Miracle-dz/UniFAR-FTUs huggingface dataset:
Willingness and Self-Perceived Competence of Final-Year Medical Students to Work as Part of the Healthcare Workforce During the COVID-19 Pandemic

Purpose The coronavirus disease 2019 (COVID-19) pandemic may increase demand for healthcare professionals (HCPs), either because of a HCP shortage due to illness or because of the need to increase surge capacity. Final-year medical students are one of the resources potentially available to expand the workforce. There is a need to explore the willingness of final-year medical students to meet this demand, examine their perceived competence, and determine how their overall perceived competence correlates with their willingness. Methods A cross-sectional study using a self-administered electronic questionnaire was used. The questionnaire included demographic data, students’ self-perceived competence derived from the patient care theme of the Saudi Medical Education Directives (SaudiMED) framework, and their willingness to be measured on a 5-point Likert scale. The study targeted final-year medical students at King Saud University, Riyadh, Saudi Arabia. Results The number of participants was 134 (56.1% response rate), of whom 47 students (34.3%) were willing to work, while 31 (23.1%) were somewhat willing. The mean total self-perceived-competence score was 58.36/88 (66.3%). Demonstration of essential clinical skills had the highest mean score 11.48/16 (71.8%) among learning outcomes. There was a positive moderate correlation between willingness and mean perceived-competence score (Spearman correlation coefficient=0.45, p<0.001). Conclusion Fifty-seven percent of medical students were willing to work as part of the healthcare workforce during the COVID-19 pandemic. Better overall self-perceived competence appeared to correlate with more willingness. Students perceive themselves to be more competent in essential clinical skills. Appropriate training and supervision are suggested in all tasks assigned to them, with additional care required in areas with a lower perceived competence, such as prescription writing and essential clinical procedures.
Production of l-Valine by 2-Thiazolealanine Resistant Mutants Derived from Glutamic Acid Producing Bacteria

Potent l-valine producers were screened among 2-thiazolealanine resistant mutants derived from three typical l-glutamic acid producing bacteria: Brevibacterium lactofermentum, Corynebacterium acetoacidophilum, Arthrobacter citreus. By strain No. 487, the best producer derived from Brevibacterium, 31 mg/ml of l-valine was produced after 72 hr when 10% glucose was supplied as a carbon source, thus giving the yield of 31% from glucose. Accumulation of the other amino acids was negligible. The addition of l-isoleucine and l-leucine in the culture medium did not reduce the l-valine production, indicating that the l-valine biosynthesis is insensitive to these end products in the l-valine producer.
The structure of the central nervous system of jumping spiders of the genus Phidippus (Araneae:Salticidae)

This work was originally published as: Hill, D. E. 1975 The structure of the central nervous system of jumping spiders of the genus Phidippus (Araneae: Salticidae). M.S. thesis, Oregon State University: 1-94. All original text and content is presented, but illustrations have been reprocessed for clarity and the text has been repaginated. In several places, pertinent material or corrections have been added [marked by red text in brackets]. A figure (6B) has also been added to Figure 6.
Analog Photonic Fractional

In this work, we provide an up to date overview on the subject of photonic fractional signal processing, including both, in-fiber and waveguide on-chip technology. Thus, we discuss in detail fractional differentiation, fractional integration, fractional Hilbert transforms, and finally, fractional Fourier transforms. In each case, the underlying mathematical principles are explained for each operation, together with a short historical discussion in the context of classical optics. After that, the different proposals to perform these operations photonically on the complex field envelope of a given light pulse are Progress in Optics, Volume 63 # 2018 Elsevier B.V. ISSN 0079-6638 All rights reserved. https://doi.org/10.1016/bs.po. 2017.10. 001 93 presented, divided according to its working principle. Finally, current applications for these operators are also discussed, as well as some of its future possibilities are envisaged.
Genetic and environmental determinants of stressful life events and their overlap with depression and neuroticism

Background Stressful life events (SLEs) and neuroticism are risk factors for major depressive disorder (MDD). However, SLEs and neuroticism are heritable traits that are correlated with genetic risk for MDD. In the current study, we sought to investigate the genetic and environmental contributions to SLEs in a large family-based sample, and quantify any genetic overlap with MDD and neuroticism. Methods A subset of Generation Scotland: the Scottish Family Health Study, consisting of 9618 individuals comprise the present study. We estimated the heritability of SLEs using pedigree-based and molecular genetic data. The environment was assessed by modelling familial, couple and sibling components. Using polygenic risk scores (PRS) and LD score regression we analysed the genetic overlap between MDD, neuroticism and SLEs. Results Past 6-month life events were positively correlated with lifetime MDD status (β=0.21, r2=1.1%, p=2.5 × 10−25) and neuroticism (β =0.13, r2=1.9%, p=1.04 × 10−37). Common SNPs explained 8% of the variance in personal life events (those directly affecting the individual) (S.E.=0.03, p=9 × 10−4). A significant effect of couple environment accounted for 13% (S.E.=0.03, p=0.016) of variation in SLEs. PRS analyses found that individuals with higher PRS for MDD reported more SLEs (β =0.05, r2=0.3%, p=3 × 10−5). LD score regression demonstrated genetic correlations between MDD and both SLEs (rG=0.33, S.E.=0.08) and neuroticism (rG=0.15, S.E.=0.07). Conclusions These findings suggest that SLEs are partially heritable and this heritability is shared with risk for MDD and neuroticism. Further work should determine the causal direction and source of these associations.
Mechanisms of Selected Plant Hormones
under Heavy Metal Stress

The present review focuses on the role of auxin (IAA), cytokinins (CKs), ethylene (ET) and abscisic acid (ABA) under heavy metal stress. From the onset of heavy metal stress in plants, cellular levels of ABA tend to increase, leading to the establishment of signal communication and encoding of genes associated with the enzymes involved in antioxidant stress responses. The mechanisms of IAA phytohormones in terms of enhancing plant tolerance to stress mainly comprise developing a link between hydrogen peroxide (H2O2) and auxin homeostasis, which can result in forming a signaling interaction between them. The primary anti-phytotoxicity mechanism of ET in response to heavy metal stress is related to modulating the interaction of ethylene with reactive oxygen species (ROS) signaling, which is reflected in a synergy between induction of ROS and synthesis of 1-aminocyclopropane-1-carboxylic acid (ACC). CKs, particularly when applied exogenously, make a positive contribution to alleviating stress emanating from plant metal exposure. This is achieved by a reduction in ROS levels inside plant cells. Generally, CKs can ameliorate heavy metal stresses via a three-way interaction involving exogenous CK, CK metabolism, and CK signaling. Our aims in this work were to review the major hormonal responses of plants elicited by heavy metal stress.
Experiences of women receiving high dose rate brachytherapy for cervical cancer at ocean road cancer institute Dar-es-salaam Tanzania

The objective of this study was to present a descriptive summary of the experiences of women treated with high dose rate brachytherapy for cervical cancer. A qualitative descriptive study design was used and 50 women who undergo high dose rate brachytherapy at Ocean Road Cancer Institute (ORCI) in Dar-es-salaam were selected. Qualitative interviewer administered questionnaire was used for data collection from cervical cancer patients treated between April 2019 to July 2019 for descriptive and thematic analysis of data. Qualitative interviewer administered questioning was done from which three themes arose: the patient perception, expectation and impressions, informational need, and psychological experiences. Many patients had good perception about brachytherapy although their first impression was bad. The study found the importance of provision of procedure information to patient which seems to improve their treatment experiences. The psychological experiences faced by patients before and during treatment included pain, distress, fear, humiliation and anxiety Pain was a major problem, as the preventative medication participants received had low efficiency in pain prevention hence did not protect them from experiencing pain. Opening and hanging their legs was a humiliating experience aggravated by the presence of doctors and nurses. Their belief that brachytherapy can cure their disease comforted them and gave them courage to endure the treatment, whilst caring staff comforted and supported them. In addition to individualized patient education, nurses should assess the level of pain women experience before, during and after receiving brachytherapy and advice the revision of pain management protocols.
IMPLEMENTATION OF AN ADAPTIVE INSTRUCTIONAL DESIGN FOR A PHYSICS MODULE IN A LEARNING MANAGEMENT SYSTEM

This article demonstrates how an adaptive instructional design for a physics module can be realized in a standard learning management system. We implemented a didactic design with physics-specific online exercises that were accompanied by either detailed or non-detailed instructions, depending on the results of the previous task (or a prior knowledge test for the very first exercise). This was realized by use of simple technological tools within the framework of a straightforward recommender system with four components. Consequently, students with less prior knowledge and/or lower learning achievements received more and different teaching assistance than those with high levels of prior knowledge or performance. This was done in the form of recommendations embedded within task feedback, suggesting which task to tackle next. We present first results which show that prior knowledge and online activity contribute to the learning progress in different ways depending on the type of task that was chosen. The detailed versions of the tasks were beneficial only to the students with lower or medium prior knowledge test scores while the students with higher levels of prior knowledge had less learning progress. In the future, our simple recommender system may serve as the basis for a more complex adaptive system, further closing the gap between research and practice in the field of technology-based adaptive learning.
Use of Metagenomics as an Approach to Relations between Algae and Bacteria: A Review

In this review, we present many different classes of ecological relationships in which are registered interactions among algal and bacterial organisms. The uses of molecular techniques as metagenomics to test hypothesis, evaluate subjacent processes and searching for patterns in microbial communities are included and discussed. Our documental research suggests that great advances have been accomplished in this discipline until now, and many of these advances were through metagenomics application. Nevertheless, some questions and aspects of relations algae-bacteria remain not answered yet and metagenomics could be a power and useful working tool.
Election Prediction on Basis of Sentimental Analysis in 3 World Countries

The detection of human behavior from social media revolutionized health, business, criminal and political prediction. Significance of it, in incentive transformation of public opinion had already proven for developed countries in improving democratic process of elections. In 3 World countries, voters poll votes for personal interests being unaware of party manifesto or national interest. These issues can be addressed by social media, resulting as ongoing process of improvement for presently adopted electoral procedures. On the optimistic side, people of such countries applied social media to garner support and campaign for political parties in General Elections. Political leaders, parties, and people empowered themselves with social media, in disseminating party’s agenda and advocacy of party’s ideology on social media without much campaigning cost. To study effectiveness of social media inferred from individual’s political behavior, large scale analysis, sentiment detection & tweet classification was done in order to classify, predict and forecast election results. The experimental results depicts that social media content can be used as an effective indicator for capturing political behaviors of different parties positive, negative and neutral behavior of the party followers as well as party campaign impact can be predicted from the analysis.
Four-classes Human Emotion Recognition Via Entropy Characteristic and Random Forest。

Human Emotion Recognition is of vital importance to realize human-computer interaction (HCI), while multichannel electroencephalogram (EEG) signals gradually replace other physiological signals and become the main basis of emotional recognition research with the development of brain-computer interface (BCI). However, the accuracy of emotional classification based on EEG signals under video stimulation is not stable, which may be related to the characteristics of EEG signals before receiving stimulation. In this study, we extract the change of Differential Entropy (DE) before and after stimulation based on wavelet packet transform (WPT) to identify individual emotional state. Using the EEG emotion database DEAP, we divide the experimental EEG data in the database equally into 15 sets and extract their differential entropy on the basis of WPT. Then we calculate value of DE change of each separated EEG signal set. Finally, we divide the emotion into four categories in the two-dimensional valence-arousal emotional space by combining it with the integrated algorithm, Random Forest (RF). The simulation results show that the WPT-RF model established by this method greatly improves the recognition rate of EEG signal, with an average classification accuracy of 87.3%. In addition, we use WPT-RF model to train individual subjects, and the classification accuracy reached 97.7%.
Real time traffic signal timing approach based on artificial neural network

As the population increases, is more and more increasing the number of vehicles in cities. The increasing number of vehicle make traffic management complicated. Difficult traffic management leads to more fuel consumption, CO2 and other harmful emissions. Therefore, real-time optimization of traffic lights (signaling) used in traffic management can make traffic management more efficient. In this study, green light time is optimized by estimating the number of vehicles in an intersection with signal lights in Konya city center through artificial neural network. The results are evaluated with different performance criteria and it has been shown that the developed estimation model can be successfully used to optimize the green light durations.
The Differences of Inpatients’ Satisfaction Level based on Socio-Demographic Characteristics

Introduction: Assessing the quality of nursing care has become a global health issue. especially for caregivers and recipients of care in the inpatient department. Patient satisfaction is one of the indicators to measure quality of nursing care. This study aimed to identify the differences of patient satisfaction level in inpatient ward based on socio-demographic characteristics at Siloam Hospitals Bali. Methods: This study was cross-sectional design with descriptive comparative and correlation methods. Patient satisfaction data were collected using the Patient Satisfaction with Nursing Care Quality Questionnaire (PSNCQQ) that was provided after the patient was discharged. Purposive sampling technique was used to determine 107 samples. The analytical tests used in this study were the Spearman correlation test, Mann-Whitney test and Kruskal-Wallis test. Results: The results of this study showed that there was a significant weak and negative correlation between the level of satisfaction and age of the patient (p = 0.017; r = -0.231; α <0.05). There were significant differences of patient satisfaction based on marital status (p = 0.036; α <0.05) and nationality status (p = 0.001; α <0.05), but there were no differences in patient satisfaction based on sex (p = 0.276; α <0.05) and education level (p = 0.434; α <0.05). Conclusion: This study concluded that social demographic characteristics of patients can influence the satisfaction, but only on age, marital and nationality status. This showed that inpatients provide good satisfaction evaluations of nursing care. The optimal nursing care needs to be maintained and improved, either routine evaluation or sustainable program development.
Spin-up Characteristics with Three Types of Initial Fields and the Restart Effects on the Forecast Accuracy in GRAPES Global Forecast System

The spin-up refers to the dynamic and thermal adjustments made at the initial stage of numerical integration in order to reach a statistical equilibrium state. The analyses on the characteristics and effects of spin-ups are of great significance for optimizing the initial field of the model and improving its forecast skills. In this paper, three different initial fields are used in the experiments: the analysis field of four-dimensional variational (4D-VAR) assimilation, the 3 h prediction field in the operational forecasting system, and the Final (FNL) Operational Global Analysis data provided by National Centers for Environmental Prediction (NCEP). Following this, the characteristics of spinups in the version 2.3.1 of GRAPES (Global/Regional Assimilation and Prediction System) global forecast system (GRAPES_GFS2.3.1) under different initial fields are compared and analyzed. In addition, the influence of the lost cloud-field information on the spin-up and forecast results of the GRAPES model in the current operation is discussed as well. The results are as follows. With any initial field, the spin-up of GRAPES_GFS2.3.1 has to go through two stages – the dramatic adjustment in the first half-hour of integration and the slow dynamic and thermal adjustments afterwards. The spin-up in GRAPES_GFS2.3.1 lasts for at least 6 h, and the adjustment is gradually completed from lower to upper layers in the model. Therefore, in the evaluation of the GRAPES_GFS2.3.1, the forecast results in the first 6 h should be avoided, and the GRAPES_GFS2.3.1 with its own analysis field performs better than the one using FNL reanalysis data for the cold start in the spin-up because the variations in amplitude of the temperature and humidity tendency are smaller and the spin-up time is slightly shorter. Based on the 4D-VAR assimilation analysis field, the forecast in the operational model is artificially interrupted and restarted after 3 h of integration. In this process, as the cloud-field information is not retained, the spin-up should repeat in the model. The characteristics of spin-up are mostly consistent with those using the 4D-VAR assimilation analysis field as the initial field. However, as the cloud-field information is not retained in the current operation, the hydrometeor content in the atmosphere at the early stage of the forecast is underestimated, affecting the calculation accuracy of the radiation and causing a systematic positive bias of temperature and geopotential height fields at 500 hPa. In addition, the precipitation is also underestimated at the early stage of the simulation, affecting the forecast of typhoon tracks.
ANALYSIS AND IMPLEMENTATION OF AN ORIENTATION-AWARE SOURCE LOCALIZATION SYSTEM WITH SMART DEVICES A Thesis by DANIEL ANTONIO TUÑÓN CORONADO

Target localization in wireless systems has experienced a great improvement during recent years given the increasing demand for location services by mobile users. Particularly, localization methods based on received signal strength indicator (RSSI) are highly attractive because hardware is readily available and cost-effective. The RSSI-based localization literature generally focuses on the propagation environment sensitivity and overlooks a major factor in signal strength variability: the relative orientation between the source and the receiver. With current advancements in hardware, especially low-cost microelectromechanical sensors, most wireless devices are orientation-aware. This offers an opportunity to enhance the performance of multi-agent localization systems. We propose to include orientation knowledge and the typical antenna radiation pattern asymmetries of the sensing devices into the inference task. We will gather experimental data using Android smartphones as sensing devices, implement the new orientation-aware algorithm and asses the improvements of our approach in simulations and real-world scenarios. We compare the new scheme with the standard setting where the orientation is unknown. The orientation-aware implementation performs significantly better than the traditional systems in terms of accuracy. These results show that orientationawareness capabilities should be accounted for whenever possible in tasks of statistical inference. Furthermore, this idea is likely to find applications beyond source localization.
INDUSTRY IMPACT ON STRUCTURE AND GENDER DIVERSITY OF COMPANY BOARDS: EVIDENCE FROM THE CZECH REPUBLIC

Paper tries to contribute to solution of the research question, which factors influence the Structure and Diversity of Company Boards as the latter one represents the ratio of female to male members as Gender Diversity. In this paper we concentrated on the impact of Industry on the Gender Diversity. Paper tries to investigate the relationship between Industry as exogenous factor and Gender Diversity in the Company Boards as endogeneous factor. The paper methodology deals with extended literature review on Gender Diversity mainly represented by Anglo-Saxon authors. The paper examines data sample collected from companies’annual reports on Company Board members at year end of 2014 in the Czech Republic.Different regression and correlation models are employed in order to demonstrate the dependence between these two variablestype of industry and gender diversity. The paper findings show that female directors are dispersed differently among the industries in the paper statistical. The paper draws conclusions that Gender Diversity is driven more by selected socio-economic factors as female education, female employment rate, birth rate and investments in female education rather than industry sector itself. The managerial role of women as chair persons on one side and as only board member is salient too.