Miracle-dz/UniFAR-FTUs huggingface dataset:
Working memory, episodic memory and sustained attention in women survivors of intimate partner violence in Spain: The Believe Battery

A growing body of literature suggests that intimate partner violence (IPV) is linked topsychopathology and lower cognitive functionality. Nonetheless, few studies have examinedneuropsychological correlates using objective neuropsychological assessments. The mainobjective of this study was to assess the relationship between cognitive functioning (specificallymemory and attention) and IPV. A group of women IPV survivors (n = 37) and a group of womenwho had not experienced IPV (n = 23) were assessed using the Believe Battery, a comprehensiveneuropsychological battery adapted for women survivors of IPV. Findings demonstrated thatwomen who have suffered IPV present lower neuropsychological scores in the domains ofworking memory, verbal episodic memory, and attention compared to women who have notexperienced IPV. These results suggest that IPV may have an impact on neuropsychologicalfunctioning among women victims and survivors, thus raising an important question aboutimplications in clinical and forensic settings. Future studies should examine whether there areadditional differences in other cognitive domains and assess how such differences are relatedto the potential causal mechanisms of violence (e.g., strangulation, head injury, chronic stress).
Optimizing dosing of the cycloserine pro-drug terizidone in children with rifampicin-resistant tuberculosis

ABSTRACT There are no pharmacokinetic data in children on terizidone, a pro-drug of cycloserine and a World Health Organization (WHO)-recommended group B drug for rifampicin-resistant tuberculosis (RR-TB) treatment. We collected pharmacokinetic data in children <15 years routinely receiving 15–20 mg/kg of daily terizidone for RR-TB treatment. We developed a population pharmacokinetic model of cycloserine assuming a 2-to-1 molecular ratio between terizidone and cycloserine. We included 107 children with median (interquartile range) age and weight of 3.33 (1.55, 5.07) years and 13.0 (10.1, 17.0) kg, respectively. The pharmacokinetics of cycloserine was described with a one-compartment model with first-order elimination and parallel transit compartment absorption. Allometric scaling using fat-free mass best accounted for the effect of body size, and clearance displayed maturation with age. The clearance in a typical 13 kg child was estimated at 0.474 L/h. The mean absorption transit time when capsules were opened and administered as powder was significantly faster compared to when capsules were swallowed whole (10.1 vs 72.6 min) but with no effect on bioavailability. Lower bioavailability (−16%) was observed in children with weight-for-age z-score below -2. Compared to adults given 500 mg daily terizidone, 2022 WHO-recommended pediatric doses result in lower exposures in weight bands 3–10 kg and 36–46 kg. We developed a population pharmacokinetic model in children for cycloserine dosed as terizidone and characterized the effects of body size, age, formulation manipulation, and underweight-for-age. With current terizidone dosing, pediatric cycloserine exposures are lower than adult values for several weight groups. New optimized dosing is suggested for prospective evaluation.
Coreset Learning-Based Sparse Black-Box Adversarial Attack for Video Recognition

In recent years, researchers have explored the use of sparse black-box video adversarial attacks, which involve selecting keyframes to reduce computational complexity and improve efficiency in generating perturbations. However, the current sparse strategy is not optimized for attack and detection steps, resulting in inaccurate frame selection. Some researchers have used reinforcement learning to train an agent to select keyframes, but this method requires additional training. To address these challenges, we propose a plug-and-play black-box sparse attack algorithm called CLVA based on the coreset concept of active learning. Our algorithm treats a video as a mini-dataset and employs the K-Center-Greedy algorithm to compute the distances between frames. We then select the frame that meets the distance condition as the key frame. We conducted extensive experiments using two attack algorithms on five mainstream recognition models and three video recognition datasets. Our results demonstrate that CLVA significantly accelerates the black-box video attack algorithm while achieving state-of-the-art performance in sparsity, time, and success rate compared to recent sparse attack algorithms. The implementation code of our CLVA method is available at https://github.com/machineNo6/CLVA.
Improved Generalization of Probabilistic Movement Primitives for Manipulation Trajectories

Imitation learning methods have proven effective in learning robotic tasks by leveraging multiple human-controlled demonstrations. However, existing approaches often struggle to generalize across a wide range of tasks, such as extrapolating to unseen object locations, incorporating via-point modulation, accurately modeling orientation, handling trajectories with multiple options, and capturing aiming actions. In this study, we propose a novel framework that combines ideas from task-parameterized Gaussian mixture models and probabilistic movement primitives to address these limitations and satisfy all the aforementioned properties within a single framework. We conduct comprehensive evaluations of our approach on four real-life tasks: pick-and-place, water pouring, shooting a hockey puck into a net, and sweeping.
The impact of physical activity and blood pressure on cardiovascular events and mortality

Hypertension is the leading risk factor for the development of cardiovascular disease (CVD). Physical activity (PA) is beneficial for preventing hypertension and decreases risk of mortality and CVD. The purpose of this thesis was to study the relationships between PA, blood pressure (BP), and how they act to impact mortality and CVD development. A systematic review examined the impact of PA on mortality in patients with high BP. Six articles evaluating over 90,000 participantswere identified. Cand all-cause mortality were shown to be inversely related to PA in all studies. Individuals with high BP who participated in any level of PA had a reduced risk of CVD mortality, and greater than two-fold increased risk of mortality was noted for inactive individuals. The second study specifically examined the main and interaction effects of different levels of PA and BP on both fatal and non-fatal CVD events, and mortality in the Scottish Health Survey. We found a significant interaction effect between PA and BP on CVDsuch that doing any level of activity for the BP groups <160 mmHg reduced risk of CVD; in those with systolic BP ≥160 mmHg, there was no change in risk. The third study evaluated the causal relationships between PA, BP, and mortality and Major Adverse Cardiovascular Events (MACE) using the Honolulu Heart Program. Advanced statistical models (i.e., Marginal structural models) were used to estimate the separate causal relationships of PA and BP on mortality/MACE, and the causal relationship of PA on BP. Being active was associated with reduced risk of mortality and MACE. BP was shown to have a dose-dependent relationship with all-cause mortality and MACE (increased BP increased risk of events). Active participants showed a reduced BP (~2.5 mmHg). Being physically active is associated with better outcomes and that BP may be a mediator of this relationship. The findings from this thesis suggest a causal relationship between greater PA and lower BP, and that high PA acts with low BP in reducing the risk of mortality and cardiovascular events. Thisoutcome supports engagement in physical activity for longevity and maintenance of healthy blood pressure.
Environmental change impacts on the C- and N-cycle of European forests: a model comparison study

Forests are important components of the greenhouse gas balance of Europe. There is considerable uncertainty about how predicted changes to climate and nitrogen deposition will perturb the carbon and nitrogen cycles of European forests and thereby alter forest growth, carbon sequestration and N2O emission. The present study aimed to quantify the carbon and nitrogen balance, including the exchange of greenhouse gases, of European forests over the period 2010–2030, with a particular emphasis on the spatial variability of change. The analysis was carried out for two tree species: European beech and Scots pine. For this purpose, four different dynamic models were used: BASFOR, DailyDayCent, INTEGRATOR and Landscape-DNDC. These models span a range from semi-empirical to complex mechanistic. Comparison of these models allowed assessment of the extent to which model predictions depended on differences in model inputs and structure. We found a European average carbon sink of 0.160 ± 0.020 kgC m−2 yr−1 (pine) and 0.138 ± 0.062 kgC m−2 yr−1 (beech) and N2O source of 0.285 ± 0.125 kgN ha−1 yr−1 (pine) and 0.575 ± 0.105 kgN ha−1 yr−1 (beech). The European average greenhouse gas potential of the carbon sink was 18 (pine) and 8 (beech) times that of the N2O source. Carbon sequestration was larger in the trees than in the soil. Carbon sequestration and forest growth were largest in central Europe and lowest in northern Sweden and Finland, N. Poland and S. Spain. No single driver was found to dominate change across Europe. Forests were found to be most sensitive to change in environmental drivers where the drivers were limiting growth, where changes were particularly large or where changes acted in concert. The models disagreed as to which environmental changes were most significant for the geographical variation in forest growth and as to which tree species showed the largest rate of carbon sequestration. Pine and beech forests were found to have differing sensitivities to environmental change, in particular the response to changes in nitrogen and precipitation, with beech forest more vulnerable to drought. There was considerable uncertainty about the geographical location of N2O emissions. Two of the models BASFOR and LandscapeDNDC had largest emissions in central Europe where nitrogen deposition and soil nitrogen were largest, whereas the two other models identified different regions with large N2O emission. N2O emissions were found to be larger from beech than pine forests and were found to be particularly sensitive to forest growth.
Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long‐term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine‐learning techniques could accurately predict hypertension risk using nutritional information. A cross‐sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine‐learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine‐learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.
Using Problem-Based Learning (PBL) in an Undergraduate Ergonomics Course

Ergonomics courses typically cover a range of topics related to the design and organization of workplaces to optimize human performance and well-being while minimizing injury risk and discomfort. This study introduces the PBL application steps to attract attention to the importance of noise not only in workplaces but also in daily life. The driving question is determined as “How can we assess and minimize the perceived daily noise exposure of people to sustain hearing health? ”. Then, the students are asked to evaluate the PBL activities at the end of the term. Among the students who completed all activities, 60% strongly agreed, 32% agreed that PBL contributed to the understanding of the subject, and only 8% stated that PBL didn’t make a significant contribution when learning the topic of concern. The PBL framework developed for “noise” has the potential to be enhanced and adapted for other topics in this course.
Using Hurst Coefficient and Lacunarity to diagnosis early breast diseases

This paper presents a study about diagnosis of breast diseases in early stages using Thermal Images, Hurst Coefficient and Lacunarity. It is based on analysis of symmetry of temperatures in breasts. The first step is extraction of region on interest: right and left breast. With these two images, a new image (image Subtraction) is generated. The second step is extraction of features. For this, Hurst Coefficient and Lacunarity were used. Using Hurst Coefficient were extracted 36 features. Others 97 features were obtained by Lacunarity. Therefore, the proposed method characterizes thermal images by 133 features. The last step is classification. With this objective, techniques of learning machine were used to classify the patients in two groups: healthy or sick. The proposed method presented excellent results (ROC Area = 0,958).
Investigating the dietary intentions of Iranian tourists regarding the consumption of local food

Introduction Attention to local food among tourists is increasing day by day. However, studies on the behavior and intention of tourists towards consuming these foods are few. Limited studies in this field prompted us to help fill the research gap by doing this research. Methods This study aims to use the extended theory of planned behavior (TPB) by adding risk perception (RP) to investigate the tourists’ intentions towards local food (TILF) in Iran. The extended model tested 313 tourists visiting local food supplies. Results Results show that the extended TPB explains 87.8% of tourist intention variance. Three variables of the original TPB had positive impacts on tourists’ intentions. A notable result of this study was the realization of a negative relationship between RP and TILF. Discussion This research has provided recommendations to strengthen tourists’ intentions toward local foods by influencing their attitudes, mental beliefs, perceived behavioral control, and risk perception.
Spectral Graph Wavelet Transform-Based Feature Representation for Automated Classification of Emotions From EEG Signal

Electroencephalogram (EEG) monitors the brain’s electrical activity and carries useful information regarding the subject’s emotional states. Due to the nonstationary and being complex in nature, proper signal-processing techniques are necessary to get meaningful interpretations. The EEG signal has been represented using a graph by incorporating the temporal dependency. In this article, a novel feature based on spectral graph wavelet transform (SGWT) for representing EEG signals has been proposed by considering the interdependency among different samples of EEG signals. SGWT is effective in finding multiscale information at the local level as well as the global level. These multiscale representations allow for the extraction of information about the EEG signal at different scales. The SGWT coefficients are used to develop machine-learning classifiers for emotion identification. Principal component analysis (PCA) is also used for feature reduction. The proposed framework is evaluated based on a publicly available SEED dataset with the help of extensive experiments. The ${k}$ -nearest neighbor (KNN) classifier provides 97.3% accuracy with a standard deviation of 1.2%. The SGWT-based representation has achieved 12.7% higher accuracy compared to the raw EEG signal, which shows the usefulness of the proposed approach. Our model for emotion recognition attains superior classification performance compared to state-of-the-art methods. Finally, the investigation of interdependency among the samples of EEG signals reveals that the SGWT-based representation of EEG signals is a useful tool for analyzing EEG signals.
How Chinese female migrants in South Korea overcome constraints to participate in leisure activities

This study aimed to investigate the leisure phenomenon of Chinese female migrants (including ethnic Koreans from China) residing in South Korea. Data were collected at three different locations in southwestern Seoul that were considered popular gathering places for Chinese migrants. A total of 455 respondents participated in measuring the constraints faced by Chinese female migrants in South Korea that keep them from participating in leisure activities. This study found that constraint negotiation negatively influenced leisure constraints, which in turn, decreased leisure participation intention. The study provides scholars and practitioners with insights on how the leisure participation of Chinese female migrants can be promoted by successfully managing the negotiation process and leisure constraints. It is also recommended to disconnect the linkage from leisure constraints to leisure participation so that possible constraints do not render negative impacts.
Seaweed Growth Monitoring with a Low-Cost Vision-Based System

In this paper, we introduce a method for automated seaweed growth monitoring by combining a low-cost RGB and stereo vision camera. While current vision-based seaweed growth monitoring techniques focus on laboratory measurements or above-ground seaweed, we investigate the feasibility of the underwater imaging of a vertical seaweed farm. We use deep learning-based image segmentation (DeeplabV3+) to determine the size of the seaweed in pixels from recorded RGB images. We convert this pixel size to meters squared by using the distance information from the stereo camera. We demonstrate the performance of our monitoring system using measurements in a seaweed farm in the River Scheldt estuary (in The Netherlands). Notwithstanding the poor visibility of the seaweed in the images, we are able to segment the seaweed with an intersection of the union (IoU) of 0.9, and we reach a repeatability of 6% and a precision of the seaweed size of 18%.
Temporal changes in the structure of the suprabenthic community from Hendaya beach (southern Bay of Biscay): A comparison with a northwestern Mediterranean beach community

In 1991-1992, the suprabenthic communities from Hendaya and Creixell beaches (southeastern bay of Biscay and northwestern Mediterranean, respectively) were monthly sampled in order to analyse and compare their qualitative and quantitative structural evolution during an annual cycle. The suprabenthos of Hendaya and Creixell beaches shows a similarity rate of 33.3 % for genera and 11.8 % at the species level, mainly amphipods and mysids. The presence of vicariant especies, especially of the genera Schistomysis, Cumposis and Eurydice indicates a certain degree of biogeographical isolation between the suprabenthos of dissipative type beaches from southeastern Bay of Biscay to northwestern Mediterranean.
Calcium silicate biocomposites: effects of selenium oxide on the physico-mechanical features and their in-vitro biological assessments

Bone tissue regenerative material serves as a prospective recovery candidate with self-adaptable biological properties of bio-activation, degradability, compatibility, and antimicrobial efficacy instead of metallic implants. Such materials are highly expensive due to chemical reagents and complex synthesis procedures, making them unaffordable for patients with financial constraints. This research produced an efficient bone tissue regenerative material using inexpensive naturally occurring source materials, including silica sand and limestone. The extracted SiO2 and CaO particles (75:25 wt%) were subjected to hydrothermal synthesis (water treatment instead of chemical solvents) to produce the CaSiO3 biomaterial (code: S). Selenium oxide was doped with calcium silicate at 3, 5, and 10 wt. % to enhance its properties, yielding biocomposite materials (i.e. S3, S5, and S10). The physico-mechanical properties of these materials were investigated with x-ray diffraction, Fourier transform infrared, FESEM-EDS, and micro-universal testing machine. The results revealed that the synthesized biocomposites have a crystalline wollastonite phase with a porously fused rough surface. From structural parametric calculations, we found that the biocomposites have reduced particle size and enhanced surface area due to the influence of selenium oxide. The biocomposite S10, having high SeO2 content, attained the maximum compressive strength of 75.2 MPa. In-vitro studies of bioactivity, biodegradability, biocompatibility, and antibacterial activity were performed. At 7 and 14 d of bioactivity, the synthesized biocomposites are capable of dissolving their ions into simulated body fluid (SBF) solution to precipitate hydroxyapatite and a required Ca/P ratio of 1.69 was achieved by S3. A comparative analysis has been performed on the degradation activity in Tris-HCl and the consequent pH changes during SBF treatment. The bio-analysis revealed that the biocomposite S3 shows enhanced bioactivity through a controlled degradation rate and secured cell viability of 88% at a concentration of 100 μg ml−1. It also offers significant bacterial inhibition potency against E.coli and S.aureus bacteria.
Gestión del Tiempo en 12 Salas Chilenas de Kindergarten: Recreo, Colación y Algo de Instrucción Time Management in 12 Chilean Kindergarten Classrooms: Recess, Snack and a Little Teaching

Palabras clave: educacion preescolar, kindergarten, lenguaje, ensenanza de lectura, gestion del tiempo en el aula. This study examined time management in 12 kindergarten classrooms from 9 Chilean schools with diverse sources of funding and socioeconomic status (SES). We conducted 33 observations in the 12 classrooms (average duration 197 minutes), and determined the amount of time devoted to different activities. More than half of the time in these classrooms was spent in non-instructional activities such as recess, snack, and managing the children’s behavior. Additionally, the distribution of instructional time is not in accordance with current fi ndings regarding the activities that are more fruitful for children’s development. These results are independent of the schools’ source of funding and SES.