Joint clinical determinants for bivariate hematological parameter among TB/HIV co-infected adults under TB/HIV treatment in university of Gondar comprehensive specialized hospital: Retrospective panel data study.

BACKGROUND: Worldwide ranking above HIV/AIDS, tuberculosis is continues to have a significant effect on public health and the leading cause of death due to high progression of HIV. The objective of current study was identify joint clinical determinants that affecting bivariate hematological parameter among TB/HIV co-infected adults under TB/HIV treatment in university of Gondar comprehensive specialized hospital. METHOD: The result of these study was conducted at university of Gondar comprehensive specialized hospital, Gondar, Ethiopia by using a retrospective cohort follow up study from September 2015-march 2022 G.C. The source of data in this study was secondary data obtained from patients chart. Bayesian approach of longitudinal linear mixed effect sub model was used in panel data set to get wide range of information about TB/HIV co-infected patients. RESULT: Out of 148 co-infected participants more than half of the patients (56.1%) and (52.7%) accounted for CPT and INH non users, of which 10.8% and 10.3% had the outcome of mortality respectively. The random intercept and slope model were selected for repeated measure hemoglobin level and hematocrit based on deviance information criteria (DIC), and probability of direction (Pd) under the full model. CONCLUSION: Current study revealed that clinical predictors red blood cell count, platelet cell count, fair and good treatment adherence, other ART regiment, IPT drug users, and viral load count < 10,000 copies/mL, were associated with high hemoglobin level concentration while, lymphocyte count, WHO clinical stage-IV,1e ART regiment, and patients with OIs results for low hemoglobin level concentration. Likewise, red blood cell count, platelet cell count, fair and good treatment adherence, IPT drug users, and viral load count < 10,000 copies/mL co-infected patients had high hematocrit, while lymphocyte count, WHO clinical stage-III,1c ART regiment, and patients with OIs significantly leads to low hematocrit. Health professionals give more attention to these important predictors to reduce progression of disease when the co-infected patients come back again in the hospital. In addition, health staff should conduct health related education for individuals to examine continuous check-up of co-infected patients.

Structural Uncertainty Analysis of High-Temperature Strain Gauge Based on Monte Carlo Stochastic Finite Element Method.

The high-temperature strain gauge is a sensor for strain measurement in high-temperature environments. The measurement results often have a certain divergence, so the uncertainty of the high-temperature strain gauge system is analyzed theoretically. Firstly, in the conducted research, a deterministic finite element analysis of the temperature field of the strain gauge is carried out using MATLAB software. Then, the primary sub-model method is used to model the system; an equivalent thermal load and force are loaded onto the model. The thermal response of the grid wire is calculated by the finite element method (FEM). Thermal-mechanical coupling analysis is carried out by ANSYS, and the MATLAB program is verified. Finally, the stochastic finite element method (SFEM) combined with the Monte Carlo method (MCM) is used to analyze the effects of the physical parameters, geometric parameters, and load uncertainties on the thermal response of the grid wire. The results show that the difference of temperature and strain calculated by ANSYS and MATLAB is 1.34% and 0.64%, respectively. The calculation program is accurate and effective. The primary sub-model method is suitable for the finite element modeling of strain gauge systems, and the number of elements is reduced effectively. The stochastic uncertainty analysis of the thermal response on the grid wire of a high-temperature strain gauge provides a theoretical basis for the dispersion of the measurement results of the strain gauge.

Theory and implementation of sub-model method in finite element analysis.

Through finite element analysis (FEA) software to study mechanical performance of a bridge structure is currently a commonly used method. The keys to obtain accurate results are to select the appropriate element type and establish a refine mesh model. With the construction of a large number of kilometer-level long-span bridges in practical projects, the time cost of establishing and analyzing a fine mesh solid finite element model (FEM) of a long-span bridge with complex structure can not be ignored. In order to find the balance between accuracy and efficiency, sub-modeling technique can be used to analyze the bridge structure. It is often thought that the sub-modeling technique is only applicable to shell and solid elements, but in fact it is also applicable to plane frame models. Based on two-dimensional (2-D) beam element model, the theory of sub-model was theoretically deduced. Meanwhile the sub-modeling technique was applied and verified by an example of plane frame model. Furthermore, based on the three-dimensional (3-D) solid FEM of a skew bridge, the influence of mesh size on the calculation accuracy was illustrated. Based on sub-modeling technique of nodal displacements, the results of the global model and the sub-model for the skew bridge were compared and studied in terms of stress field and plastic damage of concrete. It is found that the sub-model technique based on nodal displacements is suitable for solid FEM.

Ensemble system for short term carbon dioxide emissions forecasting based on multi-objective tangent search algorithm.

Carbon emissions play a crucial role in inducing global warming and climate change. Accurate and stable carbon emissions forecasting is beneficial for formulating emissions reduction schemes and achieving carbon neutrality as early as possible. Although previous studies have concentrated on employing one or several methods for carbon emissions forecasting, the improvement in forecasting performance is limited because they ignore the importance of objectively selecting the models and the necessity of interval forecasting. In this paper, a novel ensemble prediction system, composed of data decomposition, model selection, phase space reconstruction, ensemble point prediction, and interval prediction, is proposed to conduct both point and interval predictions, which has been proven to be effective in prompting carbon emissions forecasting accuracy and stability. According to the empirical results, the mean MAPE results of our proposed forecasting strategy in point prediction are 1.1102% (in Dataset A) and 1.1382% (in Dataset B), and the mean CWC values in the interval forecasting are 0.3512 and 0.1572, respectively. Thus, the proposed forecasting system improves the forecasting performance relative to other models considerably, which can provide meaningful references for policymakers.

A Multi-Scale Submodel Method for Fatigue Analysis of Braided Composite Structures.

A multi-scale fatigue analysis method for braided ceramic matrix composites (CMCs) based on sub-models is developed in this paper. The finite element shape function is used as the interpolation function for transferring the displacement information between the macro-scale and meso-scale models. The fatigue failure criterion based on the shear lag theory is used to implement the coupling calculation of the meso-scale and micro-scale. Combining the meso-scale cell model and the fatigue failure criterion based on the shear lag theory, the fatigue life of 2D SiC/SiC is analyzed. The analysis results are in good agreement with the experimental results, which proves the accuracy of the meso-scale cell model and the fatigue life calculation method. A multi-scale sub-model fatigue analysis method is used to study the fatigue damage of 2D SiC/SiC stiffened plates under random tension-tension loads. The influence of the sub-models at different positions in the macro-model element on the analysis results was analyzed. The results shows that the fatigue analysis method proposed in this paper takes into account the damage condition of the meso-structured of composite material, and at the same time has high calculation efficiency, and has low requirements for modeling of the macro finite element model, which can be better applied to the fatigue analysis of CMCs structure.

Enhanced lake-eutrophication model combined with a fish sub-model using a microcosm experiment.

Eutrophication models are effective tools for assessing aquatic environments. The lake ecosystem consists of at least three trophic levels: phytoplankton, zooplankton, and fish. However, only a few studies have included fish sub-models in existing eutrophication models. In addition, no specific value or range is available for certain parameters of the fish sub-model. In the present study, a lake microcosm experimental system was established to determine the range of fish sub-model parameters. A three-trophic-level eutrophication model was established by combining the fish sub-model and eutrophication model. The Bayesian Markov Chain Monte Carlo and genetic algorithm method was used to calibrate the parameters of the eutrophication model. The results show that the maximum relative errors were due to phosphate (5.31%), the minimum relative error was due to nitrate (1.94%), and the relative error of dissolved oxygen, ammonia N, zooplankton, and chlorophyll ranged from 3 to 4%. Compared with the two-trophic-level eutrophication model, the relative errors of ammonia nitrogen (4.17%), phosphate (- 5.31%), and nitrate (1.94%) in the three-trophic-level eutrophication model were lower than those in the two-trophic-level eutrophication model, indicating that the three-trophic-level eutrophication model can obtain highly accurate simulation results and provide a better understanding of eutrophication models for future use.