Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Control of a Real Spherical Robot

This paper presents the design and implementation of a spherical robot with an internal mechanism based on a pendulum. The design is based on significant improvements made, including an electronics upgrade, to a previous robot prototype developed in our laboratory. Such modifications do not significantly impact its corresponding simulation model previously developed in CoppeliaSim, so it can be used with minor modifications. The robot is incorporated into a real test platform designed and built for this purpose. As part of the incorporation of the robot into the platform, software codes are made to detect its position and orientation, using the system SwisTrack, to control its position and speed. This implementation allows successful testing of control algorithms previously developed by the authors for other robots such as Villela, the Integral Proportional Controller, and Reinforcement Learning.
The impact of a universal social emotional intervention on students’ mental wellbeing in a secondary public school in Qatar

Abstract This paper is based on a study that explored the impact of a practitioner-designed social emotional program on students’ wellbeing based on the results of the WEMWBS scale in a public secondary school in Qatar. This cluster randomized controlled trial study (RCT) involved the assignment of two groups; an intervention group (n = 82) to receive a universal social emotional intervention and a control group (n = 81) to continue with the usual practices without attending or participating in any social emotional interventions. Students in both groups rated their wellbeing levels before the intervention (baseline) and after the intervention (post intervention). The results revealed a significant improvement in students’ wellbeing after participating in the SEL intervention with an adjusted effect size of (EF = 0.76). The paper discusses the results of this study in the light of the program’s design and social context.
Psychometric Properties of the Moral Disengagement through Technologies Questionnaire (MDTech-Q) in a Sample of Chilean University Students

In the virtual environment, hostile and aggressive comments that could negatively affect university students who often use different digital platforms are frequently observed, more than other age groups who have little or no supervision. In this sense, moral disengagement (MD) has been linked to different negative behaviors that manifest in physical interactions and which currently take place on the Internet, creating the need for instruments that specifically address MD online. The aim of this study is to adapt and validate the Moral Disengagement through Technologies Questionnaire (MDTech-Q) with Chilean university students. The sample comprised 527 university students (43.14% men, 56.86% women), with an average age of 22.09 years (SD (Standard Deviation) = 3.59) enrolled in 12 universities. First, a linguistic adaptation of the scale took place, and the surveys were applied considering ethical principles. Then, two confirmatory factor analyses (CFA) were performed, which considered four correlated factors, and provided satisfactory indices, agreeing with the original theoretical proposal, and demonstrating suitable reliability by internal consistency. In relation to the analyses of invariance according to sex and social media use, the MDTech-Q is stable up to scalar invariance. This study provides evidence of the psychometric quality of the MDTech-Q for its use on Chilean university students.
The Effect of Hydrogen on Plastic Anisotropy of Mg and α-Ti/Zr from First-Principles Calculations

Mg and α-Ti/Zr exhibit high plastic anisotropy. In this study, the ideal shear strength across the basal, prismatic, pyramidal I, and pyramidal II slip systems in Mg and α-Ti/Zr with and without hydrogen was computed. The findings indicate that hydrogen reduces the ideal shear strength of Mg across the basal and pyramidal II slip systems, as well as of α-Ti/Zr across all four systems. Moreover, the activation anisotropy of these slip systems was analyzed based on the dimensionless ideal shear strength. The results suggest that hydrogen increases the activation anisotropy of these slip systems in Mg, while decreasing it in α-Ti/Zr. Furthermore, the activation possibility of these slip systems in polycrystalline Mg and α-Ti/Zr subjected to uniaxial tension was analyzed by utilizing the ideal shear strength and Schmidt’s law. The results reveal that hydrogen increases the plastic anisotropy of Mg/α-Zr alloy while decreasing that of α-Ti alloy.
Validation of the qSOFA and CRB-65 in SARS-CoV-2-infected community-acquired pneumonia

Rationale Prognostic accuracy of the quick sequential organ failure assessment (qSOFA) and CRB-65 (confusion, respiratory rate, blood pressure and age (≥65 years)) risk scores have not been widely evaluated in patients with SARS-CoV-2-positive compared to SARS-CoV-2-negative community-acquired pneumonia (CAP). The aim of the present study was to validate the qSOFA(-65) and CRB-65 scores in a large cohort of SARS-CoV-2-positive and SARS-CoV-2-negative CAP patients. Methods We included all cases with CAP hospitalised in 2020 from the German nationwide mandatory quality assurance programme and compared cases with SARS-CoV-2 infection to cases without. We excluded cases with unclear SARS-CoV-2 infection state, transferred to another hospital or on mechanical ventilation during admission. Predefined outcomes were hospital mortality and need for mechanical ventilation. Results Among 68 594 SARS-CoV-2-positive patients, hospital mortality (22.7%) and mechanical ventilation (14.9%) were significantly higher when compared to 167 880 SARS-CoV-2-negative patients (15.7% and 9.2%, respectively). All CRB-65 and qSOFA criteria were associated with both outcomes, and age dominated mortality prediction in SARS-CoV-2 (risk ratio >9). Scores including the age criterion had higher area under the curve (AUCs) for mortality in SARS-CoV-2-positive patients (e.g. CRB-65 AUC 0.76) compared to SARS-CoV-2 negative patients (AUC 0.68), and negative predictive value was highest for qSOFA-65=0 (98.2%). Sensitivity for mechanical ventilation prediction was poor with all scores (AUCs 0.59–0.62), and negative predictive values were insufficient (qSOFA-65=0 missed 1490 out of 10 198 patients (∼15%) with mechanical ventilation). Results were similar when excluding frail and palliative patients. Conclusions Hospital mortality and mechanical ventilation rates were higher in SARS-CoV-2-positive than SARS-CoV-2-negative CAP. For SARS-CoV-2-positive CAP, the CRB-65 and qSOFA-65 scores showed adequate prediction of mortality but not of mechanical ventilation. Mortality and mechanical ventilation rates are higher in SARS-CoV-2-positive compared to SARS-CoV-2-negative CAP. Only scores with an age criterion (CRB-65 and qSOFA-65) provide adequate mortality prediction but sensitivity is low for mechanical ventilation. https://bit.ly/41lnByb
Matchings and games on networks

We investigate computational aspects of popular solution concepts for different models of network games. In chapter 3 we study balanced solutions for network bargaining games with general capacities, where agents can participate in a fixed but arbitrary number of contracts. We fully characterize the existence of balanced solutions and provide the first polynomial time algorithm for their computation. Our methods use a new idea of reducing an instance with general capacities to an instance with unit capacities defined on an auxiliary graph. This chapter is an extended version of the conference paper [32]. In chapter 4 we propose a generalization of the classical stable marriage problem. In our model the preferences on one side of the partition are given in terms of arbitrary binary relations, that need not be transitive nor acyclic. This generalization is practically well-motivated, and as we show, encompasses the well studied hard variant of stable marriage where preferences are allowed to have ties and to be incomplete. Our main result shows that deciding the existence of a stable matching in our model is NP-complete. We then use our model to study a long standing open problem about cyclic 3D stable matchings. In particular, we prove that deciding whether a fixed 2D perfect matching can be extended to a 3D stable matching is NP-complete. This chapter is an extended version of the conference paper [33]. In chapter 5 we study a long standing open problem of whether the nucleolus of matching games can be computed efficiently. Our approach follows previous techniques that rely on obtaining a polynomial sized characterization of the least core as both the initial and crucial step in establishing an efficient algorithm. As a preliminary result, we introduce a generalisation of the least core and show that for node-weighted matching games existing polynomial sized characterizations of the least core can be extended to this generalised version. We then use this result to show that for a certain class games with general weights one can identify a node-weighted subgraph such that the least core of the original matching game is equal to the generalised least core of this subgraph. This allows us to obtain a polynomial time algorithm for computing the nucleolus for this class of matching games with general weights.
Surrogate-Assisted Combinatorial Optimization of EV Fast Charging Stations

In this work, an artificial neural network (ANN) surrogate model-based method is proposed to assist the optimal design of the modular electric vehicle (EV) fast dc charging station. This is a typical combinatorial optimization problem, which is hard to solve analytically as the key design parameters are discrete, e.g., the numbers of charging poles, power electronic converter modules, and switching contactors. In the first part of the article, the details on how to generate the expected charging power demand of the charging station are presented, where characteristic EV charging curves are considered. The charging station is designed to operate under a modified first-come-first-serve (FCFS) policy to maximize the quality of service (QoS) to the customers. The system time ratio, energy efficiency, and capital expenditure (CAPEX) are then taken as the performance indicators to evaluate different designs in correlation to expected charging demand. By varying the design parameters, we generate a group of datasets from an adjustable charging station simulation model, which is then used for supervised training of an ANN. As a surrogate model of the charging station, the trained neural network is finally used to quickly map the design parameters into the performance indicators, where optimal design parameters are found by evaluating the proposed cost function.
Ocean Data Assimilation, Initialization, and Predictions of ENSO with a Coupled GCM

Abstract A scheme for making seasonal to interannual predictions of El Nino–Southern Oscillation with a coupled atmosphere–ocean general circulation model that incorporates subsurface ocean measurements in the initial conditions is described. Anomaly initial conditions are used in order to reduce initial shock and climate drift. The ocean component of the prediction model has a nearly global domain, and the coupled model does not employ anomaly coupling or empirical statistical corrections. Initial conditions for the ocean were obtained from a near-global ocean analysis produced by an ocean data assimilation system. The assimilation system uses a variationally formulated optimal interpolation method to analyze oceanic fields from temperature observations and a first-guess field provided by integrating a global ocean general circulation model. The period of the analysis was 1986 through 1992. The anomaly initial conditions for the ocean were generated by adding the anomalies of the assimilated fields from .
Linear correlation analysis study of drinking water quality data for AlMukalla City, Hadhramout, Yemen

Ground water is the main principal source for drinking water and other activities in Yemen. It is an indispensable source of our life. The problem of ground water quality obtains high importance in this present-day, whether in the Arabic region or the other countries in the world. In this paper, a statistical regression analysis method of twenty one data points of drinking water at four fields (21 wells) for Al-Mukalla city, Hadhramout, Yemen was carried out in the study. This technique was based to study and calculate the correlation coefficients between various physicochemical parameters of drinking water at studied wells for Mukalla city. The results revealed that the systematic calculations of correlation coefficient between water parameters and regression analysis provide useful mean for rapid monitoring of water quality.
A Pilot Study of the Effects of Individualized Home Dual Task Training by Mobile Health Technology in People with Dementia

The objective of this pilot study was to evaluate the effects of dual-task training implemented by mobile health technology on performance on motor and dual-task tests in subjects with dementia. Nineteen subjects with a medical diagnosis of dementia were assigned to an experimental group (EG, n = 12) or control group (CG, n = 7). The EG participated in 24 sessions (3/week) of a homebase dual-task exercises program, in addition to their ongoing cognitive and physiotherapy treatment. The training program was implemented individually in the patient’s home by caregivers or relatives through electronic devices controlled by a mobile application. Before (Pre) and after (Post) the program, performance on motor and motor/cognitive (dual-task) tests were evaluated. Motor evaluation included gait at preferred and maximal speed, the Up and Go, and the Handgrip Strength test. Dual-task tests included gait with subtraction 3 s from 100 and naming animals (verbal fluency). The CG only performed the evaluations in addition to their cognitive and physiotherapy treatment. The statistical analysis (ANOVA Group*Test) showed a statically significant improvement for both dual-task tests in the EG after the training program, while the CG showed an impairment in the verbal fluency test. Conclusion: the implementation of a home exercise program carried out with mobile technology in people with dementia is feasible and positively affects their performance on dual tasks.
Solar Radiation Prediction using Adaboost Algorithm

Predictions of incoming solar energy are acquiring more importance, because of strong increment of solar power generation. Predictions is very useful in solar energy applications because it permits to generate solar data for locations where measurements are not available. In existing systems, solar radiation is predicted using fuzzy logic and neural networks separately. So that Mean absolute percentage error is greater than 10%. Fuzzy logic and neural networks are combined together using Takagi Sugeno Kang (TSK) method. TSK method is very efficient than mamdani method. Previous year solar radiation data is collected from National Environmental Agency and using this values neural network was trained. The graph between measured and predicted data values was plotted . Error is calculated using the difference between desired and output value. Prediction using combination of fuzzy and neural network model having Mean Absolute Percentage Error 6% was achieved. But in order to reduce Absolute Percentage Error value, we need to check the validity of the input data, so, that Adaboost algorithm is introduced in our proposed method. Adaboost algorithm is one of the best method of classification of weak learners. The algorithm classifies the training and testing data and also produces the corresponding errors. After finding the errors, it will be neglected from input data to make the predicted with more accuracy and less error. So that Mean Absolute Percentage Error -2.33% was achieved.
Is economic hardship associated with young children’s cortisol levels?

Economic hardship during childhood has been linked to poor physical and mental health. This study examines cross-sectional and longitudinal associations of a summed economic hardship score of poverty, food insecurity, and financial hardship with hair cortisol in young children. Data from 24-month (Time 1, mean age 5 years) and 36-month (Time 2, mean age 6 years) follow-up from the NET-Works obesity prevention trial (NET-Works, NCT0166891) were used. Hair cortisol measures obtained at each time point were log-transformed and regressed on economic hardship at Time 1 and a cumulative economic hardship from Time 1 to Time 2, using generalized linear regressions. All models were adjusted for child age, sex, race/ethnicity, and intervention (prevention vs. control) arm. The final analytic sample sizes ranged from 248 to 287. Longitudinal analyses indicated that for every 1-unit higher economic hardship score at Time 1, hair cortisol at Time 2 follow-up was on average 0.07 log-picograms per milligram (pg/mg) higher (95% confidence interval [CI]: 0.01, 0.13). For every 1-unit increase in the cumulative economic hardship score between Time 1 and 2, there was a 0.04 log-pg/mg (95% CI: 0.00, 0.07) average higher level of hair cortisol at Time 2 follow-up. Results show suggestive but limited evidence for an association between economic hardship and cortisol in young children.
On the general solution of the problem of the motion of a heavy rigid body in the Hess case

A solution of the Euler-Poisson equations in the Hess case is represented in the form of the family of its singular points together with the asymptotic behaviour of the solution at these points. A complete list of single-valued and finite-valued solutions in the Hess case is given. A representation for limiting periodic solutions is obtained and a precise condition for the existence of these solutions is found. Bibliography: 25 titles.
SUM OF ELEMENT ORDERS ON FINITE GROUPS OF THE SAME ORDER

For a finite group G, let ψ(G) denote the sum of element orders of G. It is known that the maximum value of ψ on the set of groups of order n, where n is a positive integer, will occur at the cyclic group Cn. In this paper, we investigate the minimum value of ψ on the set of groups of the same order.
Bayesian Adjustment of Anticipatory Covariates in Analyzing Retrospective Data

In retrospective surveys, records on important variables such as the respondent's educational level and social class refer to what is achieved by the date of the survey. Such variables are then used as covariates in investigations of behavior such as marriage and divorce in life segments that have occurred before the survey. To what extent can any change in the behavior be attributed to the misclassification of respondents across the various levels of the anticipatory variable? To what extent do they reflect real differences in the behavior across the levels? The connection is obtained by a Bayesian adjustment, by specifying a continuous-time Markov model for the incompletely observed time-varying anticipatory covariates, and by implementing standard Bayesian data augmentation techniques. The issues are illustrated by estimating effects of educational level on risks of divorce in a multiplicative piecewise-constant hazard model. Results show that ignoring the time-inconsistency of anticipatory variables may seriously plague the analyses because the relative risks across the anticipatory educational level are overestimated.
Hebbian learning, its correlation catastrophe, and unlearning

Learning and associative memory are concerned with storing and retrieving activity patterns of a neuronal net. It is considered to be a minimal requirement that the number of patterns that can be stored faithfully is extensive, i.e. is at least proportional to the number of neighbours each neuron interacts with. The main drawback of Hebbian learning, and of any one-shot local learning procedure, is that it cannot store extensively many patterns with activities which vary from pattern to pattern because, being local, it cannot discern global correlations. We critically review the performance of Hebbian unlearning—also proposed as a model of REM sleep—in a network of formal neurons with a distribution of axonal delays. Hebbian unlearning, though as local and unsupervised as Hebbian learning, eliminates undesirable global correlations, handles any spatio-temporal pattern, and improves the network performance greatly—sometimes even saturating a theoretical upper bound. Furthermore, it is shown that unlearning.
In-orbit calibration of the pyramid coarse sun sensor using Lyapunov-based conical maneuvers of the satellite: A new framework

Due to the lightweight, inexpensive, and simplicity, coarse sun sensors (CSS) are an interesting part of the small satellites with low-earth orbit. However, the CSS drawbacks such as low accuracy and nonlinear distribution of the error contours through the sensor Field of View (FOV) apply a notable limitation in its application. This forces the literature to focus on laboratory or in-orbit calibration methods. The excellence of the in-orbit calibration in comparison with the laboratory cases is clear; however, the difficulty is to achieve enough data when the satellite is launched. To solve that, in this paper, a conical maneuver based on the Lyapunov theory is designed which causes the sunlight to cover the sensor FOV. After that, using the achieved rich data pack, the CSS calibration process, including the sensor misalignment, solar cells misalignment, and solar cells characteristic curve (Fresnel effect), is accomplished. Moreover, the results show that the combination of the new calibration methods with the iterative least square (ILS) algorithm leads to 0.89° accuracy through the FOV of 120°.
An Ultra-Light Heuristic Algorithm for Autonomous Optimal Eco-Driving

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm. In this paper the authors present an ultra-light heuristic method for generating optimal Eco-Driving traces for Connected Autonomous Vehicles (CAVs) which indirectly minimizes the 1-norm of acceleration. This novel method produces EE improvements in line with previously implemented meta-heuristic methods while executing in a fraction of the time.