Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge, beliefs and perceptions among alopecia areata patients: A cross-sectional study in Faisalabad

Introduction Alopecia areata (AA) is described as a pathological condition with undesirable, nonpredictable hair loss. Being a multifactorial disease, many environmental, immunological, psychological and genetic risk factors attribute the onset and progression of AA. Objective Present study was undertaken to assess awareness, social impact, degree of stigma and management approaches among local AA patients. Methods The study was cross-sectional & conducted in Dermatology Department of Government General Hospital, Ghulam Muhammad Abad, Faisalabad. The duration of study was six months i.e. from February 2019 to July 2019 recruiting 50 patients. A questionnaire was designed and distributed among participants recruited through convenient sampling method to collect baseline information such as age, gender, age at onset, duration of AA, marital status, education along with knowledge, perceptions and behavior based-queries. Results With average age of 27.5±2.92 years, 74% participants were male. About 90% participants were married and only 3% were graduates. Majority (62%) had no knowledge about causes of AA. When asked about causative factors, almost 42% of the participants believed that germs and viruses may cause AA. Although family life of 56% respondents was unaffected by disease, yet social life of 66% patients suffered from AA. Depression was experienced by 48% subjects. Availability of economical treatment options was in the knowledge of 50-70% respondents. Conclusion It is concluded that low literacy rate and lack of knowledge about AA can lead to improper treatment options and stress.
Progress in the Diagnosis and Management of Acute Kidney Injury in Children

Pediatric acute kidney injury (PAKI) is a critical renal syndrome with multiple causes and multiple pathogenic mechanisms, and might have a poor prognosis if misdiagnosed or poorly managed. So early diagnosis is imperative to avoid delays in treatment. In recent years, some biomarkers have been developed, to predict the early occurrence of AKI, such as Neutrophil gelatinase associated lipocalin (NGAL), Kidney injury molecule-1 (KIM-1), Liver-type fatty acid binding protein (L-FABP), and Interleukin-18 (IL-18). Currently, PAKI lacks effective drug therapy, and renal replacement therapy (RRT) is the main treatment for AKI. Choosing the type of renal replacement therapy(RRT), must consider the patient's primary disease, clinical status, and the advantages and disadvantages. This article reviews the different most recent diagnostic markers of PAKI, and therapeutic interventions used. RÉSUMÉ La lésion rénale aigue en pédiatrie est un syndrome rénal grave avec de multiples causes et de multiples mécanismes pathogéniques qui pourraient avoir un mauvais pronostic si le diagnostic n’est pas posé ou mal traité. Un diagnostic précoce est donc impératif pour éviter les retards de traitement. Au cours des dernières années, certains biomarqueurs ont été développés pour prédire la survenue précoce de la lésion rénale aigue comme Neutrophil Gelatinase associated lipocalin (NGAL), Kidney injury molecule 1(KIM-1), Liver type fatty acid binding protein (L-FABP) et l’interleukin 18(IL-18). A l’heure actuelle, les lésions rénales aigues pédiatriques n’ont pas de traitement médicamenteux efficace et la thérapie de transplantation rénale est le principal traitement des lésions rénales aigues. Le choix du type de thérapie de remplacement rénal doit tenir compte de la maladie primaire du patient, de son état clinique, des avantages et inconvénients. Cet article passe en revue les différents marqueurs diagnostiques les plus récents de MRAP et les interventions thérapeutiques utilisées.
AN EMPIRICAL ASSESSMENT OF THE MACROECONOMIC EFFECT OF A SHIFT FROM INDIRECT TO DIRECT TAXES IN COTE D’IVOIRE

This paper examines the long-run effect of a shift from indirect to direct taxes for Cote d’Ivoire using data for the period 1960 to 2006. The residual-based test of Gregory and Hansen (1996) is employed to test for cointegration and the Engle-Yoo (1987) three-step procedure is used to estimate the long-run effect of tax variables on real output. The results reveal that tax burden and tax mix are negatively associated with output, with tax burden having a much greater adverse effect on GDP than tax mix. The effect of the tax mix on GDP is contingent on the level of the tax burden and diminishes as tax burden increases. Our estimates also suggest that up to a threshold level of tax burden of 17.57%, increased direct taxation relative to indirect taxation is associated with decreased output. But beyond this threshold a move from indirect to direct taxes is likely to lead to higher levels of output.
The Effects of 25 Years of Bilingual Education on Vocabulary Knowledge

One of the most fundamental, yet often overlooked, challenges facing all language learners is that of acquiring sufficient vocabulary for their needs. This study seeks to determine what kind of vocabulary knowledge has been acquired by a group of UBD undergraduates who are products of the bilingual education system some 25 years after its inception in Brunei schools. The study also seeks to determine to what extent this knowledge will meet their needs for an English medium university programme. Paul Nation’s levels tests (receptive and controlled production) are used to measure their vocabulary. These tests are administered on entry to university and again after two years of their degree programme. Participants are also surveyed in an attempt to uncover their attitudes to, and practices for, vocabulary learning. The paper then considers the reasons for the findings, including the role of bilingual education, and whether or not the students are on target to meet the standards required by the academic and social communities they currently move in. Finally, the paper also considers any implications the findings may suggest for future developments in English language teaching in Brunei.
Morphological evaluation of wheat genotypes for grain yield under arid environment of Balochistan

evaluation of wheat genotypes for grain yield under arid environment of Balochistan. Abstract Wheat is the most famous cereal crop in the world mainly used as a staple food for mankind. Drought is a leading environmental stress that restricts the productivity of wheat in water-scare regions. In this study, we evaluated the performance of 10 advanced lines and two commercial varieties for grain yield during 2018-19, at Balochistan Agriculture College, Quetta. Results revealed the highly significant differences between genotypes for number of days to heading, days to maturity, plant height at maturity (cm), number of fertile tillers plant -1 , number of grains spike 1 , 1000-grain weight (g), grain yield plant -1 and grain yield (kg/ha), while significant ( P > 0.05 ) for days to grain filling period. In addition, the principal component analysis indicated that first two principal components (PC1 and PC2) accounted for 83.77% of the total variation. The genotype BAC-51 performed superior for grain yield and other morphological traits, suggesting that BAC-51 should be tested at different locations in the upland of Balochistan and may be released as a drought-tolerant variety after the certification process.
Daily and seasonal patterns of CO2 fluxes and evapotranspiration in maize-grass intercropping

Studies that investigate the relationships between CO2 fluxes and evapotranspiration (ET) are important for predicting how agricultural ecosystems will respond to climate changes. However, none was made on the maize-grass intercropping system in Brazil. The aim of this study was to determine the ET and CO2 fluxes in a signal grass pasture intercropped with maize, in Sao Joao, Pernambuco, Brazil, in a drought year. Furthermore, the soil water storage (SWS) and leaf area index (LAI) were determined. The latent heat flux was the main consumer of the available energy and the daily and seasonal ET and CO2 variations were mainly controlled by rainfall, through the changes in soil water content and consequently in SWS. The agroecosystem acted as an atmospheric carbon source, during drier periods and lower LAI, and as an atmospheric carbon sink, during wetter periods and higher LAI values. In a dry year, the intercropping sequestered 2.9 t C ha-1, which was equivalent to 8.0 kg C ha-1 d-1. This study showed strong seasonal fluctuations in maize-grass intercropping CO2 fluxes, due to seasonality of rainfall, and that this agroecosystem is vulnerable to low SWS, with significant reduction in CO2 uptake during these periods.
DETUROPE – THE CENTRAL EUROPEAN JOURNAL OF REGIONAL DEVELOPMENT AND TOURISM

International and interregional migrations, the geographic form of human mobility have a number of social, economic and political effects. These impacts can vary depending on the reference period, region or sector as well as on the goals and aspects of our interpretation. Another important question in connection with regional competitiveness is how decision makers act and react after perceiving migratory movements. Analysing the interference between immigration and regional development, we can rely on the well-known migration theories, however, a comparison of further models and concepts relating development and regional issues can be more useful for researchers. These questions are relevant, since the issues of the regional science, such as regional competitiveness and regional policy, have in the background strong associations with migratory phenomena. The aim of this study is to introduce the theoretical background, summarizing the mainstream solutions and the controversies as well. Thus the first part presents the role of the migration theories and these of the regional science in connection with the linkages between immigration and regional competitiveness. Besides it points out the importance of immigrants ́ inclusion. The next section focuses, complemented with the outcomes of a multivariate statistical analysis, on the theories on determinants and causalities of migration, exploring the connecting points with the issues of the regional policy.
Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0

Abstract. Atmospheric aerosol microphysical processes are a significant source of uncertainty in predicting climate change. Specifically, aerosol nucleation, emissions, and growth rates, which are simulated in chemical transport models to predict the particle size distribution, are not understood well. However, long-term size distribution measurements made at several ground-based sites across Europe implicitly contain information about the processes that created those size distributions. This work aims to extract that information by developing and applying an inverse technique to constrain aerosol emissions as well as nucleation and growth rates based on hourly size distribution measurements. We developed an inverse method based upon process control theory into an online estimation technique to scale aerosol nucleation, emissions, and growth so that the model–measurement bias in three measured aerosol properties exponentially decays. The properties, which are calculated from the measured and predicted size distributions, used to constrain aerosol nucleation, emission, and growth rates are the number of particles with a diameter between 3 and 6 nm, the number with a diameter greater than 10 nm, and the total dry volume of aerosol ( N3–6 , N10 , Vdry ), respectively. In this paper, we focus on developing and applying the estimation methodology in a zero-dimensional “box” model as a proof of concept before applying it to a three-dimensional simulation in subsequent work. The methodology is first tested on a dataset of synthetic and perfect measurements that span diverse environments in which the true particle emissions, growth, and nucleation rates are known. The inverse technique accurately estimates the aerosol microphysical process rates with an average and maximum error of 2 % and 13 %, respectively. Next, we investigate the effect that measurement noise has on the estimated rates. The method is robust to typical instrument noise in the aerosol properties as there is a negligible increase in the bias of the estimated process rates. Finally, the methodology is applied to long-term datasets of in situ size distribution measurements in western Europe from May 2006 through June 2007. At Melpitz, Germany, and Hyytiala, Finland, the average diurnal profiles of estimated 3 nm particle formation rates are reasonable, having peaks near noon local time with average peak values of 1 and 0.15 cm −3 s −1 , respectively. The normalized absolute error in estimated N3–6 , N10 , and Vdry at three European measurement sites is less than 15 %, showing that the estimation framework developed here has potential to decrease model–measurement bias while constraining uncertain aerosol microphysical processes.
Constraints on global aerosol number concentration, SO2 and condensation sink in UKESM1 using ATom measurements

Abstract. Understanding the vertical distribution of aerosol helps to reduce the uncertainty in the aerosol lifecycle and therefore in the estimation of the direct and indirect aerosol forcing. To improve our understanding, we use measurements from four deployments of the Atmospheric Tomography (ATom) field campaign (ATom1-4) which systematically sampled data over the Pacific and Atlantic Oceans with near pole-to-pole coverage. We evaluate the UK Earth system model (UKESM1) against ATom observations in terms of joint biases in the vertical profile of three variables related to new particle formation: total particle number concentration (NTotal), sulphur dioxide (SO2) mixing ratio and the condensation sink. The NTotal, SO2 and condensation sink are interdependent quantities and have a controlling influence on the vertical profile of each other. Improving only one of these quantities in comparison with observations can lead to a misleading impression that overall model performance has improved. Analysing NTotal, SO2 and condensation sink simultaneously helps reduce the probability of getting the right answer for the wrong reasons. The model's condensation sink is within a factor of 2 of observations, but the NTotal and SO2 shows larger biases mainly in the tropics and high latitudes. Algorithmic improvements to the model and perturbations to key atmospheric processes help reduce tropospheric model biases consistently. We performed a series of model sensitivity tests to identify atmospheric processes that have the strongest influence on overall model performance (NTotal, SO2 and condensation sink simultaneously). In the boundary layer (which we define in this study as below 1 km altitude) and lower troposphere (1–4 km) inclusion of a boundary layer nucleation scheme (Metzger et al., 2010), which is switched off in the default version of UKESM, is critical to obtaining better agreement with observations. However, in the mid (4–8 km) and upper troposphere (> 8 km), sub-3 nm particle growth, pH of cloud droplets, DMS emissions, upper tropospheric nucleation rate, SO2 gas scavenging rate and cloud erosion rate are found to play a more dominant role. Analysing the data with altitude, we find that perturbations to boundary layer nucleation, sub 3 nm growth, cloud droplet pH and DMS emissions reduces the boundary layer and upper tropospheric model bias. We performed a combined simulation with all 4 perturbations included and found that the model's NTotal, SO2 and condensation sink biases were reduced in most cases (up to a 50 % reduction) in both the boundary layer and upper troposphere simultaneously. These perturbations are well-motivated in that they improve the physical basis of the model and are suitable for implementation in future versions of UKESM.
Adaptive gender-based thermal control system

A closed loop adaptive gender-based thermal control system (AG-TCS) is designed, modelled, analysed and tested. The system has the unique feature of adapting to the surrounding environment as a function of the number of humans present and the gender ratio. The operation of the system depends on a unique interface between a radio frequency identification (RFID) device and an imaging device, both of which are correlated and interfaced to a controller. Testing of the system resulted in smooth transition and shape conversion of the response curve, which proved its adaptability. Three mathematical equations describing the internal mechanisms of the AG-TCS are presented and have been proven to optimally reflect the original statistical data covering both genders.
Soil moisture level prediction using optical technique and artificial neural network

This research describes the use of an optical system combined with artificial neural network (ANN) for wireless and nondestructive prediction of soil moisture level. The former system comprising of near infrared (NIR) emitters of wavelengths 1200 nm and 1450 nm, and a photodetector for near real time soil moisture measurement in loams and peats holding different amount of water. There were 63 and 90 sets of data from loams and peats, respectively, used in the development of the dual stage-multiclass ANN model, wherein measurement of light attenuation (from nondestructive system) was correlated with percent soil moisture (from destructive gold standard approach) in pre-measurement stage. The result revealed a relatively good performance in the training of the NN with regression, R, of 0.8817 and 0.8881, and satisfactory error performance of 0.7898 and 1.172, for loams and peats, respectively. The testing of the system on 50 new samples of loam and peat showed a considerably high mean accuracy of 92 % for loams while 82 % was observed for peats. This study attributes the poorer performance of the system used on peats to the detection resolution of percent soil moisture, and structure and properties of the corresponding soil. This work concluded that the developed technology may be feasible for use in the future design and improvement of agricultural soil management.
Big data and remote sensing: A new software of ingestion

Currently, remote sensing is widely used in environmental monitoring applications, mostly air quality mapping and climate change supervision. However, satellite sensors occur massive volumes of data in near-real-time, stored in multiple formats and are provided with high velocity and variety. Besides, the processing of satellite big data is challenging. Thus, this study aims to approve that satellite data are big data and proposes a new big data architecture for satellite data processing. The developed software is enabling an efficient remote sensing big data ingestion and preprocessing. As a result, the experiment results show that 86 percent of the unnecessary daily files are discarded with a data cleansing of 20 percent of the erroneous and inaccurate plots. The final output is integrated into the Hadoop system, especially the HDFS, HBase, and Hive, for extra calculation and processing.
Roles of generation-interval distributions in shaping relative epidemic strength, speed, and control of new SARS-CoV-2 variants

Inferring the relative strength (i.e., the ratio of reproduction numbers, Rvar/Rwt) and relative speed (i.e., the difference between growth rates, rvar-rwt) of new SARS-CoV-2 variants compared to their wild types is critical to predicting and controlling the course of the current pandemic. Multiple studies have estimated the relative strength of new variants from the observed relative speed, but they typically neglect the possibility that the new variants have different generation intervals (i.e., time between infection and transmission), which determines the relationship between relative strength and speed. Notably, the increasingly predominant B.1. 1.7 variant may have a longer infectious period (and therefore, a longer generation interval) than prior dominant lineages. Here, we explore how differences in generation intervals between a new variant and the wild type affect the relationship between relative strength and speed. We use simulations to show how neglecting these differences can lead to biases in estimates of relative strength in practice and to illustrate how such biases can be assessed. Finally, we discuss implications for control: if new variants have longer generation intervals then speed-like interventions such as contact tracing become more effective, whereas strength-like interventions such as social distancing become less effective.
Prevalence of anxiety, depression, and stress among teachers during the COVID-19 pandemic: Systematic review

Objective: Identifying the prevalence of anxiety, depression, and stress among teachers during the COVID-19 pandemic. Methods: Systematic review of original studies published in any language. Protocol published in PROSPERO under number CRD42021240543. The search was carried out in the Web of Science, PsycINFO, Pubmed, Embase, LILACS, and SciELO databases, using the descriptors: anxiety, depression, stress, teacher, faculty, COVID-19, and their synonyms. Narrative synthesis was carried out in line with the synthesis without meta-analysis (SWiM) in systematic reviews. Results: Of the 1,372 records identified, six studies, all cross-sectional, were included in the review. The studies were carried out in China, Brazil, the United States of America, India, and Spain. Five studies included more women than men. The participants were aged from 24 to 60 years. Three studies included only school teachers, two included schools and universities teachers, and one only university teachers. Of the five studies, all dealt with remote activities and only one included teachers who returned to face-to-face classes one to two weeks ago. The prevalence of anxiety ranged from 10% to 49.4%, and depression from 15.9% to 28.9%, being considerably higher in studies with teachers who worked in schools. The prevalence of stress ranged from 12.6% to 50.6%. Conclusions: The prevalence of anxiety, depression, and stress was high among teachers during the pandemic, with great variation between studies. Anxiety and stress were more prevalent in the Spanish study. The results show the need for measures for the care of teachers" mental health, especially when returning to face-to-face classes. Keywords: anxiety, depression, stress, teachers, COVID-19.
Spider monkey optimization routing protocol for wireless sensor networks

Uneven energy consumption (UEC) is latent trouble in wireless sensor networks (WSNs) that feature a multiple motion pattern and a multi-hop routing. UEC often splits the network, reduces network life, and leads to performance degradation. Sometimes, improving energy consumption is more complicated because it does not reduce energy consumption only, but it also extends network life. This makes energy consumption balancing critical to WSN design calling for energy-efficient routing protocols that increase network life. Some energy-saving protocols have been applied to make the energy consumption among all nodes inside the network equilibrate in the expectancy and end power in almost all nodes simultaneously. This work has suggested a protocol of energy-saving routing named spider monkey optimization routing protocol (SMORP), which aims to probe the issue of network life in WSNs. The proposed protocol reduces excessive routing messages that may lead to the wastage of significant energy by recycling frequent information from the source node into the sink. This routing protocol can choose the optimal routing path. That is the preferable node can be chosen from nodes of the candidate in the sending ways by preferring the energy of maximum residual, the minimum traffic load, and the least distance to the sink. Simulation results have proved the effectiveness of the proposed protocol in terms of decreasing end-to-end delay, reducing energy consumption compared to well-known routing protocols.