Search Results (40)

Background and Objectives: During exam sessions, many students experience high levels of stress caused by the large volume of material to study, tight deadlines, and pressure to achieve top grades. This study aimed to examine the relationship between academic stress, sleep disturbances, and eating behaviors by using validated questionnaires administered to a student sample. As stress levels increased, the data revealed a higher frequency of insomnia symptoms and disordered eating, including behaviors such as compulsive eating and irregular meal patterns. Materials and Methods: This cross-sectional, correlational study was conducted on a convenience sample of 317 university students from various academic centers across Romania. Participants were recruited via online distribution of a self-administered questionnaire during a four-month period, including exam sessions. The survey included the Perceived Stress Scale (PSS), the Insomnia Severity Index (ISI), and the Eating Disorder Examination Questionnaire (EDE-Q), alongside additional items assessing perceived links between stress, sleep, and eating behaviors, and the use of medication. Data were analyzed using SPSS with Spearman correlations, t-tests, and linear regression. Results: Statistical analyses revealed significant and positive associations between perceived stress levels and insomnia, as indicated by Spearman’s correlation (p < 0.01). A similar significant correlation was identified between perceived stress and disordered eating behaviors among students. Specifically, the feeling of being overwhelmed by academic workload showed a strong positive correlation with a lack of control over eating behaviors (r = 0.568). Furthermore, linear regression analysis confirmed a significant predictive relationship between feeling overwhelmed and the tendency to eat excessively beyond initial intentions, with a standardized regression coefficient B = 0.581 (p < 0.001). A separate regression analysis focusing on exam-related stress and episodes of compulsive eating behavior demonstrated a comparable result (B = 0.573, p < 0.001), indicating a robust positive association. Additionally, independent samples t-tests demonstrated that students experiencing high levels of stress during the exam period reported significantly greater difficulties with sleep initiation and higher levels of disordered eating compared to their peers with lower stress levels. The difference in insomnia scores was highly significant (t = 11.516, p < 0.001), as was the difference in eating behavior scores (t = 10.931, p < 0.001). Conclusions: These findings underscore the need for emotional support services and effective stress management strategies, enabling students to navigate academic demands without compromising their mental or physical well-being. Full article

Background/Objectives: University students are particularly susceptible to mental health issues, exhibiting a higher prevalence of insomnia and depressive symptoms compared to the general population. These mental problems adversely affect their academic performance and overall well-being. Understanding this issue is essential for developing effective prevention and intervention strategies for the academic community. The present study investigates the complex role of problematic TikTok use and insomnia in relation to depression among university students. Methods: An online cross-sectional survey was conducted between November 2024 and January 2025, involving a sample of 173 university students in Poland. The participants had a mean age of 23 years (M = 23.09, SD = 3.92), with 73.4% being women, and 49% were enrolled in medical and healthcare faculties. The questionnaire comprised a demographic survey, the Patient Health Questionnaire (PHQ-9) for assessing symptoms of depression, the Athens Insomnia Scale (AIS-8) for measuring symptoms of insomnia, and the modified Bergen Facebook Addiction Scale (BFAS) for evaluating problematic TikTok use (PTTU). The hypotheses were verified using Student’s t-test, Pearson’s correlation, and general linear model (GLM) mediation analysis. Results: The study found no significant gender (women vs. men) and faculty (medical vs. non-medical) differences in the level of symptoms of depression, insomnia, or problematic TikTok use. Insomnia, depression, and PTTU were positively inter-related. Insomnia completely mediates the relationship between PTTU and depression in university students. However, these results must be treated with caution due to the uneven gender distribution and cross-sectional nature of these studies, which limits causal relationships. Conclusions: Excessive engagement with TikTok has been associated with an increase in insomnia symptoms, which subsequently exacerbates depression symptoms among university students. Prevention and intervention strategies should prioritize reducing TikTok usage while simultaneously enhancing sleep hygiene and mental health within the academic population, irrespective of gender and university faculty affiliation. Full article

The Eastern Tibetan Plateau (ETP), characterized by its intricate topography and pronounced altitudinal gradient, presents significant challenges for climate model simulations. This study assesses precipitation over the ETP using high-resolution (HR) and low-resolution (LR) models from CMIP6 HighResMIP. Both HR and LR models successfully reproduce the spatial distribution of annual precipitation, capturing the northwest-to-southeast increasing gradient. However, HR models significantly outperform LR models, reducing the annual mean precipitation bias from 1.09 mm/day to 1.00 mm/day (9% reduction, p < 0.05, two-tailed Student’s t-test) and decreasing RMSE by 12% (p < 0.05) in the ETP for the 1985–2014 period. Furthermore, HR models exhibit superior skill in simulating extreme precipitation events, particularly over the Sichuan Basin. For the 1985–2014 period, HR models show markedly smaller biases in representing extreme precipitation and accurately reflect observed trends. Projections for the future suggest a pronounced intensification of extreme precipitation events across the region. Process-based scaling diagnostics attribute these changes predominantly to dynamical components, which account for approximately 85% of the total scaling change in HR models and 89% in LR models. These findings underscore the pivotal role of dynamical processes in shaping extreme precipitation and highlight the advantages of HR models in enhancing simulation fidelity. This study provides critical insights into climate model performance, offering robust information to inform climate mitigation and adaptation strategies tailored for the ETP. Full article

This study proposes a generalized family of distributions to enhance flexibility in modeling complex engineering and biomedical data. The framework unifies existing models and improves reliability analysis in both engineering and biomedical applications by capturing diverse system behaviors. We introduce a novel hybrid family of distributions that incorporates a flexible set of hybrid functions, enabling the extension of various existing distributions. Specifically, we present a three-parameter special member called the hybrid-Weibull–exponential (HWE) distribution. We derive several fundamental mathematical properties of this new family, including moments, random data generation processes, mean residual life (MRL) and its relationship with the failure rate function, and its related asymptotic behavior. Furthermore, we compute advanced information measures, such as extropy and cumulative residual entropy, and derive order statistics along with their asymptotic behaviors. Model identifiability is demonstrated numerically using the Kullback–Leibler divergence. Additionally, we perform a stress–strength (SS) reliability analysis of the HWE under two common scale parameters, supported by illustrative numerical evaluations. For parameter estimation, we adopt the maximum likelihood estimation (MLE) method in both density estimation and SS-parameter studies. The simulation results indicated that the MLE demonstrates consistency in both density and SS-parameter estimations, with the mean squared error of the MLEs decreasing as the sample size increases. Moreover, the average length of the confidence interval for the percentile and Student’s t-bootstrap for the SS-parameter becomes smaller with larger sample sizes, and the coverage probability progressively aligns with the nominal confidence level of 95%. To demonstrate the practical effectiveness of the hybrid family, we provide three real-world data applications in which the HWE distribution outperforms many existing Weibull-based models, as measured by AIC, BIC, CAIC, KS, Anderson–Darling, and Cramer–von Mises criteria. Furthermore, the HLW exhibits strong performance in SS-parameter analysis. Consequently, this hybrid family holds immense potential for modeling lifetime data and advancing reliability and survival analysis. Full article

Inferences on the location parameter $\lambda$ in location-scale families can be carried out using Studentized statistics, i.e., considering estimators $\tilde{\lambda }$ of $\lambda$ and $\tilde{\delta }$ of the nuisance scale parameter $\delta$, in a statistic $T=g(\tilde{\lambda },\tilde{\delta })$ with a sampling distribution that does not depend on $(\lambda ,\delta )$. If both estimators are independent, then T is an externally Studentized statistic; otherwise, it is an internally Studentized statistic. For the Gaussian and for the exponential location-scale families, there are externally Studentized statistics with sampling distributions that are easy to obtain: in the Gaussian case, Student’s classic t statistic, since the sample mean $\tilde{\lambda }=\overline{X}$ and the sample standard deviation $\tilde{\delta }=S$ are independent; in the exponential case, the sample minimum $\tilde{\lambda }=X_{1:n}$ and the sample range $\tilde{\delta }=X_{n:n}-X_{1:n}$, where the latter is a dispersion estimator, which are independent due to the independence of spacings. However, obtaining the exact distribution of Student’s statistic in non-Gaussian populations is hard, but the consequences of assuming symmetry for the parent distribution to obtain approximations allow us to determine if Student’s statistic is conservative or liberal. Moreover, examples of external and internal Studentizations in the asymmetric exponential population are given, and an ANalysis Of Spacings (ANOSp) similar to an ANOVA in Gaussian populations is also presented. Full article

Study Design: This is anobservational, retrospective, analytical study. Objective: The aim was todetermine a statistical basis for a future line of research based on the epidemiology of a center located in a developing country, as well as defining indirect mortality predictors. Methods: Clinical files were reviewed based on diagnosis of Traumatic Brain Injury (TBI), according to the International Classification of Diseases 10th Revision (ICD-10). Sociodemographic variables as well as treatment modality of the condition during hospitalization was recorded. The patient sample was divided into two groups. Student’s t-test was performed in variables with normal distribution and Chi-square test in independent random variables with standard normal distribution. For correlations, Pearson’s correlation coefficient was used, taking the p-value < 0.05 as statistically significant. Results: A total of 150 participantswere included in this study, fromwhich 125 weremale (83.3%). The average age was 28.58 ± 16.55 years. The median hospitalization time was 9 days. Forty-five patients (30%) were treated conservatively. Fifteen patients died during hospitalization. The factors considered as predictors of mortality in the general population corresponded to Motor Vehicle Accident, Frontonasal Duct Obstruction, Neuroinfection, Glasgow Coma Scale (GCS) at admission, as well asGCS at discharge. In the patients who underwent surgery, predictors of mortality corresponded to Motor Vehicle Accident, Bilateral Frontal Craniotomy, Surgical Bleeding > 475 cc, Neuroinfection, as well as GCS at admission and discharge. Conclusions: The creation of adequate diagnostic and therapeutic algorithms in traumatic brain injury management is needed, especially in developing countries. More specific studies are needed, particularly analytical and multicentric studies, which may allow the development of these algorithms. Full article

Differential evolution (DE) is one of the most promising black-box numerical optimization methods. However, DE algorithms suffer from the problem of control parameter settings. Various adaptation methods have been proposed, with success history-based adaptation being the most popular. However, hand-crafted designs are known to suffer from human perception bias. In this study, our aim is to design automatically a parameter adaptation method for DE with the use of the hyper-heuristic approach. In particular, we consider the adaptation of scaling factor F, which is the most sensitive parameter of DE algorithms. In order to propose a flexible approach, a Taylor series expansion is used to represent the dependence between the success rate of the algorithm during its run and the scaling factor value. Moreover, two Taylor series are used for the mean of the random distribution for sampling F and its standard deviation. Unlike most studies, the Student’s t distribution is applied, and the number of degrees of freedom is also tuned. As a tuning method, another DE algorithm is used. The experiments performed on a recently proposed L-NTADE algorithm and two benchmark sets, CEC 2017 and CEC 2022, show that there is a relatively simple adaptation technique with the scaling factor changing between 0.4 and 0.6, which enables us to achieve high performance in most scenarios. It is shown that the automatically designed heuristic can be efficiently approximated by two simple equations, without a loss of efficiency. Full article

The intercomparison between different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde and atmospheric model outputs. Since instruments and/or measures are not perfectly collocated, miss-collocation uncertainty must be considered in related intercomparison uncertainty budgets. This paper is motivated by the comparison of GNSS-RO, the Global Navigation Satellite System Radio Occultation, with ERA5, the version 5 Reanalysis of the European Centre for Medium-range Weather Forecasts. We consider temperature interpolation observed at GNSS-RO pressure levels to the ERA5 levels. We assess the interpolation uncertainty using as ‘truth’ high-resolution reference data obtained by GRUAN, the Reference Upper-Air Network of the Global Climate Observing System. In this paper, we propose a mathematical representation of the interpolation problem based on the well-known State-space model and the related Kalman filter and smoother. We show that it performs the same (sometimes better) than linear interpolation and, in addition, provides an estimate of the interpolation uncertainty. Moreover, with both techniques, the interpolation error is not Gaussian distributed, and a scaled Student’s t distribution with about 4.3 degrees of freedom is an appropriate approximation for various altitudes, latitudes, seasons and times of day. With our data, interpolation uncertainty results larger at the equator, the Mean Absolute Error being $MAE\cong 0.32$ K, and smaller at a high latitude, $MAE\cong 0.21$ K at −80° latitude. At lower altitudes, it is close to the measurement uncertainty, with $MAE<0.2$ K below the tropopause. Around 300 hPa, it starts increasing and reaches about 0.8 K above 100 hPa, except at the equator, where we observed MAE about 1 K. Full article

A novel Student’s t-based robust Poisson multi-Bernoulli mixture (PMBM) filter is proposed to effectively perform multi-target tracking under heavy-tailed process and measurement noises. To cope with the common scenario where the process and measurement noises possess different heavy-tailed degrees, the proposed filter models this noise as two Student’s t-distributions with different degrees of freedom. Furthermore, this method considers that the scale matrix of the one-step predictive probability density function is unknown and models it as an inverse-Wishart distribution to mitigate the influence of heavy-tailed process noise. A closed-form recursion of the PMBM filter for propagating the approximated Gaussian-based PMBM posterior density is derived by introducing the variational Bayesian approach and a hierarchical Gaussian state-space model. The overall performance improvement is demonstrated through three simulations. Full article

This article completes our studies on the formal construction of asymptotic approximations for statistics based on a random number of observations. Second order Chebyshev–Edgeworth expansions of asymptotically normally or chi-squared distributed statistics from samples with negative binomial or Pareto-like distributed random sample sizes are obtained. The results can have applications for a wide spectrum of asymptotically normally or chi-square distributed statistics. Random, non-random, and mixed scaling factors for each of the studied statistics produce three different limit distributions. In addition to the expected normal or chi-squared distributions, Student’s t-, Laplace, Fisher, gamma, and weighted sums of generalized gamma distributions also occur. Full article

Background: Obesity has become a public health problem in our society and is associated with many diseases, including type 2 diabetes mellitus, cardiovascular diseases, dyslipidemia, respiratory diseases, and cancer. Several studies relate weight loss in obese patients to improved anthropometric measurements and cardiometabolic risk. The objective of our study was to evaluate anthropometric changes, analytical parameters, insulin resistance, fatty liver, and metabolic scales, after a personalized weight loss program, through dietary advice to increase adherence to the Mediterranean diet and a motivational booster via mobile SMS messaging. Methods: Intervention study on a sample of 1964 workers, in which different anthropometric parameters were evaluated before and after dietary intervention: the metabolic score of insulin resistance; non-alcoholic fatty liver disease using different scales; metabolic syndrome; atherogenic dyslipidemia; and the cardiometabolic index. A descriptive analysis of the categorical variables was performed, by calculating the frequency and distribution of the responses for each one. For quantitative variables, the mean and standard deviation were calculated, since they followed a normal distribution. Bivariate association analysis was performed by applying the chi-squared test (corrected by Fisher’s exact statistic when conditions required it) and Student’s t-test for independent samples (for comparison of means). Results: The population subjected to the Mediterranean diet improved in all the variables evaluated at 12 months of follow-up and compliance with the diet. Conclusions: Dietary advice on a Mediterranean diet and its reinforcement with reminder messages through the use of mobile phones may be useful to improve the parameters evaluated in this study and reduce the cardiometabolic risk of patients. Full article

Stroke is one of the leading causes of disability, including loss of hand manipulative skills. It constitutes a major limitation in independence and the ability to perform everyday tasks. Among the numerous accessible physiotherapeutic methods, it is becoming more common to apply Virtual Reality "VR”. The aim of this study was to establish whether immersive VR was worth considering as a form of physical therapy and the advisability of applying it in restoring post-stroke hand function impairment. A proprietary application Virtual Mirror Hand 1.0 was used in the research and its effectiveness in therapy was compared to classical mirror therapy. A total of 20 survivors after ischaemic stroke with comparable functional status were divided into a study group (n = 10) and control group (n = 10). Diagnostic tools included 36-Item Short Form Survey “SF-36” and the Fugl-Meyer Assessment Upper Extremity “FMA-UE”. Collected metrics showed a normal distribution and the differences in mean values were tested by the student’s t-test. In both, the study and control groups’ changes were recorded. A statistically significant outcome for FMA-UE and SF-36 measured by the student’s t-test for dependent or independent samples (p > 0.05) were obtained in both groups. Importantly, proven by conducted studies, an advantage of VR proprietary application was subjective sensations amelioration in pain and sensory impressions. Applying Virtual Mirror Hand 1.0 treatment to patients after a stroke appears to be a good solution and definitely provides the opportunity to consider VR applications as an integral part of the neurorehabilitation process. These results give a basis to plan further larger-scale observation attempts. Moreover, the development of the Virtual Mirror Hand 1.0 as an innovative application in physiotherapy may become equivalent to classical mirror therapy in improving the quality and effectiveness of the treatment used for post-stroke patients. Full article

Background: Metabolic syndrome (Met-S) is considered one of the most important health problems of the 21st century. It includes a group of metabolic disorders that increase the risk of cardiovascular diseases such as overweight and obesity, elevated lipid profile and blood pressure and insulin resistance (IR). Based on the information mentioned above in which there seems to be a relationship between IR and Met-S, the objective of this work was twofold: on the one hand, to assess the relationship between the values of different insulin resistance risk scales and Met-S determined with three different scales, and on the other, to determine whether any of the components of Met-S predispose more to the appearance of IR. Methods: A descriptive cross-sectional study of 418,343 workers. Waist circumference was measured and evaluated together with six formulas to assess the insulin resistance index. Categorical variables were evaluated by calculating the frequency and distribution of each one. For quantitative variables, mean and standard deviation were determined, and Student’s t-test was applied, while for qualitative variables, the chi-square test was performed. The usefulness of the different risk scales for insulin resistance for predicting metabolic syndrome was evaluated using ROC curves, the area under the curve (AUC), as well as their cut-off points for sensitivity, specificity, and the Youden index. Results: People with metabolic syndrome applying any criteria had higher values in the IR risk scales. The different IR scales made it possible to adequately classify people with metabolic syndrome. Of the three definitions of Met-S, the one that showed the greatest relationship with IR was IDF. Conclusions: Most risk scales for insulin resistance enable the presence of metabolic syndrome to be adequately classified, finding the best ones if the International Diabetes Federation (IDF) criteria are applied. Of the elements included in the Met-S, the one that seems to increase the risk of presenting IR the most is waist circumference; hence, the Met-S definition that is most related to IR is that of the IDF, which is the only one of the three in which a high value of waist circumference is necessary to be able to diagnose Met-S. Waist circumference can be considered the central essential component for detecting insulin resistance and, therefore, the early detection of metabolic syndrome. Full article

In econophysics, the analysis of the return distribution of a financial asset using statistical physics methods is a long-standing and important issue. This paper systematically conducts an analysis of composite index 1 min datasets over a 17-year period (2005–2021) for both the Shanghai and Shenzhen stock exchanges. To reveal the differences between Chinese and mature stock markets, we precisely measure the property of the return distribution of the composite index over the time scale $\Delta t$, which ranges from 1 min to almost 4000 min. The main findings are as follows: (1) The return distribution presents a leptokurtic, fat-tailed, and almost symmetrical shape that is similar to that of mature markets. (2) The central part of the return distribution is described by the symmetrical Lévy $\alpha$-stable process, with a stability parameter comparable with a value of about 1.4, which was extracted for the U.S. stock market. (3) The return distribution can be described well by Student’s t-distribution within a wider return range than the Lévy $\alpha$-stable distribution. (4) Distinctively, the stability parameter shows a potential change when $\Delta t$ increases, and thus a crossover region at 15 $<\Delta t<$ 60 min is observed. This is different from the finding in the U.S. stock market that a single value of about 1.4 holds over 1 $\le \Delta t\le$ 1000 min. (5) The tail distribution of returns at small $\Delta t$ decays as an asymptotic power law with an exponent of about 3, which is a widely observed value in mature markets. However, it decays exponentially when $\Delta t\ge$ 240 min, which is not observed in mature markets. (6) Return distributions gradually converge to a normal distribution as $\Delta t$ increases. This observation is different from the finding of a critical $\Delta t=$ 4 days in the U.S. stock market. Full article

We aimed to identify factors influencing the preventive behavior of COVID-19 among nursing students. A survey was conducted through an online questionnaire in December 2021 for students in the department of nursing at a university located in C city. An online questionnaire was distributed to 189 nursing students who voluntarily agreed to participate in the study, and data from 179 of them were used for the final analysis. The research tools used were COVID-19 stress scale for Korean people, COVID-19 risk-perception scale, self-efficacy scale, and COVID-19 prevention behavior scale. Data were analyzed by descriptive statistics, t-test, analysis of variance, Mann–Whitney and Kruskal–Wallis test, as well as Spearman correlation coefficient and multiple linear regression with SPSS 23.0 program. Factors significantly influencing the preventive behavior of COVID-19 were self-efficacy (β = 0.53, p < 0.001) and COVID-19 stress (β = 0.25, p = 0.001). The explanatory power of related variables was 45.0%. We found that self-efficacy and COVID-19 stress had a significant effect on the preventive behavior of nursing students. Therefore, to promote the preventive behavior of COVID-19, it is necessary to improve the self-efficacy of nursing students and manage COVID-19 stress well. Full article