Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk and Protective Factors of College Students’ Psychological Well-Being During the COVID-19 Pandemic: Emotional Stability, Mental Health, and Household Resources

Colleges and universities have increasingly worried in recent decades about college students’ well-being, with the COVID-19 pandemic aggravating these concerns. Our study examines changes to undergraduate emotional sentiments and psychological well-being from before to after the onset of the pandemic. In addition, we explore whether certain risk factors (i.e., prior mental health impairments, trait emotional stability) and protective factors (i.e., subjective socioeconomic status, parental education, household resources) predicted students’ emotions and their intraindividual changes due to the pandemic onset. We compared experience sampling method data from 120 students from before and after the pandemic onset, examining intraindividual trajectories. There was only little change in students’ emotions. Prior mental health impairment and trait emotional stability predicted students’ emotions, averaged across time points, but not emotion changes. Few associations with emotions were found for subjective socioeconomic status and parental education, but study-related household resources predicted levels and changes in emotions.
Flight Delay Prediction Using Gradient Boosting Machine Learning Classifiers

With the increasing of civil aviation business, flight delay has become a key problem in civil aviation field in recent years, which has brought a considerable economic impact to airlines and related industries. The delay prediction of specific flights is very important for airlines’ plan, airport resource allocation, insurance company strategy and personal arrangement. The influence factors of flight delay have high complexity and non-linear relationship. The different situations of various regions and airports, and even the deviation of airport or airline arrangement all have certain influence on flight delay, which makes the prediction more difficult. In view of the limitations of the existing delay prediction models, this paper proposes a flight delay prediction model with more generalization ability and corresponding machine learning classification algorithm. This model fully exploits temporal and spatial characteristics of higher dimensions, such as the influence of preceding flights, the situation of departure and landing airports, and the overall situation of flights on the same route. In the process of machine learning, the model is trained with historical data and tested with the latest actual data. The test result shows that the model and this machine learning algorithm can provide an effective method for the prediction of flight delay.
The European Union and Border Conflicts: The Transformative Power of Integration

Our article analyzes the impact of the European Union (EU) on border conflicts, in particular how integration and association are related to conflict transformation. We approach this issue from a theoretically as well as empirically grounded constructivist perspective. On this basis we propose a stage model of conflict development, based on the degree of securitization and societal reach of conflict communication. We argue that the EU can transform border conflicts and propose a four pathway-model of EU impact. This model comprises forms of EU impact that are, on the one hand, either actor-driven or indirectly caused by the integration process and have, on the other hand, as their main target either particular policies or the wider society in border conflict areas. We then apply this model to a comparative study of border conflicts, thereby analyzing the conflicts in Northern Ireland, Greece-Turkey, Cyprus, Europe's North (EU-Russia) and Israel-Palestine. We finish with a specification of the conditions of positive and negative EU impact.We are grateful to Gesa Bluhm, Olga Demetriou, Katy Hayward, Pertti Joenniemi, Kemal Kirisci, Yosef Lapid, Andrey Makarychev, David Newman, David Officer, Michelle Pace, Sergei Prozorov, Bahar Rumelili, Myria Vassiliadou, Jevgenia Viktorova, Tobias Werron, Antje Wiener, Haim Yacobi, and the reviewers and editors of this journal for their stimulating inputs, criticism, and support in the preparation of this article. Audiences at the Universities of Bielefeld, Hannover, and Osnabrück, Bilkent University (Ankara), the Viessmann Centre at Wilfrid Laurier University (Waterloo, Canada) and the copanelists at the BISA conferences 2002 and 2003, the ISA/CEEISA conference 2003, the ISA Annual Convention 2004, the ECPR Joint Session of Workshops 2004, and the ECPR SGIR Meeting 2004 have helped to shape and refine the arguments presented. We also thank Apostolos Agnantopoulos for his editorial assistance. This article builds on a research project on “The EU and Border Conflicts: The Impact of Integration and Association” (EUBorderConf), funded by a grant from the European Union's Fifth Framework Programme (SERD-2002-00144), with additional funding by the British Academy. See also 〈http://www.euborderconf. bham.ac. uk〉 for further information.
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning

Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.
Majority Based TAS/MRC Scheme in Downlink NOMA Network with Channel Estimation Errors and Feedback Delay.

The antenna selection (AS) in non-orthogonal multiple access (NOMA) networks is still a challenging problem since finding optimal AS solution may not be available for all channel realizations and has quite computational complexity when it exists. For this reason, in this paper, we develop a new suboptimal solution, majority based transmit antenna selection (TAS-maj), with significant reduction in computational complexity. The TAS-maj basically selects the transmit antenna with the majority. It is more efficient when compared to previously proposed suboptimal AS algorithms, namely max-max-max (A^3) and max-min-max (AIA) because these schemes are merely interested in optimizing the performance of the strongest and weakest users, respectively at the price of worse performance for the remaining users. On the other hand, the TAS-maj scheme yields better performance for more than half of mobile users in the NOMA networks. In this paper, we consider a multiple-input multiple-output communication system, where all the nodes are equipped with multi-antenna. Besides the TAS-maj is employed at the base station, a maximal ratio combining (MRC) is also employed at each mobile user in order to achieve superior performance. The impact of the channel estimation errors (CEEs) and feedback delay (FD) on the performance of the TAS-maj/MRC scheme is studied in the NOMA network over Nakagami-m fading channels.
ovel application of a quantitative spatial comparison tool to species istribution data sther

Comparing geographically referenced maps has become an important aspect of spatial ecology (e.g. assessing change in distribution over time). Whilst humans are adept at recognising and extracting structure from maps (i.e. identifying spatial patterns), quantifying these structures can be difficult. Here, we show how the Structural Similarity (SSIM) index, a spatial comparison method adapted from techniques developed in computer science to determine the quality of image compression, can be used to extract additional information from spatial ecological data. We enhance the SSIM index to incorporate uncertainty from the underlying spatial models, and provide a software algorithm to correct for internal edge effects so that loss of spatial information from the map comparison is limited. The SSIM index uses a spatially-local window to calculate statistics based on local mean, variance, and covariance between the maps being compared. A number of statistics can be calculated using the SSIM index, ranging from a single summary statistic to quantify similarities between two maps, to maps of similarities in mean, variance, and covariance that can provide additional insight into underlying biological processes. We demonstrate the applicability of the SSIM approach using a case study of sperm whales in the Mediterranean Sea and identify areas where local-scale differences in space-use between groups and singleton whales occur. We show how novel insights into spatial structure can be extracted, which could not be obtained by visual inspection or cell-by-cell subtraction. As an approach, SSIM is applicable to a broad range of spatial ecological data, providing a novel, implementable tool for map comparison. Publis © 2016 The Author(s).
Design of piezoresistive versus piezoelectric contact mode scanning probes

Microfabricated cantilever scanning probes are used for a variety of technological applications. Piezoresistivity and piezoelectricity are two transduction mechanisms that are commonly used to sense cantilever deflection. Despite their widespread use, their performance has not been directly compared to date. In this paper we present detailed models for the noise and sensitivity of both transducer types, design optimized cantilevers via numerical optimization for a variety of use cases, and compare their performance using a range of reported values in the literature. We find that piezoresistive sensing is advantageous for sub-micron thick cantilevers and moderate electrical power dissipation conditions, while piezoelectric sensing is preferred for thick cantilevers and power-limited applications. The wide variability in experimentally measured piezoelectric film properties is treated using the Monte Carlo method and can account for resolution variation of up to 30%, independent of geometric tolerances. We find that the preferred cantilever type depends heavily upon the fabrication and operating constraints. Accordingly, the modeling and optimization code is an open source and freely available. Importantly, we show that both piezoresistive and piezoelectric cantilevers are capable of sensing piconewton and sub-nanometer signals for a wide range of operating conditions with an appropriate design.
A stacked A-to-D converter for increased radar signal processor dynamic range

The need for increased dynamic range at the input to the radar digital signal processor has increased steadily over the last decade or so. This has been the result of lower expected radar cross sections, the need for better clutter suppression, and the desire to operate without sensitivity time control (STC) in some radar applications. The analog-to-digital converter (ADC) is the most significant bottleneck in achieving this needed dynamic range (DR). An approach for improving the effective DR utilizing multiple ADC is described. The ADC are arranged in parallel channels with different gains and the approach is referred to as a "stacked A-to-D converter", or "stacked ADC". Detailed results are presented for an experimental brassboard system assembled to demonstrate and evaluate the concept.
Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? - A computational investigation

Database hosting this document is corrupted and OSF is unable to fix it. Please refer to https://doi.org/10.34842/w3vw-s845 for openly available manuscript. ------- Previous abstract ------ Decades of research has studied how language learning infants learn to discriminate speech sounds, segment words, and associate words with their meanings. While gradual development of such capabilities is unquestionable, the exact nature of these skills and the underlying mental representations yet remains unclear. In parallel, computational studies have shown that basic comprehension of speech can be achieved by statistical learning between speech and concurrent referentially ambiguous visual input. These models can operate without prior linguistic knowledge such as representations of linguistic units, and without learning mechanisms specifically targeted at such units. This has raised the question of to what extent knowledge of linguistic units, such as phone(me)s, syllables, and words, could actually emerge as latent representations supporting the translation between speech and representations in other modalities, and without the units being proximal learning targets for the learner. In this study, we formulate this idea as the so-called latent language hypothesis (LLH), connecting linguistic representation learning to general predictive processing within and across sensory modalities. We review the extent that the audiovisual aspect of LLH is supported by the existing computational studies. We then explore LLH further in extensive learning simulations with different neural network models for audiovisual cross-situational learning, and comparing learning from both synthetic and real speech data. We investigate whether the latent representations learned by the networks reflect phonetic, syllabic, or lexical structure of input speech by utilizing an array of complementary evaluation metrics related to linguistic selectivity and temporal characteristics of the representations. As a result, we find that representations associated with phonetic, syllabic, and lexical units of speech indeed emerge from the audiovisual learning process. The finding is also robust against variations in model architecture or characteristics of model training and testing data. The results suggest that cross-modal and cross-situational learning may, in principle, assist in early language development much beyond just enabling association of acoustic word forms to their referential meanings.
Optimal reactive power support for distributed Micro Grid through PV-Grid connected inverter

Now a day, Distributed Generation in Micro Grids plays a major role in extracting energy at convenient places and distributing with much ease and efficiently. Under such circumstances maintaining the Voltage levels at the point of distribution or point of common coupling (PCC) is an important task as demanded by the loads. Hence balancing or sharing the real and reactive power among the connected inverters is a challenging task. Droop control strategies play a better role in sharing the power among the inverters, although in many circumstances maintaining the node voltages at the system network is an arduous task. In order to improve the voltage level at the distribution points/PCC of the inverters a simple technique is presented in this paper, which measures the deviation of the voltage level at PCC and feed accordingly the reactive power to boost the voltage. However, the real power sharing is undisturbed. The method is schemed and simulated using MATLAB Simulink environment. The simulated results are encouraging that the intended technique has very good efficacy in maintaining the voltage level and agreed with the expected results.
A Fractional-Order Kinetic Battery Model of Lithium-Ion Batteries Considering a Nonlinear Capacity

Accurate battery models are integral to the battery management system and safe operation of electric vehicles. Few investigations have been conducted on the influence of current rate (C-rate) on the available capacity of the battery, for example, the kinetic battery model (KiBaM). However, the nonlinear characteristics of lithium-ion batteries (LIBs) are closer to a fractional-order dynamic system because of their electrochemical materials and properties. The application of fractional-order models to represent physical systems is timely and interesting. In this paper, a novel fractional-order KiBaM (FO-KiBaM) is proposed. The available capacity of a ternary LIB module is tested at different C-rates, and its parameter identifications are achieved by the experimental data. The results showed that the estimated errors of available capacity in the proposed FO-KiBaM were low over a wide applied current range, specifically, the mean absolute error was only 1.91%.
The Effects of Brand Credibility and Prestige on Consumers Purchase Intention in Low and High Product Involvement

Given the importance and roles of brands and their component in the marketing literature, the objectives of this study was to examine the effect of brand credibility and prestige on consumers’ purchase intention under different levels of product involvement. In the research, roles of perceived value for money, Perceived quality, Perceived risk, Information cost saved investigate as moderating role between brand credibility , prestige and purchase intention. For the high and low involvement product categories, an initial pool of four product categories was selected that Mobil was high involvement and chewing gum was low involvement. Results of structural equation modeling from samples of high involvement product category (Mobil) and low involvement product category (chewing gum) identified Personal Involvement Inventory model indicated that brand credibility and brand prestige have positive impact on perceived value for money, perceived quality and information costs saved but a negative impact on perceived risk under low and high involvement product category. Overall, compared with brand prestige, brand credibility was significantly more effective in influencing brand purchase intention process when the product is low involvement.
MRI-Evaluation of Suprascapular Notch Morphometry and Its Clinical Applications

The suprascapular notch, a depression on the lateral part of the superior border of the scapula, medial to the coracoid process, is covered by the superior transverse scapular ligament, which is converted into a foramen. Sometimes it might be ossified forming a complete osseous foramen. The Suprascapular Notch (SN) served as a passage for the Suprascapular Nerve (SSN). Study the morphology of the suprascapular notch and the suprascapular transverse ligament based on MRI, variations in shape and dimensions of the suprascapular notch. A group of 100 patients underwent MRI examination of the scapular region through the period from 10th July 2019 to 15th Feb 2020. Different morphological types of the suprascapular notch were encountered in the study, the most common type was type III, while type I was less common in the study. The symmetry of the morphological feature of SN bilaterally was seen in 51% of the cases. Conclusively, the symmetry of the suprascapular notch is not a constant feature bilaterally.
Deep domain models for discourse analysis

The authors address the problem of discourse analysis, and in particular finding causal relations between facts mentioned in messages, using a detailed domain model. This work is part of the PROTEUS (Prototype Text Understanding System) project, whose objective is to understand short narrative message about equipment installed in Navy ships. Casualty reports (CASREPs) describe failures of this equipment, together with maintenance actions performed by the crew onboard. To capture the domain knowledge, the authors built a model of the equipment installed on the ship (initially, the starting air system) and demonstrated is use in several aspects of language understanding. The PROTEUS system has been substantially implemented and debugged and has been publically demonstrated operating on a small set of actual CASREPs. <<ETX>>
Quartic trigonometric B-spline algorithm for numerical solution of the regularized long wave equation

In this paper, an application of the quartic trigonometric B-spline finite element method is used to solve the regularized long wave equation numerically. This approach involves a Galerkin method based on the quartic trigonometric B-spline function in space discretization together with second and fourth order schemes in time discretization. The accuracy of the proposed methods are demonstrated by test problems and numerical results are compared with the exact solution and some previous results.
Return range and the cross-section of expected index returns in international stock markets

This study examines the cross-sectional relation between return range and future returns for the first time in literature. We show that the return range can serve as a very practical measure of total volatility instead of standard deviation due to the range's high correlation with standard deviation and strong predictive ability. Range, standard deviation, and idiosyncratic volatility are cross-sectionally linked to future returns on indexes of small size, while earnings-to-price ratio and net share issuance predict returns of mid-cap and large-cap indexes, respectively. Maximum and minimum return effects along with the momentum effect are prevalent in returns of indexes of any size but stronger for small-cap indexes.
Multi-vehicle Collisions involving Large Trucks on Highways: An Exploratory Discrete Outcome Analysis

Trucking industry is considered a driving force for logistic and supply chain systems which indirectly influences the national economy. So, any impedance in truck-flow or supply chain system eventually brings substantial consequences in terms of monetary values. As such, a growing concern related to large-truck (Gross Vehicle Weight Rating (GVWR) greater than 10,000 pounds) crashes has increased in recent years due to the potential economic impacts and level of injury severity sustained. With this in mind, this study aims to analyze the injury severities of multi-vehicle collisions involving large-trucks through an advanced econometric modeling approach to shed light on the contributing factors leading to large-truck crashes. Through a fused national crash datasets, we hope to provide a clearer understanding of the complex interactions of contributing factors (e.g., factors related to human (drivers), vehicle, and road-environment) influencing multi-vehicle crash outcomes. To capture these complexities using the national crash database and understand the underlying causal factor, discrete outcome models namely random parameter ordered probit and mixed logit (which accounts for observable factors) were estimated to predict the likelihood of five injury severity outcomes—fatal, incapacitating, non-incapacitating, possible injury, and no injury. Estimation findings indicate that the level of injury severity is highly influenced by a number of complex interactions of factors and that the effect of the some of the factors can vary across the observations.