Miracle-dz/UniFAR-FTUs huggingface dataset:
Min/max stability and box distributions

In representation learning, capturing correlations between the represented elements is paramount. A recent line of work introduces the notion of learning region-based representations, with the ob-jective of being able to better capture these correlations as set interactions. Box models use regions which are products of intervals on [0 , 1] (i.e., “boxes”), representing joint probability distributions via Lebesgue measure. To mitigate issues with training, a recent work models the endpoints of these intervals using Gumbel distributions, chosen due to their min/max-stability. In this work we analyze min/max-stability on a bounded domain and provide a speciﬁc family of such distributions which, replacing Gumbel, allow for stochastic boxes embedded in a ﬁnite measure space. This allows for a latent noise model which is a probability measure. Furthermore, we demonstrate an equivalence between this region-based representation and a density representation, where intersection is given by products of densities. We compare our model to previous region-based probability models, and demonstrate it is capable of being trained effectively to modeling correlations.
Expected Performance of the EUSO-SPB2 Fluorescence Telescope

The Extreme Universe Space Observatory Super Pressure Balloon 2 (EUSO-SPB2) is under development, and will prototype instrumentation for future satellite-based missions, including the Probe of Extreme Multi-Messenger Astrophysics (POEMMA). EUSO-SPB2 will consist of two telescopes. The first is a Cherenkov telescope (CT) being developed to identify and estimate the background sources for future below-the-limb very high energy (E>10 PeV) astrophysical neutrino observations, as well as above-the-limb cosmic ray induced signals (E>1 PeV). The second is a fluorescence telescope (FT) being developed for detection of Ultra High Energy Cosmic Rays (UHECRs). In preparation for the expected launch in 2023, extensive simulations tuned by preliminary laboratory measurements have been performed to understand the FT capabilities. The energy threshold has been estimated at 10$^{18.2}$ eV, and results in a maximum detection rate at 10$^{18.6}$ eV when taking into account the shape of the UHECR spectrum. In addition, onboard software has been developed based on the simulations as well as experience with previous EUSO missions. This includes a level 1 trigger to be run on the computationally limited flight hardware, as well as a deep learning based prioritization algorithm in order to accommodate the balloon’s telemetry budget. These techniques could also be used later for future, space-based missions.
Evaluation of two-dimensional dosimeter based on thallium (I) bromide for measurement of surface dose in radiotherapy

In radiotherapy, side effects can occur on the skin because each patient's skin has a different level of sensitivity to radiation. Therefore, in clinical practice, point dose is measured to verify skin doses, although there is a problem, in that it is difficult to analyse the dose distribution for the body surface area. In this study, we constructed a film dosimeter based on polycrystalline thallium (I) bromide, evaluated its performance, and analysed the electrical properties according to the number of bending cycles to evaluate the mechanical flexibility. Further, an array dosimeter was constructed, and the dose distribution was analysed to evaluate the clinical effectiveness. As a result of the performance evaluation, reproducibility, linearity, and dose-rate dependence all satisfied the evaluation criteria. Percent depth dose showed similar results, and a difference of 0.027 when the slope of the depth in the range of 20–10 cm was compared with that of the thimble chamber. With regard to mechanical flexibility, as the number of bending cycles increased, the sensitivity tended to decrease. The clinical efficacy evaluation showed that the difference in dose distribution between the boundary field and non-field was approximately ≥ 40%, which decreased exponentially as the distance from the boundary field increased. The gap dose distribution showed an average difference of approximately ≥ 47% compared with the beam profile central axis. This suggests the possibility of application as a radiotherapy digital dosimeter, as well as the possibility of analysing the dose distribution on the body surface.
Photoactive layer formation in the dark for high performance of air-processable organic photovoltaics

Recent progress in organic photovoltaics (OPVs) has led to an increased importance of laboratory-scale fabrication in ambient air using solution processes. However, the effect of the existence of both ambient air and light during the formation of a photoactive layer on the performance of fabricated devices has not been elucidated thus far in detail. Here, we show that photoactive layer formation in completely dark conditions enables air-processable OPVs with a high power conversion efficiency. The degradation in OPV performance caused by the coexistence of air and room light was confirmed by systematically examining atmospheric and room-light irradiation conditions during the formation and drying of the photoactive layer. Moreover, the degradation rate was much faster than that in the case of dried solid photoactive layers exposed to room light in ambient air. The photoactive layer with non-fullerene acceptors showed a much slower degradation rate, owing to room light, than that with fullerene acceptors. Based on these findings, we demonstrate that by eliminating light during formation, slot-die-coated OPVs in ambient air show comparable performance to that of spin-coated OPVs in an inert glovebox.
Simulation of 28 Element Ultrasound Transducer for Non-Destructive Evaluation (NDE)
Applications

Quantitative ultrasound based Non-Destructive Evaluation (NDE) is one of the leading test procedures used in testing of finished products in manufacturing industry. It can detect surface as well as subsurface flaws accurately. It is also employed in medical diagnostic for various purposes. The main objective of this paper is to simulate 28 element ultrasound transducer pair for through transmission testing procedure. The simulation is done using k-Wave toolbox (open source toolbox) and MATLAB. The simulation results show that the received signal strength and resolution is far more improved over single and dual element transducer probes available in the market. The simulation of wave propagation through three layered aluminum material is also shown in the results. Based on results, it can be concluded that 28 element transducer pair are the optimum solution for NDE evaluation challenges with reference to cost, size, and design complexity.
Development of heart attack prediction model based on ensemble learning

With the advent of the data age, the continuous improvement and widespread application of medical information systems have led to an exponential growth of biomedical data, such as medical imaging, electronic medical records, biometric tags, and clinical records that have potential and essential research value. However, medical research based on statistical methods is limited by the class and size of the research community, so it cannot effectively perform data mining for large-scale medical information. At the same time, supervised machine learning techniques can effectively solve this problem. Heart attack is one of the most common diseases and one of the leading causes of death, so finding a system that can accurately and reliably predict early diagnosis is an essential and influential step in treating such diseases. Researchers have used various data mining and machine learning techniques to analyze medical data, helping professionals predict heart disease. This paper presents various features related to heart disease, and the model is based on ensemble learning. The proposed system involves preprocessing data, selecting attributes, and then using logistic regression algorithms as meta-classifiers to build the ensemble learning model. Furthermore, using machine learning algorithms (Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting) for prediction on the Framingham Heart Study dataset and compared with the proposed methodology. The results show that the feasibility and effectiveness of the proposed prediction method based on group learning provide accuracy for medical recommendations and better accuracy than the single traditional machine learning algorithm.
Assessment of Bread Wheat Genotypes Under Normal Irrigation and Water Stress

Thirty-two bread wheat genotypes included two local cultivars, ten Egyptian selected line, and twenty advanced inbred line from (CIMMYT) were grown in two experiments, i.e. normal irrigation under water stress during the 2016/2017 and 2017/2018 seasons in Agronomy Dept. Research Station, Faculty of Agriculture, Assiut University. Mean squares due to irrigations and genotypes were significant for all traits i.e. plant height, spike length, number of spikes/row, number of spikelets/spike, grain yield/row and 100 grain weight in each season and over the two seasons; suggesting that all traits were markedly affected by water stress and the presence of a wide range of differences among genotypes. The variance due to interaction between genotypes and irrigation (GxI) was significant for all studied traits. Mainly, the drought stress caused a relative loss in spike length, number of spikes/row, number of spikelets/spike, grain yield/row and 100 grain weight by 8.53, 23.62, 5.51, 35.81 and 10.83%, respectively, as average of two years. The drought susceptibility index (DSI) was the powerful tool for discriminate the superior genotypes under water stress. Subsequently, the examined wheat genotypes were classified according to DSI into two groups: those were less than unity, there were tolerant to drought, while, those the DSI large than unity, there were susceptibility to drought. Some of these genotypes differed from season to season for (DSI). The superior genotypes using DSI were Giza 168 cultivar, C137 and MKI-19 and there were tolerant to drought stress. Finally the obtained results could propose the drought susceptible index as a good tool to select the most adapted genotypes under water stress condition.
New cross section determination for secondary cosmic ray electron and positrons in the light of new data from collider experiments

Dipartimento di Fisica, Università di Torino, Via P. Giuria 1, Torino, Italy Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, 10125 Torino, Italy The Oskar Klein Centre, Department of Physics, Stockholm University, AlbaNova, SE-10691 Stockholm, Sweden 2Institute for Theoretical Particle Physics and Cosmology, RWTH Aachen University, Sommerfeldstr. 16, 52056 Aachen, Germany E-mail: luca.orusa@edu.unito. it
Evaluation of Applied Machine Learning for Health Misinformation Detection via Survey of Medical Professionals on Controversial Topics in Pediatrics

In this research, we present an evaluation of a system for detection of health misinformation using applied machine learning. The system incorporates computing automation, information retrieval, and natural language processing in conjunction with evidence-based medicine to generate a veracity score based on consensus from trusted medical knowledge bases. For our study, we pre-computed the veracity scores of controversial topics in pediatrics with our proposed system, and then also solicited evaluations of these topics from medical professionals in the neurodevelopmental field via a quantitative survey. Hence, this work provides a double-blind comparison on the veracity of medical claims between our proposed system's results and medical professionals' responses. The results showed that our system's automated assessment matched professional opinions of medical personnel with 80% precision. The survey also demonstrated the inherent challenge with health misinformation detection, as there was no consensus among the medical professionals for 50% of the controversial statements. Nevertheless, this evaluation shows promising results for using objective trust metrics such as the veracity score, in contrast with subjective trust metrics that rely on potentially biased crowdsourcing, ratings, and pre-trained labelling of data.
Cross-Reactivity, Epitope Mapping, and Potency of Monoclonal Antibodies to Class 5 Fimbrial Tip Adhesins of Enterotoxigenic <named-content content-type='genus-species'>Escherichia coli</named-content>

Enterotoxigenic Escherichia coli (ETEC) is a leading diarrheagenic bacterial pathogen among travelers and children in resource-limited regions. Adherence to host intestinal cells mediated by ETEC fimbriae is believed to be a critical first step in ETEC pathogenesis. These fimbriae are categorized into related classes based on sequence similarity, with members of the class 5 fimbrial family being the best characterized. The eight related members of the ETEC class 5 fimbrial family are subdivided into three subclasses (5a, 5b, and 5c) that share similar structural arrangements, including a fimbrial tip adhesin. However, sequence variability among the class 5 adhesins may hinder the generation of cross-protective antibodies. To better understand functional epitopes of the class 5 adhesins and their ability to induce intraclass antibody responses, we produced 28 antiadhesin monoclonal antibodies (MAbs) to representative adhesins CfaE, CsbD, and CotD, respectively. We determined the MAb cross-reactivities, localized the epitopes, and measured functional activities as potency in inhibition of hemagglutination induced by class 5 fimbria-bearing ETEC. The MAbs’ reactivities to a panel of class 5 adhesins in enzyme-linked immunosorbent assays (ELISAs) revealed several reactivity patterns, including individual adhesin specificity, intrasubclass specificity, intersubclass specificity, and class-wide cross-reactivity, suggesting that some conserved epitopes, including two conserved arginines, are shared by the class 5 adhesins. However, the cross-reactive MAbs had functional activities limited to strains expressing colonization factor antigen I (CFA/I), coli surface antigen 17 (CS17), or CS1, suggesting that the breadth of functional activities of the MAbs was more restricted than the repertoire of cross-reactivities measured by ELISA. The results imply that multivalent adhesin-based ETEC vaccines or prophylactics need more than one active component to reach broad protection.
A BRIEF REVIEW OF METAL OXIDE (HYDROXIDE)-GRAPHENE NANOCOMPOSITES SYNTHESIS BY LAYER-BY-LAYER DEPOSITION FROM SOLUTIONS AND SYNTHESIS OF CuO NANORODS-GRAPHENE NANOCOMPOSITE

Principle conditions for layer-by-layer synthesis of nanocomposite layers incorporating graphene and metal oxides or hydroxides are formulated in the review. Practical applications of such nanocomposites in the electrodes of lithium-ion batteries, supercapacitors, or electrochemical sensors, systems of water purification and environmental control, photocatalysts, etc, are considered. As an example of this method the synthesis of nanocomposite containing graphene nanosheets and CuO nanorods is described. This material was first synthesized in our study using Cu[NH 3 ] 4 (OAc) 2 solution and water suspension of graphene. Analysis of the synthesized thin films by SEM, XRD, and Raman spectroscopy have shown that the size of the graphene nanosheets in the films is about 2 m and the size of the CuO nanorods is approximately 10-12 by 150-200 nm. This nanocomposite layers were deposited on the surface of nickel foam and their electrochemical properties were studied for application in supercapacitor electrode. It was shown that the capacitance of the supercapacitor is 700 F/g, and its value after 500 cycling is reduced by 6% only. Also some recommendations are given concerning potential applications of these layers in the electrodes of electrocatalysts or electrochemical sensors.
A Deep Learning Approach to Multi-Channel and Multi-Microphone Acoustic Echo Cancellation

Building on deep learning based acoustic echo cancellation (AEC) in the single-loudspeaker (single-channel) and single-microphone setup, this paper investigates multi-channel (multi-loudspeaker) AEC (MCAEC) and multi-microphone AEC (MMAEC). A convolutional recurrent network (CRN) is trained to predict the near-end speech from microphone signals with far-end signals used as additional information. We ﬁnd that the deep learning based MCAEC approach avoids the non-uniqueness problem in traditional MCAEC algorithms. For the AEC setup with multiple microphones, rather than employing AEC for each microphone, we propose to train a single network to achieve echo removal for all microphones. Combining deep learning based AEC with supervised beamforming further improves the system performance. Experimental results show the effectiveness of deep learning approach to MCAEC and MMAEC. Furthermore, deep learning based methods are capable of removing echo and noise simultaneously and work well in the presence of nonlinear distortions.
Electronic Structure of Manganese Phthalocyanine Modified via Potassium Intercalation: a Comprehensive Experimental Study

Potassium (K) intercalated manganese phthalocyanine (MnPc) reveals vast changes of its electronic states close to the Fermi level. However, theoretical studies are controversial regarding the electronic configuration. Here, MnPc doped with K was studied by ultraviolet, X-ray, and inverse photoemission, as well as near edge X-ray absorption fine structure spectroscopy. Upon K intercalation the Fermi level shifts toward the lowest unoccupied molecular orbital filling it up with donated electrons with the appearance of an additional feature in the energy region of the occupied states. The electronic bands are pinned 0.5 eV above and 0.4 eV below the Fermi level. The branching ratio of the Mn L3 and L2 edges indicate an increase of the spin state. Moreover, the evolution of the Mn L and N K edges reveals strong hybridization between Mn 3d and N 2p states of MnPc and sheds light on the electron occupation in the ground and n-doped configurations.
Examining the relationship between university students' social media addiction and goal commitment

This study aims to examine the relationship between university students' goal commitment and social media addiction. The participants consist of undergraduate and graduate students studying in the 2019-2020 period. Total of 697 university students, including 406 (58.2%) women and 291 (41.8%) men were joined in the study. The online questionnaire used in the study consisted of 3 sections and 20 questions. The personal information form, the goal commitment scale with 5 items, which were developed by Klein et al. (2001), and a social media addiction scale consisting of 7 items developed by Günüç (2009) were used. The scale has a 5-point Likert type scale structure. The internal consistency of the goal commitment scale is 0.74. The coefficient of the scale (Cronbach’s Alpha) is 0.85. According to the results, there was a significant difference between the participants' goal commitment (F (3-693) = 2.618; p > 0.05) and age variable scores between the ages of 21-23 and those between the ages of 24-26 in favor of the participants between the ages of 24-26 (p < 0.05). There was a significant difference between the social media addiction (F (4-692) = 3.985; p > 0.05) scores of the participants and their monthly income levels. According to the results of the analysis, the significance was seen between those who had 6500 TL and above, and those who had 0 to 1500 TL. There was no statistically significant difference between the participants' social media scores and goal commitment scores (p > 0.05). It was seen that there was a statistically negative relationship between social media addiction and goal commitment. In other words, as the level of social media addiction increased, students' commitment to their goals decreased.
On future occupational safety and health challenges in industrialized countries — a systematic literature review

Occupational safety and health (OSH) have constantly changed and evolved throughout the industrial revolutions and in recent years. The aim of this systematic literature review was to give an overview of future OSH challenges in industrialized countries. A search within the Scopus database returned 31 peer-reviewed journal articles published since 2014 on new and emerging OSH risks and challenges in the future. A review of articles identified future overall OSH challenges, OSH challenges caused by climate change, and OSH challenges related to sustainable development and responsibility. In addition, branch-specific challenges in industries and health care were determined. Individual employees can experience a wide range of OSH challenges from heat stress to poison absorption, musculoskeletal disorders, increased psychosocial load, ergonomic issues, and unexpected accidents and injuries. OSH management is discussed and future studies proposed.
High efficiency slotted photonic crystal waveguides for the determination of gases using mid-infrared spectroscopy

ABSTRACT We present the design and analysis of a high efficiency slotted photonic crystal waveguide for gas sensing applications in the mid-infrared wavelength range. We designed the slotted photonic crystal waveguide structure by engineering the interfaces between the input and output slot waveguides and a resonant coupler. Coupling and transmission spectra of the sensor have been modeled using the three-dimensional finite-difference time domain method. The sensing principle is based on the shift of the upper band edge of the sensor output transmission spectrum which arises due to changes in the ambient refractive index. Transmission spectrum and sensitivity of the proposed sensor are analyzed by tuning the radii of the air holes localized on each side of the slot. The results show that a change of the refractive index of the gas by 10−4 leads to a shift of the upper band edge wavelength by 540 pm which corresponding to a sensitivity of 1720 nm per refractive index unit. The proposed design may be an ideal platform for developing mid-infrared gas sensing devices characterized by high coupling efficiencies and high sensitivities.
Sistem Informasi Peminjaman Laboratorium pada Cross-Platform dengan Metode Prototyping (Studi Kasus: Politeknik Caltex Riau)

Received 2020-01-13 Revised 2020-02-19 Accepted 2020-04-01 The Laboratory information system is one of the information systems that was built by the BSTI of Polytechnic Caltex Riau to provide information about laboratory lending. This system is also being intended to the person who is in charge of approving laboratory lending such as the head of the laboratory, and laboratory assistant in case of monitoring some of the laboratories that are lent. However, the features provided by this information system are not enough to support the process of laboratory lending. Therefore, a mobile application with the quality of CrossPlatform was built, in other words, it can be used on iOS and Android platforms. One of the technologies that can be used to build a cross-platform application is the React Native. This application was built with a prototype method that is used to get more perspective points from users with interviews and can simplify the development of this application to be effective. This application was developed with Laravel, Firebase Authentication, and MySQL. Because there are several problems with the old system, an application that offers several solutions consists of avasisten instruktur laboratoriumbility in the lending laboratory, receiving legal paper of laboratory lending, and receiving notification were developed in this study. The main feature provided in this application is the evidence letter of laboratory lending that doesn’t have to be printed so that can reduce paper usage. As a result, 94.1% of respondents stated that this application’s features can run well and make it easy for college students to propose laboratory lending. Keyword: