Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral Element-Based Multi-Physical Modeling Framework for Axisymmetric Wireless Power Transfer Systems

This paper concerns a multi-physical modeling framework based on the spectral element method (SEM) for axisymmetric wireless power transfer systems. The modeling framework consists of an electromagnetic and a thermal model. The electromagnetic model allows for eddy currents in source- and non-source regions to be included in the analysis. The SEM is a numerical method, which is particularly advantageous in 2D problems for which the skin-depth is several orders of magnitude smaller compared to the object dimensions and complex geometrical shapes are absent. The SEM applies high-order trial functions to obtain the approximate solution to a boundary-value problem. To that end, the approximation is expressed as an interpolation at a set of nodal points, i.e., the nodal representation. The trial functions are Legendre polynomials, which reduces the complexity of the formulation. Furthermore, numerical integration is performed through Gaussian quadratures. In order to verify the SEM, a benchmark system is modeled using both the SEM and a finite element-based commercial software. The differences in the SEM solutions, i.e., magnetic vector potential and temperature distribution, and the discrepancies in essential post-processing quantities are assessed with respect to the finite element solutions. Additionally, the computational efforts of both methods are evaluated in terms of the sparsity, number of degrees of freedom, and non-zero elements.
E-LEARNING ADOPTION DURING COVID-19 CRISIS AND ITS EFFECT ON ACHIEVING STUDENTS’ PERFORMANCE – EVIDENCE FROM BUSINESS COLLAGES – JORDANIAN UNIVERSITIES

This study aimed to know the effect of E-Learning with all its components on students’ performance at Business Colleges-Jordanian Universities during at COVID-19 crisis. To achieve study goals, a questionnaire was distributed to the students at Jordanian Universities, (870) valid questionnaires were recovered. The study found that: The level of E-Learning and its components at Jordanian Universities were at middle rates, students’ performance also comes at a moderate level. E-Learning and its components (Technological facilities readiness, Electronic applications readiness, Databases & E-resources readiness, People readiness, E-Learning management system readiness, Evaluation system readiness, teaching methods diversity, Educational curricula and electronic content readiness) have a significant effect on students’ performance at Jordanian Universities at significance (α ≤ 0.05). Whereas sub components of (E-Learning organizational environment readiness and legislations and regulations readiness) have no effect separately on students’ performance. The study recommended Jordanian Universities to improve E-Learning in all its components in the Universities, reinforce using E-Learning in education, and exploit the benefits of E-Learning to improve students’ performance because it is the main alternative to face emerging crises like Coronavirus.
Microirradiation techniques in radiobiological research

The aim of this work is to review the uses of laser microirradiation and ion microbeam techniques within the scope of radiobiological research. Laser microirradiation techniques can be used for many different purposes. In a specific condition, through the use of pulsed lasers, cell lysis can be produced for subsequent separation of different analytes. Microsurgery allows for the identification and isolation of tissue sections, single cells and subcellular components, using different types of lasers. The generation of different types of DNA damage, via this type of microirradiation, allows for the investigation of DNA dynamics. Ion microbeams are important tools in radiobiological research. There are only a limited number of facilities worldwide where radiobiological experiments can be performed. In the beginning, research was mostly focused on the bystander effect. Nowadays, with more sophisticated molecular and cellular biological techniques, ion microirradiation is used to unravel molecular processes in the field of radiobiology. These include DNA repair protein kinetics or chromatin modifications at the site of DNA damage. With the increasing relevance of charged particles in tumour therapy and new concepts on how to generate them, ion microbeam facilities are able to address unresolved questions concerning particle tumour therapy.
Observation of the Pinatubo Volcanic Cloud by Lidar Network in Japan

The latitudinal and vertical distribution of stratospheric aerosols injected by eruptions of Mt. Pinatubo on June 15, 1991 was obtained in Japan by a lidar network extending from Naha (26.2°N) to Wakkanai (45.4°N). Results from June to October 1991 are reported. An increase of aerosols origmating from the Pinatubo eruptions was observed first on June 28, 1991, at approximately 16km. The layer was observed continuously since then. The upper layer, above 20km, was observed first on July 15, thereafter it disappeared at all stations, but reappeared on August 6. The upper layer was sporadic, and not observed in July over Nagano, some 200km from Tsukuba, where it was observed. This indicates the non-uniform density of the cloud in the upper region.
ORIGINAL PAPER: SOL-GEL AND HYBRID MATERIALS FOR OPTICAL, PHOTONIC AND OPTOELECTRONIC APPLICATIONS

A propylene oxide (PO)-driven fast sol-gel route followed with a post high temperature calcination were utilized to synthesize CaxSr1-xAl2O4: Eu 2+ cationic solid solution phosphors with micro-spherical morphologies and improved internal quantum efficiencies. The polyethylene oxide (PEO) was also employed to induce the phase separation of CaxSr1-xAl2O4: Eu precursors to get monodispersed spherical morphologies in the sol-gel system. Benefit from uniform copolymerization of each chemical components and pyrolysis of organic matter, Ca, Sr, Al and O were homogenously distributed in the produced CaxSr1-xAl2O4: Eu 2+ cage like microspheres. It helps to efficiently trap incident light and to improve the PL efficiencies. By changing the ratio of Ca/Sr, infinite solid solution polycrystalline phosphors, CaxSr1-xAl2O4: Eu 2+ microspheres with x= 0~1, can be obtained and their PL emission can be gradually tuned from green to white to blue. Micro-spherical SrAl2O4: Eu , CaxSr1-xAl2O4: Eu 2+ and CaAl2O4: Eu 2+ phosphors exhibited internal quantum efficiencies as high as 71.1, 35.3~45.9 and 75.3%, respectively.
The city matters: urbanization, regional analysis and urban segregation in times of the COVID-19 pandemic

In early 2020, urban settlements worldwide have experienced the rapid expansion of severe acute respiratory syndrome (SARS-CoV-2). Cities were the hotspots of virus contamination within countries presenting significant COVID-19 notifications. In this study, we first investigate the COVID-19 dissemination between compact and sprawling big cities, examining aspects such as urban density, location of people and jobs and commuting patterns. Previous literature and recent data from three distinct big cities (New York, Los Angeles and Sao Paulo) support our discussion. Our literature review demonstrates that urban morphology, infrastructure, mobility projects and economic activities are relevant aspects of urban development that might affect interactions among citizens and COVID-19 dissemination. In addition, we show that regional scale and urban network analysis are also relevant in studies investigating COVID-19 growth. Finally, our literature review shows that urban socio-spatial vulnerability is also relevant in times of pandemic, highlighting the associations between COVID-19 dissemination and socio aspects within cities, such as poverty and inequality.
An exogenous fibrolytic enzyme mixture enhances in vitro ruminal degradability of red grape pomace by-product

Usefulness of red grape pomace (GP) by-product in ruminant rations is restricted by its high fibre content. Pre-treatment with exogenous carbohydrases may partly resolve this problem. Therefore, the effects of various levels of an exogenous fibrolytic enzyme mixture (Viscozyme ® L) on chemical nutrient content and in vitro ruminal fermentation of GP were studied. This product, with an enzyme activity of ≥100 fungal betaglucanase per gram (FBG/g) and a density of 1.2 g/mL, was applied to GP at 0 (ENZ0), 20 (ENZ20), 30 (ENZ30), 35 (ENZ35), and 45 g/kg DM (ENZ45). Neutral detergent fibre (NDF), acid detergent fibre (ADF) and insoluble condensed tannins (ICT) declined linearly as enzyme levels increased. Significant negative linear trends were observed for the rate of gas production at 36 and 48 hours, whereas quadratic trends were observed at 12and 24-hours incubation in response to enzyme levels. Cumulative gas production at 12 hours and 24 hours and in vitro organic matter degradability at 96 hours post incubation increased linearly as treatment levels increased. Effective gas production declined linearly (P =0.041; R 2 =0.085), whereas the slowly degradable fraction (P =0.042, R 2 =0.084), the fractional degradation rate (P =0.003, R 2 =0.175) and potential gas production (P =0.043, R 2 =0.083) showed quadratic trends as enzyme levels increased. Pretreatment with Viscozyme reduced the fibre levels of GP and increased in vitro ruminal organic matter degradability. The optimum level for inclusion of Viscozyme was 5 g/kg. ______________________________________________________________________________________
Monitoring 3D Changes in Urban Forests Using Landscape Metrics Analyses Based on Multi-Temporal Remote Sensing Data

Rapid urbanization is causing changes in green spaces and ecological connectivity. So far, urban ecosystem research has mainly focused on using landscape metrics (LM) in two-dimensional (2D) space. Our study proposes three-dimensional (3D) measures of urban forests (UF) and LM calculations using LiDAR technology. First, we estimated the UF volume of Krakow (Poland) and the distribution of vegetation (low, medium, high) using a voxel-based GEOBIA approach based on the ALS LiDAR point cloud, satellite imagery, and aerial orthophotos at specific timestamps: 2006, 2012, 2017. Then, the appropriate landscape metrics were selected (NP, AREA_MN, CONTIG_MN, LPI, PARA_MN, SPLIT, MESH, PD, DIVISION, LSI) to quantify the differences between the 2D- and 3D-derived vegetation structures and detect changes in the urban landscape. The results showed that areas with low vegetation decreased due to the expansion of built-up areas, while areas with medium and high vegetation increased in Krakow between 2006, 2012, and 2017. We have shown that the lack of information on the vertical features of vegetation, i.e., 2D greenery analysis, leads to an overestimation of landscape connectivity. In the 3D vegetation classes, it was observed that low vegetation was the best connected, followed by high vegetation, while medium vegetation was dispersed in the city space. These results are particularly relevant for the urban environment, where the distribution of green space is crucial for the provision of ecosystem services.
NDT_A_356414 1133..1143

Purpose: There is variation in the safety profile of antidepressants. Rates of adverse events along with the costs of treating them can be an important factor influencing the choice of depression treatment. This study sought to estimate the comparative safety of commonly prescribed antidepressants, and how the costs of treating these varied across European countries. Methods: A systematic literature review was conducted (in Medline, Embase, PsycINFO and CENTRAL) to identify placebocontrolled trials reporting rates of at least one type of sexual dysfunction, weight change, insomnia, anxiety, and anhedonia. Eight antidepressants were considered: duloxetine, escitalopram, fluoxetine, paroxetine, sertraline, trazodone, venlafaxine, and vortioxetine. This evidence was synthesised via Bayesian random effects network meta-analyses to provide comparative estimates of safety. A systematic search identified country-specific costs of managing depression and adverse events of antidepressants. Evidence on costs and safety was combined in an economic model to provide country-specific costs for Bulgaria, the Czech Republic, Greece, Hungary, Italy, Romania, Slovakia, Portugal, and Poland. Results: Trazodone had the lowest rates of both insomnia (odds ratio 0.66, 95% credible interval 0.31 to 1.38) and anxiety (0.13, <0.01 to 1.80). All antidepressants were associated with increased rates of sexual dysfunction relative to placebo. Weight change was largest for fluoxetine (kg change −1.01, −1.40 to −0.60) and sertraline (−1.00, −1.36 to −0.65), although heterogeneity was extreme for this outcome. No evidence was identified for anhedonia. Total costs were lowest for trazodone in all nine of the countries evaluated. This was primarily due to reduced rates of treatment discontinuation. Conclusion: Trazodone generally had the best safety profile of the antidepressants evaluated. This led to healthcare costs being lowest for trazodone in all nine European countries, emphasising the importance of considering rates of adverse events when choosing a pharmacological treatment to treat symptoms of depression.
On-Demand Collaboration in Programming

Many teams have shifted to online remote collaboration as a result of COVID-19, from professional development teams to programming classes to computing-related workspaces (e.g., makerspaces). This paper explores on-demand remote help seeking in programming, a type of collaboration that occurs when developers seek online support for their tasks as needed, and argues that a key challenge in scaling remote on-demand collaboration in programming is to facilitate effective context capturing and workforce coordination. Traditionally, this collaboration happens within teams and organizations where people are familiar with the context of the tasks. Recently, this collaboration has become ubiquitous due to the success of paid online crowdsourcing marketplaces (e.g., Upwork) and Q&A sites (e.g., Stack Overflow). We discuss prior work on on-demand collaboration in programming, analyze how the idea can be tested in physical computing as well, and examine existing and new challenges that should be further explored.
What are we PSIgning up for? Analyzing Applications of Two-Party Private Set Intersection

A private set intersection (PSI) protocol allows two (or more) parties, each with sets from a common domain, to learn the intersection of their sets, while being “assured” that no information about their sets beyond the intersection has been revealed to the other parties. The level of this “assurance” provided depends on the model that is used while proving security of the protocol. We review some of the common models of security for two-party protocols used in the literature, and relate them to each other via implications and separations. PSI protocols have found use in a wide variety of applications in diﬀerent walks of life, from measuring the eﬀectiveness of online advertisements and checking whether passwords have been compromised, to genomics and public health problems such as contact tracing. In this work, we look at a number of applications using two-party PSI protocols or common variants, and study the appropriateness of the applied security model to the problem at hand. A common trend in existing work is the use of the weaker semi-honest model of security. While this allows for more eﬃcient constructions, we point out, both via an explicit example and via a generic construction, how we can break security via minor deviations from the protocol speciﬁcation when a protocol is secure only against semi-honest adversaries.
Effect of hemoglobin, triglyceride, and urea in different concentrations on compatibility between methods used in HbA1c measurement

Abstract Objectives The variability between methods is seen in the existence of hemoglobin (Hb), triglyceride (TG), blood urea nitrogen (BUN) and glucose in the specimens, which can affect the measurement. Therefore, this study aimed to compare boronate affinity chromatography and turbidimetric inhibition immunoassay methods for glycosylated hemoglobin (HbA1c) levels in samples containing different levels of Hb, TG, BUN and glucose. Methods In this study, a total of 412 consecutive samples were used for method comparison. The samples were divided into groups according to HbA1c, TG, BUN, Hb, and glucose levels. Clinically significant changes for HbA1c are defined as ≥0.5%. Results The mean relative difference as % was determined as 1.4% by the Bland–Altman plot of Trinity against Roche. The Passing–Bablok analysis indicated good agreement (r=0.985) between two methods. When compared the group with and without a clinically significant difference, we found differences only between glucose levels (p=0.003). Conclusions High glucose levels may cause a difference between the methods. Therefore, we suggest that a method’s performance and properties should be considered prior to clinical usage.
The explicit formula for Gauss-Jordan elimination applied to flexible systems

Abstract Flexible systems are obtained from systems of linear equations by adding to the elements of the coefficient matrix and the right-hand side scalar neutrices, which are convex groups of (non-standard) real numbers. The neutrices model imprecisions, giving rise to calculation rules extending informal error calculus. Stability conditions for flexible systems are given in terms of relative imprecision and size of determinants. We then apply the explicit formula for the elements of the successive intermediate matrices of the Gauss-Jordan elimination procedure to find the solution of flexible systems, keeping track of the error terms at every stage. The solution respects the original imprecisions in the right-hand side and is the same as the one given by Cramer’s rule.
Combining traditional machine learning and anomaly detection for several imbalanced Android malware dataset's classification

As the number of mobile devices has exploded in recent years, so has the amount of advanced Android malware. Among the popular Android malware on the market today, click fraud malware, adware, banking Trojans, spyware, etc. are usually disguised and hidden in the heap of good Android applications. These advanced malwares lurk in the third-party application market trusted by users, and potentially endanger the security of the user's smart device causing privacy or economic loss. Therefore, this paper leverages and combines traditional machine learning and anomaly detection methods to detect specific classes of Android malware in three highly imbalanced Android datasets (entertainment + social app vs. click fraud malware + adware; financial services app vs. banking trojan; communication app vs. spyware). The experiment results show that our proposed combined methods have great performance on the three sub-datasets, achieving the average f1-score over 0.98 on three imbalanced datasets, which performs better than the traditional machine learning algorithm used alone. In addition, we use the combined methods to analyze the correlation between the top features of the dataset, and provide interpretable insights for other researchers focusing on Android malware classification in the coming future.
Comparison of different clustering methods applied to omics datasets

Nowadays, omics techniques have been widely used to study cancer and other related problems, but there are many cancer subtypes in a certain type of cancer which are unclear or even completely unknown, and thus unsupervised learning are only suitable approaches to address the problem. Cluster analysis refers to the analysis process of grouping data into multiple classes composed of similar data points. In this paper, six omics datasets are used to compare three clustering methods, in order to find a more suitable clustering method for omics datasets.
The exact complexity of pseudorandom functions and the black-box natural proof barrier for bootstrapping results in computational complexity

Investigating the computational resources we need for cryptography is an essential task of both theoretical and practical interests. This paper provides answers to this problem on pseudorandom functions (PRFs). We resolve the exact complexity of PRFs by proving tight upper and lower bounds for various circuit models. * PRFs can be constructed in 2n + o(n) size general circuits assuming the existence of polynomial-size PRFs, simplifying and improving the O(n) upper bound by Ishai, Kushilevitz, Ostrovsky, and Sahai (STOC 2008). Moreover, if PRFs exist in NC1, we can further guarantee the depth of our construction to be (1+є) logn. We show that our construction is almost optimal by giving an unconditional 2n−O(1) lower bound. * PRFs can be constructed in AC0[2] circuits of o(n) gates and 2n+o(n) wires assuming the existence of polynomial-size AC0[2] PRFs. We show the optimality of our construction with a 2n+Ω(√n) wire complexity lower bound. * PRFs can be constructed with wire complexity n1+O(1.61−d) in depth-d TC0 circuits assuming the existence of polynomial-size TC0 PRFs. We also present an n1+Ω(c−d) wire complexity lower bound against depth-d TC0 circuits for some c>1.61. As a byproduct, we prove unconditional tight upper and lower bounds for ”almost” universal hash functions that are of independent interest. Following the natural proof barrier of Razborov and Rudich (J. Comput. Syst. Sci. 1997), we observe that our exact complexity results are closely related to the ”bootstrapping phenomena” in circuit complexity (such as hardness magnification and quantified derandomization). We introduce the black-box natural proof barrier and show that a large range of techniques for bootstrapping results cannot be combined with ”black-box” lower bound proofs to obtain a breakthrough.
Graph-based Similarity for Document Retrieval in the Biomedical Domain

The growing amount of available data in the biomedical domain turns out to be beneficial for decision-making, but a sufficiently accurate DR system is required. Plenty of NLP techniques and models have been proposed for semantic similarity in DR, but few of them have been able to consider the variations of the language and relationship between distant words in texts. This work is focused on formulating a Graph-based Similarity for DR method (GBS-DR) for the biomedical domain and comparing the obtained results with traditional DR paradigms. The graph-based methods were selected to prove the importance of analyzing the semantic, syntactic, and long-distant word relationships in texts. It will be demonstrated that through the graph's topology the system can extract the structural information of documents, which solves relevant issues that are faced in this research area. CCS CONCEPTS • Information Systems • Information Retrieval • Retrieval Models and Ranking • Learning to Rank
Application of Weight-Based Feature Selection in Artificial Neural Network with Transition Layer

In the traditional artificial intelligence network model, the interpret ability of the model is often not strong due to the intricate associations between neurons, and the importance of input layer neurons in the model is difficult to be judged intuitively. This paper attempts to propose a new type of artificial neural network structure, which adds a transition layer between the input layer and the first hidden layer to enhance the interpret ability of the model for further feature selection. Simulated data set is applied to test the accuracy of feature selection and prediction, and a variant group lasso is added to improve the effect of feature selection. Result shows that the performance of new-structured artificial neural network improves in feature selection with the variant group lasso added.