Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Analysis of Reading Self-Efficacy of Turkology Students

The Turkology Departments in countries other than Turkey fulfill two functions. First, they aim to equip their students with sufficient Turkish language proficiency. Second, they offer students a university degree from the Language, History and Literature program. Therefore, regarding the students who are studying in these departments, their competence in Turkish language skills on an academic level is significant. Thus, the aim of this study is to determine the reading skills self-efficacy of Turkology students studying in Bosnia and Herzegovina and Algeria. The study explored if the students’ reading self-accicay changed in line with any of the variables of country origin, gender, and student achievement level. The research data were collected by adopting the “Reading self-efficacy scale of those who learn Turkish as a foreign language” prepared by Güngör and Kan (2020), and the data obtained was analyzed using IBM SPSS Statistics 25.0 package program. According to the results of the study, it was seen that the reading self-efficacy of Bosnia and Herzegovinian students was higher than the Algerians. Additionally, it was found out that as the achievement scores and language proficiency levels of the students increased their reading self-efficacy scores also increased. Next, the self-efficacy scores of the graduate students were found to be higher than those of the undergraduate students. In addition, there was no significant difference between the self-efficacy level of the students participating in the study according to their gender. Finally, based on the findings, suggestions were given to increase the reading self-efficacy of the students at the end of the study.
Potential Suitable Habitat of Two Economically Important Forest Trees (Acer truncatum and Xanthoceras sorbifolium) in East Asia under Current and Future Climate Scenarios

Acer truncatum Bunge and Xanthoceras sorbifolium Bunge are small deciduous trees distributed in East Asia and have high ecological and nutrient value due to their strong environmental adaptability and seed oil abundant in nervonic acid and unsaturated fatty acids. However, their natural distribution remains unclear, which will also be affected by the changing climatic conditions. The main purpose of this study was to map and predict the current and future potential suitable habitats of these two species using MaxEnt based on the presence location of species and environmental variables. The results showed that A. truncatum was more suitable for warm and humid climates and was more durable to climate change compared to X. sorbifolium. Under the current environmental conditions, the suitable habitat of A. truncatum was mainly concentrated in Inner Mongolia Plateau, Loess Plateau, Sichuan Basin, Northeast Plain, North China Plain, Korean Peninsula, as well as Japan, with an area of 115.39 × 104 km2. X. sorbifolium was mainly distributed in Inner Mongolia Plateau and Loess Plateau with an area of 146.15 × 104 km2. Under future climate scenarios, the model predicted that higher concentrations of greenhouse gas emissions could result in greater expansion of the potential distribution of both species. Meanwhile, the study also revealed that the two species migrated to the north by east to varying degrees with the change in suitable habitats. This work could provide scientific basis for resource protection and utilization of the two economic forest trees.
A Review Article on Triazoles and its Pharmacological Activities

Despite the fact that triazole was first synthesised over a century ago, it continues to unique the interest of chemists, biologists, technologists, and other experts. Triazoles have been shown to have antiviral, anti-inflammatory, anti-fertility, anti-tubercular, antimicrobial, anti-cancer, and anti-corrosion properties in recent years. The goal of this review is to describe the structures, synthesis, reactions, and spectral properties of triazoles in order to highlight their potential applications in a variety of bioactive phenomena and analytical applications.
Urban air pollution exposure: an assessment exploiting world settlement footprint and land use data

The majority of the population in Europe that is exposed to air pollution levels exceeding the WHO limit values lives in metropolitan areas. There are already several studies that assess the linkage between air pollution and adverse effects on health. With the technology at our disposition, today, we can identify air pollution hotspots. The assessment of the pollution situation alone represents, however, only one component of the whole picture. In order to be able to build a scale that identifies the most critical regions in higher need of intervention, also the probability of exposure and the number of people exposed to defined pollution concentrations must be considered. For this purpose, we can benefit from satellite-derived data products of settlement extent, population density and land use. To improve the health risk assessment, novel data sets have been synergistically exploited for the first time. In this work a method is proposed to perform an assessment of the increased health risk within urban areas in Europe due to the exposure to PM2.5 and to calculate the health burden index HBI: a useful parameter for the assessment of health risk that provides a measure of the impact of air pollution and enables to perform comparisons between different cities. This is a first approach showing the potential of this easily scalable tool that can be of support in the decision-making process and in the research on air pollution/health relationship. Further work is required for the verification and tuning of the initial hypotheses by means of validation with real-life data.
International Students Labour and School Attendance: Evidence from China

Since 2000, an increasing number of scholarship international students have been entering Chinese universities. Government scholarships provide them money each month to pay for a degree, but most of them are engaged in part-time work even if china officially doesn’t allow international students to work during their studies. This Research aimed to explore the key determinants of international students labour and schooling in China and labour experience contribution to their study. We used the Labor Market Survey (SLM) conducted by LARES (Laboratory of analysis and research in economic and social research) in the year of 2019 in Beijing, Shanghai, Guangzhou, Zhejiang and Shandong. The sample includes 400 scholarship students at the Bachelor, Master and PhD level. They were from different major areas, aged between 18 and 35 years old. Results from logistic regression analysis showed that developing relationship, practicing Chinese, tutor income, age, were the key factors that influence scholarship international student’ work while study. The students benefited both positively and negatively from working and studying.
Role of Atlantic SST in the Recent ENSO Predictability Change in NMME Model Hindcasts

The present study analyses the possible change in the seasonal prediction skill of El Nino Southern Oscillation (ENSO) in association with the reported climate modification in the tropical Pacific during the early 21st century. The hindcasts of nine models that participated in the National Multimodel Ensemble Project (NMME) are used for the analysis. Both the boreal summer (JJAS) and winter (DJF) seasons ENSO indices from 4 months and 1-month lead for the period 1981-2018/19 are studied. The analysis shows that all the models have reduced interannual variability as observations for both seasons. There is not much skill (both actual and potential) difference for DJF season for all the models for both the lead times. Summer skill loss for Feb IC is more for models such as CanSIPv2, CCSM3 and NEMO, while it is minimum for CCSM4. There is an increase of skill for Feb IC hindcasts for three GFDL models for JJAS season. Most of the models failed to simulate the ENSO events during the second period. The summer season ENSO pattern in the recent period are influenced by spring time north Atlantic SST anomalies. The models with maximum decrease of skill after 2000 fail to simulate the tropical Atlantic SST anomalies during the initialization months and also the summer season SST anomalies induced by these SST anomalies. The models with better or close to observed patterns with Atlantic SST induced ENSO patterns are only able to maintain the same skill as previous decades.
Automation

We study the economic incentives for automation when labor and machines are perfect substitutes. Labor may still be employed in production, even when it is a costlier input than robots on a productivity-adjusted basis. This occurs if firms face uninsurable idiosyncratic risk, adjusting the stock of machines is costly, and workers can be hired and fired quickly enough. Even though labor survives, jobs become less stable, as workers are hired in short-lived bursts to cope with shocks. We calibrate a general equilibrium, multi-industry version of our model to match data on robot adoption in US manufacturing sectors, and use it to compute the employment and labor share consequences of progress in automation technology. A fall in the relative price of robots leads to relatively few jobs losses, while reductions in adjustment costs, or improvements in relative robot productivity, can be far more disruptive. The model-implied semi-elasticity of aggregate employment to robot penetration ranges between 0.01% and 0.1%, depending on the underlying source of increased robot adoption. Adding reduced-form hiring and firing costs to our benchmark model reveals that the scare of automation is justified when regulations impose substantial rigidity on employment relations.
Non‐fragile state estimation for second‐order memristive neural networks with unbounded time‐varying delays

Without converting the second‐order memristive neural networks (SMNNs) into the usual first‐order systems, this article works on the non‐fragile state estimation of SMNNs with unbounded time‐varying delays. The memristor is treated as uncertainty by recommending some measurable functions. According to Lyapunov functional approach, inequality techniques, and combining Barbalat lemma, a new Lyapunov functional is proposed to straightway discuss the asymptotic stability for the error system, and several sufficient conditions are obtained to assure the existence of the preset estimators. The nonfragile estimators are designed and several explicit descriptions of the estimators are given by solving linear matrix inequalities. To overcome the uncertainty of state variables, the problem of parameter mismatch is as well considered. Finally, two examples are given to verify the availability of the results.
A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation

Limited navigation capabilities of many current robots and UAVs restricts their applications in GPS denied areas. Large aircraft with complex navigation systems rely on a variety of sensors including radio frequency aids and high performance inertial systems rendering them somewhat resistant to GPS denial. The rapid development of computer vision has seen cameras incorporated into small drones. Vision-based systems, consisting of one or more cameras, could arguably satisfy both size and weight constraints faced by UAVs. A new generation of thermal sensors is available that are lighter, smaller and widely available. Thermal sensors are a solution to enable navigation in difficult environments, including in low-light, dust or smoke. The purpose of this paper is to present a comprehensive literature review of thermal sensors integrated into navigation systems. Furthermore, the physics and characteristics of thermal sensors will also be presented to provide insight into challenges when integrating thermal sensors in place of conventional visual spectrum sensors.
Infestation of Early- and Late-Flushing Trees by Spring Caterpillars: An Associational Effect of Neighbouring Trees

In temperate forests within Europe, early-flushing (EF) deciduous trees are often heavily infested by early spring leaf-eating Lepidoptera, while late-flushing (LF) trees are better protected in a phenological manner against such heavy infestations, as spring moth larvae begin to appear before their bud burst. The associational effects of EF trees on LF ones are only poorly known. We studied whether or not the infestation of LF trees by spring Lepidoptera can be affected by EF ones if they grow in the immediate vicinity. We compared spring assemblages of leaf-eating larvae of Lepidoptera on LF Quercus cerris L. with those on EF Q. pubescens Willd. in several microhabitats in Slovakia, Central Europe. Larvae were collected from mature and young trees. Mature trees sampled were growing: (1) in a closed-canopy forest; (2) in small groups; or (3) as a lone tree. Forest and tree groups are both constituted by oak species. Tree groups and lone trees were 20–50 m distant from forest edges. Young trees were growing (1) under mature Q. pubescens trees in a forest or (2) as a lone tree within forest gaps or near the edges. In the closed-canopy forest where LF trees (Q. cerris) were surrounded by EF ones (Q. pubescens), the caterpillars on mature LF trees were in abundance, almost as on mature EF ones. The species composition of larval assemblages on the two oak species was similar. In contrast, on small groups and on lone trees, the lepidopteran larvae were significantly less abundant on LF trees than EF ones. In the case of young trees, the abundance of larvae and their composition assemblages on both oaks were comparable in the forest. In the open habitat, LF trees were less infested by larvae than EF ones and the assemblages of moth larvae differed between the two. Our results reveal the effect (associational susceptibility) of EF trees on LF ones when growing in a close vicinity. It means that the phenological protection of LF trees may not be sufficient if they grow close to or are surrounded by EF ones.
Convolutional Neural Networks for event classification

Cutting-edge Artificial Intelligence is being implemented in a wide range of tasks in High Energy Physics (HEP) in order to facilitate the analysis of large datasets. However, visual recognition has not been explored as much in HEP for event classification. This study shows how Convolutional Neural Networks could be applied for such an important task, for which a novel method to represent the event information in images is explored. This technique is applied for a classification problem corresponding to a search for Dark Matter in proton-proton collisions. The results obtained with this technique are also compared with the performance of a Boosted Decision Tree.
Network Model Analysis of Residual Chlorine to Reduce Disinfection Byproducts in Water Supply Systems in Yangon City, Myanmar

In Yangon City, chlorination commenced in January 2020 to supply drinkable water; therefore, there is as yet no information on chlorine decay and DBP formation in the water supply system. This study aimed to find methods to optimize chlorine dosage in Yangon City. Onsite sampling and laboratory analyses of residual chlorine and trihalomethane (THM) formation, as well as water quality simulations, were conducted to find the chlorine decay and THM formation kinetics. Due to a high chlorine dose of 2 mg/L for both pre- and post-chlorination, disinfection was effective despite the low removal efficiency of turbidity. However, THMs were found in high levels in both treated and tap water due to the high THM formation potential of raw water. The re-contamination and/or transformation of dissolved organic matter were found in the distribution network by increases in specific ultraviolet absorption (SUVA) values and excitation-emission matrix (EEM) fluorophores, which brought about variations of THMs in the networks. The EPANET models were run assuming there to be no water leakages; it was found that the chlorine dose could be decreased to 0.8 mg/L to meet the guidelines for THMs and residual chlorine. The methods employed in this study could be also applied in other water supply systems in tropical developing countries with limited water quality monitoring data.
Differences in spike generation instead of synaptic inputs determine the feature selectivity of two retinal cell types

The output of spiking neurons depends both on their synaptic inputs and on their intrinsic properties. Retinal ganglion cells (RGCs), the spiking projection neurons of the retina, comprise over 40 different types in mice and other mammals, each tuned to different features of visual scenes. The circuits providing synaptic input to different RGC types to drive feature selectivity have been studied extensively, but there has been substantially less research aimed at understanding how the intrinsic properties of RGCs differ and how those differences impact feature selectivity. Here, we introduce an RGC type in the mouse, the Bursty Suppressed-by-Contrast (bSbC) RGC, whose contrast selectivity is shaped by its intrinsic properties. Surprisingly, when we compare the bSbC RGC to the OFF sustained alpha (OFFsA) RGC that receives similar synaptic input, we find that the two RGC types exhibit starkly different responses to an identical stimulus. We identified spike generation as the key intrinsic property behind this functional difference; the bSbC RGC undergoes depolarization block in conditions where the OFFsA RGC maintains a high spike rate. Pharmacological experiments, imaging, and compartment modeling demonstrate that these differences in spike generation are the result of differences in voltage-gated sodium channel conductances. Our results demonstrate that differences in intrinsic properties allow these two RGC types to detect and relay distinct features of an identical visual stimulus to the brain.
Engineering Pyrrolysyl-tRNA Synthetase for the Incorporation of Non-Canonical Amino Acids with Smaller Side Chains

Site-specific incorporation of non-canonical amino acids (ncAAs) into proteins has emerged as a universal tool for systems bioengineering at the interface of chemistry, biology, and technology. The diversification of the repertoire of the genetic code has been achieved for amino acids with long and/or bulky side chains equipped with various bioorthogonal tags and useful spectral probes. Although ncAAs with relatively small side chains and similar properties are of great interest to biophysics, cell biology, and biomaterial science, they can rarely be incorporated into proteins. To address this gap, we report the engineering of PylRS variants capable of incorporating an entire library of aliphatic “small-tag” ncAAs. In particular, we performed mutational studies of a specific PylRS, designed to incorporate the shortest non-bulky ncAA (S-allyl-l-cysteine) possible to date and based on this knowledge incorporated aliphatic ncAA derivatives. In this way, we have not only increased the number of translationally active “small-tag” ncAAs, but also determined key residues responsible for maintaining orthogonality, while engineering the PylRS for these interesting substrates. Based on the known plasticity of PylRS toward different substrates, our approach further expands the reassignment capacities of this enzyme toward aliphatic amino acids with smaller side chains endowed with valuable functionalities.
Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection

A non-local patch regression (NLPR) denoising-enhanced differential broadband photoacoustic (PA) sensor was developed for the high-sensitive detection of multiple trace gases. Using the edge preservation index (EPI) and signal-to-noise ratio (SNR) as a dual-criterion, the fluctuation was dramatically suppressed while the spectral absorption peaks were maintained by the introduction of a NLPR algorithm. The feasibility of the broadband framework was verified by measuring the C2H2 in the background of ambient air. A normalized noise equivalent absorption (NNEA) coefficient of 6.13 × 10−11 cm−1·W·Hz−1/2 was obtained with a 30-mW globar source and a SNR improvement factor of 23. Furthermore, the simultaneous multiple-trace-gas detection capability was determined by measuring C2H2, H2O, and CO2. Following the guidance of single-component processing, the NLPR processed results showed higher EPI and SNR compared to the spectra denoised by the wavelet method and the non-local means algorithm. The experimentally determined SNRs of the C2H2, H2O, and CO2 spectra were improved by a factor of 20. The NNEA coefficient reached a value of 7.02 × 10−11 cm−1·W·Hz−1/2 for C2H2. The NLPR algorithm presented good performance in noise suppression and absorption peak fidelity, which offered a higher dynamic range and was demonstrated to be an effective approach for trace gas analysis.
Cryptic vocal behavior of foraging humpback whales on feeding grounds in West Greenland.

Male humpback whales (Megaptera novaeangliae) sing in mating aggregations in the form of song displays, but much less is known about how both sexes use sound on their feeding grounds. Here, we test different hypotheses about the function of vocalizations in 14 foraging humpback whales tagged with sound and movement recording Dtags in Greenland. We show that this population of foraging humpback whales have an overall low call rate of 11.9 calls h-1 (inter-quartile range = 12.1) with no support for the hypotheses that they employ sound in the localization or manipulation of prey nor in the coordination of lunge feeding. The calls had a mean received level of 135 ± 5dB re 1 μPa, which is some 30 dB lower than maximum levels of song recorded on similar deployed tags, suggesting a much smaller active space of these vocalizations. This reduced active space might, in concert with low call rates, serve to mitigate eavesdropping by predatory killer whales or conspecifics competing for the same prey resources. We conclude that feeding humpback whales in Greenland produce low level, infrequent calls suggesting that calling is not a prerequisite for successful feeding, but likely serves to mediate within group social interactions.
Molecular Epidemiology, Antimicrobial Surveillance, and PK/PD Analysis to Guide the Treatment of Neisseria gonorrhoeae Infections

The aim of this study was to apply molecular epidemiology, antimicrobial surveillance, and PK/PD analysis to guide the antimicrobial treatment of gonococci infections in a region of the north of Spain. Antibiotic susceptibility testing was performed on all isolates (2017 to 2019, n = 202). A subset of 35 isolates intermediate or resistant to at least two antimicrobials were selected to search for resistance genes and genotyping through WGS. By Monte Carlo simulation, we estimated the probability of target attainment (PTA) and the cumulative fraction of response (CFR) of the antimicrobials used to treat gonorrhea, both indicative of the probability of treatment success. In total, 2.0%, 6.4%, 5.4%, and 48.2% of the isolates were resistant to ceftriaxone, cefixime, azithromycin, and ciprofloxacin, respectively. Twenty sequence types were identified. Detected mutations were related to antibiotic resistance. PK/PD analysis showed high probability of treatment success of the cephalosporins. In conclusion, multiple populations of N. gonorrhoeae were identified. We can confirm that ceftriaxone (even at the lowest dose: 250 mg) and oral cefixime are good candidates to treat gonorrhea. For patients allergic to cephalosporins, ciprofloxacin should be only used if the MIC is known and ≤0.125 mg/L; this antimicrobial is not recommended for empirical treatment.