Miracle-dz/UniFAR-FTUs huggingface dataset:
MULTIDISCIPLINARY: APPLIED BUSINESS AND

This study examines the interplay between personality traits, gender, and entrepreneurial tendencies. Drawing from the framework of the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism), we investigate the moderating role of gender on the relationship between these personality traits and various entrepreneurial behaviors. The sample consists of 500 individuals from diverse professional backgrounds, who completed self-report measures assessing their personality traits and entrepreneurial tendencies. The results reveal that gender plays a significant moderating role in the relationship between personality traits and entrepreneurial behaviors. Specifically, we find that gender moderates the effects of Agreeableness and Extraversion on Opportunity Seeking and Persistence. Additionally, gender moderates the effects of Openness and Conscientiousness on Commitment to Work Contract, and Openness and Extraversion on Risk Taking. However, no significant gender moderation is observed for the effects of personality traits on Demand for Quality and Efficiency. These findings highlight the complex interaction between personality traits, gender, and entrepreneurial tendencies. Understanding these dynamics has important implications for practitioners and policymakers aiming to foster entrepreneurship and support diverse entrepreneurial ecosystems. By acknowledging the differential impact of personality
Systematic Literature Review and Bibliometric Analysis of Grammatical Violations in Financial Reports

Objective: This study presents a systematic literature review and bibliometric analysis of grammatical violations in financial statements and their effects on the capital market. Similar to previous studies, the conceptual framework concerns the violation of grammatical norms associated with the quality of financial statements. Method: The method included two alternative and complementary paths: i) a systematic review in which the grammatical violation proxy is presented as a valuable tool to measure the quality of accounting information by analyzing the texts of explanatory notes of financial statements, and ii) a bibliometric analysis based on the journals listed in Web of Science, and Bibliometrix, an R-Tool for mapping analysis. Results and Contributions: The in-depth literature analysis through a systematic review and a comprehensive analysis performed via bibliometrics allow this study to serve as a conceptual framework for future research. There are discussions on the potential directions of accounting research on the subject, which is strongly developed in the United States, but little explored in Brazil and other countries. Finally, the analysis suggests that the framework through which data become available to the market might restrict the application of this proxy.
Taguchi Optimization of Geometrical Factors of a Polymer Composite Patch in Crack Repair

- For the repair of a crack in thin aluminium sheets, polymer composite patching is one of the better repair techniques. During service, when the load acts, the patch separates from the substrate due to the stresses developed at the interface. This separation of the patch from the substrate largely depends on the geometrical factors of the patch, i.e
INTRODUCTION TO MARTINGALES WITH AN APPLICATION IN FINANCE

Martingales are an important construct in probability theory, which find great utility in mathematically modelling many real-life processes. This paper introduces martingales in a rigorous measure-theoretic manner, along with some interesting properties. Then, we discuss an application of martin- gales in the realm of finance - specifically the behavior of futures prices. We assume elementary knowledge of measure theory and probability.
Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections

Despite increased interest in wearables as tools for detecting various health conditions, there are not as of yet any large public benchmarks for such mobile sensing data. The few datasets that are available do not contain data from more than dozens of individuals, do not contain high-resolution raw data or do not include dataloaders for easy integration into machine learning pipelines. Here, we present Home-kit2020: the first large-scale public benchmark for time series classification of wearable sensor data. Our dataset contains over 14 million hours of minute-level multimodal Fitbit data, symptom reports, and ground-truth laboratory PCR influenza test results, along with an evaluation framework that mimics realistic model deployments and efficiently characterizes statistical uncertainty in model selection in the presence of extreme class imbalance. Furthermore, we implement and evaluate nine neural and non-neural time series classification models on our benchmark across 450 total training runs in order to establish state of the art performance.
Privacy-Preserving Energy-Based Generative Models for Marginal Distribution Protection

We consider learning generative models for sensitive financial and healthcare data. While previous work incorporates Differential Privacy (DP) into GAN training to protect the privacy of individual training instances, we consider a different privacy context where the primary objective is protecting the privacy of sensitive marginal distributions of the true generative process. We propose and motivate a new notion of privacy: α -Level Marginal Distribution Privacy ( α -LMDP), which provides a statistical guarantee that the sensitive generative marginal distributions are different from the observed real data. We then propose Privacy-Preserving Energy Models (PPEMs) , a novel energy-based generative model formulation where the representations for these attributes are isolated from other attributes. This structured formulation motivates a learning procedure where a penalty based on a statistical goodness of fit test, the Kernel Stein Discrepancy , can be applied to only the attributes requiring privacy so that α -LMDP may be satisfied without affecting the other attributes. We evaluate this approach using financial and healthcare datasets and demonstrate that the resulting learnt generative models produce high fidelity synthetic data while preserving privacy. We also show that PPEMs can incorporate both α -LMDP and DP in contexts where both forms of privacy are required.
Robust Variable and Interaction Selection for High-Dimensional Classification via Logistic Regression

Discriminant analysis and logistic regression are fundamental tools for classification problems. Quadratic discriminant analysis has the ability to exploit interaction effects of predictors, but the selection of interaction terms is non-trivial and the Gaussian assumption is often too restrictive for many real problems. Under the logistic regression framework, we propose a forward-backward method, SODA, for variable selection with both main and quadratic interaction terms, where in the forward stage, a stepwise procedure is conducted to screen for important predictors with both main and interaction effects, and in the backward stage SODA remove insignificant terms so as to optimize the extended BIC (EBIC) criterion. Compared with existing methods on quadratic discriminant analysis variable selection (e.g., [Murphy et al., 2010], [Zhang and Wang, 2011] and [Maugis et al., 2011]), SODA can deal with high-dimensional data with the number of predictors much larger than the sample size and does not require the joint normality assumption on predictors, leading to much enhanced robustness. Theoretical analysis establishes the consistency of SODA under high-dimensional setting. Empirical performance of SODA is assessed on both simulated and real data and is found to be superior to all existing methods we have tested. For all the three real datasets we have studied, SODA selected more parsimonious models achieving higher classification accuracies compared to other tested methods.
Student Dropout in Higher Education in Southern Mexico: A Qualitative Approach (2011-2020)

Student dropout is a problem that cuts across several disciplines, educational levels, and contexts. More than 40% of students who enroll in higher education in Latin America do not complete their program (Fonseca & García, 2016). Several organizations evaluating educational programs in Mexico have urged universities to reduce student dropout rates. However, accomplishing this goal requires identifying its causes. Previous research suggests students abandon their studies for numerous reasons, including psychological, socio-environmental, economic, organizational, and interactional factors (Tinto, 1987); and economic, administrative, academic, and affective factors (Authors, 2012). This paper reports on the findings obtained from a qualitative exploratory case study conducted at a university located in the southeastern region of Mexico. The main goal was to explore the causes for which students abandoned their studies in higher education. Data were collected from 118 participants from undergraduate and graduate programs across five campuses and seven colleges using an electronic survey. Findings suggest that three main types of causes influenced students’ decision to drop out of their program, namely academic, personal, and economic reasons. Results also demonstrate the complexity and multi-factorial reasons for early leaving. This paper offers suggestions for strategic university-wide decisions for student support program design.
Abordagem FEW Nexus (nexo água-energia-alimento) para o desenvolvimento sustentável através dos ODS da ONU

Objetivo/Finalidade: Analisar como a Abordagem do FEW Nexus pode contribuir na promoção do desenvolvimento sustentável, tendo os ODS da ONU como meta e servir como uma nova abordagem epistemológica e ontológica na resolução de problemas relacionados a uma realidade complexa dos dias atuais, como o desenvolvimento sustentável. Desenho/metodologia/abordagem: Revisão da literatura sobre o estado atual das pesquisas envolvendo o uso da Abordagem do FEW Nexus, a partir de um panorama global até o cenário da pesquisa sobre o tema no Brasil. Também foi feita a caracterização da abordagem de FEW Nexus, e sua ligação com os conceitos do desenvolvimento sustentável. Resultados: Se demonstrou que abordagem de FEW Nexus, quando relacionada ao conceito de desenvolvimento sustentável, pode buscar nos Objetivos do Desenvolvimento Sustentável as metas tangíveis que, eventualmente, este paradigma é criticado por não oferecer. A análise auxilia no entendimento de como, nessa perspectiva, o FEW Nexus considera os tradeoffs na gestão de recursos naturais, as possibilidades de direcionamento de políticas públicas e programas sociais e econômicos, utilizando como metas os ODS da ONU. Originalidade/valor: Demonstrar a aplicabilidade da Abordagem do FEW Nexus dentro das Ciências Sociais Aplicadas, onde essa abordagem é utilizada numa frequência muito menor em comparação às Ciências exatas. Possibilidade de oferecer uma contribuição para a elaboração de política públicas que possam atender um problema crucial para a sustentabilidade, que é o ataque a pobreza em nível de Brasil e mundial.
The role of gratitude on stress and psychological wellbeing among parent of children with cancer in Aceh

Cancer is a disease with a high incidence that pediatric patients need parents during treatment. The situation became a source of stress for parents. One way to deal with stress is to be grateful that the psychological wellbeing of the caregivers who accompany children with cancer is maintained. This study aims to determine the relationship between stress and psychological wellbeing with the moderation of gratitude in the parents of children with cancer in Aceh. This study used a quantitative approach, involving 14 participants at the shelter for children with cancer undergoing treatment in Banda Aceh aged 20–40 years. Participants were selected using the non-probability sampling method and the incidental sampling technique. Individual stress levels were measured using the Perceived Stress Scale, wellbeing using the Warwick-Edinburgh Mental Wellbeing Scale, and gratitude using the Indonesian Grateful Scale. The data were analyzed using the Rank Spearman nonparametric test, showing no relationship between stress and psychological wellbeing, but there is a relationship between stress and gratitude and gratitude with psychological wellbeing. This result shows partially that gratitude has a relationship with stress and psychological wellbeing. It means that with gratitude, stress can be controlled and impacts the psychological wellbeing of parents accompanying children with cancer.
The Mediating Effects of Self-Concept in the Relationship of Brand Image and Purchasing Buying Behavior: The Case of A Skincare 13

This study aims to determine the effect of brand image on purchasing buying behavior of a skincare products for men. It proposes self-concept as a mediating variable and answers how brand image affects purchasing behavior. The participants of the study are the male users of the brand. To answer the research questions, this study used structural equation modeling. The results suggested that there is a positive relationship between brand image and purchasing buying behavior that is the higher the brand image the more likely that they will purchase the skincare for men brand. As a result, providing customers with more agency and focusing exclusively on meeting their needs will typically result in improved consumer behavior. The output revealed also a mediating effect of self-concept in the association between brand image and purchasing behavior
Geodesics on the Momentum Phase Space with metric $^{{C}}{g}$

In this paper, a system of the differential equations giving geodesics on the momentum phase space with pseudo Riemann metric $^{C}g$ of a Hamilton space is found by using the Euler Lagrange equations. Then, space like geodesics on pseudo hyperbolic 2-space $H_{1}^{2}$ are obtained. Finally, a system of the differential equations giving geodesics on the cotangent bundle with pseudo Riemann metric $^{C}g$ of $H_{1}^{2}$ is get.
Short Paper: DeFi Deception – Uncovering the prevalence of rugpulls in cryptocurrency projects

DeFi has attracted legitimate investors and scammers alike. The paper presents an empirical investigation into the prevalence of rug-pulls, a scam where cryptocurrency project developers exit without fully delivering and leave investors in the wind. Using forum data, 101 rug-pulls from 6 different types of DeFi services are documented. ICOs form the majority of the rugpulls, most of which were active for less than six months before scamming out. ICOs rugpulled in 2021 were active for a much longer time than those that were rugpulled later on, perhaps pointing to new entrants intending to pull the rug. Through qualitative thematic analysis, we discover that these schemes primarily use author-itative and financial lures at the announcement stage of the project to mimic legitimate projects.
DEACT: Hardware Solution to Rowhammer Attacks

: Dynamic Random Access Memory ( DRAM) is a crucial component in modern computing devices. Improvements in process technology have significantly increased the speed and storage capacity of memory devices. However, as memory cells become smaller and closer to one another, annoying circuit disturbance errors such as the Row-hammer problem have become significant. Studies show that attackers can systematically exploit such errors to induce bit flips and take control of local/remote systems. Even though several hardware and software-based mitigation techniques have been proposed, it is still continuing to be a big threat to system security. In this research, we propose DEACT, a counter-based hardware mitigation to the Rowhammer attack. Contrary to existing countermeasures that refresh victim rows or throttle memory access upon excessive row activation, DEACT uses additional row buffers to keep hot rows and prevent further activation. The size of our counter uses 1.67 times less space than the optimal of existing implementations. DEACT not only eliminates the Rowhammer problem, but it also improves the performance of DRAM. We tested DEACT on the TPC and CPU-2006 benchmarks; the average hit rate has increased by 41% when compared to standard DRAM
Characterization of c‐, L‐ and ϕk‐optimal designs for a class of non‐linear multiple‐regression models

Optimal designs for multiple‐regression models are determined. We consider a general class of non‐linear models including proportional hazards models with different censoring schemes, the Poisson and the negative binomial model. For these models we provide a complete characterization of c‐optimal designs for all vectors c in the case of a single covariate. For multiple regression with an arbitrary number of covariates, c‐optimal designs for certain vectors c are derived analytically. Using some general results on the structure of optimal designs for multiple regression, we determine L‐ and ϕk ‐optimal designs for models with an arbitrary number of covariates.
Stochastic differential equations with Sobolev diffusion and singular drift and applications

In this paper, we study properties of solutions to stochastic differential equations with Sobolev diffusion coefficients and singular drifts. The properties we study include stability with respect to the coefficients, weak differentiability with respect to starting points and the Malliavin differentiability with respect to sample paths. We also establish Bismut–Elworthy–Li’s formula for the solutions. As an application, we use the stochastic Lagrangian representation of incompressible Navier–Stokes equations given by Constantin– Iyer [Comm. Pure Appl. Math. 61 (2008) 330–345] to prove the local wellposedness of NSEs in Rd with initial values in the first-order Sobolev space
Divergence of cochlear transcriptomics between reference‑based and reference‑free transcriptome analyses among Rhinolophus ferrumequinum populations

Differences in gene expression within tissues can lead to differences in tissue function. Understanding the transcriptome of a species helps elucidate the molecular mechanisms underlying phenotypic divergence. According to the presence or absence of a reference genome of for a studied species, transcriptome analyses can be divided into reference‑based and reference‑free methods, respectively. Presently, comparisons of complete transcriptome analysis results between those two methods are still rare. In this study, we compared the cochlear transcriptome analysis results of greater horseshoe bats (Rhinolophus ferrumequinum) from three lineages in China with different acoustic phenotypes using reference‑based and reference‑free methods to explore their differences in subsequent analysis. The results gained by reference-based results had lower false-positive rates and were more accurate because differentially expressed genes among the three populations obtained by this method had greater reliability and a higher annotation rate. Some phenotype-related enrichment terms, including those related to inorganic molecules and proton transmembrane channels, were also obtained only by the reference-based method. However, the reference‑based method might have the limitation of incomplete information acquisition. Thus, we believe that a combination of reference‑free and reference‑based methods is ideal for transcriptome analyses. The results of our study provided a reference for the selection of transcriptome analysis methods in the future.
The relationship between ambient temperature and match running performance of elite soccer players

The influence of environmental factors on key physical parameters of soccer players during competitive match-play have been widely investigated in the literature, although little is known on the effects of sub-zero ambient temperatures on the performance of adult elite soccer players during competitive matches. The aim of this study was to assess how the teams’ match running performance indicators are related to low ambient temperature during competitive matches in the Russian Premier League. A total of 1142 matches played during the 2016/2017 to 2020/2021 seasons were examined. Linear mixed models were used to assess the relationships between changes in ambient temperature at the start of the match and changes in selected team physical performance variables, including total, running (4.0 to 5.5 m/s), high-speed running (5.5 to 7.0 m/s) and sprint (> 7.0 m/s) distances covered. The total, running and high-speed running distances showed no significant differences across temperatures up to 10°C, while these showed small to large decreases at 11 to 20°C and especially in the >20°C ranges. On the contrary, sprint distance was significantly lower at temperature of -5°C or less compared to higher temperature ranges. At sub-zero temperatures, every 1°C lower reduced team sprint distance by 19.2 m (about 1.6%). The present findings show that a low ambient temperature is negatively related to physical match performance behavior of elite soccer players, notably associated with a reduced total sprint distance.
Bayesian compartmental model for an infectious disease with dynamic states of infection

ABSTRACT Population-level proportions of individuals that fall at different points in the spectrum [of disease severity], from asymptomatic infection to severe disease, are often difficult to observe, but estimating these quantities can provide information about the nature and severity of the disease in a particular population. Logistic and multinomial regression techniques are often applied to infectious disease modeling of large populations and are suited to identifying variables associated with a particular disease or disease state. However, they are less appropriate for estimating infection state prevalence over time because they do not naturally accommodate known disease dynamics like duration of time an individual is infectious, heterogeneity in the risk of acquiring infection, and patterns of seasonality. We propose a Bayesian compartmental model to estimate latent infection state prevalence over time that easily incorporates known disease dynamics. We demonstrate how and why a stochastic compartmental model is a better approach for determining infection state proportions than multinomial regression is by using a novel method for estimating Bayes factors for models with high-dimensional parameter spaces. We provide an example using visceral leishmaniasis in Brazil and present an empirically-adjusted reproductive number for the infection.
Final design and assembly of the GHOST Cassegrain unit

GHOST is a high resolution spectrograph system currently being built for the Gemini South Observatory in Chile. In the Cassegrain unit, the observational targets are acquired on integral field units and guided during science exposures, feeding the fiber cable to the temperature-stabilized echelle spectrograph. The Cassegrain unit is mounted on the Gemini telescope, and consists of a main structural plate, the two object positioners and ballast frame. The image from each of the two science beams passes through a field lens and a mini-atmospheric dispersion corrector and is then captured by the integral field unit. The positioner moves each corrector-integral field unit assembly across the focal surface of the telescope. The main structural plate provides the interface for the positioner and ballast frame to the telescope structure. In this paper we describe the final design and assembly of the GHOST Cassegrain unit and report on the outcome of on-sky testing at the telescope in Chile.