Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Comparison and Internalization as Mediators between Weight Status and Body Esteem in Lebanese Adolescents

This study examined the role of social comparison and internalization of social attitude as mediators between weight status and body esteem in male and female high school students. Participants were 426 Lebanese students (M = 17.71, SD = 1.48) who completed self-report questionnaires. Using structural equation modeling (SEM), results revealed that while the same measurement and structural models exhibited good fit for both genders, structural weights were significantly different for the two genders. Further, weight status in females was significantly related to social comparison and internalization of social attitude, which in turn negatively impacted body esteem. For males, social comparison and internalization of social attitude were not significant mediators between weight status and body esteem. These findings indicate the need for school prevention programs that incorporate formative influences and processes such as internalization of societal norms and social comparison in the construction of guidance and counseling strategies
Cell Tracking with Multifeature Fusion]{Cell Tracking with Multifeature Fusion

Cell tracking is currently a powerful tool in a variety of biomedical research topics. Most cell tracking algorithms follow the tracking by detection paradigm. Detection is critical for subsequent tracking. Unfortunately, very accurate detection is not easy due to many factors like densely populated, low contrast, and possible impurities included. Keeping tracking multiple cells across frames suﬀers many diﬃculties, as cells may have similar appearance, they may change their shapes, and nearby cells may interact each other. In this paper, we propose a uniﬁed tracking-by-detection framework, where a powerful detector Attentio-nUnet++, a multimodal extension of the ECO algorithm, and an eﬀective data association algorithm are included. Experiments show that the proposed algorithm can outperform many existing cell tracking algorithms.
The role of customer and service provider mindfulness on trust recovery after a service failure

Consumers’ perception towards trust recovery tactics in the presence of a service failure might be dependent upon a variety of factors (e.g., customer’s cognitive, emotional and behavioral reactions). Additionally, consumers’ mindfulness levels (high vs. low) could play a role on the trust recovery tactics of a service failure. Moreover, mindfulness in services may be addressed also to the level of mindfulness of the service provider. Although some research has given attention to the emotional role of consumers to service failure responses, investigation regarding the influence of mindfulness is still very limited. This paper aims to fill this gap by examining whether trust recovery tactics of a mindful service provider will be more effective in recovering trust of mindful customers ( vs. low mindful customers). The theoretical foundations for this examination are drawn and some propositions are made for future empirical investigation.
Evaluation of the differences in audit service quality: Assurance in local independent auditing firms

One profession that is distinguished by tightly related legal numbers, papers, and audit reports is auditing. In addition to data accuracy, audit work must also be truthful, impartial, transparent, and adhere to the principles. Small inaccuracies or omissions in audited reports or invoices can have a detrimental impact on both the client company and the audit firm. The main objective of this study was to identify, analyze, and measure the audit service quality: assurance in local independent auditing firms in Hanoi through qualitative and quantitative research methods. Based on an overview of previous studies and experts' opinions, the study has identified, analyzed, and measured four attributes of audit service quality: assurance in local independent auditing firms in Hanoi. There is no statistically significant difference in the level of audit quality: assurance in local independent auditing firms in Hanoi between participants of these different genders, these different job positions, and these three groups of work experiences. Based on the findings, some recommendations are given for local independent auditing firms to improve the quality of their audit services.
Real-Time Control of Robotic Hand by Human Hand at Low Cost

: In this paper, the design and implementation of control system for robotic hand at low cost is achieved. The proposed control is based on the Flexible sensor and the Arduino controller. It is used glove to transferring the gestures to simulate the motion of the five fingers of human hand by using five fingers of the robotic hand. The task of the robotic hand is to catch any object under the real-time control. MEMS gyroscope is used to control the rotation of the elbow of robotic hand toward right or left. The robotic hand is a tendon finger manipulation type. It can be considered the developed robotic system in this paper is very important especially in the industrial sector where there are some places are not reachable by human hands, therefore it can be used this robotic hand easily.
The length-biased weighted frechet distribution: Properties and estimation

In this paper, we introduce a new class of length-biased weighted Frechet (LBWF) distribution. The statistical properties of this distribution including the mean, variance, coefficient of variation, harmonic mean, raw and incomplete moments, skewness, kurtosis, mode, moment and probability generating functions, reliability, hazard and reverse hazard functions, Lorenz, Bonferroni and Zenga curves, Renyi of entropy, order statistics and record statistics are studied. The moment and maximum likelihood estimates are obtained for the model parameters.
Reproducibility of in-vivo 1 electrophysiological measurements 2 in mice 3

20 Understanding whole-brain-scale electrophysiological recordings will rely on the collective work 21 of multiple labs. Because two labs recording from the same brain area often reach diﬀerent 22 conclusions, it is critical to quantify and control for features that decrease reproducibility. To 23 address these issues, we formed a multi-lab collaboration using a shared, open-source 24 behavioral task and experimental apparatus. We repeatedly inserted Neuropixels multi-electrode 25
The Moderating Role of e-WOM

Except for perceived fee, which has a negative effect on perceived value, the results demonstrate that all hypotheses of latent correlations in TAM and VAM were strongly significant. Furthermore, attitude and perceived value have a significant role in determining consumer adoption of e-learning. Consumers’ perceived value will be driven by the high and low levels of the moderating influence of e-word of mouth, influencing their intention toward e-learning. Since e-learning is an effective sustainable education system, the result of this study can provide a good solution to facilitate e-learning in current and future conditions.
DESIGNING OF ELECTRICAL VEHICLE CHARGER USING MATLAB

: Due to their reduced environmental effect and lower operating costs when compared to conventional gasoline-powered vehicles, electric vehicles (EVs) are becoming more and more popular. However, the absence of a thorough charging infrastructure prevents the wide-scale adoption of EVs. Therefore, creating effective and efficient EV chargers is essential for the market's success. A MATLAB-based simulation of an EV charger is described in this project. A power electronic converter, a control algorithm, and a battery management system are all included in the simulation model. In order to charge the EV battery, the converter transforms AC electricity from the grid into DC power. The charging of the battery is made safe and effective by the control algorithm. The battery management system keeps an eye on how fully charged the battery is and guards against either overcharging or undercharging. The simulation model is tested under different scenarios, including varying input voltages and loads, and the performance is evaluated in terms of charging time and efficiency. The simulation results show that the designed charger is capable of charging an EV battery within a reasonable time while maintaining a high level of efficiency. Overall, this project demonstrates the feasibility and effectiveness of using MATLAB to design and simulate an EV charger. The simulation results can be used to optimize the charger's design and improve its performance, leading to the development of more efficient and reliable EV charging systems
Back to the future-Conservative grassland management for 2 Anthropocene soils in the changed landscapes of Uruguay?

The ‘soils of the anthropocene’ are predominately agricultural. To understand them, 13 we analysed agri-and silvicultural intensification of Uruguayan grasslands in a country wide 14 survey on fertility proxies, pH and trace metals in topsoils originating from different land uses. 15 We observed a loss of nutrients, trace metals and organic matter from grassland, crops and 16 timber plantations, and its accumulation in the topsoils of riverine forests. The translocation of 17 nutrients and organic matter across the landscape to the erosion base depends on local land use 18 trajectories. Increasing soil acidification is driving a positive feedback loop, and land use 19 intensification is leading to degradation of local black soils within a few decades. Our data 20 raises questions about the resilience and carrying capacity of Uruguayan soils with regard to 21 currently implemented highly productive management forms, including the use of timber 22
Load Fluctuations in Turbulent Flow

Measurements of lift and drag force fluctuations on elementary building shapes in turbulent flow are reported. It is shown that fluctuations in the drag force can be related to the turbulent velocity fluctuations with the use of a simple momentum model. Tests were conducted in a uniform stream, with a relative intensity of turbulence of approximately 10% and a scale of turbulence of the same order as the major dimensions of the models used. The investigation of load fluctuations in turbulent flow was carried out as part of a study directed towards the assessment of wind loads and wind effects on tall buildings.
Reply letter to RC 2 : Review of the manuscript entitled « Clustering of eruptive events from high precision strain signals recorded during the 2020-2022 lava fountains at Etna volcano ( Italy ) » by

Explosive eruption eventsLava fountains at Etna are hazardous explosive eruptions that pose risk to civil infrastructures and aviation. The characterization of such events is essential for both monitoring the events and assessing the related hazard. We appliedhave been clustered by machine learning techniques applied on the strain signal recorded by highprecision borehole strainmeters. We focus on the extraordinary intense and frequent eruptive activity at Etna in the period December 2020 – February 2022 when more than 60 lava fountains occurred. We apply tThe k-means algorithm was applied 10 on the associated strain variations associated to the eruptive episodes which are representative of the eruptivetheir dynamics. A novel procedure was developed to ensure a high-quality clustering process and obtain robust results. The analysis identified four distinct groups of strain variations which stand out forcharacterize the events in terms of their amplitude, and duration and time derivative of the signal. The temporal distribution of the clusters provides useful insightshighlighted transition in the different types of eruption revealing thus the importance of clustering the strain variations for monitoring and hazard 15 assessment purposes. into the evolution of the volcano activity and reveals transitions in the eruptive style.
A Comparison Framework for Product Matching Algorithms

A Comparison Framework for Product Matching Algorithms Jeremy Foxcroft Advisor: University of Guelph, 2021 Dr. Luiza Antonie Product matching is a specific application of record linkage where different digital records that refer to the same product are identified. In this thesis, we design a framework to compare state-of-the-art product matching systems including traditional machine learning models and more recent deep learning approaches. We then employ this system to perform comparisons on both open source product matching benchmarks and real-world modern day industrial product data, measuring performance with both F1 measure and precision-recall curves. We find that traditional machine learning techniques remain superior for clean, structured data and that this superior performance translates seamlessly from the open source product matching benchmarks to the real-world data. We also propose a new application for product matching: forecasting demand for products that are new to market.
Family size and domestic violence: An instrumental variable analysis in the Pacific

How does having larger family size affect the prevalence of domestic violence? This paper extends studies on children quantity-quality trade-off by examining the impact on domestic violence against female and children. We investigate the causal impact of childbearing on the prevalence of domestic violence using the data set from Kiribati, Samoa, and Tonga. Identification is based on instrumental variable strategy using three types of instruments for fertility: (1) dummy for multiple second births, (2) dummy for same-sex sibling pairs in families with two or more children, and (3) dummy for first child is female. We apply these three IVs as the exogenous variation to family size, and then examine its effect on domestic violence within the context of Pacific Island Countries. In general, the results reveal a correlation between the number of children and the prevalence of domestic violence against women and children. In a similar vein, attitudes that rationalize violent behaviour among both males and females also have a positive association with an increase in the number of children. However, the estimation results using 2SLS to determine the causal effect of family size indicate little evidence of effects. Despite the non-detectable effects, the sign of the coefficients remain consistent with the theoretically predicted outcomes.
DESIGN OF A COMPACT CP ANTENNA WITH ENHANCED BANDWIDTH USING A NOVEL HEXAGONAL RING BASED REACTIVE IMPEDANCE SUBSTRATE

A novel hexagonal ring-based reactive impedance surface (RIS) has been proposed and comprehensively employed as a ground plane of a circular polarized patch antenna (CPPA) for enhancing impedance bandwidth (IBW) and axial ratio bandwidth (ARBW), simultaneously. Furthermore, a simple analytical model analysis has been developed to estimate the resonance frequency and to predict the surface characteristics of the RIS structure. Two slits over the patch render the antenna to radiate circular polarized (CP) wave. The RIS has improved the CP antenna performance in terms of compactness, improved gain and increased efficiency. The proposed loaded structure has been numerically and experimentally studied with a layout of 40 × 40 × 3.2 mm3 at 3.7 GHz. The measured results indicate that the prototype antenna has produced a relatively wider IBW and ARBW of 9.32% and 2.1%, respectively with peak gain about 2.98 dBiC. Both gain and efficiency of the loaded structure have been improved owing to the low conductive loss of the ring-shaped RIS. The proposed CP antenna might be suitable for radar application used in S-band.
Protistology 15 (4), 312–320 (2021)

1 Laboratory of Cytology of Unicellular Organisms, Institute of Cytology RAS, 194064 St. Petersburg, Russia 2 National Research Tomsk State University, 634050 Tomsk, Russia 3 Institute of Molecular and Cellular Biology, 630090 Novosibirsk, Russia 4 Department of Applied Ecology, Faculty of Biology, St. Petersburg State University, 199034 St. Petersburg, Russia 5 Center for Algorithmic Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia 6 Department of Cytology and Histology, St. Petersburg State University, 199034 St. Petersburg, Russia 7 Department of Invertebrate Zoology, Faculty of Biology, St. Petersburg State University, 199034 St. Petersburg, Russia
Predicting prenatal depression and assessing model bias using machine learning models

Perinatal depression (PND) is one of the most common medical complications during pregnancy and postpartum period, affecting 10-20% of pregnant individuals. Black and Latina women have higher rates of PND, yet they are less likely to be diagnosed and receive treatment. Machine learning (ML) models based on Electronic Medical Records (EMRs) have been effective in predicting postpartum depression in middle-class White women but have rarely included sufficient proportions of racial and ethnic minorities, which contributed to biases in ML models for minority women. Our goal is to determine whether ML models could serve to predict depression in early pregnancy in racial/ethnic minority women by leveraging EMR data. We extracted EMRs from a hospital in a large urban city that mostly served low-income Black and Hispanic women (N=5,875) in the U.S. Depressive symptom severity was assessed from a self-reported questionnaire, PHQ-9. We investigated multiple ML classifiers, used Shapley Additive Explanations (SHAP) for model interpretation, and determined model prediction bias with two metrics, Disparate Impact, and Equal Opportunity Difference. While ML model (Elastic Net) performance was low (ROCAUC=0.67), we identified well-known factors associated with PND, such as unplanned pregnancy and being single, as well as underexplored factors, such as self-report pain levels, lower levels of prenatal vitamin supplement intake, asthma, carrying a male fetus, and lower platelet levels blood. Our findings showed that despite being based on a sample mostly composed of 75% low-income minority women (54% Black and 27% Latina), the model performance was lower for these communities. In conclusion, ML models based on EMRs could moderately predict depression
Learning to Hash for Indexing Big Data V A Survey This paper provides readers with a systematic understanding of insights, pros, and cons of the emerging indexing and search methods for Big Data.

| The explosive growth in Big Data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the straightforward solution using exhaustive comparison is infeasible due to the prohibitive computational complexity and memory requirement. In response, approximate nearest neighbor (ANN) search based on hashing techniques has become popular due to its promising performance in both efficiency and accuracy. Prior randomized hashing methods, e.g., locality-sensitive hashing (LSH), explore data-independent hash functions with random projections or permutations. Although having elegant theoretic guarantees on the search quality in certain metric spaces, performance of randomized hashing has been shown insufficient in many real-world applications. As a remedy, new approaches incorporating data-driven learning methods in development of advanced hash functions have emerged. Such learning-to-hash methods exploit information such as data distributions or class labels when optimizing the hash codes or functions. Importantly, the learned hash codes are able to preserve the proximity of neighboring data in the original feature spaces in the hash code spaces. The goal of this paper is to provide readers with systematic understanding of insights, pros, and cons of the emerging techniques. We provide a comprehensive survey of the learning-to-hash framework and representative techniques of various types, including unsupervised, semisupervised, and supervised. In addition, we also summarize recent hashing approaches utilizing the deep learning models. Finally, we discuss the future direction and trends of research in this area.
The importance of the metal reinforcement of low voltage cables in the process of identifying defects

Regarding the identification of defect in the electrical energy cables, in exploatation, usual or high-performance methods are used, based in particular on the phenomenon of reflection and refraction of the impulse transmitted in the cable, phenomenon generated by the impedance change at the defect location. In order to identify the defect, an important role is played by the existence of a current path on the cable route between the ends of the electrical cable and the place where the defect occurred. In the case of low voltage cables, there are situations where, following the sleeve process the continuity of the mechanical protection armature of the steel strip or the copper screen has not been restored. To measure the distance to the defect location, location equipment uses low and high voltage pulses which moves to the defect site with a certain speed and which is repeated after a selected time interval. These impulses are transmitted to the location of the defect at one end of the cable, through the circuit generated by the short circuit between the faulted conductor and the metal armature, which is usually found at ground potential. At the same time, there is situations where the defect is difficult to identify precisely because of the continuity of the reinforcement or screen and their connection to ground potential. This paper describes the stages of fault identification in a low-voltage power cable and presents a case study that highlights the importance of the metal reinforcement in the case of the high-voltage impulse method. Ways to detect defects using the arc reflected method (ARM) using the shock wave generator as well as the inductive method are also briefly presented.
Characterizing 3D Magnetic Structures in Sunspot Light Bridges

Light bridges (LBs) are narrow structures dividing sunspot umbra, and their role in active region evolution is yet to be explored. We investigated the magnetic structure of the two LBs: a narrow LB (with width ∼810 km) and a considerably wider LB (2475 km) in the active region NOAA 12371. We employed: (1) the high-spatial-resolution spectropolarimetric data obtained by the Near InfraRed Imaging Spectropolarimeter (NIRIS) of the 1.6 m Goode Solar Telescope (GST) for studying the magnetic structure at the photosphere, and (2) the nonlinear force-free field (NLFFF) models, extrapolated from both the photospheric magnetogram from GST/NIRIS and from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, for studying the three-dimensional (3D) magnetic structure on a larger scale. Our observations reveal the presence of a field-free (or, more precisely, weak-field) region and the different velocity structures inside the two LBs. Analysis of the 3D NLFFF model shows a low-lying magnetic canopy as well as the enhanced current system above the LBs. The substantial difference between the LBs and the umbrae is found in the overall magnetic topology in that the field lines emanating from the two LBs are more twisted than that from the neighboring umbrae.
A2_2 The Detection Limits of the E-ELT on the Proxima Centauri System

The development of the European Extremely Large Telescope (E-ELT) allows for a more in-depth analysis of exoplanets. We explore the Proxima Centauri system as a potential target for the E-ELT. Our results show that the instrument would allow us to resolve the separation of the star and planet, thus making it possible to conduct spectral analysis on the atmosphere of the planet.