A Structural Equation Model of Social Support, Stress and Depression in Pregnant Women During Epidemic Period

Objective: To investigate the connections among social support, stress, and depression. Design: Cross-sectional study. Setting: Guizhou Province in China. Population or Sample: An aggregate of 1,056 expectant ladies, had finished our questionnaire during pregnancy from March to April in 2020. Methods: The Edinburgh prenatal sadness scale, an independent pressure scale, and social support scale evaluated the downturn, stress, and social support of pregnant people during the pestilence. An auxiliary condition model was utilized to examine the immediate and aberrant connection between social support and prenatal misery. Main Outcome Measures: Incidence of depression. Results: During the pandemic time, 73.01% of pregnant ladies experienced prenatal misery. The model is suitable (chi-square = 11.96, CFI = 0.97, RMSEA = 0.07, RMR = 0.03). The auxiliary condition model indicated that the immediate pathway of social support to depression was critical (normalized pathway coefficient = - 0.34), and the aberrant pathway of stress to depression via social support was additionally huge (normalized pathway coefficient = 0.50). Stress partially intercedes the connection between social support and depression. Conclusion: Our discoveries posit that social support is related to an expanded danger of depression. Stress is decidedly corresponding to depression and assumes an interceding position between social support and stress. Thusly, directed mediation ought to be completed to lessen the depression of pregnant ladies and improve their psychological wellness status. Keywords: stress, depression, social support, structural equation model

Selected aspects of Sustainable Urban Mobility Planning (sump)

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Energy and pointwise bounds in some non-standard parabolic problems

We study a class of initial-boundary-value problems for which an auxiliary condition of the form is prescribed. We determine bounds on an energy expression by means of differential inequalities and derive pointwise bounds for the solution and its gradient by use of a parabolic maximum principle.

Ergodicity of diffusion and temporal uniformity of diffusion approximation

Let {XN (t), t ≧ 0}, N = 1, 2, … be a sequence of continuous-parameter Markov processes, and let TN (t)f(x) = Ex [f(XN (t))]. Suppose that limN→∞ T N (t)f(x)= T(t)f(x), and that convergence is uniform over x and over t ∈ [0, K] for all K < ∞. When is convergence uniform over t ∈ [0, ∞)? Questions of this type are considered under the auxiliary condition that T(t)f(x) converges uniformly over x as t → ∞. A criterion for such ergodicity is given for semigroups T(t) associated with one-dimensional diffusions. The theory is illustrated by applications to genetic models.

Ergodicity of diffusion and temporal uniformity of diffusion approximation

Let {XN(t), t ≧ 0}, N = 1, 2, … be a sequence of continuous-parameter Markov processes, and let TN(t)f(x) = Ex[f(XN(t))]. Suppose that limN→∞TN(t)f(x)= T(t)f(x), and that convergence is uniform over x and over t ∈ [0, K] for all K < ∞. When is convergence uniform over t ∈ [0, ∞)? Questions of this type are considered under the auxiliary condition that T(t)f(x) converges uniformly over x as t → ∞. A criterion for such ergodicity is given for semigroups T(t) associated with one-dimensional diffusions. The theory is illustrated by applications to genetic models.