Recently, many algorithms have been proposed to identify potential forced oscillation sources using synchrophasors. However, the synchrophasor coverage is limited in several systems, and the oscillation source may not be monitored by nearby synchrophasor measurements. In this regard, SCADA measurements are helpful because of their extensive presence in the power grids worldwide. Although SCADA has a much lower reporting rate than synchrophasors, the asynchronous polling nature of SCADA data can be utilized to estimate the amplitude of oscillations at different locations. This paper proposes a rigorous algorithm based on inferential statistics to analyze the amplitude of oscillations seen in the SCADA data. The amplitude analysis offers valuable insights into the potential source and nature of forced oscillations, as demonstrated in the paper. Several simulation-based test cases and archived event data from the European and Western American interconnections have been tested with the proposed algorithm, which provides the correct ranking of generator oscillation amplitudes in all these events.

Evaluating virtual reality communication training in nursing and medical education: A multi-country cross-sectional study.

Knowledge, attitudes and practices of diagnostic radiographers regarding non-accidental injuries in Zambia.

Virtual reality (VR) has been adopted in the education field, providing students with innovative methods to acquire and practice skills. With the increasing importance of English education in vocational colleges, learning method (LM) should be transformed and improved to enhance learning efficiency. VR is recognized as a valuable tool to improve learning outcomes, yet its application in English education for vocational colleges remains in the early stages. This study investigates the factors affecting VR adoption and examines whether gender difference among students impacts its practical application. Quantitative research method was conducted utilizing questionnaire to examine the factors affecting vocational students’ VR adoption in their English learning. Total 520 vocational students were selected as respondents to complete the questionnaire, and the data were analyzed using descriptive statistics (mean and standard deviation) and inferential statistics (independent sample t-test) to identify the differences between gender. The findings revealed that identified factors moderately influence VR adoption and no significant gender differences regarding the challenges vocational students face when using VR to learn. This study contributes to promoting VR adoption in English learning and helps to stimulate students’ interest in actively using VR, thereby enhancing learning outcomes and driving the transformation of educational methods.

Assessing medical student preparedness for the clinical phase: perspectives of clinical faculty across specialties and medical school types in Indonesia

Enhancing engagement in biobanking research among Black women with endometrial cancer.

Influences of vegan status on protein intake, lean body mass, and strength in lightly active, young women: A cross-sectional study.

Discerning the underlying trends in electrocatalytic hydrogen evolution activity of MOF/MOF-derived materials using data-driven approach

Construction and multivariate influence pattern analysis of Mass Energy Coupling Coefficient (MEC) for steel rolling reheating furnaces

To determine the association between diabetic neuropathy (DN) and quality of life (QoL) in patients with type 2 diabetes mellitus (DM2). Family Medicine Unit No. 33 (UMF 33), El Rosario, Azcapotzalco, Mexico City, Mexico. Observational, analytical, cross-sectional. 365 participants aged 18 years or older with a diagnosis of DM2, ascribed to a specialized diabetes care program. A sociodemographic questionnaire and two standardized instruments were applied: the Michigan Neuropathy Screening Instrument (MNSI), in its questionnaire (MNSIQ) and physical examination (MNSIEF) components and the SF-12v2 questionnaire to assess QoL through its physical (PCS) and mental (MCS) components. Frequencies and percentages were analyzed. The chi-square test was used for inferential statistics. According to the MNSIQ, 35.6% presented signs of ND, while the MNSIEF it was detected in 99.2% of the participants. 59.5% reported good physical QoL, while 61.4% reported poor mental QoL. Although descriptive differences were notable, the statistical power was limited to demonstrate consistent associations between ND and QoL. The findings suggest a discordance between patient self-perception and physical examination. ND could have a negative impact on QoL, especially on the mental component (MCS). Active screening for ND in primary care settings may enhance comprehensive care and improve perceived well-being in people with DM2.

Network analyses of cognitive performance in psychiatric disorders: A scoping review.

Selective Attention Dynamics in Adults With Attention-Deficit/Hyperactivity Disorder: A Role for Sensory Processing Asymmetry?

Tobacco use is highly prevalent among individuals with schizophrenia and exacerbates craving, a key METHODS: Data were collected from 1769 patients within the FondaMental Expert Centers for Schizophrenia network. Sociodemographic, clinical, addictive, suicidal, and treatment-related variables were analyzed using both univariate and multivariate logistic regression models. Mediation analyses were performed to ensure robust statistical inference. The prevalence of tobacco smoking was 52.8% (n = 878). Higher tobacco craving scores were significantly correlated with increased suicidal ideation (p = 0.004), more frequent suicide attempts (p < 0.001), and higher suicide scores (p < 0.001). In the multivariable analysis, no significant associations were found between craving scores and all suicide outcomes. Regression models confirmed a strong association between tobacco craving and lifetime alcohol use disorder (AUD) (p < 0.001). Mediation analyses showed that AUD does not mediate the craving-suicide relationship (indirect effects p > 0.05). These findings highlight the complex interplay between clinical severity, substance use, and suicidality in shaping craving levels among patients with schizophrenia. Mediation analyses showed that AUD does not explain the craving-suicide relationship, indicating that these represent separate, parallel risk pathways. Assessment of tobacco craving and comorbid substance use, particularly AUD, is essential when assessing suicide risk. These findings have important implications for suicide prevention and treatment strategies in individuals with schizophrenia and co-occurring substance use disorders.

To investigate a dilemma of statistical and computational efficiency faced by long-run variance estimators, we propose a decomposition of kernel weights in a quadratic form and some online inference principles. These proposals allow us to characterize efficient online long-run variance estimators. Our asymptotic theory and simulations show that this principle-driven approach leads to online estimators with a uniformly lower mean squared error than all existing works. We also discuss practical enhancements such as mini-batch and automatic updates to handle fast streaming data and optimal parameters tuning. Beyond variance estimation, we consider the proposals in the context of online quantile regression, online change point detection, Markov chain Monte Carlo convergence diagnosis, and stochastic approximation. Substantial improvements in computational cost and finite-sample statistical properties are observed when we apply our principle-driven variance estimator to original and modified inference procedures.

This study aims to examine the relationship between the use of the Ibis Paint X application and the motivation to learn to design fashion at SMK Negeri 1 Salatiga. This research is a quantitative approach research with a correlational design. The population in this study is all students of class XI fashion at SMK Negeri 1 Salatiga which totals 71 students, and the sample in this study uses total sampling, The sampling technique uses total sampling, namely making the population as a sample, there is a total sampling because the population is less than 100. The data collection technique used a Likert scale questionnaire. The data in this study were analyzed using descriptive and inferential statistics. Hypothesis test using Pearson correlation. The results of the study obtained that there was a significant relationship between the use of the Ibis Paint X application and the motivation to learn to design the fashion of grade XI students, the fashion show had a pearson correlation coefficient value of 0.559 and a significance value (sig. 2-tailed) of 0.000. This shows that there is a relationship between the use of the Ibis Paint X application and the motivation to learn to design fashion in grade XI students of fashion at SMK Negeri 1 Salatiga with the correlation category being.

Mathematical competence is a critical skill in engineering education, particularly in a rapidly digital and technology-driven environment. This study focused on exploring the predictive influence of language proficiency and technology competence on the mathematical competence of first-year engineering students. A causal research design using survey method was employed. The participants were 65 first-year engineering students selected through stratified random sampling. The research instrument is a combination of adapted and researcher-made subjected to face and content validity by two expert validators. After checking the assumptions for regression, descriptive and inferential statistics were used to analyze the data at 0.05 level of significance. Results revealed that students demonstrated high levels of language proficiency, technology competence, and mathematical competence. Both language proficiency and technology competence significantly predicted mathematical competence, with technology competence emerging as the stronger predictor. Among the specific dimensions, speaking and internet use were identified as the only significant individual predictors. The findings suggest that strengthening students’ speaking skills and fostering the use of internet play a significant role in improving mathematical performance in engineering education.

This study examined the impact of the School Feeding Program on pupils’ attendance and academic performance in Basic Science and Technology in public primary schools in Katsina Metropolis, Katsina State, Nigeria. The study adopted a quasi-experimental research design, specifically a non-equivalent control group design. The population comprised all Primary 5 and 6 pupils in public primary schools in Katsina Metropolis. A sample of 360 pupils was selected using multistage sampling techniques, consisting of 180 pupils from School Feeding Program beneficiary schools and 180 pupils from non-beneficiary schools. Two instruments were used for data collection: Attendance Record Checklist (ARC) and Basic Science and Technology Achievement Test (BSTAT). The instruments were validated by experts, and a reliability coefficient of 0.81 was obtained using Cronbach’s Alpha method. Data were analyzed using descriptive statistics (mean and percentage) and inferential statistics (independent samples t-test and Pearson Product Moment Correlation) at 0.05 level of significance. The findings revealed that pupils in beneficiary schools recorded significantly higher attendance rates and performed better in Basic Science and Technology than pupils in non-beneficiary schools. A significant positive relationship was also found between participation in the School Feeding Program and academic performance. However, challenges such as irregular food supply, weak monitoring, poor food quality, and inadequate kitchen facilities were identified. The study concluded that the School Feeding Program significantly enhances attendance and academic performance, and recommended program expansion, improved monitoring, and better infrastructural support for sustainability.

Gain-of-function screening in Drosophila melanogaster provides a powerful approach for identifying genes that modulate lifespan; however, induction strength and environmental stress can substantially influence phenotypic outcomes. Here, we performed a pilot Gene Search (GS)-based overexpression screen using a heat-inducible hs-GAL4 driver and compared lifespan analyses at 25°C and 30°C to evaluate the impact of induction conditions on the detectability of lifespan-modulating genes. Induction at 30°C caused uniformly shortened lifespans across genotypes and did not reveal robust lifespan-extending candidates. In contrast, screening at 25°C, where moderate hs-GAL4 induction produces robust and detectable transgene expression, revealed multiple longevity-promoting lines. Lifespan measurements at the two temperatures were poorly correlated, indicating that elevated temperature imposes a dominant physiological burden that masks gene-specific effects. Using this strategy, we characterized a candidate line overexpressing Drosophila Ankyrin repeat and MYND domain-containing protein 2 (dAnkmy2). Overexpression of dAnkmy2 significantly extended adult lifespan and enhanced resistance to oxidative stress without detectable changes in canonical antioxidant gene expression. In contrast, loss of dAnkmy2 caused larval lethality, indicating an essential developmental function. Given the conserved role of Ankmy2 in ciliary biology, our results raise the possibility that cilia-associated processes may be involved in lifespan regulation. Collectively, this study establishes a proof-of-principle framework for detecting subtle genetic modulators of aging in genetically robust systems.

The aim of this study was to investigate tooth extractions in a Swedish public general dental practice setting, including the proportion of endodontically treated teeth, reasons for extraction, and subsequent prosthetic replacement. A prospective cohort study was conducted in 20 clinics within the Public Dental Service of Västra Götaland, Sweden. During an 8-week period, general dental practitioners consecutively registered reasons for tooth extraction. Patients' pain levels were assessed. Pre-extraction radiographs were assessed for tooth status. Five-year follow-up data from electronic dental records were used to determine whether the extracted teeth had been prosthetically replaced and to classify the type of replacement. Descriptive and inferential statistics were used. A total of 133 patients (61 men and 72 women; mean age 54.0 years, SD = ± 15.8) underwent extractions. Endodontic pathology (36.8%) and fractures (24.8%) were the most common reasons. Sixty-one patients had previous endodontic treatment, and one-third of extracted teeth were root-filled. Thirty-five teeth were prosthetically replaced, most often with removable prostheses (45.7%). Endodontically treated teeth, including those with initiated or completed root canal treatment, were markedly overrepresented among extractions, yet prosthetic replacement was infrequent. Younger patients less often opted for replacement, warranting further investigation of factors influencing replacement decisions.

Modeling of physical phenomena has been introduced by many physicists. In this paper, we explore some particle models with the Bose–Einstein distribution. The statistical inference of these distributions including moments, maximum likelihood estimation, Fisher information, entropy, and Kullback–Leibler divergence is analyzed. Given the maximum likelihood estimator and Fisher information estimate, we can use the Wald test to build confidence intervals and test hypotheses. Random sample generation based on the Metropolis–Hastings algorithm and the integral probability transformation theorem is investigated. We prefer to use the integral probability transformation theorem because of its speed and high accuracy. Also, for two independent random variables from the Bose–Einstein distributions, the distribution of their functions, as well as their moments, is examined. To evaluate the goodness-of-fit of the proposed models, we analyze real-world salary data across different levels. Graphical tools including histograms, empirical density functions, empirical cumulative distribution functions, and Q–Q plots are employed to visually compare the data with the fitted distributions. Bootstrap and permutation methods are used to compute the Kolmogorov–Smirnov statistic and the Kullback–Leibler divergence, while Wald, likelihood ratio, and score tests are applied for hypothesis testing. Comparisons with other common distributions (exponential, Weibull, log-normal, and gamma) are also conducted. The results consistently indicate that the Bose–Einstein type I distribution provides a substantially better fit to the empirical data compared to the Bose–Einstein type II distribution and the other models considered, as confirmed by residual analysis, descriptive measures, and formal test statistics. Finally, modeling with real data is carried out. In the conclusion section, suggestions are made for future research that can be conducted as a continuation of this study. Also, the R codes are reported in Appendix A.