Sample Correlation Coefficient Research Articles

Chaos and feasibility analysis of a healthy lifestyle’s sustainability using fractional-order polycystic ovarian syndrome’s effect on infertility

In this study, we developed a fractional-order model for polycystic ovarian syndrome issue transmission in women through four compartments with the Mittag-Leffler kernel. For biological feasibility, we discussed the positivity and boundedness of solutions with fixed point mappings in Banach spaces. Sensitivity analysis is performed using different variables as response functions each time, employing Latin Hyper-cube Sampling (LHS) and Partial Rank Correlation Coefficient (PRCC) with 200 iterations. Further, the reproductive number is determined for the model’s biological feasibility with an analysis of parameter impact through Matlab software. The solutions are examined for global stability using the Lyapunov function with a first derivative. Chaos Control will use the regulate for linear responses approach to bring the system to stabilize according to its points of equilibrium. Numerical solutions are derived by using a Newton polynomial in the generalized form of the Mittag-Leffler kernel, which illustrates the effect of the fractional operator and parameters involved in the system. Results are also analyzed with different assumptions of parameter values and initial conditions to observe the different impacts of treatment to overcome the risk of infertility in women. Such type of investigation will be useful to investigate the spread of disease as well as helpful in developing control strategies to overcome the risk of problems and best management care pre-detection of the issue and for treatment.

A point cloud segmentation algorithm based on multi-feature training and weighted random forest

Abstract Point cloud segmentation is the process of dividing point cloud data into a series of coherent subsets according to its attributes. It has been widely used in target recognition, digital protection of cultural relics, medical research and other fields. To improve the classification accuracy of point cloud and achieve accurate segmentation of objects or scenes, a point cloud segmentation algorithm based on multi–features training and weighted random forest (RF) is proposed. Firstly, the feature vector composed of 3D coordinate value, RGB value, echo intensity, point cloud density, normal direction and average curvature is used to train the SVM classifier, and the ‘one–to–one’ strategy is adopted to achieve the initial multivariate rough segmentation of point cloud. Then, the maximum information coefficient and sample correlation coefficient (SCC) are used to evaluate the correlation of the decision tree, and the decision tree is weighted accordingly to build a weak correlation weighted RF, so as to achieve further accurate segmentation of the point cloud. The experiment verifies the effectiveness of the proposed algorithm by segmenting the outdoor scene point cloud data model. The results show that the segmentation algorithm based on multi–features training and weighted RF can achieve accurate point cloud segmentation, and is an effective point cloud segmentation method.

Sensitivity and perturbation analysis of alpha-eigenvalue with time-dependent Monte Carlo perturbation techniques

Sensitivity analysis and perturbation calculation methods for prompt neutron decay constants α using the Monte Carlo method, such as iterative fission probability (IFP), differential operator sampling (DOS), and correlated sampling (CS), have been proposed thus far. However, these methods require a significant amount of memory storage. The method developed in this study applies DOS and CS techniques during a time-dependent Monte Carlo simulation for the pulsed neutron method in subcritical systems. The time variation of the neutron flux is fitted to an exponential function during the time range when the flux decays with the fundamental mode α. By applying the least squares method to the results of time-dependent DOS and CS, the sensitivity coefficients with respect to nuclear data and the change in α due to perturbation can be calculated. In contrast to the existing methods that require storing the total fission or time sources per generation over several (∼10) generations, the proposed method only needs to store the weighting coefficient of a single source particle until it dies. The additional memory storage per parameter is comparable to that of conventional time-dependent Monte Carlo simulations and much less than that of existing methods. Numerical tests for the proposed method have demonstrated the accuracy and usefulness of the time-dependent DOS and CS methods.

Tool Developments in the OpenMC Code: Correlated Sampling and Transient Fission Matrix Approach Coupled to OpenFOAM

This article outlines the advancements made in broadening the application scope of the OpenMC neutron transport code to include thermohydraulic coupling and nuclear data uncertainty propagation. These developments primarily involve the incorporation of the correlated sampling (CS) technique, facilitating the propagation of thermal feedback or cross-section sampling on neutronic calculations through neutron weight adjustments. The CS technique is integrated with computer-aided-design (CAD)–based meshing and the Transient Fission Matrix (TFM) approach. Together, these components enable comprehensive handling of neutronics-thermohydraulic coupling: The TFM approach addresses neutron kinetics via precalculated neutron transport matrices, the CS technique accounts for thermal feedback impacts on the matrices, and CAD meshing defines volumes corresponding to each matrix bin to align results with computational fluid dynamics codes, specifically OpenFOAM. Implementation details and verification procedures are elaborated, alongside an analysis on existing limitations and possible perspectives.

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokinces on muscle regeneration.

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular-Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSCs), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple timepoints following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting extracellular matrix [ECM]-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokines on muscle regeneration

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular-Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSCs), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple timepoints following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting extracellular matrix [ECM]-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

Characterizations of Correlation Coefficient

The probability density function of correlation coefficient conditional of chi-square variables has been derived. Then the pdf of the correlation coefficient has been easily derived. The conditional moments of correlation coefficient have been derived. The well known moments of correlation coefficient can be easily and quickly derived from the conditional moments. The intermediate steps of the derivation in terms of conditional distributions or moments seem to be worth recording and will be instructive to learners and instructors. A random decomposition of sample correlation coefficient has been discussed. An algorithm has been presented to calculate sample correlation coefficient by using only sample variance.

Goodness of fit test for a skewed generalized normal distribution

ABSTRACT The skewed generalized normal (SGN) distribution with four parameters is a versatile distribution that can effectively model data with skewness and heavy or light tails. In this paper, we conduct two classes of goodness of fit tests for the SGN distribution based on the empirical distribution function (edf) and the sample correlation coefficient. The first class involves transforming the sample into approximately mixed gamma observations, and then applying five classical parametric bootstrap edf-based goodness of fit tests. The second class is based on the inverse probability transformation and utilizes the sample correlation coefficient as the test statistic. We compare the finite sample performances of the proposed tests for different sample sizes and alternative distributions by extensive numerical studies. The simulation results demonstrate that the proposed tests provide a valid alternative to the standard tests using the original data, and the analysis of real data illustrates its application.

RESULTS OF MONITORING THE SETTLEMENT OF SILO FOUNDATIONS REINFORCED WITH SOIL-CEMENT ELEMENTS

During the first cycle of loading and in the subsequent process of steel silos operation to store grain, special attention goes to the observation of base settlement under silo foundations and their tilting. Traditionally, silo settlement tends to be monitored by geodetic measurements. To analyse the results of monitoring for the silos base settlement, we used the linearity changing of the base stiffness approach. The base stiffness is the ratio of the average pressure p (in kPa) under the foundation sole to the average settlement S (in mm). This approach relies on the following assumption: in the process of silo loading, thus gradually increasing the average pressure under the sole of the foundation, the modulus of deformation of the base soil gradually decreases, and the deformability of the base increases until the stresses in the soil mass under the foundation do not exceed the values of the calculated resistance and the development of plastic deformations begins. For grain storage elevator silos 10.1–10.12 located in the Khmelnytskyi region, we obtained the ratio of the average pressure p (in kPa) under the foundation sole to the average settlement S (in mm) during the geodetic monitoring, and then, we used it to construct the stiffness graphs of the silo base. The base stiffness indicators of silos 10.2 and 10.3 most closely match the calculated base stiffness. The specified values of base stiffness exceeding the measured ones by 3–5% are probably due to the higher calculated mechanical characteristics of the base reinforced with soil-cement elements. The rigidity of the silo base and the average pressure in the base under the foundations calculated based on geodetic measurement data are independent random variables, as evidenced by the graphs of the rigidity of the silo base 10.1–10.12. The study of the correlation between the specified random variables is of practical interest. The obtained value of the sample correlation coefficient r = –0.4687 indicates a negative correlation between the samples of the average pressure p (in kPa) under the foundation sole and the stiffness of the base (p / S, kPa/mm). The existence of negative correlation dependence has a clear physical meaning, such as when the pressure on the base increases, its stiffness decreases while increasing the deformability of the base, as confirmed by the results of silo geodetic monitoring. The calculated stiffness of the base decreases with increasing pressure due to the involvement of a greater depth of the compressible stratum, and, accordingly, the calculated deformability of the base increases. To determine the physical and mechanical properties of the base soil, the generally accepted pressure range during the tests does not exceed 250 kPa. A priori, this pressure range corresponds to the elastic section of the soil deformation diagram and the gradual decrease in mechanical characteristics. The average stresses under the foundations of the studied silos did not exceed 125 kPa. Therefore, the proposed method makes it possible to obtain not only quantitative conclusions – the magnitude of base settlement, but also to analyse qualitative indicators related to the stiffness characteristics (р / S, kPa/mm) of the base and to assess its deformation indicators acceptability. Keywords: foundation settlement, geodetic monitoring, reinforced concrete foundation, steel silo, base stiffness.

Exploring Perceptions and Practices of Interprofessional Shared Decision-Making Education in Palliative Care Settings.

Palliative care teams provide support to patients and their caregivers during terminal illness, which requires interprofessional collaboration. One of the foundational skills is to assist patients with decision-making. This can be facilitated through interprofessional shared decision-making (IP-SDM). So far, IP-SDM education frameworks have only been used to a limited extent in the area of palliative care. This study aims to explore perceptions and practices of faculty members, health professionals, and students toward IP-SDM education in palliative care and to indicate associated factors to implement an IP-SDM in undergraduate health professions education in palliative care settings. We used a cross-sectional study design in which the data was obtained via an online self-administered questionnaire adapted from existing validated tools. The questionnaire was distributed to faculty members and health professionals (n = 125) and students (n = 334) at King Abdulaziz Medical City in Jeddah, Saudi Arabia. The sampling technique was a non-probability convenience sampling. Bivariate statistics, such as independent sample t-tests, one-way ANOVA, correlation coefficient, and linear multiple regression were conducted. The response rate was 54% (85 faculty members and health professionals and 164 students). Perceptions on IP-SDM did not differ between participants. From those who had previous experience with IP-SDM, the mean practices score was slightly higher for faculty members and health professionals (M = 83.1, SD = 15.9) than for students (M = 74.1, SD = 11.5), which was significant (p < 0.05). Factors such as gender, age, discipline, nationality, level of education, years of study, and previous experience that were associated with perceptions and practices were varied among participants. The findings show high levels of perception with low levels of practice of IP-SDM in palliative care. Other factors that could be associated with the topic should be addressed in further studies.

Using EEG total energy as a noninvasively tracking of intracranial (and cerebral perfussion) pressure in an animal model: A pilot study

Purpose: This study aims to describe the total EEG energy during episodes of intracranial hypertension (IH) and evaluate its potential as a classification feature for IH. New methods: We computed the sample correlation coefficient between intracranial pressure (ICP) and the total EEG energy. Additionally, a generalized additive model was employed to assess the relationship between arterial blood pressure (ABP), total EEG energy, and the odds of IH. Results: The median sample cross-correlation between total EEG energy and ICP was 0.7, and for cerebral perfusion pressure (CPP) was 0.55. Moreover, the proposed model exhibited an accuracy of 0.70, sensitivity of 0.53, specificity of 0.79, precision of 0.54, F1-score of 0.54, and an AUC of 0.7. Comparison with existing methods: The only existing comparable methods, up to our knowledge, use 13 variables as predictor of IH, our model uses only 3, our model, as it is an extension of the generalized model is interpretable and it achieves the same performance. Conclusion: These findings hold promise for the advancement of multimodal monitoring systems in neurocritical care and the development of a non-invasive ICP monitoring tool, particularly in resource-constrained environments.

Simulating chemical reaction dynamics on quantum computer.

The electronic energies of molecules have been successfully evaluated on quantum computers. However, more attention is paid to the dynamics simulation of molecules in practical applications. Based on the variational quantum eigensolver (VQE) algorithm, Fedorov et al. proposed a correlated sampling (CS) method and demonstrated the vibrational dynamics of H2 molecules [J. Chem. Phys. 154, 164103 (2021)]. In this study, we have developed a quantum approach by extending the CS method based on the VQE algorithm (labeled eCS-VQE) for simulating chemical reaction dynamics. First, the CS method is extended to the three-dimensional cases for calculation of first-order energy gradients, and then, it is further generalized to calculate the second-order gradients of energies. By calculating atomic forces and vibrational frequencies for H2, LiH, H+ + H2, and Cl- + CH3Cl systems, we have seen that the approach has achieved the CCSD level of accuracy. Thus, we have simulated dynamics processes for two typical chemical reactions, hydrogen exchange and chlorine substitution, and obtained high-precision reaction dynamics trajectories consistent with the classical methods. Our eCS-VQE approach, as measurement expectations and ground-state wave functions can be reused, is less demanding in quantum computing resources and is, therefore, a feasible means for the dynamics simulation of chemical reactions on the current noisy intermediate-scale quantum-era quantum devices.

GLOBAL SENSITIVITY ANALYSIS AND OPTIMAL CONTROL OF TYPHOID FEVER TRANSMISSION DYNAMICS

This paper presents a mathematical model aimed at studying the global behaviour and optimal control strategies for Typhoid fever. The primary objective of this study is to identify the most effective control strategy that minimizes the spread of the disease. To achieve this, we calculate the effective and basic reproduction numbers and utilize them to investigate the existence and stability of the equilibria. Furthermore, we investigate the global impact of each model parameter on the variables using Latin Hypercube Sampling and Partial Rank Correlation Coefficient. The necessary conditions of the optimal control problem are analyzed using Pontryagin’s maximum principle, and the numerical values of the model parameters are estimated using the maximum likelihood estimator. The results indicate that the optimal use of vaccination for susceptible individuals, as well as the screening and treatment of asymptomatic infected individuals, have a significant impact on reducing the spread of the disease in endemic regions.

Modelling of tuberculosis dynamics incorporating indirect transmission of contaminated environment and infectivity of smear-negative individuals: A case study for Xinjiang, China

Xinjiang has been one of the high incidence areas of pulmonary tuberculosis (PTB) in China. Besides being infected by direct contacting with active PTB individuals (direct infection), the susceptible would be infected because of the exposure to the environment contaminated by Mycobacterium tuberculosis (indirect infection). Active PTB individuals include not only the smear-positive PTB (PTB+) but also the smear-negative PTB (PTB–) who are infectious due to their ability to release tiny Mycobacterium tuberculosis particles even in the absence of visible Mycobacterium tuberculosis in sputum. By taking account of direct/indirect infection and the difference between PTB+ and PTB– individuals in transmission capability, a periodic dynamical PTB transmission model is proposed. The model is fitted to the newly monthly PTB+ and PTB– cases in Xinjiang from 2008 to 2017 by Markov Chain Monte Carlo algorithm. Moreover, global sensitivity analysis is constructed to address the uncertainty of some key parameters by using Latin hypercube sampling and partial rank correlation coefficient methods. Basic reproduction number R0 for PTB transmission in Xinjiang is estimated to be 2.447 (95% CrI:(1.203, 3.844)), indicating that PTB has been prevalent in Xinjiang over the study period. Our results suggest that reducing the direct/indirect transmission rates, early screening, isolating and treating the latent, PTB+ and PTB– individuals, and enhancing the clearance of Mycobacterium tuberculosis in the environment could more effectively control PTB transmission in Xinjiang. The model fits the reported PTB data well and achieves acceptable prediction accuracy. We believe that our model can provide heuristic support for controlling PTB transmission in Xinjiang.

The effectiveness of higher mental programs in developing students' performance skills in theatrical acting from the teachers' point of view

This study aimed to identify (the effectiveness of higher mental programs in developing the performance skills of students in theatrical acting subject from the point of view of teachers). The researcher intended to build a measure of teachers' attitude towards higher mental programs, and the validity and reliability of the (tool) were verified, and it finally consisted of (50) items that were applied to the research sample, and after collecting information and processing it statistically using the t-test for one sample and Pearson's correlation coefficient. The researcher concluded: The attitudes of theatrical acting teachers towards the use of higher mental programs have a high positive level. The researcher came out with a number of recommendations and proposals.

Micro Finance Institutions and its Contribution on the Growth of Small and Medium Size Businesses in Bambili-Bambui Area

This study examines the contribution of micro finance institution on the growth of small and medium size businesses in Bambili-Bambui area. The quantitative and descriptive research design was used for this study with a sample size of 100 respondents. The simple random sampling technique was adopted for this study. A four Likert scale questionnaire was adopted as instrument used for data collection in this study (Primary source of data) among the sampled SMBs in Bambili and Bambui area. The data collected was coded and analyzed using Statistical Package of Social Science (SPSS) version 20 using descriptive and inferential statistics. Result from the first component “Microloan” data analysis on the aggregate score indicate that 307(62.3%) of the respondent agreed on all the indicators of the Microloan. The sample correlation coefficient(R) was 0.647a indicating that 51.9% of the variation in the growth of SMBs was accounted by the contribution of microloan from MFIs. Also, the F calculated was (36.992) with p = 0.000 indicating a significant effect of microloan on the growth of SMBs Hence, H01 upheld by concluding that microloan significantly affects the growth of SMBs. Also, the second component “Business training and education”, the aggregate score of 332(66.4%) agreed to the important of all the variables with a sample correction coefficient (R) of 0.714 indicating that 56.3 % of the variation in growth of SMBs is accounted for by business training and education provided by MFI and the F calculated was (34.471) with p = 0.000 showing a significant effect of business training and education on the growth of SMBs. Hence, H02 upheld, by concluding that there was a significant effect of business training and education on the growth of SMBs. Lastly, the third component “saving account”, the aggregate score of 350(70.0%) of the responses agreed to all the variables with a sample correction coefficient (R) of 0.742 indicating that 55.1 % of the variation in growth of SMBs was accounted for by saving account provided by MFI and the F calculated was (34.324) with p = 0.000 indicating a significant effect of saving account on the growth of SMBs. Hence, H03 upheld by concluding that there existed a significant effect of saving account on growth of SMBs. Results from the Pearson Correlation Matrix on all the indicators under study reveals that there is a strong positive correlation between microloan and growth of SMBs with a value 0.451*at 0.05level of significant. Again it shows that there is a positively moderate correlation between training and education with a value 0.310** at 0.01 level of significant. And lastly, the result shows that there is a weak correlation between saving account and growth with a value of 0.171 but there exists a positively strong correlation between saving account and business training and education with a value of 0.759** at 0.01 level of significant. The researcher recommends in view of the result obtained from data analysis that microloan should be client-oriented and not product- oriented. Proper and extensive monitoring activities should be provided for members who are granted loans. Microfinance institution can research into very profitable business lines and offer loan to members who have the capacity to exploit such business lines and lastly business and financial training should be provided by microfinance institutions on a regular basis and in most cases should be tailored toward the training needs of their members. Lastly the researcher suggested that since the examined microloan, business training and education and saving account revealed a positive and significant effect on the growth of small and medium businesses, further research should be conducted to determine the effect of other factors, such as access to market, infrastructures and technology in influencing the growth of small and medium size businesses.

Mathematical Analysis of Malaria Epidemic: Asymptotic Stability With Cost‐Effectiveness Study

Malaria is an old, curable vector‐borne disease that is devastating in the tropics and subtropical regions of the world. The disease has unmatched complications in the human host, especially in children. Mathematical models of infectious diseases have been the steering wheel, driving scientists towards elucidation of the dynamic behaviour of epidemics and providing tailored strategic management of diseases. With the ongoing vaccination programs for vector‐borne diseases, the research proposes a nonlinear differential equation model for the malaria disease that provides public health with a shift from the classical understanding of nonpharmaceutical preventive malaria control to pharmaceutical measures of vaccines. The asymptotic dynamic behaviour of the model is studied at the model’s equilibria. The bifurcation type invoked at the disease‐free state is analysed, and the result revealed that the convention that is the condition for eradicating the disease is not always sufficient when the system undergoes backward bifurcation. Furthermore, sensitivity analysis was investigated to quantify the amount of influence each parameter has on . With the Latin hypercube sampling and partial rank correlation coefficient method, the uncertainty in is computed with a 95% confidence interval, with the mean, and 5th and 95th percentiles, respectively, simulated as 0.143788, 0.01545, and 0.41491. An intervention model was derived from the nonintervention model to experiment with and evaluate the respective effects of the various pairings of interventions on the dynamics of the disease. Lastly, an in‐depth cost analysis was studied to identify the most cost‐effective intervention regarding rewarding the desired outcome. From the analysis, we recommend that besides the nonpharmaceutical measure of bed nets and insecticide spray, public health should target the pharmaceutical intervention of vaccine as it can close the gap in malaria prevention.

Психологические аспекты профессиональной деятельности и благополучие женщин в сфере образования: возрастная динамика и гендерное сравнительное корреляционное исследование

The problem of the psychological well-being of women employed in the educational field currently still has a fairly wide range of unexplored issues. In this paper, well-being is considered in connection with the components of professional satisfaction and motivation, taking into account gender and age aspects. The study involved 140 people, including 103 women aged 20 to 75 years. The study was carried out using theoretical analysis of scientific literature, the Method for diagnosing the subjective well-being of a person (Shamionov &amp; Beskova), Motivation Sources Inventory (version of Sidorenko), Job Satisfaction Survey (P. Spektor), Maslach Burnout Inventory (version by Vodopyanova &amp; Starchenkova), Izard's Differential Emotions Scale adapted by Leonov and Kapitsa, test “Personal differential” (adapted at the Bekhterev Research Institute). Statistical methods: Shapiro-Wilk test, non-parametric Kruskal-Wallis test, pairwise comparison using the Dunn method, Spearman's rank correlation coefficient, Cohen's q and z- test for comparing two sample correlation coefficients. As a result, differences between different age groups of women were revealed in terms of professional stress, well-being and burnout, as well as differences for women and men in relationship between the psychological aspects of their professional activities and personality traits, emotions and well-being.

A non-integer order model for Zika and Dengue co-dynamics with cross-enhancement

A novel fractional derivative model with nine compartments is formulated to investigate the transmission dynamics of zika and dengue co-infection. The Atangana–Baleanu fractional derivative in the Caputo sense was employed. The conditions for a unique solution are identified, and the solutions’ positivity and boundedness are demonstrated. The disease-free equilibrium point (DFE) and basic reproduction number, R0, were obtained. The DFE was shown to be locally asymptotically stable when the basic reproduction number is less than one. Zika-associated reproduction number, R0z, and dengue-associated reproduction number, R0d, were estimated to be 1.0144 and 1.1724, respectively. The system was shown to be generalized Ulam Hyers–Rassias stable, and the Adam–Bashforth method was used to provide its’ numerical solution. Sensitivity analysis using the Latin Hyper-cube Sampling (LHS) and Partial Rank Correlation Coefficient (PRCC) (|PRCC|> 0.45) with 200 runs was carried out using various variables as response functions per time. The most significant parameters were found to be zika human-to-human transmission rate, βhz1, vector death rate, μv, zika recovery rate, γhz1 and dengue vector-to-human transmission rate, βhd. Real data from Espirito Santo in Brazil is used to validate the model and fit needed parameter values. Numerical simulations illustrated the impact of varying the fractional order derivative, recovery rates, transmission rates, and cross-enhancement parameters on the infected human compartments. The zika Human-to-human transmission rate, βhz1, was found to be a very significant parameter in the control of zika disease transmission. Increasing the vector death rate, μv, was more important in curbing dengue prevalence and incidence than the attainment of recovery from the dengue disease, and the absence of the zika Vector-to-human transmission rate, βhz3, was almost insignificant in the presence of the zika Human-to-human transmission rate, βhz1, for disease eradication. This study suggested control measures and strategies to decrease the dengue and zika human-to-human transmission rates.

A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer

AbstractTo achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory. The hybrid model decomposes the hmF2 time data into multiple subsequences through CEEMDAN and reconstructs the subsequences by sample entropy and correlation coefficient into high and low‐frequency sequences, which effectively shortens the calculation time of the model. Then, we determine the optimal hyperparameters of the LSTM models through ISOA, achieving high‐precision forecasting of the hmF2. In single‐step forecasting, the forecasting values of the hybrid model in diurnal and seasonal changes are highly consistent with the observation, which can better capture the severe changes in the hmF2. The model's RMSE, MAE, MAPE, and CC evaluation metrics are 15.86, 11.03 km, 4.76%, and 0.93 in the test set. Compared to IRI, GRU, and LSTM models, taking RMSE as an example, the forecasting accuracy of the models increased by 65.24%, 29.89%, and 29.60%, respectively. In multi‐step forecasting, the proposed model is better at forecasting the changing trend of hmF2, and the forecasting accuracies are significantly better than the IRI model. The data from multiple stations also verified the applicability of the proposed model for hmF2 forecasting. The above results indicate that the hybrid model has high accuracy in hmF2 short‐term forecasting and good applicability in multiple multi‐step forecasting, which can further improve the accurate forecasting of space weather.