Search Results (127)

Testing the validity of the cosmological principle’s assumption of large-scale isotropy remains crucial for modern cosmology. We investigate the angular distributions of gamma-ray bursts using the GRB catalog from Neil Gehrels Swift Observatory (Swift) for an independent probe of isotropy. Using the HEALPix spherical harmonic decomposition, we estimate the dipole and quadrupole amplitudes and compare them against the null hypothesis obtained from 500 isotropic Monte Carlo realizations. Our results show 2.9$\sigma$ dipole and 7.2$\sigma$ quadrupole amplitude when applied to the raw data. To account for observational biases, we then create an exposure map using the pointing history, roll angle, and the partial coding fraction of the Swift Telescope. Reevaluating the null hypothesis using this map reduces the significance of these anisotropies to less than $1\sigma$. Therefore, our findings confirm statistical isotropy of the GRB sky using the Swift data, consistent with previous studies. We have also made the Swift exposure map publicly available. Full article

This study examines whether international food price dynamics provide a reliable signal of undernourishment and human development outcomes relevant to the attainment of SDG 2 (Zero Hunger) by 2030. We apply wavelet coherence analysis to the FAO Food Price Index and the prevalence of undernourishment (SDG Indicator 2.1.1) over 2001–2023, testing statistical significance against an AR(1) red-noise null hypothesis. Hybrid ARIMA–Random Forest models generate probabilistic price forecasts through 2030. Despite strong raw coherence (R² ≈ 0.77), only 7.8% of time–frequency cells achieve statistical significance, indicating that apparent co-movement largely reflects autocorrelation rather than substantive dependence. Where significant coherence emerges, it concentrates at medium-run horizons (3–6 years), consistent with undernourishment as a habitual dietary adequacy measure linked to sustained affordability pressures affecting health, productivity, and human capital formation. Rolling correlation analysis reveals suggestive evidence of a regime change around 2012—from negative to positive correlation—coinciding with a slowdown in progress toward reducing hunger, although the 5-year rolling windows yield only 19 observations, limiting the power of formal structural break tests. Price forecasts exhibit rapidly widening confidence intervals (by ±131 index points by 2030), underscoring fundamental limits to predictability. The annual PoU series comprises only 23 observations, which constrains the estimation of long-run (8–12-year) wavelet cycles; results at those horizons should therefore be interpreted with caution. These findings caution against mechanistic inferences from global price indices to hunger and human development outcomes, redirecting policy emphasis toward domestic transmission channels and nutrition-sensitive safety nets. Full article

Almost all publications in biomedical literature have employed statistical tests, with p-values being considered of particular importance in the assessment of the presence of a link between two variables. However, these tests and p-values have been the subject of considerable criticism. It may appear paradoxical that tools utilised by the scientific community for nearly a century could possess all the flaws attributed to them. This paradox can partially be explained by the counterintuitive nature of p-values and the fact that the test that generates them is the result of a combination of two tests that were developed to answer statistical questions of a very different nature. The respective characteristics of these two tests are essentially unknown to the majority of users of p-values. The aforementioned paradox can be partially explained by the paucity of publications that seek to elucidate these concepts for users of p-values, the majority of whom are not statisticians. The recently introduced Bayesian methods have properties that enable us to understand the limitations of traditional methods. In Bayesian methods, the use of a specific interpretation of probability allows for better exploitation of clinical research data. The aim of this article is to highlight the limits of non-Bayesian methods and explain the principles and functioning of Bayesian methods to a non-statistical audience. Full article

The COVID-19 pandemic constitutes one of the most significant exogenous shocks to global financial markets in recent history, raising questions about the robustness of market efficiency under extreme uncertainty. This study examines whether the pandemic affected the weak-form efficiency of the Bitcoin market or merely heightened volatility without introducing return predictability. Using daily Bitcoin log returns from January 2013 to February 2026, the analysis first evaluates weak-form market efficiency through the Variance Ratio (VR) test. The VR statistics remain close to unity across multiple holding horizons, and the null hypothesis of a random walk cannot be rejected, indicating that daily Bitcoin returns are consistent with weak-form efficiency. Building on this baseline, an Interrupted Time Series (ITS) framework is employed to assess whether the onset of the COVID-19 pandemic in March 2020 led to structural changes in Bitcoin return dynamics. The ITS results reveal no statistically significant changes in level or slope following the outbreak. To further account for autoregressive and moving-average dynamics while explicitly modelling the intervention, an ARIMAX (0, 0, 7) model with COVID-19 intervention variables is estimated. Both the pandemic dummy and its interaction term are statistically insignificant, indicating no material change in the return-generating process after controlling for serial dependence. The moving-average structure indicates that shocks dissipate over approximately one trading week, consistent with weekly trading cycles and liquidity patterns in cryptocurrency markets rather than persistent return predictability. Diagnostic checks, including the Ljung–Box and Shapiro–Wilk tests, confirm the absence of residual autocorrelation and support the model’s white-noise properties. Although volatility increased during the pandemic period, daily Bitcoin returns continued to align with weak-form market efficiency. The evidence, therefore, suggests that COVID-19 served as a stressor without generating persistent inefficiencies. These findings reinforce the distinction between volatility and predictability, demonstrating that heightened uncertainty does not necessarily undermine informational efficiency. Full article

Caries control is essential for the long-term success of dental restorations, and the frequent use of mouthwashes may influence the esthetic properties of restorative materials. This study aimed to evaluate the effect of several commercially available mouthwashes on the color stability of resin-based CAD/CAM restorative materials. Three CAD/CAM materials were investigated: a hybrid ceramic (Cerasmart), a polymer-infiltrated ceramic (Vita Enamic), and a reinforced composite (Brilliant Crios). Rectangular specimens were immersed in Eludril Classic, Listerine Total Care, Listerine Advanced White, and distilled water (control). Color measurements were performed at baseline and after immersion periods up to 24 h using a digital spectrophotometer, and color differences (ΔE₀₀) were calculated. Statistical analysis revealed that both mouthwash type and immersion time had a statistically significant effect on color change (p ≤ 0.05). All tested materials exhibited ΔE₀₀ values exceeding the clinically acceptable threshold. These findings suggest that prolonged mouthwash use may compromise the esthetic longevity of resin-based CAD/CAM restorations. The null hypothesis was that prolonged use of mouthwash does not influence the color of resin-based CAD/CAM materials especially for those containing alcohol. Full article

Background/Objectives: Dental anxiety is a common clinical problem that negatively affects patient cooperation, treatment acceptance, and physiological stability during dental procedures. This randomized controlled clinical trial study aimed to evaluate the effectiveness of music-based interventions in reducing dental anxiety and stress responses during restorative dental treatment. The null hypothesis was that music exposure would not result in significant differences in anxiety levels or physiological stress parameters compared with standard dental care. Methods: Seventy-five patients with moderate to high pre-treatment dental anxiety (MDAS ≥10) were randomly assigned to three groups: classical music, Turkish music, and control (no music) (n = 25 per group). Anxiety levels were assessed using the Modified Dental Anxiety Scale (MDAS). Restorations were performed using a standardized adhesive protocol. Physiological parameters, including systolic and diastolic blood pressure (SBP, DBP), heart rate (HR), and oxygen saturation (SpO₂), as well as salivary cortisol and alpha-amylase levels, were measured before and after restorative treatment. Salivary cortisol and amylase levels were measured using a Human ELISA Kit. Statistical analysis was performed using paired t-tests and one-way ANOVA with Tukey’s post hoc test (p < 0.05). Results: Both music groups showed significant reductions in SBP, DBP, HR, cortisol, amylase, and MDAS scores compared to the control group (p < 0.05). Oxygen saturation increased significantly in the music groups, whereas it decreased significantly in the control group. There were no significant differences between classical and Turkish music regarding their anxiety-reducing effects. Conclusions: Music-based interventions effectively reduce dental anxiety and physiological stress during restorative dental procedures. This study is novel in simultaneously evaluating subjective anxiety scores and multiple physiological and biochemical stress markers in adult patients undergoing restorative treatment, supporting music as a simple and non-invasive adjunct in clinical dentistry. Full article

Background/Objective. The objective of this study is to test the hypothesis that there is no significant difference between intraoral scanners and in-lab scanners. An additional objective is to test the hypothesis that there is no significant difference between the accuracy of two types of polyvinyl siloxane PVS impression materials and between PVS impression materials and intraoral scanners. Material and Methods. Fourteen subjects received a set of maxillary and mandibular removable complete dentures [RCD]. Impressions of each RCD were obtained using two PVS impression materials (heavy [H] or medium [M] body with light [L] body). Each RCD was then scanned utilizing two intraoral scanners, Trios [TR] and Omnicam [OM]. The PVS impressions were sent to the lab to be further scanned by an in-lab scanner. STL files of the intraoral and in-lab scans were obtained and trimmed using the GeoMagic X Software. The files were merged [TR vs. OM] and [TR vs. in Lab], [HL vs. ML], [HL vs. TR] and [ML vs. TR] and the gap was measured in sixty points for each merged file. Results. There was no significant difference between [TR vs. OM] with a mean of 44 ± 10 μm. There was a statistically significant difference between [TR vs. In lab] with a mean of 62 ± 21 μm and [ML vs. TR] and [HL vs. ML] with means of 66 ± 24 μm and 50 ± 21 μm, respectively. There was no significant difference between [HL vs. TR] with a mean of 37 ± 10 μm. Conclusions. Intraoral scanners provide a similar quality of scans. The lab scanner depends on the impression materials used. The first null hypothesis was rejected. Using a heavy and light body PVS impression material provides more accurate and dimensionally stable impressions, particularly in full-arch applications, and is comparable to intraoral scanners. The second null hypothesis was rejected, there were significant differences between the PVS groups. These findings guide clinicians in selecting impression methods for complete dentures. Full article

Background/Purpose: The study aimed to analyze the dimensions and width-to-length ratios of the maxillary anterior teeth in young native Cambodian adults and to assess their relationship with the golden proportion, symmetry, and sexual dimorphism. Materials and Methods: Maxillary study casts of 193 eligible Cambodian subjects, aged 18 to 25 years, were retrospectively evaluated. The width and length of their maxillary anterior teeth were measured using a digital caliper. Descriptive statistics, independent-samples t-test at 95% confidence intervals, Kolmogorov-Smirnov, Shapiro-Wilk, and Kruskal-Wallis tests were performed to analyze the data. Results: There was a high level of similarity between first and second quadrant measurements. Females showed slightly higher standard deviations for central incisors and lateral incisors than males across most ratios, indicating more variability in the width-in-length ratios for females. Males exhibited significantly greater tooth dimensions than females. The following results showed statistical significance with p < 0.05 and 95% confidence intervals. The mean crown width of the central incisors was 8.16 mm in males (CI: 8.03–8.29) and 7.87 mm in females (CI: 7.78–7.96). For the lateral incisors, the mean crown width was 6.69 mm in males (CI: 6.53–6.85) and 7.64 mm in females (CI: 7.43–7.85). The width-to-length ratio of the central incisors was higher in females (mean = 0.88; CI: 0.86–0.91) compared with males (mean = 0.87; CI: 0.84–0.89). Overall, proportional relationships remained consistent across genders. The golden proportion guideline was not applicable, as observed ratios ranged from 0.90 to 1.67 (all below 1.618), and RED values exceeded 80%. The null hypothesis was rejected due to the significant gender differences found in tooth dimensions and width-to-length ratios. Conclusions: There was no significant difference in maxillary anterior tooth dimensions for the right and left sides among the Cambodian population. Males had statistically larger teeth than females. Width-to-length ratios were greater in females for central incisors; however, the proportional relationships between the genders remained relatively consistent. The golden proportion and RED proportions did not exist within this population. A smaller size characterizes Cambodian dentition compared to that of other ethnic groups. Finally, these results can serve as an indicator for planning customized esthetic treatment in Cambodians. Future studies with larger sample sizes are needed to ensure the representation of the whole Cambodian population. Full article

This paper introduces a new hybrid model, HAR-RV-CARMA, which combines the Heterogeneous Autoregressive model for Realized Volatility (HAR-RV) with the Continuous Autoregressive Moving Average (CARMA) model. The key innovation of this study lies in the use of a Kalman filter-based dynamic state weighting mechanism to optimally combine the predictive capabilities of both models while mitigating overfitting. The proposed model is applied to five major Covered Call Exchange-Traded Funds (ETFs), QYLD, XYLD, RYLD, JEPI, and JEPQ, utilizing daily realized volatility data from 2019 to 2024. Model performance is evaluated against standalone HAR-RV and CARMA models using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Quasi-Likelihood (QLIKE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Additionally, the study assesses directional accuracy and conducts a Diebold-Mariano test to compare forecast performance against the standalone models statistically. Empirical results suggest that the HAR-RV-CARMA hybrid model significantly outperforms both HAR-RV and CARMA in volatility forecasting across all evaluation criteria. It achieves lower forecast errors, superior goodness-of-fit, and higher directional accuracy, with Diebold-Mariano test outcomes rejecting the null hypothesis of equal predictive ability at significant levels. These findings highlight the effectiveness of dynamic model weighting in improving predictive accuracy and offer a strong framework for volatility modeling in financial markets. Full article

In this article, we present an extension of the Full Bayesian Significance Test (FBST) for nonparametric settings, termed NP-FBST, which is constructed using the limit of finite dimension histograms. The test statistics for NP-FBST are based on a plug-in estimate of the cross-entropy between the null hypothesis and a histogram. This method shares similarities with Kullback–Leibler and entropy-based goodness-of-fit tests, but it can be applied to a broader range of hypotheses and is generally less computationally intensive. We demonstrate that when the number of histogram bins increases slowly with the sample size, the NP-FBST is consistent for Lipschitz continuous data-generating densities. Additionally, we propose an algorithm to optimize the NP-FBST. Through simulations, we compare the performance of the NP-FBST to traditional methods for testing uniformity. Our results indicate that the NP-FBST is competitive in terms of power, even surpassing the most powerful likelihood-ratio-based procedures for very small sample sizes. Full article

To date, the ordinal pattern-based method has been applied to problems in natural and social sciences. We report, for the first time to our knowledge, an attempt to apply this methodology to a topic in the humanities. Specifically, in an effort to investigate the applicability of the methodology in analyzing the quality of texts that are translated into a language preserving the so-called vowel harmony, computed results are presented for the metrics of divergence between the back-translated and the original texts. As a specific language we focus on Japanese, and as metrics the Hellinger distance as well as the chi-square statistic are employed. Here, the former is a typical information-theoretical measure that can be quantified in natural unit, nat for short, while the latter is useful for performing a non-parametric testing of a null hypothesis with a significance level. The methods are applied to three cases: a Japanese novel along with a translated version available, the Preamble to the Constitution of Japan, and seventeen translations of an opening paragraph of a famous American detective story, which include thirteen human and four machine translations using DeepL and Google Translate. Numerical results aptly show unexpectedly high scores of the machine translations, but it still might be too soon to speculate on their unconditional potentialities. Both our attempt and results are not only novel but are also expected to make a contribution toward an interdisciplinary study between physics and linguistics. Full article

This study investigates whether firm-specific narratives extracted from the news add predictive content to monthly stock return models. Using bidirectional encoder representations from transformer-based topic modeling (BERTopic), we processed Microsoft (MSFT) news and constructed monthly narrative activations (binary presence and decay weighting). These narrative activations are used in autoregressive moving-average models with exogenous regressors (ARIMA-X) to analyze MSFT monthly log returns alongside the U.S. Economic Policy Uncertainty (EPU) index from February 2021 to March 2025. Decay models using a similarity-distilled BERT embedding yielded three significant narratives: Media and Public Perception (MPP) (β = 0.0128, p = 0.002), Currency and Macro Environment (CME) (β = −0.0143, p < 0.001), and Tech and Semiconductor Ecosystem (TSE) (β = −0.0606, p = 0.014). Binary activation identifies reputational shocks: the Media and Public Perception (MPP) indicator predicts lower returns at one- and two-month lags (β = −0.0758, p = 0.043; β = −0.1048, p = 0.007). A likelihood-ratio test comparing ARIMA-X models with narrative regressors to a baseline ARIMA (no narratives) rejects the null hypothesis that narratives add no improvement in fit (p < 0.01). Firm-level narratives enhance monthly forecasts beyond conventional predictors; decay activation and similarity-distilled embeddings perform best. Demonstrated on Microsoft as a proof of concept, the ticker-agnostic design scales to multiple firms and sectors, contingent on sufficient firm-tagged news coverage for external validity. Full article

This study is the first to directly compare natural dynamic penumbra shadows with experimentally replicated constant-intensity shadows on photovoltaic modules, providing new insights into the limitations of conventional shadow approximations found in the existing body of knowledge. Neutral density filters were deemed the most appropriate method for replicating a constant-intensity shadow, as they reduce visible light relatively uniformly across the primary silicon wavelength range. Preliminary experiments established the intensity values for each neutral density filter chosen to be able to match with the 29 dynamic penumbra shadows being replicated by both the size of shadow and the averaged intensity. The results revealed that while constant-intensity shadows and dynamic penumbra shadows produced similar overall power loss magnitudes, the constant-intensity shadows consistently led to higher losses, averaging 9.65% more, despite having the same average intensity and shadow size. Regression modelling showed similar curvature trends for both shading types (Adjusted R² = 0.895 for constant-intensity shadows and Adjusted R² = 0.743 for dynamic-intensity shadows), but statistical analyses, including the Mann–Whitney U-test (p = 0.00229), confirmed a significant difference between the power loss output for the two penumbra shadow conditions. Consequently, the null hypothesis was rejected, confirming that the simplified constant-intensity shadows represented in the literature cannot accurately replicate the behaviour of dynamic-intensity penumbra on photovoltaic modules. Full article

The research focuses on the National Rural Development Programme (NRDP) during the transition period, assessing the absorption level of sub-measure 4.1, “Investments in agricultural holdings”, which impacts rural development in the agricultural sector in western Romania. A quantitative and qualitative analysis of all selection reports associated with sub-measure 4.1 submitted during the transition period (2021–22) was conducted to investigate a potentially relevant link between the number of beneficiaries identified in the analysed region and their location. Fisher’s exact tests indicate that the null hypothesis, which postulates independence between county and measure in the observed dataset, cannot be rejected. Further empirical analysis was conducted using panel data analysis to identify any relevant regression traits. Tests indicate that funding allocation, the spatial dimension and the temporal dimension are all statistically and substantively significant. Larger budget allocations are associated with a higher volume of proposals. Two out of the four analysed counties systematically outperformed the predicted values in the model by submitting more proposals than would be expected given their budgets. Later application stages yielded a greater number of successful proposals, which is consistent with residual demand capture in sequential competitive calls. Full article

Core mathematics courses are fundamental to the academic success of engineering students in higher education. These courses equip students with skills and knowledge applicable to their specialized fields. However, first-year engineering students often face significant challenges in mathematics due to a range of factors, including insufficient preparation, mathematics anxiety, and difficulty connecting theoretical concepts to real-life applications. The transition from secondary to tertiary mathematics remains a key area of educational research, with ongoing discussions about effective pedagogical approaches for teaching engineering mathematics. This study utilized a belief survey to gain general insights into the attitudes of first-year mathematics students towards the subject. In addition, it employed the activity theory framework to conduct a deeper exploration of the experiences of first-year engineering students, aiming to identify contradictions, or “tensions,” encountered within a flipped-classroom learning environment. Quantitative data were collected using surveys that assessed students’ self-reported confidence, competence, and knowledge development. Results from Friedman’s and Wilcoxon’s Signed-Rank Tests, conducted with a sample of 20 participants in 10 flipped-classroom sessions, statistically showed significant improvements in all three areas. All of Friedman’s test statistics were above 50, with p-values below 0.05, indicating meaningful progress. Similarly, Wilcoxon’s Signed-Rank Test results supported these findings, with p values under 0.05, leading to the rejection of the null hypothesis. The qualitative data, derived from student questionnaire comments and one-to-one interviews, elucidated critical aspects of flipped-classroom delivery. The analysis revealed emerging contradictions (“tensions”) that trigger “expansive learning”. These tensions encompassed the following: student expectation–curriculum structure; traditional versus novel delivery systems; self-regulation and accountability; group learning pace versus interactive learning; and the interplay between motivation and anxiety. These tensions are vital for academic staff and stakeholders to consider when designing and delivering a first-year mathematics course. Understanding these dynamics can lead to more effective, responsive teaching practices and support student success during this crucial transition phase. Full article