aimed at correcting the basic CFTR defect have become available, leading to substantial clinical improvement of CF patients. The restoration or increase of CFTR function affects the airway microenvironment, improving local defence mechanisms. CFTR modulator drugs might therefore affect the development of chronic airway infections and/or improve the status of existing infections in CF. Thus far, however, the full extent of these effects of CFTR-modulators, especially in the long-term remains still unknown. This review aims to provide an overview of current evidence on the potential impact of CFTR modulators on airway infections in CF. Their role in affecting CF microbiology, the susceptibility to infections as well as the potential efficacy of their use in preventing/decreasing the development of chronic lung infections and the recurrent acute exacerbations in CF will be critically analysed.
Chromosome-Scale Assembly and Characterization of the Albino Northern Snakehead, Channa argus var. (Teleostei: Channidae) Genome

Northern snakehead, Channa argus (C. argus), is an important economic and ecological fish species. The wild population of the species was sharply declined in the last decade. A high-quality reference genome could lay a solid foundation for the genetic and conservation studies for C. argus. In this work, we report a chromosomal genome assembly with PacBio and Hi-C technology using the albino northern snakehead, a color variety of C. argus. A 644.1-Mb genome with 24 chromosomes was obtained with a contig and scaffold N50 of 11.78 and 27.8 Mb, respectively. We inferred that C. argus diverged from A. testudineus around 85.6 million years ago. 514 expanded gene families and 214 positively selected genes were identified in the C. argus genome. The chromosome-level genome provides a valuable high-quality genomic resource for population, as well as genetic and evolutionary studies for C. argus and other species in Channidae.
COREAT: Developing a Mobile Application to Assess the Severity of Repetitive Behavior in Autism

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterised by difficulties in communication and repetitive behaviors. The early detection of ASD is a clinical priority in education centres and medical services. COREAT is a computer adaptation of the Repetitive Behavior Scale-Revised (RBS-R) which has been designed to facilitate the diagnosis of individuals with ASD and intellectual disability. This article describes the development, an initial evaluation of the feasibility and the preliminary outcomes of COREAT. The development of COREAT consists of two phases. Phase 1: Performing a prior analysis of the psychometric properties of the RBS-R scale. Phase 2: Developing the mobile application (pilot tests, testing real cases n = 11, and final feedback). Descriptive data of the pilot study shows that 91.31% of participants indicate that COREAT is very accessible and intuitive. COREAT proves to be a useful diagnostic resource for professionals and families. Cross-cultural differences must be analysed.
Recursive-YOLOv5 Network for Edible Mushroom Detection in Scenes with Vertical Stick Placement

In many rural edible mushroom growing sites, due to their growing environments and economic factors, growers are often unable to use industrial automation to pick edible mushrooms, resulting in losses of hundreds of millions of dollars each year due to missing the optimal picking cycles of edible mushrooms. For this reason, this project aimed to find an object detection algorithm that can be used with industrial cameras to help detect the growth statuses of edible mushrooms in real time and can be applied to future automatic picking machines. In this paper, based on the You Only Look Once version 5 (YOLOv5) network, by introducing the idea of recursion, remerging the first output with the convolutional layer of the backbone part of the network, introducing atrous spatial pyramid pooling (ASPP) instead of SPP (as in the original algorithm), utilizing the complete intersection over union (CIOU) and distance IOU (DIOU) instead of the generalized IOU (GIOU), and employing DIOU_nonmaximum suppression (NMS) instead of the original NMS algorithm, we propose a new deep learning network called Recursive-YOLOv5. This network can effectively identify 98% of edible mushrooms when dealing with large-resolution, small-target situations; this is a 12.87% improvement over the accuracy of YOLOv5X. Although the algorithm proposed in this paper utilizes almost double the parameters of the original network, with today’s computing power and the rapid development of cloud computing, this sacrifice of computing power for accuracy is very cost-effective.
Cost Burden of Endothelial Keratoplasty in Fuchs Endothelial Dystrophy: Real-World Analysis of a Commercially Insured US Population (2014–2019)

Purpose To assess the incremental burden of corneal transplant surgery for US commercially insured patients with Fuchs endothelial corneal dystrophy (FECD) treated with endothelial keratoplasty (EK) compared to controls. Methods The study design was retrospective cohort using IBM® MarketScan® claims (January 2014–September 2019) and included EK-treated (N=1562) and control patients (N=23,485) having ≥12 months’ enrollment before and after diagnosis, who were subsequently matched on select characteristics. The index date was the beginning of the pre-operative period (3 months before EK); synthetic EK index was assigned for controls. All-cause, eye-disease, and complication-related healthcare resource utilization (HCRU) and costs were compared up to 36 months post index. For a small subset of patients, patient data were linked to the Health and Productivity Management supplemental database, which integrates data on productivity loss and disability payments. Results Matched cohorts included 804 EK-treated and 1453 controls with average age 65.7 years, 1383 (61%) female. Over 12 months of follow-up, all-cause ($41,199 vs $20,222, p<0.001) and eye-disease related costs ($22,951 vs $1389, p<0.001) were higher among EK-treated patients than controls. The cost differential increased additionally by $1000–$2000 per annum by 36 months of follow-up. While balanced at baseline, over follow-up EK-treated patients had higher prevalence of glaucoma, elevated intraocular pressure, cataract, cataract surgery, diagnosis of cornea transplant rejection, retinal edema. By 36 month of follow-up, EK-treated patients had 9 more short-term disability days, resulting in $2992 additional burden of disability payments. Conclusion This study found a higher cost burden among FECD patients receiving EK treatment versus those who did not. With a shift in management of FECD, cost burden estimates generated in this study could serve as an important benchmark for future studies.
Near‐field atmospheric inversions for the localization and quantification of controlled methane releases using stationary and mobile measurements

This study evaluates two local‐scale atmospheric inversion approaches for the monitoring of methane (CH4) emissions from industrial sites based on in situ atmospheric CH4 mole fraction measurements from stationary or mobile sensors. We participated in a two‐week campaign of CH4 controlled‐release experiments at TotalEnergies Anomaly Detection Initiatives (TADI) in Lacq, France in October 2019. We analyzed releases from various points within a 40 m × 50 m area with constant rates of 0.16 to 30 g CH4 s−1 over 25 to 75 mins, using fixed‐point and mobile measurements, and testing different inversion configurations with a Gaussian dispersion model. An inlet switching system, combining a limited number (6–7) of high‐precision gas analyzers with a higher number (16) of sampling lines, ensured that a sufficient number of fixed measurement points sampled the plume downwind of the sources and the background mole fractions for any wind direction. The inversions using these fixed‐point measurements provide release rate estimates with approximately 23%–30% average errors and estimates of the location of the releases with approximately 8–10 m average errors. The inversions using the mobile measurements provide estimates with approximately 20%–30% average errors for the release rates and approximately 30 m average errors for the release locations. The precision of the release rate estimates from both inversion frameworks corresponds to the best estimation precision documented on site‐scale CH4 inversions. However, the use of continuous measurements from fixed stations provides much more robust estimates of the source locations than that of the mobile measurements.
Microbiological Analysis of Drinking Water from Different Areas of Lahore

Access to safe drinking water and its regular monitoring is essential to ensure the health and safety of the public. This study aimed to analyze the microbiological quality of drinking water supplies (filtered and unfiltered) from various towns of Lahore, Pakistan. In total, 135 water samples, with and without filtration (n=90) and additional 45 bottled drinking water samples of different brands from the local market were collected from 9 towns of Lahore. To determine the microbiological quality, all samples were subjected to total plate count, total coliform, E. coli, Streptococci and Salmonella detection. According to the findings, the total plate count from various water supplies was significantly higher as compared to bottled drinking water samples. Drinking water from supplies with filtration and without filtration was found to be contaminated with total coliform (51%), E. coli (51%) and total coliform (84.5%), E. coli (84.5%), faecal Streptococci (15.5%), and Salmonella (11.1%) respectively. In total, 22 (49%) of the filtered and 7 (15.5%) of the unfiltered water samples were found to be microbiologically safe for drinking, whereas 41 (91.1%) bottled drinking water samples met the WHO criteria. To summarize, bottled drinking water is safer than both filtered and unfiltered drinking water. However, all bottled water should not be assumed to be safe for use. The microbial contamination in drinking water supplies, as well as bottled water samples, insinuates for strict monitoring of drinking water by regulatory authorities, and immediate action is required to avoid public health hazards.
Behind the Political Connections Under Emerging Democracies

ABSTRACT In light of inconclusive findings on the effect of political connections, this study explores conditions that affect the effectiveness of political connections on firm performance in democratic systems. First, using a resource dependence rationale, this study stresses the importance of variation in political connections and classifies direct and indirect political connections based on sources of power and the mechanisms for developing connections. Second, this study recognizes that influencing political outcomes is an entire political process in which political power matters. Furthermore, as power is central to the resource dependence rationale unlike exchange in transaction cost economics, this study explores how the effectiveness of political connections is contingent on the dynamics of de jure political power. We find that the effect of direct political connections is susceptible to changes in de jure political power due to its dyadic relationship with de jure power, while indirect political connections are more robust to such changes due to their connections with informal networks holding de facto political power. Further, the positive effects of political connections on firm performance are mediated by operational capability. 摘要 由于目前对政治联系对企业绩效的研究结果莫衷一是，本文试图借助新兴民主体制的独特情境，来探讨不同类型的政治联系对企业绩效的影响及其边界条件。 首先，作者基于资源依赖理论分析了权力的最终来源及产生机制，并在此基础上区分了直接政治联系和间接政治联系。 本研究发现政治联系对于企业绩效的影响取决于政治权力发挥作用的过程，并发现政治联系的作用随着执政党的更替而动态变化。 进一步的，政治联系能通过提高企业的运作能力促进其绩效。 然而，在执政党更替的动态环境中，与直接政治联系相比，间接政治联系通过与各个执政党成员建立非正式的关系，对企业绩效的影响更为显著。