Skewness Values Research Articles

An examination of the perception of future time among university students from the perspective of demographic variables

This study aimed to examine the future time perspective of students in the faculty of sports sciences and the factors influencing this perception in relation to various variables. The population of the study consists of university students, while the sample includes a total of 266 students (128 males, 48.1%, and 138 females, 51.9%) studying at the Faculty of Sports Sciences at Kütahya Dumlupınar University. The data collection instruments include a personal information form developed by the researcher, as well as the Future Time Perspective Scale (FTPS), developed by Husman and Shell (1996) and adapted to Turkish culture with validity and reliability studies conducted by Avcı and Erden (2009). In this study, a non-experimental causal-comparative model, one of the quantitative research methods, was employed to identify the current situation. The data were analyzed using the SPSS program. Descriptive statistics such as means, standard deviations, and percentages were included in the analysis. Skewness and kurtosis values were examined, revealing a normal distribution, and Independent Samples T-Test and MANOVA were used to compare the differences. According to the findings, no statistically significant differences were found regarding students' gender, participation in recreational sports, and academic success (p>0.05). However, significant differences were found in favor of students in upper classes based on the class variable (p<0.05). These findings indicate that students become more focused on career goals as they approach graduation, which creates differences in their future time perspectives.

Experimental investigation of the flow disturbance near the trailing edge of slotted and non-slotted blades using skewness and kurtosis

The present study conducts an experimental investigation into the flow characteristics over a cascade of both slotted and non-slotted compressor blades. The focus is on evaluating potential statistical correlations between higher-order moments of velocity fluctuations, including Skewness and Kurtosis. The linear axial cascade comprises three NACA6409 blades, which are tested in both normal and slotted blade. Data acquisition was conducted using a hot wire anemometer at three measurement stations within the cascade. The hot wire sensor was positioned at distances of x/c = 0.125, 0.25, and 0.5 from the trailing edge (x), where x represents the distance from the probe to the trailing edge of the airfoil and (c) is the chord length of the blade. Measurements were taken at a blade angle of attack of 0° and two Reynolds numbers: 22,750 and 45,500. Results indicate that blade grooving enhances flow velocity, postpones separation and improves flow control in the trailing edge region. By comparing calculated Skewness and Kurtosis values with those predicted by existing boundary layer and free jet relations, more consistent correlations were identified. To provide more accurate relationships, two polynomials of the second and third order have been used. It was found that high-order values of velocity were obtained more accurately with the relations presented in this research. Although the accuracy of these predictions is significantly reduced in the proportion of larger distances. In distant stations, the loss velocity has decreased. As the distance from the model increases, the amount of inhomogeneities in the sequence increases and the values of the opposite Skewness become zero. Turbulence intensity has been reduced by slotting the blades.

Flow structure and characteristics of fluid undergoing a sudden contraction to an annular gap under dynamic boundary

Pipeline transport serves as an effective means to alleviate traffic congestion and reduce carbon emissions from transportation. The hydraulic delivery system, which employs pipeline cars as carriers, addresses the limitations of existing systems. However, its transportation efficiency is affected by variations in the flow structure within the pipelines. During the acceleration of the pipeline car, the sudden contraction flow field from circular to annular gap formed in the vicinity of the end face under dynamic boundary conditions. This study utilized particle image velocimetry (PIV) to visualize and measure the sudden contraction flow field. Based on the obtained experimental results, it investigated the impact of dynamic boundary velocity on the flow structure, velocity characteristics, and energy dissipation of the annular gap. The acceleration process of the dynamic boundary is the conversion of flow energy into the kinetic energy of the annular gap flow and the kinetic energy of the pipeline car. This process is accompanied by phenomena of velocity slip and velocity overshoot. As the velocity of the pipeline car increases, the recirculating vortex within the annular gap dissipates and eventually disappears. The velocity slip gradually decreases, the location of the overshoot point shifts radially, and the magnitude of the overshoot diminishes before ultimately vanishing. From static to steady, the probability density distribution of the slipstream face transitions from a distribution with high skewness and low peak value to a normal distribution with high peak value and low skewness. The irreversible losses that arise in a sudden contraction flow field can be quantified by the increase in entropy. Due to the similarity of the solving processes of large Eddy simulation and PIV, a combined sub-grid stress model is used to solve the flow losses in the flow field. The turbulent dissipation occurs mainly in the recirculation region, shear layer, and high-speed shear regions near the wall.

Effect of midwives’ attitudes towards evidence-based practices on mothers’ perceptions regarding childbirth practices and satisfaction

ABSTRACT Objective The aim was to determine the effect of midwives’ attitudes towards evidence-based practices in childbirth on mothers’ perception and satisfaction with childbirth practices. Material and Method This cross-sectional study was carried out with the participation of 34 midwives working in the delivery rooms of three public hospitals in a province in the south of Türkiye and 287 postpartum women whose deliveries were facilitated by these midwives. A Personal Information Form and the Midwives’ Evidence-Based Practices Attitude Scale during Labor (MEBPAS) were administered to the midwives. A Personal Information Form, the Birth Practices Perception Scale (BPPS), and the Birth Satisfaction Scale-Revised (BSS-R) were administered to the postpartum women. Results Because the skewness and kurtosis values were between −2 and + 2, the data showed normal distribution. The multiple linear regression model showed that the dimensions of MEBPAS (Interventional Practices, Supportive Care Practices, Movement and Nutrition Practices, Early Postpartum Period Practices) explained 29.4% of the total variance in BSS-R (F = 30.798; p = 0.041) and 53.4% of the total variance in BPPS (F = 83.094; p < 0.001). According to the Structural Equation Modeling, Interventional Practices, Movement and Nutrition Practices, and Early Postpartum Period Practices had statistically significant positive effects on BSS-R (respectively, β = 0.286, p < 0.001; β = 0.479, p = 0.016, and β = 1.009, p < 0.001), while Interventional Practices, Supportive Care Practices, and Early Postpartum Period Practices had statistically significant negative effects on BPPS (respectively, β=-0.048, p < 0.001; β=-0.026, p = 0.027, and β=-0.039, p = 0.034). Conclusion It was found that midwives’ positive attitudes towards evidence-based practices in childbirth positively affected women’s perceptions and satisfaction with birth practices.

A Stacking Ensemble Learning Algorithm for Anomaly Early Detection of Liquid Levels in High-Pressure Heaters of Turbine Reheat Systems

In the steam turbine reheating system, maintaining the high-pressure heater liquid level is crucial to prevent equipment damage and safety accidents. High liquid levels may cause steam to enter the feed system, while low liquid levels may lead to overheating and damage to the heater tube bundle. In this study, the stacking ensemble learning model for combined data decomposition is proposed for high-precision liquid level prediction and early warning. Initially, outliers in the high-pressure heater liquid level data from the DSC system are processed. The Adaptive Local Iterative Filtering (ALIF) algorithm is used for the first data decomposition, followed by Empirical Mode Decomposition (EMD) for a second decomposition. Time-domain features such as summation, maximum value, skewness, and peak value are extracted to construct reconstructed data. Random Forest (RF) reduces the dimensionality of the reconstructed data. The prediction model combines Deep Belief Network (DBN), Deep Neural Networks (DNN), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (xGBoost) as base models, with Echo State Network (ESN) as the meta-learner. Finally, based on historical data, warning level values are set, and the system outputs warnings when the actual liquid level significantly deviates from the predicted value.

Experimental Study of Wind Characteristics at a Bridge Site in Mountain Valley Considering the Effect of Oncoming Wind Speed

The topography of mountainous areas is characterized by large undulations, which lead to a very complex wind field at bridge sites in mountain valleys. The influence of oncoming wind speed on long-span bridges built in mountain valleys is quite pronounced. To investigate the wind characteristics at a bridge site in a mountain valley under different oncoming wind speeds, a wind tunnel test of a terrain model with a scaling ratio of 1:1000, where a long-span bridge would be built in the V-shaped canyon, was conducted. Uniform and atmospheric boundary layer (ABL) inflows were both applied, and the effect of different oncoming wind speeds (basic wind speeds of 6 m/s, 8 m/s, 10 m/s, 12 m/s, and 14 m/s) under three wind directions (0°, 30°, and 180°) on the wind characteristics at the main beam and two bridge towers were studied. The results indicate that increasing oncoming wind speed leads to decreased wind profiles and wind speed amplification factors and increased wind attack angles, while wind yaw angles remain largely unchanged. In addition, compared to ABL inflow, the variation of fluctuating wind characteristics is more pronounced with the oncoming wind speed under uniform inflow. Under uniform inflow conditions, increasing the oncoming wind speed causes decreased turbulence intensity, reduces the peak frequency of the power spectrum, and slows down the high-frequency decay rate. Under ABL inflow conditions, turbulence intensity and the power spectrum remain unchanged with different oncoming wind speeds. Additionally, the turbulent integral scale derived from fitting with the von Kármán wind spectrum is sufficiently accurate, and the variation in the turbulent integral scale is greatly influenced by the terrain. Furthermore, higher wind speeds result in stronger coherence between two points. When two points are at different locations but with the same spacing, the coherence function remains roughly the same. Locations with higher kurtosis and skewness values exhibit steeper probability density functions, with larger kurtosis and skewness coefficients typically found on the leeward side. High wind speeds are more detrimental to bridge safety, and appropriate preventive measures should be implemented in advance to address extreme conditions that may arise at high wind speeds.

DUS Topp-Leone-G Family of Distributions: Baseline Extension, Properties, Estimation, Simulation and Useful Applications.

This study introduces the DUS Topp-Leone family of distributions, a novel extension of the Topp-Leone distribution enhanced by the DUS transformer. We derive the cumulative distribution function (CDF) and probability density function (PDF), demonstrating the distribution's flexibility in modeling various lifetime phenomena. The DUS-TL exponential distribution was studied as a sub-model, with analytical and graphical evidence revealing that it exhibits a unique unimodal shape, along with fat-tail characteristics, making it suitable for time-to-event data analysis. We evaluate parameter estimation methods, revealing that non-Bayesian approaches, particularly Maximum Likelihood and Least Squares, outperform Bayesian techniques in terms of bias and root mean square error. Additionally, the distribution effectively models datasets with varying skewness and kurtosis values, as illustrated by its application to total factor productivity data across African countries and the mortality rate of people who injected drugs. Overall, the DUS Topp-Leone family represents a significant advancement in statistical modeling, offering robust tools for researchers in diverse fields.

Feasibility of Prostate Apex Cancer Diagnosis Based on the Combination of Magnetic Resonance Imaging Radiomics and Biomarkers.

Traditional diagnostic methods have limitations in accurately identifying and characterising prostate apex cancer. Therefore, exploring innovative approaches such as magnetic resonance imaging (MRI) radiomics, biomarker assessments and clinical pathological features is essential to improve diagnostic accuracy. This retrospective study evaluated diagnostic data from 52 patients with prostate apex cancer and 52 healthy individuals. MRI radiomics features-including grey-level non-uniformity, co-occurrence homogeneity, first order skewness, grey level co-occurrence matrix (GLCM) correlation, wavelet-low-high-low (wavelet-LHL) energy and prostate apparent diffusion coefficient (ADC) values-were compared between the groups. Biomarker levels, including Free Prostate-Specific Antigen (fPSA), Prostate-Specific Antigen (PSA), Ratio of Free to Total Prostate-Specific Antigen (f/tPSA), Prostate Volume (PV) and Prostate-Specific Antigen Density (PSAD), were also measured and analysed. Statistical analyses included t-tests, chi-square tests, correlation analyses and receiver operating characteristic (ROC) analyses. Significant differences were observed between the healthy and cancer groups in several MRI radiomics features: Grey-level non-uniformity (57.23 ± 7.31 vs. 69.54 ± 9.84, p < 0.001), co-occurrence homogeneity (0.29 ± 0.05 vs. 0.21 ± 0.07, p < 0.001), first order skewness (2.91 ± 0.61 vs. 3.85 ± 0.71, p < 0.001), GLCM correlation (0.72 ± 0.06 vs. 0.62 ± 0.07, p < 0.001), wavelet-LHL energy (264.14 ± 30.12 vs. 311.24 ± 42.13, p < 0.001) and prostate ADC value (1.29 ± 0.25 vs. 0.98 ± 0.15 × 10-3 mm2/s, p < 0.001). Biomarker levels also differed significantly: fPSA (0.93 ± 0.50 vs. 1.97 ± 1.69 ng/mL-1, p = 0.032), PSA (6.69 ± 2.55 vs. 17.45 ± 7.85 ng/mL-1, p = 0.048), f/tPSA (0.14 ± 0.07 vs. 0.11 ± 0.07 ng/mL-1, p = 0.020), PV (42.16 ± 8.32 vs. 38.43 ± 8.92 mL, p = 0.030) and PSAD (0.17 ± 0.08 vs. 0.49 ± 0.29 µg/L/mL-1, p = 0.040). The combined model of these parameters achieved a sensitivity of 0.865, a specificity of 0.962 and an area under the curve of 0.913. The integration of MRI radiomics, biomarker assessments and clinical pathological features presents a promising approach for diagnosing prostate apex cancer.

A supergene affects androgen concentrations during early development in a bird with alternative reproductive morphs

Species with alternative reproductive tactics typically show pronounced phenotypic variation between and within sexes. In some species, this variation culminates in discrete reproductive morphs that are genetically determined, facilitating studies on how genetic variation translates into phenotypic variation. In ruffs (Calidris pugnax), an autosomal inversion polymorphism underlies three reproductive morphs (Independents, Satellites and Faeders), which differ in circulating steroid concentrations in adults. Yet, it remains unknown whether morph differences in steroid concentrations already arise before adulthood. We examined variation in circulating testosterone, androstenedione and progesterone concentrations between morphs and sexes in ruff chicks and juveniles and compared the differences to those in adults. Since measured hormone concentrations only provide momentary states and show high within- and between-individual variation, we took repeated measurements to compare means, variances and skewness between groups. We found clear differences between morphs but not the sexes in early life. Between morphs, androgen concentrations in young ruffs differed in variance and skewness, but not in their means. For testosterone, Independents had a higher variance than Satellites/Faeders, whereas for androstenedione, we observed the opposite pattern. For progesterone, we did not detect clear differences between groups. Skewness values mirrored differences in morph variances. Compared to adults, premature ruffs had lower androgen concentrations. In both life stages, we detected morph-specific associations between androgen concentrations: androstenedione concentrations increased with testosterone concentrations more in Satellites/Faeders than in Independents. These observed morph differences during early life are consistent with a supergene-mediated regulation of androgen variation that underlies the diversification of adult behavioural phenotypes.

Fractal analyses of AlxGa1−xN thin film surfaces on AlN at different annealing temperatures

The determination of heteromorphological structures formed on the surface during annealing of AlxGa1−xN thin film grown on sapphire substrate using the metal organic chemical vapor deposition technique at different temperatures was investigated by fractal analysis method. The images of the surfaces of the thin films were taken by atomic force microscopy (AFM) at temperatures of 900, 1000, 1050 and 1200 °C respectively. AFM images were digitised in bitmap format according to the annealing temperatures. It was determined that the fractal dimensions obtained a linear correlation with the annealing temperatures. The results confirm the hypothesis that surface relaxation by the thermal action can produce fractal-like structures at particle or cluster boundaries. It is found that the observed cluster formation of superficial particles decreases as increasing temperature. The increase in temperature reduces the rate of superficial particle coating. To determine the surface roughness of the AlxGa1−xN thin film according to the annealing temperature, the AFM images were digitized in tagged image file format and the statistical root mean square, mean value, mean roughness, skewness and kurtosis values of the films were calculated. The roughness is a result of the thin film surface heteromorphology formed due to the specific annealing process. It is proved that the fractal dimensions of the AlxGa1−xN thin film increase as the annealing temperature rises. The particles coalesce on the surface and cluster in different types and sizes at each different annealing temperature, forming islets of different sizes. The skewness and kurtosis values show a different and irregular change.

Incomplete ordinal panel responses with initial condition and skew-normal random effect

This paper presents a model for the ordinal categorical panel data with skew-normal random effects, endogenous initial condition, and non ignorable (NI) non monotone missing data. We assumed three separate models for the initial condition, subsequent responses, and missing procedure. The baseline or initial response variable and subsequent response variables are modeled as ordinal logistic, whereas the logistic binary model is considered for the missing process where the maximum likelihood estimates are obtained using Monte Carlo methods. The results show that in the presence of missing data, the skew-normal random effects produce better results compared to the normal one, and considering the endogenous initial conditions is effective in modeling the results of the observed and missing responses such that the patients whose severity of schizophrenia is higher at the beginning of the process have a higher chance of missing on the subsequence times. In this research, schizophrenia data and then simulation studies have been used to check the performance of the proposed model. The results show that the skew-normal random effect using the NI non monotone missing mechanism by increasing the skewness value produces better results in the analysis of simulation studies.

Asymmetric Laplace scale mixtures for the distribution of cryptocurrency returns

AbstractRecent studies about cryptocurrency returns show that their distribution can be highly-peaked, skewed, and heavy-tailed, with a large excess kurtosis. To accommodate all these peculiarities, we propose the asymmetric Laplace scale mixture (ALSM) family of distributions. Each member of the family is obtained by dividing the scale parameter of the conditional asymmetric Laplace (AL) distribution by a convenient mixing random variable taking values on all or part of the positive real line and whose distribution depends on a parameter vector $$\varvec{\theta }$$ θ providing greater flexibility to the resulting ALSM. Advantageously concerning the AL distribution, our family members allow for a wider range of values for skewness and kurtosis. For illustrative purposes, we consider different mixing distributions; they give rise to ALSMs having a closed-form probability density function where the AL distribution is obtained as a special case under a convenient choice of $$\varvec{\theta }$$ θ . We examine some properties of our ALSMs such as hierarchical and stochastic representations and moments of practical interest. We describe an EM algorithm to obtain maximum likelihood estimates of the parameters for all the considered ALSMs. We fit these models to the returns of two cryptocurrencies, considering several classical distributions for comparison. The analysis shows how our models represent a valid alternative to the considered competitors in terms of AIC, BIC, and likelihood-ratio tests.

Scalar mixing and entrainment in an axisymmetric jet subjected to external turbulence

The present study aims to understand the process of turbulent entrainment into a jet, as affected by background turbulence, using scalar statistics. Planar laser-induced fluorescence was employed to capture the orthogonal cross sections of the jet at a fixed downstream station with varying background turbulence intensities and length scales. The conditional scalar profiles revealed that the thickness of the scalar turbulent/turbulent interface is greater than that of the traditional turbulent/non-turbulent interface, and the interfacial thickness is an increasing function of the background turbulence intensity. Although nibbling remains the primary entrainment mechanism in the far field, increased occurrence of concentration “holes” within the interfacial layer in the presence of ambient turbulence suggests a more significant role of large-scale engulfment in the turbulent/turbulent entrainment process (although still below 1% of the total mass flux). Enhanced contribution of the area of detached jet patches (i.e., “islands”) to that of the main jet is hypothesized to be evidence of intense detrainment events in the background turbulence. This can potentially contribute to a reduced net entrainment into the jet, which manifests as less negative values of scalar skewness within the jet core.

Investigation of the Relationship Between Burnout Levels and Time Management of Students of the Faculty of Sport Sciences

The aim of the research is to examine the relationship between time management and burnout levels of students studying in sports sciences. The population of the research consisted of 640 students studying at faculty of sports sciences, and the sample group consisted of a total of 153 volunteer students randomly selected. The research was conducted using the survey model, one of the quantitative research methods. Participants in the study were asked to answer the personal information form and the time management and athlete burnout scale in the literature. In order to analyze the data obtained, frequency, arithmetic mean, standard deviation, kurtosis and skewness values were calculated. Independent t test was used for pairwise comparisons, One-Way Anova test was used for multiple comparisons, and Pearson correlation analysis was applied to examine the relationship between variables. As a result, no significant differences were detected in the participants' time management and athlete burnout scales in terms of gender and sport type variables. In the time planning sub-dimension of the time management scale; It has been determined that individuals who read books plan their time better than individuals who do not read, and individuals who participate in recreational activities do better than individuals who do not participate, depending on the age variable, the department studied.

Microstructure and corrosion properties of Al1-xWxN coatings for potential use in gas turbine blades

Herein, a numerical approach of Al1-xWxN thin film coatings on Inconel 738LC superalloys for thermal barrier applications has been studied and the experimental analysis includes deposition of Al1-xWxN films on Si (100), glass and transparent quartz substrates at 40 % N2 flow rate using a dual target DC reactive magnetron sputtering technique to investigate the morphology, microstructure, chemical bonding and corrosion behaviour. AFM analysis revealed the rms roughness, Sq and average roughness, Sa increased with the increasing W content from 22 to 70 at.%. It was found that Al1-xWxN films tend to form tiny valleys on the film surface because the values of surface skewness, Ssk lie below zero. The TEM image showed a branched snowflake structure for the specimen of Al0.58W0.42N films with discrete spots in the SAED pattern, confirming the microcrystalline nature. The blue-shifting of Ecorr towards positive potential confirmed the lower corrosion resistance of WN films than those of AlN. XRD spectra of Al0.58W0.42N films showed an amorphous nature devoid of peaks, which is also confirmed by the SAED pattern. The quasi-passive oxide layer formation was spontaneous for WN films compared to the AlN films. The bending vibrations of AlN and WN showed the coordination environment of the metal centre in the Al1-xWxN films. The computational results indicated that the thermal barrier properties of Al1-xWxN films decreased with the increasing W content, i.e., AIN > Al0.58 W0.42N > WN.

DETERMINING THE RELATIONSHIP BETWEEN SOCIAL MEDIA APPEARANCE CONSCIOUSNESS AND SELF-ESTEEM IN ADOLESCENTS

Introduction: Adolescents' active use of social media raises curiosity about how they appear on social media platforms. This situation may cause changes in their personality. This study was conducted to determine the relationship between social media appearance consciousness and self-esteem in adolescents. Method: This cross-sectional study was conducted between January and March 2023. The study involved 1103 students from three different high schools located in a province in eastern Turkey. As part of the study process, data were collected using the "Questionnaire", "Appearance-Related Social Media Consciousness Scale" and "Rosenberg Self-Esteem Scale". To analyze the data, percentages, averages, independent sample t-tests, one-way ANOVA, Pearson correlation, and regression analysis were performed. The significance level in all tests was set at 0.05. Shao's method was employed to assess the conformity of the data to normal distribution. As a result, the Skewness value was 0.409, the Kurtosis value was -0.370, and it was determined that the data showed a normal distribution between -3 and +3. Results: The mean age of the students in the study was 26.8% 15 years old, 62.2% were male, and 27% were 9th-grade students. The mean total score of the students was 41.11±17.74 for the Appearance-Related Social Media Consciousness Scale and 29.36±6.35 for the Rosenberg Self-Esteem Scale. It was determined that there was a positive correlation between the scores of the Appearance-Related Social Media Consciousness Scale and the Rosenberg Self-Esteem Scale (p&lt;0.05). Conclusions: In conclusion, it was observed that increasing social media appearance consciousness scores was associated with higher self-esteem levels.

Real-time identification of noise type contaminated in surface electromyogram signals using efficient statistical features

Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

Characterization of Multi-Layer Rolling Contact Fatigue Defects in Railway Rails Using Sweeping Eddy Current Pulse Thermal-Tomography

Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting surface and near-surface defects, Eddy Current Pulsed Thermography (ECPT) has garnered significant attention from researchers. However, detecting multi-layer RCF defects remains a challenge. This paper introduces a sweeping Eddy Current Pulsed Thermal-Tomography system (ECPTT) to detect multi-layer RCF defects effectively. This system utilizes varying excitation frequencies to heat defects, altering skin depth and facilitating feature extraction to distinguish multi-layer RCF defects. Skewness and thermographic signal reconstruction (TSR) values are employed as features in the experiments. These features are qualitatively analyzed to differentiate the layers and depths of multi-layer RCF defects. Additionally, five different coils were compared and analyzed quantitatively. The results indicate that the ECPTT system can effectively detect and distinguish multi-layer RCF defects, thereby providing more detailed defect information and enhancing railway safety and maintenance efficiency.

The Framework of Quantifying Biomarkers of OCT and OCTA Images in Retinal Diseases.

Despite the significant advancements facilitated by previous research in introducing a plethora of retinal biomarkers, there is a lack of research addressing the clinical need for quantifying different biomarkers and prioritizing their importance for guiding clinical decision making in the context of retinal diseases. To address this issue, our study introduces a novel framework for quantifying biomarkers derived from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images in retinal diseases. We extract 452 feature parameters from five feature types, including local binary patterns (LBP) features of OCT and OCTA, capillary and large vessel features, and the foveal avascular zone (FAZ) feature. Leveraging this extensive feature set, we construct a classification model using a statistically relevant p value for feature selection to predict retinal diseases. We obtain a high accuracy of 0.912 and F1-score of 0.906 in the task of disease classification using this framework. We find that OCT and OCTA's LBP features provide a significant contribution of 77.12% to the significance of biomarkers in predicting retinal diseases, suggesting their potential as latent indicators for clinical diagnosis. This study employs a quantitative analysis framework to identify potential biomarkers for retinal diseases in OCT and OCTA images. Our findings suggest that LBP parameters, skewness and kurtosis values of capillary, the maximum, mean, median, and standard deviation of large vessel, as well as the eccentricity, compactness, flatness, and anisotropy index of FAZ, may serve as significant indicators of retinal conditions.

Exploring Teacher Awareness of Artificial Intelligence in Education: A Case Study from Northern Cyprus.

This study investigates the level of awareness among teachers regarding the use of artificial intelligence (AI) in education, focusing on whether this awareness varies according to socio-demographic characteristics, access to technology, and specific knowledge and beliefs about AI. Conducted in Northern Cyprus during the 2023-2024 academic year, this study employed a survey model with purposive and snowball sampling methods, involving 164 teachers. Teachers at different levels, namely, primary school, secondary school, high school, and university, were included in this study. The "Artificial Intelligence Awareness Scale", developed by Ferikoğlu and Akgün (2022), was used to measure AI awareness. Data normality was verified through skewness and kurtosis values, allowing for parametric statistical tests such as t-tests, one-way ANOVA, logistic regression, and chi-square analysis. This study explored the distribution of AI use across different school types and educational levels and assessed the impact of sub-dimensions of AI awareness on its application in teaching. Findings revealed no significant influence of teacher demographics (age, gender, education level, type of school, institution level, and monthly income) on AI awareness. However, usage patterns indicated that university lecturers were more likely to incorporate AI in their teaching, followed by primary and high school teachers, with secondary school teachers using it the least. A Multilayer Neural Network Analysis identified practical knowledge as the most critical factor influencing the use of AI in teaching (importance weight of 0.450), followed by beliefs and attitudes (0.298), relatability (0.148), and theoretical knowledge (0.104). These results highlight the importance of practical knowledge for fostering AI integration in educational practices, underscoring significant implications for teacher training and professional development programs.