Search Results (110)

Background: Claustrophobia is defined as the fear of enclosed spaces, and it is a rather common specific phobia. Although the Claustrophobia Questionnaire (CLQ) is a valid questionnaire to measure claustrophobia, there have been no studies validating this tool in Greek. Thus, our aim was to translate and validate the CLQ in Greek. Methods: We applied the forward–backward translation method to translate the English CLQ into Greek. We conducted confirmatory factor analysis (CFA) to examine the two-factor model of the CLQ. We examined the convergent and divergent validity of the Greek CLQ by using the Fear Survey Schedule-III (FSS-III-CL), the NEO Five-Factor Inventory (NEO-FFI-NL-N), and the Spielberger’s State-Trait Anxiety Inventory (STAI). We examined the convergent validity of the Greek CLQ by calculating Pearson’s correlation coefficient between the CLQ scores and scores on FSS-III-CL, NEO-FFI-NL-N, STAI-S (state anxiety), and STAI-T (trait anxiety). We examined the divergent validity of the Greek CLQ using the Fisher r-to-z transformation. To further evaluate the discriminant validity of the CLQ, we calculated the average variance extracted (AVE) score and the Composite Reliability (CR) score. We calculated the intraclass correlation coefficient (ICC) and Cronbach’s alpha to assess the reliability of the Greek CLQ. Results: Our CFA confirmed the two-factor model of the CLQ since all the model fit indices were very good. Standardized regression weights between the 26 items of the CLQ and the two factors ranged from 0.559 to 0.854. The convergent validity of the Greek CLQ was very good since it correlated strongly with the FSS-III-CL and moderately with the NEO-FFI-NL-N and the STAI. Additionally, the Greek CLQ correlated more highly with the FSS-III-CL than with the NEO-FFI-NL-N and the STAI, indicating very good divergent validity. The AVE for the suffocation factor was 0.573, while for the restriction factor, it was 0.543, which are both higher than the acceptable value of 0.50. Moreover, the CR score for the suffocation factor was 0.949, while for the restriction factor, it was 0.954. The reliability of the Greek CLQ was excellent since the ICC in test–retest study was 0.986 and the Cronbach’s alpha was 0.956. Conclusions: The Greek version of the CLQ is a reliable and valid tool to measure levels of claustrophobia among individuals. Full article

Background: Autism spectrum disorder (ASD) is characterised by atypical behavioural and cognitive diversity, yet the neural underpinnings linking brain activity and individual presentations remain poorly understood. In this study, we investigated the relationship between resting-state functional magnetic resonance imaging (fMRI) signal complexity and intelligence (full-scale intelligence quotient (FIQ); verbal intelligence quotient (VIQ); and performance intelligence quotient (PIQ)) in male adults with ASD (n = 14) and matched neurotypical controls (n = 15). Methods: We used three complexity-based metrics: Hurst exponent (H), fuzzy approximate entropy (fApEn), and fuzzy sample entropy (fSampEn) to characterise resting-state fMRI signal dynamics, and correlated these measures with standardised intelligence scores. Results: Using a whole-brain measure, ASD participants showed significant negative correlations between PIQ and both fApEn and fSampEn, suggesting that increased neural irregularity may relate to reduced cognitive–perceptual performance in autistic individuals. No significant associations between entropy (fApEn and fSampEn) and PIQ were found in the control group. Group differences in brain–behaviour associations were confirmed through formal interaction testing using Fisher’s r-to-z transformation, which showed significantly stronger correlations in the ASD group. Complementary regression analyses with interaction terms further demonstrated that the entropy (fApEn and fSampEn) and PIQ relationship was significantly moderated by group, reinforcing evidence for autism-specific neural mechanisms underlying cognitive function. Conclusions: These findings provide insight into how cognitive functions in autism may not only reflect deficits but also an alternative neural strategy, suggesting that distinct temporal patterns may be associated with intelligence in ASD. These preliminary findings could inform clinical practice and influence health and social care policies, particularly in autism diagnosis and personalised support planning. Full article

Background/Objectives: The combination of venetoclax (VEN) and hypomethylating agents (HMA), such as azacitidine (AZA) or decitabine (DEC), has transformed the treatment landscape for acute myeloid leukemia (AML) in patients unfit for intensive chemotherapy. However, optimal management of neutropenia and the impact of post-remission treatment interruptions (washouts) remain unclear. This study aimed to evaluate the safety and efficacy of post-remission washouts and their effect on clinical outcomes. Methods: We conducted a retrospective single-center study of 44 AML patients treated with HMA/VEN between 2020 and 2021. Clinical, molecular, and treatment-related data were collected, including treatment duration, post-remission washout duration, response rates, disease-free survival (DFS), and overall survival (OS). Statistical analyses included Fisher’s exact test and univariate and multivariate Cox models. Results: Overall, 61% of patients responded to therapy, with significantly higher response rates among those potentially eligible for the VIALE-A trial (86% vs. 39%, p = 0.002). Neither treatment duration nor post-remission washout length was associated with DFS or OS. DFS was significantly longer in patients treated with AZA compared to DEC (p = 0.006). Median OS was 7.7 months, with longer OS observed in patients who did not meet VIALE-A trial eligibility criteria (p = 0.021). Achieving complete remission (CR) was associated with improved OS (14.5 months). Conclusions: Post-remission treatment interruptions (washouts) did not negatively impact DFS or OS, suggesting they may be a safe strategy to support hematologic recovery. However, the choice of HMA appears to influence response duration, with AZA outperforming DEC in maintaining disease control. Full article

Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs’ linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices. Full article

The time-fractional generalized Burger–Fisher equation (TF-GBFE) is utilized in many physical applications and applied sciences, including nonlinear phenomena in plasma physics, gas dynamics, ocean engineering, fluid mechanics, and the simulation of financial mathematics. This mathematical expression explains the idea of dissipation and shows how advection and reaction systems can work together. We compare the homotopy perturbation transform method and the new iterative method in the current study. The suggested approaches are evaluated on nonlinear TF-GBFE. Two-dimensional (2D) and three-dimensional (3D) figures are displayed to show the dynamics and physical properties of some of the derived solutions. A comparison was made between the approximate and accurate solutions of the TF-GBFE. Simple tables are also given to compare the integer-order and fractional-order findings. It has been verified that the solution generated by the techniques given converges to the precise solution at an appropriate rate. In terms of absolute errors, the results obtained have been compared with those of alternative methods, including the Haar wavelet, OHAM, and q-HATM. The fundamental benefit of the offered approaches is the minimal amount of calculations required. In this research, we focus on managing the recurrence relation that yields the series solutions after a limited number of repetitions. The comparison table shows how well the methods work for different fractional orders, with results getting closer to precision as the fractional-order numbers get closer to integer values. The accuracy of the suggested techniques is greatly increased by obtaining numerical results in the form of a fast-convergent series. Maple is used to derive the approximate series solution’s behavior, which is graphically displayed for a number of fractional orders. The computational stability and versatility of the suggested approaches for examining a variety of phenomena in a broad range of physical science and engineering fields are highlighted in this work. Full article

Large Language models have shown a remarkable ability to “converse” with humans in a natural language across myriad topics. Despite the proliferation of these models, a deep understanding of how they work under the hood remains elusive. The core of these Generative AI models is composed of layers of neural networks that employ the Transformer architecture. This architecture learns from large amounts of training data and creates new content in response to user input. In this study, we analyze the internals of the Transformer using Information Theory. To quantify the amount of information passing through a layer, we view it as an information transmission channel and compute the capacity of the channel. The highlight of our study is that, using Information-Theoretical tools, we develop techniques to visualize on an Information plane how the Transformer encodes the relationship between words in sentences while these words are projected into a high-dimensional vector space. We use Information Geometry to analyze the high-dimensional vectors in the Transformer layer and infer relationships between words based on the length of the geodesic connecting these vector distributions on a Riemannian manifold. Our tools reveal more information about these relationships than attention scores. In this study, we also show how Information-Theoretic analysis can help in troubleshooting learning problems in the Transformer layers. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

In this paper, we develop an analytical approximate solution for the nonlinear time-fractional Fisher’s equation using a right starting space function and a unique analytic-numeric technique referred to as the Laplace residual power series approach. The generalized Taylor’s formula and the Laplace transform operator are coupled in the aforementioned method, where the coefficients, obtained through fractional expansion in the Laplace space, are determined by applying the limit concept. In order to validate and illustrate the theoretical methodology of the LRPS technique, as well as to show its effectiveness, adaptability, and superiority in solving various types of nonlinear time and space fractional differential equations, numerical experiments are generated. The obtained analytical solutions are compatible with the precise solutions and concur with those proposed by the other approaches. The outcomes show that the Laplace residual power series strategy is incredibly successful, straightforward to implement, and well suited for handling the complexity of nonlinear problems. Full article

Background: This study investigates the association between synchronous and metachronous metastases across various cancer types, evaluating whether that relationship has evolved over time. Methods: Data from the Surveillance, Epidemiology, and End Results (SEER)-8 dataset from 1975 to 2020 were retrospectively reviewed. For each of the 18 solid tumor types, the crude rates of synchronous and metachronous metastases were estimated from the SEER database. For each of the years assessed (from 1975 to 2015 at 10-year increments), linear regression analyses were conducted to quantify the relationship between the rates of metachronous metastasis and synchronous metastasis across all cancer sites. The degrees of association over time were compared using a Fisher’s z-transformation. Results: At all time points considered, there was a significant association between the rates of metachronous and synchronous metastases (all p values < 0.05 for 5-year follow-up data). The degree of that association tended to increase over time (R = 0.59, 0.60, 0.66, 0.80, and 0.87 for 1975, 1985, 1995, 2005, and 2015, respectively), with the p value of the z-score comparing the many R values over time varying from 0.04 to 0.48. Conclusions: There appears to be an increasing association between the rates of synchronous and metachronous distant metastases over time. The exact cause of this increasing association is unknown. However, it appears to have occurred somewhat contemporaneous with the increasing use of more-accurate imaging studies (e.g., FDG-PET). Newer, targeted radiotracers for PET may provide the landscape for a prospective evaluation of the role of imaging. Full article

Background/Objectives: Compliance with healthcare standards is an absolute must for every healthcare organization seeking accreditation. Several factors were found to affect compliance, and in Saudi Arabia, certain standards were observed for non-compliance. Therefore, this systematic review and meta-analysis seeks to identify the factors associated with non-compliance with healthcare accreditation in Saudi Arabia. Methods: This study adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The population, intervention, comparison, and outcome (PICO) model was used to refine the research question. The Peer Review of Electronic Search Strategies (Press) guidelines were used to improve the search strategy. The databases used for the search were PubMed, Web of Science, Scopus, and Google Scholar. The dates searched were from 1 January 2000 to 1 November 2024. We used a data extraction form for study characteristics and outcome data, which was piloted on five studies in this review. The risk of bias was assessed by using the Joanna Briggs Institute (JBI) tool and the Mixed Methods Appraisal Tool (MMAT). The analysis was carried out using the Fisher r-to-z transformed correlation coefficient as the outcome measure. A random-effects model was fitted to the data. Results: A total of ten studies were included for qualitative synthesis and five for quantitative synthesis. Several factors were observed for non-compliance, including insufficient training organization hurdles, a lack of implementation strategies, and the attitudes of healthcare providers. The estimated average correlation coefficient based on the random-effects model was 0.2568 (95% CI: −0.1190 to 0.6326). Conclusions: The dimension of quality in healthcare through pooled correlations from various studies highlighted a weak association among these dimensions. Full article

The improvement of energy efficiency (EE) and growing consumption of renewable energy sources (RES) in the commercial and public services sector are playing important roles in seeking to pursue sustainable development in the Baltic States and contributing to the transition to a low-carbon economy. This paper provides findings from a detailed analysis of energy intensity trends in economic sectors from 2005 to 2022 in three countries, considering the role of transformations in the energy and climate framework of the European Union (EU). Based on the Fisher Ideal Index application, the different contributions from improving EE and structural changes are revealed. The dominant role of EE improvements in energy savings is identified in Estonia and Lithuania, and structural changes are dominant in Latvia. Changes in energy-related greenhouse gas (GHG) emissions in the commercial and public services sector and the main determinants of their reduction are examined. Based on applying the Kaya identity and the logarithmic mean Divisia index (LMDI) method, decreasing energy intensity is the most important determinant in all three countries. Due to the different extents of RES deployment, their role was very important in Estonia and Latia but was less effective in Lithuania. Reduction in emission intensity has the largest impact in Latvia. The GHG emissions decreased by 34.1% in Estonia, 17.5% in Latvia, and 16.7% in Lithuania. The results confirm the need for new policies, implementation of relevant EE measures, and the growing contribution from RES in Latvia and Lithuania. Full article

Background/Objectives: DAPK-1 plays a crucial role among molecules that may be affected by DNA hypermethylation. The aim of this study is to investigate the DNA methylation of DAPK-1 gene in oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) compared to normal oral epithelium and to evaluate the possible role of methylated DAPK-1 as an indicator of the early onset of malignant transformation of oral potentially malignant disorders. Methods: The paraffin embedded tissue samples were retrieved from the archives of the Department of Oral Medicine/Pathology, School of Dentistry, Aristotle University of Thessaloniki, Greece and St Lukas Hospital of Thessaloniki, Greece during the period of 2014–2019. The tissue samples included 83 OPMDs samples, 39 OSCC samples and 12 samples of normal oral epithelium. The PCR process followed, targeting four different DAPK-1 gene primers. Results: Regarding OSCC, it was found that all 39 OSCCs samples were methylated in DAPK-1 promoter region, whereas only 2 out of 12 normal tissues samples showed DAPK-1 promoter hypermethylation (p < 0.001 Fisher’s exact test). A total of 17 out of 83 OPMDs were DAPK-1 methylated (five erosive oral lichen planus samples, three non-dysplastic oral leukoplakias, eight mildly dysplastic oral leukoplakias and one sample belonging to the group of moderately and severely dysplastic oral leukoplakia). Conclusions: Since epigenetic changes occur early in carcinogenesis and are potentially reversible, they could be used as disease biomarkers for diagnosis, prognosis and prediction, as well as therapeutic targets. DAPK-1 methylation is mostly present in the early stages of dysplasia as well as in all cases of oral cancer. Full article

Heavy-tailed Pearson diffusions provide a natural alternative to well-known Ornstein–Uhlenbeck and Cox–Ingersoll–Ross processes in applications that require addressing heavy-tailed behavior. In this paper, all three heavy-tailed Pearson diffusions, having inverse gamma, Fisher–Snedecor and Student stationary distributions, are constructed via an absolutely continuous time-change process employed in a specific functional transformation of CIR or OU. Moreover, time-change rates in stochastic clocks are continuous functionals of the CIR process. Full article

Background/Objectives: BMS-202, is a potent small molecule with demonstrated antitumor activity. The study aimed to comprehensively characterize the physical and chemical properties of BMS-202 and evaluate its suitability for topical formulation, focusing on uniformity, stability and safety profiles. Methods: A range of analytical techniques were employed to characterize BMS-202. Scanning Electron Microscopy (SEM) was used to assess morphology, Differential Scanning Calorimetry (DSC) provided insights of thermal behavior, and Hot-Stage Microscopy (HSM) corroborated these thermal behaviors. Molecular fingerprinting was conducted using Raman spectroscopy and Fourier Transform Infrared (FTIR) spectroscopy, with chemical uniformity of the batch further validated by mapping through FTIR and Raman microscopies. The residual water content was measured using Karl Fisher Coulometric titration, and vapor sorption isotherms examined moisture uptake across varying relative humidity levels. In vitro safety assessments involved testing with skin epithelial cell lines, such as HaCaT and NHEK, and Transepithelial Electrical Resistance (TEER) to evaluate barrier integrity. Results: SEM revealed a distinctive needle-like morphology, while DSC indicated a sharp melting point at 110.90 ± 0.54 ℃ with a high enthalpy of 84.41 ± 0.38 J/g. HSM confirmed the crystalline-to-amorphous transition at the melting point. Raman and FTIR spectroscopy, alongside chemical imaging, confirmed chemical uniformity as well as validated the batch consistency. A residual water content of 2.76 ± 1.37 % (w/w) and minimal moisture uptake across relative humidity levels demonstrated its low hygroscopicity and suitability for topical formulations. Cytotoxicity testing showed dose-dependent reduction in skin epithelial cell viability at high concentrations (100 µM and 500 µM), with lower doses (0.1 µM to 10 µM) demonstrating acceptable safety. TEER studies indicated that BMS-202 does not disrupt the HaCaT cell barrier function. Conclusions: The findings from this study establish that BMS-202 has promising physicochemical and in vitro characteristics at therapeutic concentrations for topical applications, providing a foundation for future formulation development focused on skin-related cancers or localized immune modulation. Full article

Color transfer, an essential technique in image editing, has recently received significant attention. However, achieving a balance between holistic color style transfer and local detail refinement remains a challenging task. This paper proposes an innovative color transfer method, named BHL, which stands for Balanced consideration of both Holistic transformation and Local refinement. The BHL method employs a statistical framework to address the challenge of achieving a balance between holistic color transfer and the preservation of fine details during the color transfer process. Holistic color transformation is achieved using optimal transport theory within the generalized Gaussian modeling framework. The local refinement module adjusts color and texture details on a per-pixel basis using a Gaussian Mixture Model (GMM). To address the high computational complexity inherent in complex statistical modeling, a parameter estimation method called the unit-wise Riemannian information gradient (uRIG) method is introduced. The uRIG method significantly reduces the computational burden through the second-order acceleration effect of the Fisher information metric. Comprehensive experiments demonstrate that the BHL method outperforms state-of-the-art techniques in both visual quality and objective evaluation criteria, even under stringent time constraints. Remarkably, the BHL method processes high-resolution images in an average of 4.874 s, achieving the fastest processing time compared to the baselines. The BHL method represents a significant advancement in the field of color transfer, offering a balanced approach that combines holistic transformation and local refinement while maintaining efficiency and high visual quality. Full article