Miracle-dz/UniFAR-FTUs huggingface dataset:
Levels of stress in medical students due to COVID-19

For medical schools, the COVID-19 pandemic necessitated examination and curricular restructuring as well as significant changes to clinical attachments. With the available evidence suggesting that medical students’ mental health status is already poorer than that of the general population, with academic stress being a chief predictor, such changes are likely to have a significant effect on these students. This online, cross-sectional study aimed to determine the impact of COVID-19 on perceived stress levels of medical students, investigate possible contributing and alleviating factors, and produce recommendations for medical schools to implement during future healthcare emergencies. The majority (54.5%) of respondents reported levels of stress ranging from moderate to extreme. Higher levels of stress were significantly associated with female gender (p=0.039) and international status (p=0.031). A significant association was also noted between reported stress and the transition to online learning (p<0.0001) and online assessment formatting (p<0.0001), concerns for personal health (p<0.0001) and for the health of family members (p<0.0001). Students who reported higher stress levels were less confident in their government’s management of the crisis (p=0.041). Additionally, students who reported lower stress agreed highly that their medical school had an appropriate response to the crisis (p<0.0001), had provided sufficient information regarding the crisis (p=0.015), that they trust their school in handling the continuing of their education (p=0.020) and that their school had appropriate plans in place to support the continuing of education (p=0.017)
Breakeven criteria for the GaInNAs junction in GaInP/GaAs/GaInNAs/Ge four‐junction solar cells

The four‐junction GaInP/GaAs/GaInNAs/Ge solar cell structure holds the promise of efficiencies exceeding those of the GaInP/GaAs/Ge three‐junction cell, which at present is the benchmark for high‐efficiency multijunction cell performance. The performance of GaInNAs junctions demonstrated to date has been insufficient for the realization of these projected efficiency gains, owing to poor minority‐carrier properties in the GaInNAs. However, incremental improvements in the GaInNAs junctions have brought this breakeven point within sight. In this paper, we use a semiempirical approach to estimate the efficiency of the GaInP/GaAs/GaInNAs/Ge four‐junction solar cell structure as a function of the performance parameters of the GaInNAs third junction. The results provide guidance on the extent to which the current and voltage of present‐day GaInNAs junctions will need to be improved in order for the resulting four‐junction cell to realize its potential for efficiencies higher than that of GaInP/GaAs/Ge benchmark. Published in 2002 by John Wiley & Sons, Ltd.
Synthesis, structure and magnetic properties of graphite carbon encapsulated Fe3C nanoparticles for applications as adsorbents

This paper reports the synthesis of graphite carbon encapsulated Fe3C nanoparticles (Fe3C/GC NPs). Melamine is used as a carbon source for the generation of Fe3C, and hexadecyltrimethylammonium bromide (CTAB) is used as a templating agent for the generation of mesoporous carbon and conversion of Fe to Fe3C in the synthetic process. The Fe3C/GC NPs exhibited a high saturation magnetization value (61.5 emu g−1). Due to their mesoporous structure, the Fe3C/GC NPs are employed to remove methylene blue from waste water, and they exhibited excellent adsorption properties. The current synthetic approach is versatile and scalable, and thus opens the door towards the larger-scale synthesis of mesoporous and graphite carbon-based metal materials.
Thermodynamic Study of Benzamide, N-Methylbenzamide, and N,N-Dimethylbenzamide: Vapor Pressures, Phase Diagrams, and Hydrogen Bond Enthalpy

A static method was used to measure the vapor pressures of crystalline and liquid phases of benzamide, N-methylbenzamide, and N,N-dimethylbenzamide, in the temperature ranges (344.8 to 438.2) K, (322.9 to 388.4) K, and (297.0 to 361.7) K, respectively. The vapor pressures of the crystalline phase of benzamide were also measured using the Knudsen effusion method in the temperature range (324.1 to 344.2) K. From the experimental results, the standard molar Gibbs energies and enthalpies of sublimation and of vaporization, at T = 298.15 K, and the triple points coordinates (p,T) for the three compounds were derived. The temperatures and molar enthalpies of fusion were also determined using differential scanning calorimetry and were compared with the values derived from the vapor pressure measurements. The results enabled the determination of the hydrogen bond enthalpies in the studied compounds.
Thermal Degradation Behavior and Kinetic Analysis of Ultra High Molecular Weight Polyethylene Based Multi-Walled Carbon Nanotube Nanocomposites Prepared Via in-situ Polymerization

Thermal degradation behavior of multi-wall carbon nanotubes (MWCNTs)/ultra high molecular weight polyethylene (UHMWPE) nanocomposites, with different nanotubes contents (0.5, 1.5 and 3.5 wt%) prepared via in-situ polymerization technique have been investigated using thermal gravimetric analysis (TGA). TGA spectra revealed that these nanocomposites had enhanced thermal stability and no significant mass loss (<0.4 wt%) occurred up to 350°C. Thermal degradation of these UHMWPE/MWCNT nanocomposites was investigated in terms of parameters such as the onset temperature of degradation (T10), the decomposition temperature at 50% wt loss (T50), the degradation temperature of maximum rate of the weight loss (Tm), and the residual yields (WR) from TGA. The degradation activation energies (E) of virgin UHMWPE and its nanocomposites were estimated using the Friedman, the Ozawa, Flynn, and Wall (OFW), and the Kissinger's methods. Results indicated that the degradation activation energy for the virgin UHMWPE was 281.3 kJ/mol. The activation energy increased with increasing nanotube loading up to 1.5 wt% indicating that MWCNTs had a stabilizing effect on the degradation of the matrix. However, loadings of 3.5 wt% of nanotube or more could slightly decrease the activation energy. The decrease in the activation energy for degradation of nanocomposites with higher MWCNT concentrations might be attributed to the catalytic effects of nanotubes and polymerization catalyst residues. The “model fitting” method indicated a mechanism of n th-order auto-catalysis from the form of the conversion curves for UHMWPE/MWCNTs nanocomposites prepared via in-situ polymerization.
Oxidative Coupling and Reforming of Methane with Carbon Dioxide Using a High-Frequency Pulsed Plasma†

We studied the oxidative coupling and reforming of CH4 with CO2 to C2H4, CO, and H2 using a pulsed plasma with a pulse frequency ranging from 166 to 3050 PPS. The largest selectivity of C2H4 was 64% with 31% CH4 conversion and 24% CO2 conversion at 2920 PPS and 500 °C. Selectivities of CO and H2 were about 20% and 100%, respectively. Ratios of H2 to CO and (CO + CO2) were, respectively, 7.1 and 2.5, which are acceptable for methanol production from (CO + H2) and (CO2 + H2) over a catalyst. The results indicated that the pulsed plasma with a high frequency can promote conversion of CH4 and CO2. The energy efficiency of the pulsed plasma was improved using a high pulse frequency and a high reaction temperature. We suggested that a pulsed plasma with a high pulse frequency is useful for oxidative coupling and reforming of CH4 with CO2 in industry.
Comparison of backwashing with conventional cleaning methods in slow sand filters for small-scale communities

AbstractThe application of slow sand filtration technology for domestic water supply was investigated, with particular attention to the issue of periodic maintenance (cleaning the filter layer). The study used raw lake water with a high algal density to allow results to be obtained in a short time. Two filters were constructed with the same filter layer characteristics and were operated in parallel under the same conditions (filtration rate 0.16 m/h and hydraulic load variable maximum 0.80 m). Periodic maintenance was performed when the hydraulic load on the filtration layer reached 0.80 m. One filter was cleaned conventionally and the other by backwash. Nine filtration runs were performed, with an average duration of 14 d. Both filters provided significant improvements in water quality: filtered water turbidity less than 1 NTU, apparent color average of 15 Pt/Co, and removal of total coliforms and Escherichia coli around 1.5 log. In general, the quality of filtered water from the two filters was similar.
Evidence of Segregation of a Quinolone Antibiotic in Dipalmitoylphosphatidylcholine Environment

AbstractBiophysical techniques as Photon correlation spectroscopy (PCS), Diferential Scaning Calorimetry (DSC) and Epifluorescence and monolayer studies, were used to explore the interaction between the quinolone, 1-cyclopropyl-6-fluoro-l,4-dihydro-4-oxo-7-(N-3-methyl-piperazinyl)-3-quinoline carboxilic acid (CNV8912) and dipalmitoylphosphatidyl-choline (DPPC). CNV8912 caused a reduction in the size and polydispersity of the liposomes, and a reduction in the rate of change in these parameters near the usual pretransition temperature of DPPC. DSC showed that the quinolone could be incorporated initially in relatively large amounts into the DPPC bilayers, but repetitive cycling through the gel to liquid crystal phase transition led to a substantial exclusion of CNV8912 into separate domains. CNV8912 caused expansion of monolayers of DPPC, although CNV8912 alone did not spread well at the air-water interface. CNV8912 in monolayers of DPPC caused the production of more, smaller, condensed domains when the fil.
Ageing of poly(lactic acid) films plasticized with commercial polyadipates

BACKGROUND: Amorphous poly(lactic acid) (PLA) was plasticized with two polyadipates with different molar masses. Some physical properties were studied over time to evaluate the stability of these blends. The aim of this study was to improve PLA ductility and consider the feasibility of its use in flexible films for food packaging. RESULTS: The addition of polyadipates caused a decrease of the glass transition temperature (Tg) and an increase of PLA chain mobility. Samples with Tg values above the storage temperature suffered physical ageing with a reduction in free volume. All the unaged blends were mainly amorphous, but samples with Tg below the storage temperature developed crystallinity during ageing leading to phase separation. Ductile properties of films improved with plasticizer content immediately after blending, but there was a deterioration in such properties upon ageing due to matrix densification and crystallization of PLA chains. CONCLUSION: PLA can be efficiently plasticized by polyadipates and the results have shown that some of the prepared films remain flexible with no phase separation after 150 days. Copyright © 2009 Society of Chemical Industry
Deep Learning for Text Attribute Transfer on Auto Encoder Models

Natural Language Generation requires models that are able to fully grasp all connotations of the human language in order to generate new meaningful sentences. Current models can eﬀectively capture the grammatical structure and basic content of texts. However, the current state-of-the -art-models are not capable of understanding text speciﬁc attributes such as sentiments, politeness, humour and inclinations. This thesis aims to determine whether the current Au-toencoder models are suitable for capturing such text attributes. To that end, it will review and compare the performance of current Autoencoder models in terms of Text Style Transfer, the task of modifying a sentences attribute while preserving its content and ﬂuency. We argue that the nature of content and sentiment related information in a text are inherently diﬀerent. The content relates to what is said, whereas sentiment relates to how the message is conveyed, which implies a multitude of potential nuances. Therefore, understanding and generating sentiment, which is required for Text Style Transfer, is a more complex and intricate task. This thesis analyses the performance of four diﬀerent Autoencoder models on the task of Text Style Transfer, which is performed by three diﬀerent adversarial attacks on two datasets. The experiments showed, that all Autoencoder models, except for the VAE model delivered useful results. The SWAE model outperformed the others due to its ability to keep the perplexity low, while still achieving a high Style Transfer Accuracy. We conclude that while Autoencoder models, in general, are capable of capturing sentiments, the models diﬀer in their eﬃciency of transferring sentiment.
Exploring the Performance of Synthetic Minority Over-sampling Technique (SMOTE) to Predict Good Borrowers in P2P Lending

This study aims to identify good borrowers within the context of P2P lending. P2P lending is a growing platform that allows individuals to lend and borrow money from each other. Inherent in any loans is credit risk of borrowers and needs to be considered before any lending. Specifically in the context of P2P lending, traditional models fall short and thus this study aimed to rectify this as well as explore the problem of class imbalances seen within credit risk data sets. This study implemented an over-sampling technique known as Synthetic Minority Over-sampling Technique (SMOTE). To test our approach, we implemented five benchmarking classifiers such as support vector machines, logistic regression, k-nearest neighbor, random forest, and deep neural network. The data sample used was retrieved from the publicly available LendingClub dataset. The proposed SMOTE revealed significantly improved results in comparison with the benchmarking classifiers. These results should help actors engaged within P2P lending to make better informed decisions when selecting potential borrowers eliminating the higher risks present in P2P lending.
B EYOND T RIVIAL C OUNTERFACTUAL G ENERATIONS WITH D IVERSE V ALUABLE E XPLANATIONS

Explainability of machine learning models has gained considerable attention within our research community given the importance of deploying more reliable machine-learning systems. Explanability can also be helpful for model debugging. In computer vision applications, most methods explain models by displaying the regions in the input image that they focus on for their prediction, but it is difficult to improve models based on these explanations since they do not indicate why the model fail. Counterfactual methods, on the other hand, indicate how to perturb the input to change the model prediction, providing details about the model’s decision-making. Unfortunately, current counterfactual methods make ambiguous interpretations as they combine multiple biases of the model and the data in a single counterfactual interpretation of the model’s decision. Moreover, these methods tend to generate trivial counterfactuals about the model’s decision, as they often suggest to exaggerate or remove the presence of the attribute being classified. Trivial counterfactuals are usually not valuable, since the information they provide is often already known to the system’s designer. In this work, we propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model’s prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA and Synbols demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. We will make the code public.
FIRST DIRECT IMAGING AND PROFILING TOF-SIMS STUDIES ON CUTOUTS FOM CAVITIES PREPARED BY STATE-OF-THE-ART TREATMENTS*

Small amounts of interstitial impurities in the penetration depth of superconducting radio frequency (SRF) cavities have a dramatic effect on the quality factors and maximum accelerating gradients. Here we report the first TOFSIMS studies of cutouts from cavities prepared by all modern surface treatments, which allow a direct correlation of the impurity distribution with the observed cavity performance. Imaging capability of our instrument allows to avoid the possible issues associated with the ̀ `ghost'' depth profiles appearing as a consequence of particulate surface contamination, which likely caused the inconclusive SIMS results on e.g. oxygen diffusion in the past.
Overconfidence and the Diffusion of Medical Technology

A variety of models explain why new technologies such as hybrid corn, agricultural fertilizer, and new medical treatments diffuse so slowly. A common characteristic of these models is that agents (or countries) quickest to diffuse do so optimally because of relative gains from the new technology; the debate is around why others are so slow. In this paper, we develop a nested Bayesian model of diffusion and learning with heterogeneous agents that allows for overconfidence, which can cause early innovators to exhibit below-average productivity. We apply the model to the case of implantable cardiac defibrillators (ICDs), a medical device approved in 2005 to help prevent cardiac death in patients with weakened hearts (congestive heart failure). Using a unique clinical registry of every ICD implanted during 2006-13 linked to Medicare claims data, we find remarkable variations in the speed of diffusion across hospitals and regions. The structural model matches both aggregate moments, and individual hospitallevel trajectories, of mortality and utilization. We find that overconfidence raises mortality by 8% on average (and more among those most overconfident), and can explain roughly threequarters of variation in diffusion rates and risk-adjusted mortality. In addition, the model predicts, correctly, that the most overconfident hospitals are the ones that scale back quickest. These results suggest caution in equating rapid diffusion to productivity gains, particularly in health care. We are grateful for financial support from the National Institute on Aging (P01-AG19783 and U01-AG046830). In addition, we are indebted to Greg Roth, Peter Groeneveld, Kimon Bekelis, and to seminar participants at the World Bank, the University of Chicago, Princeton University, the University of Virginia, Emory University, NBER, and the Federal Reserve Bank of Chicago for very helpful comments and suggestions. Weiping Zhou provided essential programming and analysis.
Anaerobic Codigestion of Kitchen Waste with Cattle Manure for Biogas Production

Kitchen waste (KW), cattle manure (CM), and the mixture of KW and CM were anaerobically digested. The performances of single digestion with KW or CM and of codigestion with KW and CM were investigated and compared. Two loading rates of 10 and 20 g volatile solid (VS) L−1 were used for KW, CM, and their mixture digestion, respectively. NaOH was used as supplementary for KW, and sulfuric acid pretreatment was used for CM to explore the effects of alkalinity and acidification on methane production to verify the roles of codigestion. Scanning electron microscopy (SEM) was used to analyze the structural changes of CM fibers. The results showed that the codigestion of KW and CM increased methane yield by 44% as compared to the single digestion of KW, and the increase could be attributed to the synergistic effect in the codigestion process. A 32% more methane yield was achieved for the KW with NaOH addition than raw KW, which was due to increased alkalinity and buffering capacity. The methane yield and VS reduct.
Brazilian Group of Gastrointestinal Tumours’ consensus guidelines for the management of oesophageal cancer

Oesophageal cancer is among the ten most common types of cancer worldwide. More than 80% of the cases and deaths related to the disease occur in developing countries. Local socio-economic, epidemiologic and healthcare particularities led us to create a Brazilian guideline for the management of oesophageal and oesophagogastric junction (OGJ) carcinomas. The Brazilian Group of Gastrointestinal Tumours invited 50 physicians with different backgrounds, including radiology, pathology, endoscopy, nuclear medicine, genetics, oncological surgery, radiotherapy and clinical oncology, to collaborate. This document was prepared based on an extensive review of topics related to heredity, diagnosis, staging, pathology, endoscopy, surgery, radiation, systemic therapy (including checkpoint inhibitors) and follow-up, which was followed by presentation, discussion and voting by the panel members. It provides updated evidence-based recommendations to guide clinical management of oesophageal and OGJ carcinomas in several scenarios and clinical settings.
Auditory neural networks for attention prefer biologically significant sounds and exhibit sexual dimorphism in anurans

Allocating attention to biologically relevant stimuli in a complex environment is critically important for survival and reproductive success. In humans attention modulation is regulated by the frontal cortex, and often reflected by changes in specific components of the event-related potential (ERP). Although brain networks for attention modulation have been widely studied in primates and in avian species, little is known about attention modulation in amphibians. The present study aims to investigate the attention modulation networks in an anuran species, the Emei music frog (Babina daunchina). Male music frogs produce advertisement calls from within underground nest burrows that modify the acoustic features of the calls and both male and females prefer calls produced from inside burrows. We broadcast call stimuli to male and female music frogs while simultaneously recording electroencephalographic (EEG) signals from the telencephalon and mesencephalon. Granger causal connectivity analysis was used to elucidate functional brain networks within the time window of ERP components. The results show that calls produced from inside nests which are highly sexually attractive result in the strongest brain connections; both ascending and descending connections involving the left telencephalon were stronger in males while those in females were stronger with the right telencephalon. Our results indicate that the frog brain allocates neural attention resources to highly attractive sounds within the window of early components of ERP, and that such processing is sexually dimorphic presumably reflecting the different reproductive strategies of males and females. Keyword: auditory attention; brain network; Granger causal connectivity analysis (GCCA); sexual dimorphism; music frog Jo ur na l o f E xp er im en ta l B io lo gy • A cc ep te d m an us cr ip t