Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing brain connectivity group graphs from EEG time series

ABSTRACT Graphical analysis of complex brain networks is a fundamental area of modern neuroscience. Functional connectivity is important since many neurological and psychiatric disorders, including schizophrenia, are described as ‘dys-connectivity’ syndromes. Using electroencephalogram time series collected on each of a group of 15 individuals with a common medical diagnosis of positive syndrome schizophrenia we seek to build a single, representative, brain functional connectivity group graph. Disparity/distance measures between spectral matrices are identified and used to define the normalized graph Laplacian enabling clustering of the spectral matrices for detecting ‘outlying’ individuals. Two such individuals are identified. For each remaining individual, we derive a test for each edge in the connectivity graph based on average estimated partial coherence over frequencies, and associated p-values are found. For each edge these are used in a multiple hypothesis test across individuals and the proportion rejecting the hypothesis of no edge is used to construct a connectivity group graph. This study provides a framework for integrating results on multiple individuals into a single overall connectivity structure.
Learning models that can improve elementary school students' creative thinking ability

There are several abilities that can accommodate students in facing the 21st century, one of which is the ability to think creatively, but based on educational report cards, students' ability to think creatively is low. This study aims to describe a learning model that can stimulate the emergence of creative thinking abilities of elementary school students through the literature study method. Based on the results of the research and discussion, the researchers concluded that increasing the ability to think creatively can be pursued through three types of learning models, including (1) problem-based learning models, (2) discovery-based learning models, and (3) project-based learning models. These learning models include problem-solving, problem-posing, problem-based learning, guided inquiry learning, discovery learning, and project-based learning. This learning model was chosen because it has been proven by many researchers and has syntactic relations and indicators of creative thinking ability
Predictive Discontinuous Modulation Strategy With Embedded Inter-Phase Cluster Voltage Control for Cascaded H-Bridge StatComs

The discontinuous pulsewidth modulation (DPWM) in cascaded H-bridge (CHB) static compensators (StatComs) is achieved via adding a common mode voltage to the converter voltage references. Hence, in discontinuously operated CHB StatComs, the common mode voltage has two functions, namely, interphase cluster voltage control and clamping a converter voltage. These two functions are conventionally achieved by two independently designed control stages and interferences between them can cause the loss of cluster voltage control during unbalanced grid conditions, since the fundamental frequency common mode voltage for discontinuous operation is significant. To solve this control issue, this article proposes a DPWM strategy with embedded interphase cluster voltage control using a finite control set model predictive control approach. The proposed predictive DPWM outperforms the conventional DPWM in switching loss reduction while preventing the cluster voltages from diverging during unbalanced grid conditions. The effectiveness of the proposed strategy is verified experimentally considering various grid conditions.
Vitamin D Deficiency and Cardiovascular Mortality: Retrospective Analysis “Siena Osteoporosis” Cohort

Vitamin D is a fat-soluble vitamin that plays a key role in bone metabolism, particularly concerning the regulation of calcium and phosphate homeostasis. Cardiovascular disease (CVD) is the main cause of morbidity and mortality in Western countries. Knowledge of the role of vitamin D in CVD arose from evidence of the vitamin D receptor (VDR) inside the cardiovascular system. In this retrospective analysis, we investigated the relationships between vitamin D status and hospitalization for heart failure (HF), overall mortality and cardiovascular mortality. Between 2004 and 2009, age-stratified, random sampling of elderly men and postmenopausal women in the primary care registers of Siena residents was performed. In total, 174 males (mean ± SD, 65.9 ± 6 years) and 975 females (62.5 ± 6 years) were enrolled in the study. We investigated the association between 25OHD status and hospitalization for HF or causes of mortality. A total of 51 subjects (12 males and 39 females) had been hospitalized for acute HF. At the end of the survey, 931 individuals were alive, while 187 had died (43 males and 144 females). A greater proportion of deceased patients showed low 25OHD (particularly patients with levels below 20 ng/mL). A similar trend was observed concerning the prevalence of patients with 25OHD levels below 20 ng/mL who died from stroke (RR = 2.15; 95% CIs 0.98–4.69; p = 0.06). Low 25OHD levels may be predictive of cardiovascular mortality. Whether vitamin deficiency represents a primitive cause or is a simple bystander in increased cardiovascular mortality should be further investigated in prospective large cohort studies specifically designed to assess CVD risk, including a detailed assessment of cardiac dysfunction and the characterization of atherosclerotic lesions.
SARS-CoV-2 Infection Increases the Risk of Muscle Injury in Professional Male Soccer Players—A Retrospective Analysis of the Italian and Spanish Major Leagues

A retrospective cohort study on professional soccer players from the Serie A and LaLiga was conducted to investigate the correlation between SARS-CoV-2 infection and muscle injuries. Players were divided into two groups based on whether they contracted the SARS-CoV-2 infection (C+) or not (C−) during the 2020/2021 season. In the 2019–2020 season, both championships showed a similar number of muscular injuries (MI) between C+ and C− (Serie A: p = 0.194; 95% CI: −0.044 to 0.215, LaLiga p = 0.915; 95% CI: −0.123 to 0.137). In the 2020–2021 season, C+ had a significantly higher number of MI compared to C− in both championships (Serie A: p < 0.05; 95% CI 0.731 to 1.038; LaLiga: p < 0.05; 95% CI: 0.773 to 1.054). Multiple linear regression analysis confirmed that belonging to C+ in the season 2020/2021 was the variable that most strongly influenced the probability of having a muscle injury. Survival analysis revealed a hazard ratio of 3.73 (95% CI 3.018 to 4.628) and of 5.14 (95% CI 3.200 to 8.254) for Serie A and LaLiga respectively. We found an association between SARS-CoV-2 infection and increased risk of muscle injury, emphasizing the importance of carefully considering the infection in the decision-making process for returning to sport. Therefore, SARS-CoV-2 infection should be judged as a real injury requiring specific assessment and training programs.
Reversible jump MCMC to identify dropout mechanism in longitudinal data

Abstract Existence of missing values is an inseparable part of longitudinal studies in epidemiology, medical and clinical studies. Usually researchers, for simplicity, ignore the missingness mechanism while, ignoring a not at random mechanism may lead to misleading results. In this paper, we use a Bayesian paradigm for fitting selection model of Heckman, which allows the non-ignorable missingness for longitudinal data. Also, we use reversible-jump Markov chain Monte Carlo to allow the model to choose between non-ignorable and ignorable structures for missingness mechanism, and show how the selection can be incorporated. Some simulation studies are performed for illustration of the proposed approach. The approach is also used for analyzing two real data sets.
Mode Characterization and Sensitivity Evaluation of a Surface Acoustic Wave (SAW) Resonator Biosensor: Application to the Glial-Fibrillary-Acidic-Protein (GFAP) Biomarker Detection

Biosensors based on surface acoustic waves (SAWs) offer unique advantages due to their high sensitivity, real-time response capability, and label-free detection. The typical SAW modes are the Rayleigh mode and the shear-horizontal mode. Both present pros and cons for biosensing applications and generally need different substrates and device geometries to be efficiently generated. This study investigates and characterizes SAW resonator biosensors on lithium niobate in terms of modes generated and biosensing performance. It reveals the simultaneous presence of two typical SAW modes, the first around 1.6 GHz and the second around 1.9 GHz, differently polarized and clearly separated in frequency, which we refer to as slow and fast modes. The two modes are studied by numerical simulations and biosensing experiments with the glial-fibrillary-acidic-protein (GFAP) biomarker. The slow mode is generally more sensitive to changes in surface properties, such as temperature and mass changes, by a factor of about 1.4 with respect to the fast mode.
Linearization and Stability of Nonholonomic Mechanical Systems

The stability of an equilibrium point of a nonlinear system is typically analyzed in two ways: (1) stability of its linearization, and (2) Lyapunov stability. An unconstrained simple mechanical system is a type of nonlinear system with a special structure, and so the methods for stability analysis can be specialized for this particular class of nonlinear systems. For a simple mechanical system subject to velocity constraints, the situation becomes more complicated. If the constraints are holonomic, then the problem can simply be reduced to that of an unconstrained simple mechanical system by restricting analysis to a certain submanifold of the configuration space. If the constraints are nonholonomic, this approach cannot be taken. In this report we study the differences and additional complexities that arise in these nonholonomic mechanical systems, and derive results with regards to linearization and stability of its equilibria.
Non-Parabolic Band Hydrodynamic Model for Silicon Quantum Wires

ABSTRACT Electron transport phenomena in silicon nanowires are investigated for square and equilateral triangle cross-sections of the wire by using an Extended Hydrodynamic model coupled to the Schrödinger–Poisson system. This model has been formulated by closing the moment system derived from the Boltzmann equation on the basis of the maximum entropy principle of Extended Thermodynamics, including scattering of electrons with acoustic and non-polar optical phonons and by considering the conduction band described by the Kane dispersion relation. Applications to the case of bulk silicon for different cross-sections of the wire are presented.
A fast optical proximity sensor skin that contains an analog computing circuit and can cover an entire link

This paper proposes a novel optical proximity sensor skin that can cover entire robot links and has high responsiveness, wire-saving properties, and suitability for covering narrow surfaces. Moreover, it can extract information that can help to control robots. Although implementing these characteristics simultaneously has been problematic for sensor skins covering links, the proposed sensor skin achieves this using a newly developed analog computing circuit. This analog computing circuit only consists of several resistors and diodes and is based on the splitting ratio of photocurrents variation based on the design of the resistance values. If the output from the proximity sensing element is interpreted as a virtual force, the processed output denotes its magnitude and the location of the point of action. Even if multiple objects are detected, the outputs are associated with the resultant virtual force because photocurrents are merged. Therefore, the characteristics such as high responsiveness are maintained. This work fabricated a prototype to evaluate the proposed sensor skin. Then, collision avoidance experiments against human hands were conducted to validate the efficiency of the proposed sensor skin for generating robot motions. These results verified that the proposed sensor skin is suitable for robots reacting quickly to unknown environments. GRAPHICAL ABSTRACT
Backchannel responses in Hungarian conversations: a corpus-based study on the effect of the partner’s age and gender

Backchannel responses (BChs) have been an important research topic in conversation analysis since Yngve (1970) drew a distinction between speech produced by the person holding the turn and talking in the ‘main’ channel, while the listener occupying the ‘back’ channel of the communication. However, much less is known about the acoustic-phonetic characteristics of BChs. The aim of the present study is twofold: 1. it seeks to provide a general analysis of BChs as a formal group in 10 Hungarian conversations, using a formal, phonetic approach. 2. Additionally, another sub-study was conducted concerning the contextual variability of BChs investigating their change, according to the age of the conversation partner in three-party conversations
Treatment of Uranium-Contaminated Ground Water Using Adsorption Technology via Novel Mesoporous Silica Nanoparticles

Contamination of underground water by uranium (U) and other heavy metals is a growing concern. Mesoporous silica nanoparticles (MSNs) have shown great potential as an adsorbent material for heavy metal removal. This study synthesized a novel MSN using surface-initiated atom transfer radical polymerization (SI-ATRP) and evaluated its effectiveness for removing uranium from aqueous solutions under different conditions. The particle size was reduced to 150–240 nm to enhance adsorption. Fourier transform infrared characterization and thermogravimetric analysis confirmed successful synthesis and modification. Results showed that the MSN adsorbent was highly effective in removing U, with a removal rate of 85.35% at 120 min. Temperature had a significant impact, with the highest removal rate of 96.7% achieved at 25 °C and a U concentration of 10 ppm. The highest removal rate of 91.89% was achieved at a pH of 6 and a U concentration of 50 ppm. The highest removal rate of 95.16% was achieved at 25 mg and a U concentration of 50 ppm at room temperature for 60 min. The MSNs also showed a 58.27% removal rate in a mixture solution at room temperature for 60 min. This study demonstrates the effectiveness of the MSN adsorbent for removing U under different conditions.
Cognitive Adaptation to Stress and Trauma: The Role of Self-Appraised Problem-Solving in Posttraumatic Stress Disorder

Background Cognitive appraisals play a fundamental role in mental health outcomes following exposure to trauma. Appraisals influence emotional reactions, coping responses, and adaptation to stress and represent a modifiable factor that can serve as a central focus for intervention. Most studies have primarily focused on the role of dysfunctional cognitions in the persistence of posttraumatic stress disorder (PTSD). In this study, we extend research in this area by investigating the role of problem-solving appraisal, an adaptive cognitive strategy, in the association between stress and PTSD. Methods A total of 322 participants completed the Perceived Stress Scale, the problem-solving inventory (PSI), and the PTSD Checklist for DSM-5. Descriptive statistics were generated, and intercorrelations and mediation analysis were performed. Results Problem-solving confidence and personal control partially mediated the relationship between stress and PTSD. However, contrary to existing research, the approach-avoidance style, which is a subscale of the PSI, did not mediate the relationship between these variables. Conclusion Interventions for PTSD should incorporate a complementary focus on developing and increasing adaptive cognitions pertaining to personal control and confidence in problem-solving abilities. This could potentially form part of a broader process of rebuilding the individual's cognitive worldview following exposure to trauma.
Additive Manufacturing of Cu Using Graphene-Oxide-Treated Powder

Additive manufacturing of Cu is interesting for many applications where high thermal and electric conductivity are required. A problem with printing of Cu with a laser-based process is the high reflectance of the powder for near-infrared wavelengths making it difficult to print components with a high density. In this study, we have investigated laser bed fusion (L-PBF) of Cu using graphene oxide (GO)-coated powder. The powder particles were coated in a simple wet-chemical process using electrostatic attractions between the GO and the powder surface. The coated powder exhibited a reduced reflectivity, which improved the printability and increased the densities from ~90% for uncoated powder to 99.8% using 0.1 wt% GO and a laser power of 500 W. The coated Cu powders showed a tendency for balling using laser powers below 400 W, and increasing the GO concentration from 0.1 to 0.3 wt. % showed an increase in spattering and reduced density. Graphene-like sheet structures could be observed in the printed parts using scanning electron microscopy (SEM). Carbon-filled inclusions with sizes ranging from 10–200 nm could also be observed in the printed parts using transmission electron microscopy (TEM). The GO treatment yielded parts with higher hardness (75.7 HV) and electrical conductivity (77.8% IACS) compared to the parts printed with reference Cu powder.
STEM-PjBL Learning: The Impacts on Students’ Critical Thinking, Creative Thinking, Communication, and Collaboration Skills

The purpose of this study was to analyze the effectiveness of STEM-PjBL learning on students' critical thinking, creative thinking, communication, and collaboration (4C) skills. This research is quantitative with a one-group pretest-posttest design. The research samples were 106 class XI State Senior High School 16 Semarang students. Quantitative data on critical thinking skills were analyzed using the N-gain test, while other data were analyzed descriptively. The results showed increased students' critical thinking skills based on the N-gain test. The paired sample t-test results in the SPSS 28 program obtained a significance value/Sig. (2-tailed) of 0.001, there were differences in critical thinking skills before and after STEM-PjBL learning. STEM-PjBL learning effectively trained students' creative, collaborative, and communicative thinking skills with high categories. This learning effectively trained students' critical thinking, creative thinking, communication, and collaboration skills
Data envelopment analysis based on team reasoning

Existing approaches to data envelopment analysis focus mainly on the derivation of the efficiency of the individual decision-making unit (DMU) or on the calculation of the weights of multiple inputs or outputs, but pay little attention to the team interest of all the DMUs. Motivated by the idea of team reasoning, in which the benefit of the team is of higher importance than that of each individual, this paper considers all the DMUs as a team and introduces the team indexes including the overall efficiencies, variance, boundaries of all the DMUs, and relationships between DMUs. Several models are first developed to estimate values of the team indexes based on which decision makers can provide their preferences regarding them. Then, models are established to obtain the interval efficiencies of individual DMUs under the condition that the team indexes are satisfied. Several examples are given to illustrate the proposed approaches and verify their applicability.
Electromagnetic properties and microstructures of in situ MgB2 wires made from three types of boron powders

In powder-in-tube processed MgB2 wires, the choice of boron powder as a starting material crucially affects their performance. In this paper, we prepared in situ MgB2 wires from three types of boron powders in various heat-treatment conditions and investigated their electromagnetic properties and microstructures. Their critical current density, Jc, varied over a wide range from sample to sample. The difference in Jc is understood to be caused by the effect of changes in the electrical connectivity, K, and intrinsic residual resistivity, ρ0. Here, K represents the effective cross-sectional area for current, and ρ0 reflects the degree of the charge carrier scattering caused by lattice defects. It was found that the use of boron powder with a large specific surface area leads to a large degree of lattice defects in MgB2 grains and enhances ρ0, resulting in improving Jc. The boron powder produced by thermal decomposition of B2H6 has a large specific surface area. Hence, this boron powder is the most suitable as a starting material for MgB2. Meanwhile, dry pulverization of low-cost boron powder, which is largely produced by active-metal reduction of B2O3, is also effective to increase its specific surface area without introducing impurities, resulting in the enhancement of Jc in the entire magnetic field region. This finding broadens the choice of boron powder and contributes to realizing superconducting applications with excellent balance between performance and cost.
Inference from the incomplete longitudinal design under an ARMA covariance structure

JAMES ROCHON. Inference from the Incomplete Longitudinal Design under an ARMA Cdvariance Structure (Under the direction of RONALD W. HELMS). A stochastic model is presented for the analysis of the longitudinal design, appropriate when some of the response variables are missing. The general linear model is used to relate these dependent variables to other variables which are thought to account for inherent variation. An ARMA time series representation is used to model disturbance terms, resulting in a characteristic structure in the covariance matrix among the repeated measures. Maximum likelihood estimation procedures are performed. An unbiased estimator for the general linear model parameter vector is derived, and this estimator is observed to enjoy favourable large-sample properties. There is no convenient expression for the ARMA parameter estimator, and non-linear optimization procedures are prescribed to iterate to a maximizing value. Two broad classes of hypotheses among the indigenous parameters are considered, and procedures to test these hypotheses are derived. The first examines the covariance structure among the repeated measures in an effort to verify the ARMA model. The second considers more substantive hypotheses among the linear model parameters. Two datasets are studied in detail to illustrate the procedures. The first was generated at random from a known stochastic mechanism, while the second was derived from a paper published in the statistical literature. The underlying stochastic patterns are largely verified by the methodology. It is concluded that while the assumptions underpinning the ARMA covariance models may be somewhat restrictive for many practical situations, they nevertheless offer a wide variety of covariance structures, which can have a salutary effect, particularly in the presence of missing values.