Miracle-dz/UniFAR-FTUs huggingface dataset:
Experiences of the Transgender and Gender Non-Conforming Community in the University Setting

Background: Transgender and gender non-conforming individuals face daily challenges in higher education due to a lack of gender-inclusive resources. This shortcoming is rooted in the minimal knowledge about what barriers affect this population. Aims: The aims of this study were to identify fitness-related barriers faced by the transgender community in higher education and emphasize the need to develop fitness standards for the transgender population to reduce those barriers in higher education. Methods: Participants completed one or both aspects of this study. A survey was sent to students to gather information about experiences in fitness at a university and how it relates to their gender. Several of the survey respondents then agreed to participate in a series of fitness assessments to evaluate muscular strength, endurance, and body composition. Results: Seventy-nine students responded to the survey (n = 68 cisgender females, n = 5 cisgender males, n = 2, non-binary, n = 4 transgender males). Proportionally, more transgender and non-binary people (84%) felt that gendered fitness standards did not apply to them when compared to cisgender people (31%). Further, more transgender and non-binary respondents (50%) reported that they were not comfortable working out in all areas of their gym when compared to the cisgender respondents (37%). Twenty-eight students participated in the fitness assessment. The BOD POD assessment of body composition utilizes gender to calculate body fat percentage. When using both male and female classifications, results were not different, meaning the estimated body fat percentage was calculated as the same despite a different gender in the calculation. Conclusions: This study begins to identify fitness-related barriers faced by the transgender community in higher education. Additionally, these small-scale results provide a starting point for future research on developing gender-neutral fitness standards.
Une Méthode Hybride pour le Problème de Tournées de Véhicules avec Contraintes de Temps

L'objectif de ce travail est de proposer une methode exacte pour resoudre un ensemble de problemes de tournees de vehicules. Une nouvelle formulation est proposee et resolue en utilisant des methodes d'acceleration. Ces methodes se basent sur des heuristiques primitives (primal heuristics), des tests de faisabilite ainsi que des bornes inferieures dediees. Les resultats compares avec les anciennes formulations ou les solveurs standards montrent l'efficacite de ce type d'hybridation.
CPs Move Rightward, Not Leftward

Several recent proposals hold that CP complements appear rightmost in many languages by a two-step leftward movement process: first the CP moves leftward, and then a remnant phrase carries all other material to the left of that moved position. I show here that this analysis faces insurmountable problems. In contrast, a simple rightward movement analysis explains all the facts. Importantly, if binding is computed on the basis of precede-and-command rather than c-command (Bruening 2014), the rightward movement analysis accounts for all binding facts. The remnant movement analysis fails to account for binding, since apparent rightward movement does not pattern with clear cases of remnant movement like partial VP fronting. Furthermore, there is a leftward-rightward asymmetry in syntactic category when CPs undergo movement. I show that it is difficult to capture this in a theory that only has leftward movement. I also address preposition stranding in English, which Moulton (2015) presents as an argument for the leftward movement analysis. The results of a large-scale survey reveal that there is actually significant speaker variability in whether prepositions can be stranded when CPs move to the right. I spell out a non-grammatical account of this variability within the rightward movement analysis. The overall conclusion is that rightward movement is not effected by a two-step leftward movement process, and we need a theory of grammar that allows movement upward and to the right.
Development and validation of an implantable sensor for measuring activity and body temperature for long-term studies in small wild and laboratory animals

ABSTRACT In the present study, accelerometer data obtained from a sensor prototype and data obtained from commercial animal-activity recording systems were recorded simultaneously and processed to identify the parameters most fully describing the locomotor activity of laboratory mice. A linear combination of the found parameters explained up to 66% of the variance of activity measured by the PhenoMaster system and up to 86% of the variance of activity measured by the LABORAS system, with 20-min averaging of the parameters measured every minute. Correlation coefficient between body temperature and activity measured by the prototype was higher than that for activity measured by the PhenoMaster system. It has been demonstrated that the developed sensor can be used to assess an animal’s locomotor activity at least with 20-min averages, and when powered by a 5 mAh battery with a total sensor weight of 0.9 g, the resulting runtime is expected to exceed 6 months effectively making it a data logger suitable for long-term field research.
Design and Optimization of High-Gain Bidirectional DC–DC Converter for Electric Vehicles

To better leverage and cope with the characteristics of low-voltage electric vehicle battery pack, this work optimizes the design of on-board bidirectional dc–dc converter. Based on the dual active half-bridge (DAHB) converter, combined with partial switch sharing, a high-gain bidirectional dc–dc converter is proposed. This converter has 48-V and 24-V output voltage levels, and the multiple voltage levels, which are often required by new energy vehicles, are able to reduce the number of on-board converters. The theoretical part of this work analyzes the design process of the converter, and in parallel, establishes the mathematical models of transmission power, soft switching, current stress, and reactive backflow loss. Under the condition of high gain, the system has good soft-switching characteristics and low reactive backflow loss. In the experimental part, a prototype with an input voltage of 400 V is built under an operating frequency of 100 kHz. The voltage of the two output ports is stable and a peak efficiency of 94.7% is achieved. The experimental results verify the validity of the theoretical analysis.
Light Nouns and Term Creation in Basque

This paper investigates the activation of specialized values in a set of Basque Light Nouns (LNs) such as talde ‘group’, from the viewpoint of the referentiality scale (RS) (Simone & Masini, 2009, 2014). We demonstrate that the terminologization of LNs happens in the sense described by Adelstein (2004, 2007) for ultranouns, and we postulate that paraterms can be characterized as “specialized support nouns” positioned lower than terms in the RE.
Resilient distributed model‐free adaptive predictive control for multiarea power systems under denial of service attack

This paper proposes a novel load frequency control (LFC) strategy for power systems based on distributed model‐free adaptive predictive control. First, a power system dynamic model is established by the input and output signals of the power system. Then, a distributed model‐free adaptive predictive control algorithm is established for the power system under denial‐of‐service attack by the established power system dynamic model, and a predictive compensation algorithm is designed to compensate the impact of DoS attack. Based on the designed distributed model‐free adaptive predictive control algorithm, the frequency tracking error of the multiarea power system is bounded. The scheme is independent of the structure of the power system and does not need to measure any state signals of the power system, relying only on the input and output data of the power system. The results of simulations demonstrate the effectiveness and superiority of the design.
The Analysis of Face Perception MEG and EEG Data Using a Potts-Mixture Spatiotemporal Joint Model

In this paper we analyze magnetoencephalography (MEG) and electroencephalography (EEG) data from a single subject with the objective of determining the location and dynamics of brain activity when this subject is repeatedly presented with stimuli corresponding to pictures of scrambled faces and required to make a symmetry judgement. To meet this objective we must consider the ill-posed inverse problem arising when MEG and EEG are used to measure electromagnetic brain activity over an array of sensors at the scalp and the goal is to map these data back to the sources of neural activity within the brain. A novel challenge arising with the current dataset is that the study involves combined MEG and EEG data, and it is of interest to combine these two different modalities together. We propose a new Bayesian finite mixture state-space model that builds on previously developed models and incorporates two major extensions that are required for our application: (i) We combine EEG and MEG data together and formulate a joint model for dealing with the two modalities simultaneously; (ii) we incorporate the Potts model to represent the spatial dependence in an allocation process that partitions the cortical surface into a small number of latent states termed mesostates. We formulate the new spatiotemporal model and derive an efficient procedure for simultaneous point estimation and model selection based on the iterated conditional modes algorithm combined with local polynomial smoothing. The proposed method results in a novel estimator for the number of mixture components and is able to select active brain regions which correspond to active variables in a high-dimensional dynamic linear model. The methodology is investigated using synthetic data and then applied to our motivating application to examine the neural response to the perception of scrambled faces.
A simple integral approach to compute flows in ducts with variable cross-section

We introduce in this paper a new approach based on an integral formulation in order to get a relevant approximation of the mean flow patterns in one-dimensional ducts with variable cross-sections. Numerical results are compared with those obtained by using the standard one-dimensional formulation associated with a well-balanced scheme, and also with numerical results provided by a multi-dimensional code. The comparison includes ducts with smooth or discontinuous cross sections. This new formulation is shown to be robust wrt to sudden and high cross-section variations, even when a total obstruction occurs, and to be accurate when compared with the reference solution.
Variable Stiffness Mechanism for Suppressing Unintended Forces in Physical Human–Robot Interaction

This paper presents the design of a two-degrees-of-freedom (DoFs) variable stiffness mechanism and demonstrates how its adjustable compliance can enhance the robustness of physical human–robot interaction. Compliance on the grasp handle is achieved by suspending it in between magnets in preloaded repelling configuration to act as nonlinear springs. By adjusting the air gaps between the outer magnets, the stiffness of the mechanism in each direction can be adjusted independently. Moreover, the capability of the proposed design in suppressing unintended interaction forces is evaluated in two different experiments. In the first experiment, improper admittance controller gain leads to unstable interaction, whereas in the second case, high-frequency involuntary forces are caused by the tremor.
Various Feature Extraction and Selection Techniques for Lexicon Based and Machine learning Sentiment Classification

Introduction: Natural Language Processing (NLP) is a kind of software that gives computers the ability to comprehend human languages. Words are often used as the fundamental unit for grammatical and semantic analysis on a deeper level, and the major objective of most natural language processing (NLP) projects is word segmentation Objectives: Proposed System uses the feature extraction method for generating the hybrid feature to get good accuracy for classification in order to solve the practical problem of huge structural differences between different data modalities in a multi-modal environment, where traditional machine learning methods cannot be directly applied to solve the problem. Methods: In order to do so, this paper introduces the method for generating the hybrid feature. In this System, we utilise a various feature extraction and selection technique from large text. The data has collected from students’ feedback and these feature extraction techniques has applied Results: Each technique provides different feature extraction while NLP based dependency features provides homogeneous feature set with relationship. Conclusions: In extensive experimental analysis NLP based features obtains higher precision over the other feature extraction techniques in classification.
Conceptual Metaphor Analysis of The Great Gatsby

Lakoff and Johnson's Metaphor We Live By published in 1980 has made the study of metaphor from the perspective of cognitive linguistics a new development and become a hot research topic. Metaphor is no longer limited to a simple rhetorical device used in literature, but a cognitive mode connected with our way of thinking and perceptual experience. Its essence is to use one thing to understand another thing and people use metaphors to understand and create connections between two different things. The Great Gatsby, written by Francis Scott Fitzgerald, is one of the greatest American works of the 20th century and is full of metaphors, which are the key to building the profound meaning of the book. It paints a panorama of the Jazz Age and the disillusionment of the American Dream. From the perspective of cognitive linguistics, this paper studies the metaphor of classic sentences in this book, hoping to help readers better understand this book. At the same time, the interpretation of conceptual metaphor theory based on text is helpful for us to grasp the daily application of metaphor effectively.
Scientometric Analysis of Islamic Bank in Indonesia

This study aims to identify and map research related to the extent to which the development of Islamic banking research in Indonesia is in scientific literature published by Scopus-indexed journals. The method used is descriptive qualitative using data analyzed from 471 published documents from 1987 to 2022, which were then processed and analyzed using Bibliometric and VOSViewer software to determine bibliometric maps. The results show that the number of articles published by Scopus-indexed journals on the topic of Islamic Banks in Indonesia has grown quite a bit in the early 2010s and will have the potential to continue to grow and increase in line with the increasingly rapid technological developments around the world. In addition, Table 6 provides an overview of the bibliometric review, which shows that some keywords are frequently used, relevant journals, author affiliations, and the author's country. Thus, the results of this study are expected to be developed further, especially in the specific field of Islamic banking.The time frame is limited, and the results are still dynamic. The database used is Scopus. Suggestions for further research can use a better database such as WoS for more comprehensive results. This research can be used as a basic reference to see how the graphical visualization of the development of research trends with the theme of Islamic banking research in Indonesia is published in scientific research so that experts can further develop it.
Robotic Process Automation for Stock Selection Process and Price Prediction Model using Machine Learning Techniques

The usage of AI in the financial industry has increased the reliance on stochastic models for forecasting market behaviour. Quantitative analysts are constantly working to increase the precision with which machine learning models predict stock returns. Regression models using Support Vector Machine (SVM) and Random Forest are well renowned for their ability to predict closing prices with high accuracy. This study suggests a method for analysing and forecasting stock prices using an ensemble of these algorithms.Using basic market price data from India's National Stock Exchange (NSE), datasets are used that have been preprocessed to add common technical indicators as features. The size of the training dataset is decreased by using feature selection techniques to rank the features according to their impact on the final closing price. Sentiment analysis is also used in the study to examine the effect of investor sentiment on stock prices. Twitter postings with a specific corporate hashtag are rated as good or bad using a trained Word2Vec model. The ensemble model is then trained on a fresh dataset made up of counts of both positive and negative tweets across time as well as technical indicator data.This paper makes a contribution to the area by offering an ensemble model for stock price prediction that blends SVM and Random Forest regression models. The study illustrates the significance of feature selection in lowering dataset size as well as the limited influence of aggregated sentiment analysis from Twitter data on the performance of the model as a whole. For researchers and quantitative analysts looking to improve the precision of stock price prediction models in the financial industry, these findings offer invaluable insights.
Fake it till you break it: evaluating the performance of synthetically optimized adversarial patches against real-world imagery

Deep neural networks (DNNs), enabled by massive open datasets like ImageNet, have produced impressive results in a wide range of fields and applications. ImageNet, a database of over 15 million high-resolution images categorized into 22,000 categories, has revolutionized the field of computer vision with state-of-the-art models achieving 98% accuracy. However, this performance comes at a cost. Recent advances in adversarial machine learning have revealed inherent vulnerabilities in DNN-based models. Adversarial patches have been successfully used to disrupt the performance of artificial intelligence (AI) systems that leverage DNN-based computer vision models, but the trade space of these attacks is not fully understood; adversarial attack generation and validation methods are still nascent. In this paper we explore the generation and performance of synthetically-trained attacks against models trained on real data like MSCOCO, VIRAT and VisDrone. Using a synthetic environment tool built on the Unreal Engine, we generate a synthetic dataset consisting of pedestrians and vehicles, train synthetic object detection models, and optimize adversarial patch attacks on the synthetic feature space of those models. We then apply our synthetic attacks to real image data and examine the efficacy of synthetic patch attacks against models trained on real-word image data. The implications of synthetically optimized attacks are broad: a much larger attack surface for DNN-based computer vision models, development of simulation-based validation pipelines, more effective attacks, and stronger defenses against adversarial examples.
Black-Scholes Equation with the Variable Risk-free Interest Rate

As for B-S model with constant coefficients, it can be obtained analytical solution directly by converting into parabolic partial differential equation with constant coefficients, however, the research on the explicit solution of the parabolic partial differential equation under the condition of time-varying is not yet mature. The risk-free interest rate as the discount rate is not always fixed, which is changeable. Consequently, the real option pricing method palys an important role both in theoretical and practical. There are two different methods which dealing with the B-S equation under the condition of the risk-free interest rate is time-varying, one of them is the method of infinite series expansion which is put forward by Kazemi. Another is the method based on decomposition of the path which is put forward by Yun Feng. The two methods both are the summary and improvement of the predecessors' experience. In this paper, we studied the Black-Scholes equation under certain assumptions, we obtain the solution to the problem. And we have proved the compare principle by using advantage function method. Under the compare principle, which is the innovation of this paper, we can find the solution of the Black-Scholes equation with the variable risk-free interest rate by combining with the idea of stage by stage.
An adaptive simplex cut‐cell method for high‐order discontinuous Galerkin discretizations of conjugate heat transfer problems

In this paper, we present a solution framework for high‐order discretizations of conjugate heat transfer problems on non‐body‐conforming meshes. The framework consists of and leverages recent developments in discontinuous Galerkin discretization, simplex cut‐cell techniques, and anisotropic output‐based adaptation. With the cut‐cell technique, the mesh generation process is completely decoupled from the interface definitions. In addition, the adaptive scheme combined with the discontinuous Galerkin discretization automatically adjusts the mesh in each sub‐domain and achieves high‐order accuracy in outputs of interest. We demonstrate the solution framework through several multi‐domained conjugate heat transfer problems consisting of laminar and turbulent flows, curved geometry, and highly coupled heat transfer regions. The combination of these attributes yield nonintuitive coupled interactions between fluid and solid domains, which can be difficult to capture with user‐generated meshes. Copyright © 2016 John Wiley & Sons, Ltd.
Study of Two Common Brain Disorders using Statistical Parameters

Seizures are characterized by excessive brain activity or jumpiness in the patient. Usually the patient suffers from excessive shaking of body and moments of blankness. By predicting the on-set, a physician can administer the required drugs and prevent/control the seizure thereby reducing the pain to the patients. EEG is a random process and linear analysis will not provide us with detailed information. Seizures are non-stationary in nature; therefore they are distinguished by anomalies in values of non-linear parameters such as Bi-correlation, Approximate Entropy (ApEn), Hurst Exponent. Statistical measures, time series plots are the basic ways of differentiating the changes in nature of EEG when a seizure has occurred in a subject. This methodology of analysis confirms the occurrence of this event and appropriate measures can be taken to treat the subject. We can define depression as that state of mind when we perceivably become slow in thoughts as well as action. Brain is too entangled in a maze of its own design in this state and it is among those brain disorders that lead to slow electrical activity.