Variance Of Variables Research Articles

Learning capabilities to resolve tilt-translation ambiguity in goldfish.

Spatial orientation refers to the perception of relative location and self-motion in space. The accurate formation of spatial orientation is essential for animals to survive and interact safely with their environment. The formation of spatial orientation involves the integration of sensory inputs from the vestibular, visual, and proprioceptive systems. Vestibular organs function as specialized head motion sensors, providing information regarding angular velocity and linear acceleration via the semicircular canals and otoliths, respectively. However, because forces arising from the linear acceleration (translation) and inclination relative to the gravitational axis (tilt) are equivalent, they are indistinguishable by accelerometers, including otoliths. This is commonly referred to as the tilt - translation ambiguity, which can occasionally lead to the misinterpretation of translation as a tilt. The major theoretical frameworks addressing this issue have proposed that the interpretation of tilt versus translation may be contingent on an animal's previous experiences of motion. However, empirical confirmation of this hypothesis is lacking. In this study, we conducted a behavioral experiment using goldfish to investigate how an animal's motion experience influences its interpretation of tilt vs. translation. We examined a reflexive eye movement called the vestibulo-ocular reflex (VOR), which compensatory-rotates the eyes in response to head motion and is known to reflect an animal's three-dimensional head motion estimate. We demonstrated that the VORs of naïve goldfish do not differentiate between translation and tilt at 0.5 Hz. However, following prolonged visual-translation training, which provided appropriate visual stimulation in conjunction with translational head motion, the VORs were capable of distinguishing between the two types of head motion within 3 h. These results were replicated using the Kalman filter model of spatial orientation, which incorporated the variable variance of process noise corresponding to the accumulated motion experience. Based on these experimental and computational findings, we discuss the neural mechanism underlying the resolution of tilt-translation ambiguity within a context analogous to, yet distinct from, previous cross-axis VOR adaptations.

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.

Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related retinal detachment (HMRD) is crucial for effective treatment and prevention of additional vision impairment. Consequently, our objective was to streamline and validate a machine-learning model based on clinical laboratory omics (clinlabomics) for the early detection of RD in HM patients. We extracted clinlabomics data from the electronic health records for 24,440 HM and 5607 HMRD between 2015 and 2022. Lasso regression analysis assessed fifty-nine variables, excluding collinear variables (variance inflation factor > 10). Four models based on random forest, gradient boosting machine (GBM), generalized linear model, and Deep Learning Model were trained for HMRD diagnosis and employed for internal validation. An external test of the models was done. Three random data sets were further processed to validate the performance of the diagnostic model. The primary outcomes were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) to diagnose HMRD. Nine variables were selected by all models. Given the AUC and AUCPR values across the different sets, the GBM model was chosen as the final diagnostic model. The GBM model had an AUC of 0.8550 (95%CI = 0.8322-0.8967) and an AUCPR of 0.5584 (95%CI = 0.5250-0.5879) in the training set. The AUC and AUCPR in the internal validation were 0.8405 (95%CI = 0.8060-0.8966) and 0.5355 (95%CI = 0.4988-0.5732). During the external test evaluation, it reached an AUC of 0.7579 (95%CI = 0.7340-0.7840) and an AUCPR of 0.5587 (95%CI = 0.5345-0.5880). A similar discriminative capacity was observed in the three random data sets. The GBM model was well-calibrated across all the sets. The GBM-RD model was implemented into a web application that provides risk prediction for HM individuals. GBM algorithms based on nine features successfully predicted the diagnosis of RD in patients with HM, which will help ophthalmologists to establish a preliminary diagnosis and to improve diagnostic accuracy in the clinic.

Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations.

Contribution of climatic variables and their interactions to reference evapotranspiration changes considering freeze-thaw cycles in the Tibetan Plateau during 1960–2022

While the direct impact of climate change on reference evapotranspiration (ET0) has been extensively studied, there is limited research on the indirect impact resulting from the interaction between climatic variables. This gap hinders a comprehensive understanding of climate change effects on ET0. Additionally, there is scarce exploration into the quantitative effect of freeze-thaw cycles on ET0 variation. In this study, we employed path analysis and dependent variable variance decomposition methods to discern the direct and interactive effects of climatic variables on ET0 in the Tibetan Plateau from 1960 to 2022. Annual ET0 exhibited variation across basins, with the coefficient of variability during the thawed period smaller than that during the non-thawed period. On an annual scale, the largest contribution to ET0 variation came from water vapor pressure deficit (VPD) at 47.7%. This contribution was amplified by its coupled interaction with temperature (T) at 47.1%, although the contribution was partially offset by the interactive effects of VPD with downward shortwave radiation and wind speed at −2.4% and − 27.6%, respectively. During different freezing-thawing periods, VPD primarily controlled ET0 variation, with its interaction with other climatic variables enhancing its impact. Furthermore, soil moisture, influenced by freeze-thaw cycles, exhibited a strong correlation with T and VPD, indicating the significant effect of freeze-thaw cycles on ET0 variation. The weak correlation between ET0 and NDVI suggested that vegetation growth had a limited regulatory effect on ET0. These findings provide valuable insights into the impact of interactions between climatic variables on hydrological processes, enhancing our understanding of the interactive roles of hydrometeorological variables.

Решение оптимизационной задачи оценивания моделей полносвязной линейной регрессии

<p>This article is devoted to the problem of estimating fully connected linear regression models using the maximum likelihood method. Previously, a special numerical method was developed for this purpose, based on solving a nonlinear system using the method of simple iterations. At the same time, the issues of choosing initial approximations and fulfilling sufficient conditions for convergence were not studied. This article proposes a new method for solving the optimization problem of estimating fully connected regressions, similar to the method of estimating orthogonal regressions. It has been proven that, with equal error variances of interconnected variables, estimates of b-parameters of fully connected regression are equal to the components of the eigenvector corresponding to the smallest eigenvalue of the inverse covariance matrix. And if the ratios of the error variances of the variables are equal to the ratios of the variances of the variables, then the b-parameter estimates are equal to the components of the eigenvector corresponding to the smallest eigenvalue of the inverse correlation matrix, multiplied by the specific ratios of the standard deviations of the variables. A numerical experiment was carried out to confirm the correctness of the developed mathematical apparatus. The proposed method for solving the optimization problem of estimating fully connected regressions can be effectively used when solving problems of constructing multiple fully connected linear regressions.</p>

Prevalence and factors associated with sarcopenia among Brazilian older adults: An exploratory network analysis

ObjectivesThis study aimed to verify the prevalence of sarcopenia and its associations with sociodemographic, clinical and psychological factors in community-dwelling older adults. Study designA randomized cross-sectional study was extracted from a probabilistic cluster conducted on individuals aged 65 years or older residing in the community. MethodsSarcopenia was defined according to the criteria of the European Working Group on Sarcopenia in Older People (EWGSOP2). Body composition was assessed using dual-energy X-ray absorptiometry (DXA). Associations were analyzed using networks based on mixed graphical models. Predictability indices of the estimated networks were assessed using the proportion of explained variance for numerical variables and the proportion of correct classification for categorical variables. ResultsThe study included 278 participants, with a majority being female (61 %). The prevalence of sarcopenia was 39.57 %. Among those with sarcopenia, 67 % were women and 33 % were men. In the network model, age, race, education, family income, bone mass, depression, cardiovascular disease, diabetes, total cholesterol levels and rheumatism were associated with sarcopenia. The covariates demonstrated a high accuracy (62.9 %) in predicting sarcopenia categories. ConclusionThe prevalence of sarcopenia was high, especially in women. In addition, network analysis proved useful in visualizing complex relationships between sociodemographic and clinical factors with sarcopenia. The results suggest early screening of sarcopenia for appropriate treatment of this common geriatric syndrome in older adults in Brazil.

Development of the Couples' Sleep Conflict Scale: A new tool to assess conflict around sleep in romantic relationships

ObjectiveThe purpose of this study was to evaluate the psychometric properties of the Couples' Sleep Conflict Scale, a new measure designed to identify the extent of conflict around sleep in romantic relationships. MethodsData from an individual sample (N = 158) and dyadic sample (N = 143 mixed-gender couples) in romantic relationships were used to examine the psychometric properties of the Couples' Sleep Conflict Scale, including internal consistency, convergent and divergent validity, and whether the factor structure differed between couples with concordant and discordant chronotypes. ResultsResults revealed that the Couples' Sleep Conflict Scale fit a 1-factor solution of 5 items, a summed or mean score can be used, and that it is reliable for both men and women. In addition, more relational sleep conflict was associated with both their own and their partners’ poorer sleep hygiene, worse sleep quality, and more daytime sleepiness as well as more general relationship conflict, lower relationship satisfaction, and higher anxious and avoidant attachment. Finally, we found partial measurement invariance for factor loadings, intercepts, and latent variable variances between couples with concordant vs. discordant chronotypes. ConclusionThe Couples' Sleep Conflict Scale is a brief measure that can be used in both research and in health care settings to examine how sleep-related conflict can affect both sleep and relationship quality among couples.

Weed Species’ Diversity and Composition as Shaped by the Interaction of Management, Site, and Soil Variables in Olive Groves of Southern Greece

Gaining a comprehensive understanding of how weed communities respond to both environmental and human-induced factors is of paramount importance in developing effective and ecologically sound weed control strategies. The objectives of the current research were to (1) assess the effect of the main weed management practices used in Greek olive groves on weed species’ diversity; (2) explore the filtering effect of management, site, and soil variables in determining weed species’ composition; and (3) shed light on the association between weed species’ composition and the diversity of the understory vegetation of olive groves. To accomplish these objectives, winter weed species’ coverage was assessed in 116 olive groves, both conventional and organic, distributed across three provinces in southern Greece. The investigation encompassed 29 explanatory variables, categorized into three groups: soil (22), management practices (6), and site conditions (1). It was confirmed that glyphosate use may lower biodiversity and species richness; however, this trend was not universal. In fact, the negative influence of the presence of Oxalis pes-caprae L. on species richness and diversity far outweighed the effect of spraying glyphosate. Redundancy analysis (RDA) revealed that among the 29 variables used to describe the ecological niche, eight (i.e., Mn, Mg, chemical spraying, mowing, rotary tiller, grazing, irrigation, and elevation) were significant and explained 21.5% of the total variation in weed species’ data. Interestingly, the soil Mn concentration was identified as the most influential one, highlighting the importance of soil micronutrients in determining weed species’ composition. The variation partitioning procedure demonstrated that the effect of the management variables on weed species’ composition accounted for 2.2 times the variance of soil variables and 4.5 times the variance of elevation. The present findings might help to enhance optimal management in olive groves that can sustain the biodiversity of flora and, in turn, provide various ecosystem services to agro-ecosystems.

Clinical Variables and Peripheral Biomarkers Associated with Substance-Induced Psychotic Disorder: Differences Related to Alcohol, Cannabis, and Psychostimulant Abuse.

Background: The present retrospective observational study aims to identify differences in clinical features and peripheral biomarkers among patients affected by substance-induced psychotic disorder (SIPD) according to the primary substance of abuse. Methods: A sample of 218 patients was divided into three groups according to the type of consumed substance: alcohol, cannabis, and psychostimulants. The three groups were compared using one-way analyses of variance (ANOVAs) for continuous variables and χ2 tests for qualitative variables. After excluding the alcohol-induced psychotic disorder group, the same analyses were repeated. The statistically significant variables from these subsequent analyses were included in a binary logistic regression model to confirm their reliability as predictors of cannabis- or psychostimulant-induced psychotic disorder. Results: Psychotic cannabis abusers were younger (p < 0.01), with illness onset at an earlier age (p < 0.01). Alcohol consumers presented a longer duration of illness (p < 0.01), more frequent previous hospitalizations (p = 0.04) and medical comorbidities (p < 0.01), and higher mean Modified Sad Persons Scale scores (p < 0.01). Finally, psychostimulant abusers had a higher frequency of lifetime history of poly-substance use disorders (p < 0.01). A binary logistic regression analysis revealed that higher mean Brief Psychiatric Rating Scale scores (p < 0.01) and higher sodium (p = 0.012) and hemoglobin (p = 0.040) plasma levels were predictors of cannabis misuse in SIPD patients. Conclusions: Different clinical factors and biochemical parameters con be associated with SIPD according to the main substance of abuse, thus requiring specific management by clinicians.

Enlarged cavum septum pellucidum and small thymus as markers for 22q11.2 deletion syndrome.

Enlarged cavum septum pellucidum (CSP) and hypoplastic thymus are proposed extra-cardiac fetal markers for 22q11.2 deletion syndrome. We sought to determine if they were part of the fetal phenotype of our cohort of fetuses with 22q11.2 deletion syndrome. Case-control study of fetuses evaluated from 2016 to 2022. The study group included fetuses with laboratory confirmation of 22q11.2 deletion syndrome. The control group included pregnancies with conotruncal cardiac anomalies with normal microarray as well as structurally normal fetuses with normal microarray. The CSP and thymus were routinely measured during anatomical ultrasound in all patients at their initial visit at 27.1±4.7weeks. The CSP and thymus measurements were classified as abnormal if they were >95% or <5% for gestational age, respectively. The groups were compared using analysis of variance or Kruskal-Wallis for continuous variables and Fisher's exact test for categorical variables. Logistic regression was performed, and a Receiver Operating Characteristic (ROC) curve was constructed. We identified 47 fetuses with 22q11.2 deletion syndrome and compared them to 47 fetuses with conotruncal anomalies and normal microarray and 47 structurally normal fetuses with normal microarray. 51% (24/47) of fetuses with 22q11.2 deletion syndrome had an enlarged CSP compared to 6% (3/47) of fetuses with a conotruncal anomaly and normal microarray and none of the structurally normal fetuses (p<0.001). Of the fetuses with 22q11.2 deletion syndrome, 83% (39/47) had a hypoplastic or absent thymus compared to 9% (4/47) of the fetuses with a conotruncal anomaly and normal microarray and none of the structurally normal fetuses (p<0.001). 87% (41/47) of the fetuses with 22q11.2 deletion syndrome had conotruncal cardiac anomalies. Logistic regression revealed that both enlarged CSP and hypoplastic/absent thymus were associated with 22q11.2 deletion syndrome. The area under the ROC curve for the two markers was 0.94. An enlarged CSP and hypoplastic/absent thymus appear to be part of the fetal phenotype of 22q11.2 deletion syndrome. These markers are associated with conotruncal anomalies in the setting of 22q11.2 deletion syndrome but not in normal controls or fetuses with conotruncal defects and normal microarrays.

Characteristics of Safety Personality

For high-risk organisations, it is essential that employees demonstrate safety-awareness behaviour, as a reflection of their safety personality, to avoid accidents. Several studies confirm that personality is the individual difference that is most associated with the likelihood of an accident occurring; it is therefore necessary to address the identification and expression of the characteristics of safety personality. This paper aims to present a personality questionnaire the authors have created, which was administered to employees of a large Hungarian company in the electricity supply business with altogether 1273 employees obligated to prioritise workplace safety. The final version of the questionnaire contained 59 items. Based on the statistical results, 58.08% of the variance of total variables was explained by 10 factors; the scales of the questionnaire developed are otherwise reliable. The paper also examines the validation of the scales using different psychological measures.

A Multidimensional Approach to Talent Identification in Youth Volleyball through Declarative Tactical Knowledge and Functional Fitness.

This study aimed to assess which multidimensional performance indexes were the best predictors of talent identification in volleyball. Fifty-five female players (age: 13.8 ± 1.81 years; mass: 55.12 ± 8.12 kg; height: 158.23 ± 7.62 cm) were clustered into two groups according to some physical characteristics (i.e., the first group included players with more favorable performance predictors). Musculoskeletal Fitness (MSF), Functional Motor Competence (FMC), and Declarative Tactical Knowledge (DTK) were measured as multidimensional indexes of performance. Moderate-to-large differences between groups were found for each index in favor of the first group. Regression analyses were performed to examine the variance explained by MSF, FMC, and DTK in the two groups. A model with FMC components explained slightly more variance in the group predictor variables (R2 = 0.53) than a model using only MSF components (R2 = 0.45). Among FMC components, the score of the Throw-and-Catch test resulted in the best predictor (Odds Ratio = 1.58) for determining group selection, followed by the score of the Supine-to-Stand-and-Go test (Odds Ratio = 0.02). An additional model composed by MSF and FMC significant predictors (i.e., functional fitness index) and DTK explained 63% of the variance (R2 = 0.63), and these were significant predictors of group membership (Odds Ratio = 6.32 and Odds Ratio = 1.51, respectively). A more comprehensive multidimensional analysis of youth performances is warranted to identify and monitor the best players in a youth volleyball context.

Gender Differences in Children's Conduct Problems: A Multigroup Analysis of Latent Profiles Based on Temperament and Psychopathic Traits.

Despite the high prevalence of conduct problems in children, and their social and health impact, little is known about gender differences in their characteristics, determinants and implications. This three-year longitudinal study explored the diversity of individual trait configurations in children with conduct problems, assessing whether 1) the same profiles can be identified in boys and girls and, 2) the predictors and outcomes of these profiles are invariant across genders. A multigroup analysis of latent profiles based on temperamental and psychopathic traits was performed on a sample of 401 young children (50.87% girls) with high scores in conduct problems. Both the number of profiles and the means and variances of compositional variables were similar across genders, but the distributions were different. Four profiles were identified, and girls belonged to the least problematic group more frequently. While the predictors were invariant across genders, outcomes were not. This study contributes to the literature on heterogeneity in conduct problems by identifying specific constellations of traits in both boys and girls and shows the importance of considering gender in understanding the progression of conduct problems.

Levenberg–Marquardt backpropagation neural networking (LMB-NN) analysis of hydrodynamic forces in fluid flow over multiple cylinders

The mathematical formulation of the flowing liquid stream around and through confined multiply connected domains brings a complex differential system. Due to this, one cannot provide a complete description of flow field properties. The current pagination provides a numerical remedy by the use of artificial intelligence in this direction. To be more precise, fluid is considered in the rectangular channel. The circular, square, and triangular-shaped cylinders are positioned as an obstacle to the continuous fluid. The channel’s left wall is viewed as an inlet and two different velocity profiles are introduced at an inlet that are constant and parabolic profile. To discretize the computational domain, hybrid meshing is used. The variance in basic variables, namely, the velocity of the liquid and the distribution of the liquid pressure, is recorded via graphs. The nine different meshed grades are tested for the drag and lift coefficients around the right-angle triangle, square, and circular barrier. The neural networking model is constructed by using 99 datasets of sample values for drag coefficient when characteristic length, the density of fluid, the dynamic viscosity of the fluid, and mean flow velocity are taken as inputs. The training of the neural network takes up 69 slots (about 70%), while the testing and validation of the neural network each take up 15 slots (15%). The Levenberg–Marquardt backpropagation algorithm is used for training. We have observed that for the parabolic profile, the drag coefficient is higher in intensity for each obstacle compared to the constant profile, while the lift coefficient shows opposite patterns.

Knowledge, attitude, and perceived risks related to diabetes mellitus among university students in Southwestern Nigeria

IntroductionDiabetes mellitus (DM) has recently shown a demographic shift in epidemiology among adolescents and young adults. Adolescents and young adults are an important demographic of study since studies have shown an increasing prevalence in low-income countries including Nigeria. This study assessed the knowledge, attitude, and perceived risks of DM among university students in selected universities in southwestern Nigeria. MethodsThis study was a cross-sectional study carried out among university undergraduates in southwestern Nigeria. An online questionnaire was used to collect data via students’ WhatsApp groups. The students' knowledge was ranked as good (≥70%), fair (50–69%), and poor (<50%) while attitude was categorized into positive (≥50%), and negative (<50%). The difference in knowledge based on demographics, disease profile, and source of information was analysed using the independent sample t-test and one way analysis of variance for variables with 2 and > 2 groups respectively. Analyses were conducted using SPSS software version 25. ResultsA total of 349 students consented to participate in the study of whom 55.3% were female. 25.2% had good knowledge while 92.3% of the participants were aware of DM. Positive attitudes and perceived risks were reported among all of the students. Source of information, family history, level of study, and age were significantly associated with knowledge of diabetes. ConclusionA small proportion of the students exhibited good knowledge, while all demonstrated a positive attitude and perceived risks associated with DM. There is a clear need for educational interventions, such as health programs and awareness campaigns, to enhance their knowledge of DM.

The relationship between muscle quality index and physical function in older adults

BACKGROUND: The muscle quality index (MQI) has been proposed as a diagnostic tool to detect individuals at risk of limited physical function. OBJECTIVE: Our goal was to examine the relationship of the MQI with physical function and compare its predictive ability with other muscle parameters of strength and mass in an older population. METHODS: Sixty-eight healthy older adults (34 male, 34 female) aged between 69 and 89 years were tested for body composition, MQI, maximum isokinetic concentric KES and physical function including sit-to-stand (STS) time, normal and maximum walking speed (WS), Timed Up and Go (TUG) and static balance. Pearson’s correlation was applied to examine the relationship between muscle parameters. Linear regression analysis including age and sex as additional covariates was performed to assess their predictive ability for physical functions. RESULTS: MQI correlated significantly with relative KES (r= 0.611, p&lt; 0.001), total KES (r= 0.829, p&lt; 0.001) and leg lean tissue mass (r= 0.690, p&lt; 0.001). The MQI was not a significant predictor for any physical function (p&gt; 0.05). STS time was a significant predictor for normal WS and TUG time (p&lt; 0.001). Relative KES was a significant predictor for all physical functions except static balance (p&lt; 0.001). No muscle parameter and only age was a significant predictor for static balance. Models explained 20.4%–57.3% of variances of dependent variables. CONCLUSIONS: The MQI is a useful tool to assess leg lean tissue mass and strength of the knee extensor muscles and outperforms STS time. However, STS time and relative KES are more closely related to physical function than the MQI.

Concussion Symptoms and Neurocognitive Performance of Children and Adolescents on Antidepressants.

There is a well-established association between preexisting depression/anxiety and greater postconcussion symptom burden, but the potential impact of antidepressant medications has not been fully explored. The primary objective of this study was to compare preinjury/baseline and postinjury concussion symptom scores and neurocognitive performance of athletes on antidepressant medications, both with healthy controls and with those with depression/anxiety not on antidepressants. This is a cross-sectional study using data collected from 49,270 junior and high school athletes from computerized neurocognitive assessments (Immediate Post-Concussion Assessment and Cognitive Test [ImPACT]) administered between 2009 and 2018 held by the Massachusetts Concussion Management Coalition. The main outcome measures were symptom scores and neurocognitive performance measures, all of which were assessed both at baseline and postinjury. Statistical analysis included analysis of variance and Tukey pairwise comparisons for continuous variables and Fisher's exact test for categorical variables. Multivariate regression models were used to adjust for potential confounding variables. Both at baseline and postinjury, athletes with depression/anxiety had mean total symptom scores that were more than double that of healthy controls regardless of antidepressant use. Although there were no significant differences in neurocognitive performance at baseline, depression/anxiety was associated with small but significant decreases in postinjury visual memory and visual motor scores. Both at baseline and after sustaining a concussion, young athletes with depression/anxiety experience significantly greater symptom burden compared with healthy controls regardless of antidepressant use.

Comparative Analysis of the Volatility Structures of the Stock Prices of Energy Companies Traded on the Kazakhstan Stock Exchange and International Gold and Oil Prices

The return of its stock exchange and the companies traded within are one of the important indicators for a national economy. Due to the global structure of stock markets, returns are closely related to both national and international market variables. This study makes a comparative analysis of the volatility structures of the energy companies traded in the Kazakhstan Stock Exchange (KASE) and the combined stock market index and gold and oil prices in international markets for the period between January 01, 2021, and June 31, 2023. The research focused on two issues. The first is the analysis of the volatility structure of the six series examined. For this purpose, four different models were examined. The second focus is to determine whether the returns in international indices have a causal effect on the Kazakhstan stock market (composite stock market index) and the returns of oil and energy companies traded in the stock market. The results revealed that other indices and returns have a similar variable variance structure, except for the KASE. The relevant coefficient estimation was found to be significant in both conditional standard deviation models for the KASE index. The coefficient estimate of the GARCH-M(1,1) model in the OIL index was significant, whereas conditional standard deviation models and the relevant coefficients of both conditional standard deviation models were found to be statistically insignificant in the other returns. This is an indication of the structural compatibility of Kazakhstan's stock market composite index and energy and oil companies with international markets. Furthermore, the causality analysis results showing that international indices have a causal effect on KASE and KZAP is another indicator that the Kazakhstan market works in harmony with the international markets.

DPIM-based global reliability analysis method for the cable-stayed bridges and the determination of global target reliability index

This study presents an approach to analyzing the dynamic global reliability of cable-stayed bridges using the direct probability integral method. The analysis considers the degradation of resistance in the concrete main beam, tower, and cables, as well as the time-varying nature of the load effect. Six types of limit state functions for failure modes are established, including bending failure of the beam, static torsional failure of the beam, wind vibration failure of the beam, transverse buckling failure of the tower, longitudinal strength failure of the tower, and strength failure of the stay cable due to fretting fatigue and corrosion. A time-dependent extreme value mapping is constructed in the time domain, then the joint probability density integral equation of time-varying extreme value mapping of multiple performance functions is established. According to the mean and variance of random variables in the evaluation reference period, the direct probability integral method with Heaviside function (DPIM-H) is employed to solve the equation, and the dynamic global reliability index of cable-stayed bridge is obtained. As an example, the Brotonne cable-stayed bridge’s global reliability is analyzed, and the reasonable value of the global target reliability index is studied considering the structural performance degradation.

Analysis of the physical morphology and athletic performance of local and naturalised players in the Chinese Super League

ABSTRACT Background: The integration of naturalized players in Chinese soccer since 2019 has sparked significant debate, with mixed results raising questions about the effectiveness of this policy in improving the national team’s performance. Objectives: This study aims to analyze the differences in physical morphology and athletic performance between local and naturalized players in the Chinese Super League to assess the impact of naturalization on the national team’s competitiveness. Methods: Utilizing the AMISCO computerized tracking system, we collected and analyzed data from 561 matches spanning the 2019-2021 seasons. Employing mixed linear models, we examined the variance in key physical morphology and performance-related variables, with standardized differences assessed through Magnitude-Based Inference (MBI). Results: Our findings indicate modest but significant differences in physical morphology between local and naturalized defenders (P &lt; 0.05, -0.32 &lt; ES &lt; -0.42). Performance-wise, naturalized defenders outperformed local counterparts in both technical and tactical aspects and demonstrated superior high-intensity running capabilities. Similarly, naturalized midfielders showed a slight edge over local midfielders in technical, tactical, and running proficiency. Conversely, local forwards exhibited marginally better technical and tactical performance compared to their naturalized counterparts, albeit with minimal differences in running ability. Conclusion: This study reveals that while naturalized players demonstrate certain advantages in physical morphology and athletic performance compared to local players in the Chinese Super League, these advantages are not consistent across all positions.