Understanding Drivers of Vaccine Hesitancy among Pregnant Women in Nigeria: A Longitudinal Study

Background: Vaccine-preventable diseases are major contributors to the disease burden in Sub-Saharan Africa, accounting for many childhood illnesses, disabilities, and mortality. There is dearth of knowledge on the drivers of vaccine hesitancy in Nigeria and the extent of its impact on coverage. Pregnant women are a particularly important vulnerable and at-risk group and, additionally, very relevant for childhood vaccination decisions. However, this group is understudied in Nigeria. This study’s aims are to adapt Confidence, Complacency, Constraints, Calculation, and Collective Responsibility, also known as the 5C psychological antecedence scale for the Nigerian context and to measure vaccine hesitancy to predict the intention to vaccinate among pregnant women (prenatal) and subsequent vaccination behavior (postnatal). Method: It is a longitudinal study that used multi-stage sampling procedure. One healthcare facility was selected from each district in five regional clusters, from which 255 pregnant women were randomly drawn. A standardized questionnaire was used to collect data on demographic characteristics, sources of vaccination information, and the 5C psychological antecedents of vaccination. Additional variables tested included the importance of religion, masculinity, and rumor/conspiracy theory. The scale’s reliability was explored, and a backward elimination regression analysis was performed to identify the major determinants of childhood vaccination intention among pregnant women (T1) and their postnatal behavior (T2). Results: The prenatal (T1) findings revealed low reliability of the 5C subscales in Nigeria’s setting. Pregnant women’s intention to vaccinate unborn children was lower if they were Muslims, had lower confidence in public authorities or the health system, if husband approval was important for vaccination, and if they believed in rumor. Postnatal (T2) findings revealed that vaccination was more likely to follow mothers’ religious beliefs, when confidence in vaccine effectiveness was high and when mothers felt responsible for the collective. However, higher levels of everyday stress (constraints) were related to less vaccination behavior, and intention did not predict actual vaccination behavior. Conclusion: The 5C scale is incompletely adaptable in Nigeria but is a better tool for measuring vaccination behavior than intention. Overall, the vaccination intention did not predict behavior among pregnant women. The additional variables are good instruments that need further exploration.

Stress-Constrained Topology Optimization Using Approximate Reanalysis with on-the-Fly Reduced Order Modeling

Most of the methods used today for handling local stress constraints in topology optimization, fail to directly address the non-self-adjointness of the stress-constrained topology optimization problem. This in turn could drastically raise the computational cost for an already large-scale problem. These problems involve both the equilibrium equations resulting from finite element analysis (FEA) in each iteration, as well as the adjoint equations from the sensitivity analysis of the stress constraints. In this work, we present a paradigm for large-scale stress-constrained topology optimization problems, where we build a multi-grid approach using an on-the-fly Reduced Order Model (ROM) and the p-norm aggregation function, in which the discrete reduced-order basis functions (modes) are adaptively constructed for both the primal and dual problems. In addition to reducing the computational savings due to the ROM, we also address the computational cost of the ROM learning and updating phases. Both reduced-order bases are enriched according to the residual threshold of the corresponding linear systems, and the grid resolution is adaptively selected based on the relative error in approximating the objective function and constraint values during the iteration. The tests on 2D and 3D benchmark problems demonstrate improved performance with acceptable objective and constraint violation errors. Finally, we thoroughly investigate the influence of relevant stress constraint parameters such as the coagulation factor, stress penalty factor, and the allowable stress value.

Topological Design of Multi-Material Compliant Mechanisms with Global Stress Constraints

This paper presents an approach for the topological design of multi-material compliant mechanisms with global stress constraints. The element stacking method and the separable stress interpolation scheme are applied to calculate the element stiffness and element stress of multi-material structures. The output displacement of multi-material compliant mechanisms is maximized under the constraints of the maximum stress and the structural volume of each material. The modified P-norm method is applied to aggregate the local von Mises stress constraints for all the finite elements to a global stress constraint. The sensitivities are calculated by the adjoint method, and the method of moving asymptotes is utilized to update the optimization problem. Several numerical examples are presented to demonstrate the effectiveness of the proposed method. The appearance of the de facto hinges in the optimal mechanisms can be suppressed effectively by using the topology optimization model with global stress constraints, and the stress constraints for each material can be met.

An Application Process of Additive Manufacturing Based on Digital Simulation and BESO Topology Optimization

Additive manufacturing has now entered a wide range of areas and plays an important role. There are many factors affecting the application of additive manufacturing, such as the amount of printing supplies, print product strength, print speed and so on. These factors potentially hinder the application of additive manufacturing in some typical areas, such as spare parts producing for on-orbit maintenance in space environments. Based on the improvement of the above factors, an additive manufacturing application process based on topology optimization of variable density method and digital simulation was proposed. Print volume of product was used as an explicit constraint, and the design goal of the product, such as strength and modal, was transformed into implicit stress constraints in the topology optimization of three-dimensional model, then stress constraints were independently extracted for secondary verification, finally the checked model is put into print. This process saves computational resources during optimization calculations and printing time, reduces print product’s weight, conserves supplies, and meets initial strength or modal design goals. This process greatly exploited the advantages of additive manufacturing in product manufacturing and made up for the shortcomings of traditional manufacturing processes that can not directly output a relatively abstract model after topological optimization. Under the constraints of saving material and increasing strength, it becomes optimum solution in the manufacture of specific products.

Topology optimization of structures subject to self-weight loading under stress constraints

PurposeThe purpose of this paper is to present an approach for structural weight minimization under von Mises stress constraints and self-weight loading based on the topological derivative method. Although self-weight loading topology has been the subject of intense research, mainly compliance minimization has been addressed.Design/methodology/approachThe resulting minimization problem is solved with the help of the topological derivative method, which allows the development of efficient and robust topology optimization algorithms. Then, the derived result is used together with a level-set domain representation method to devise a topology design algorithm.FindingsNumerical examples are presented, showing the effectiveness of the proposed approach in solving a structural topology optimization problem under self-weight loading and stress constraint. When the self-weight loading is dominant, the presence of the regularizing term in the formulation is crucial for the design process.Originality/valueThe novelty of this research work lies in the use of a regularized formulation to deal with the presence of the self-weight loading combined with a penalization function to treat the von Mises stress constraint.

Topology optimization in concrete construction: a systematic review on numerical and experimental investigations

Structural optimization within concrete construction has been increasingly taken up in research within the last two decades. Possible drivers are the need for material-reduced and thus resource-efficient structures as well as recent advancements in automated concrete construction. However, structural concrete is characterized by nonlinear material behavior. Consequently, the merge of structural concrete design and topology optimization is not trivial. This paper reviews and assesses the topic of topology optimization within concrete construction, carrying out an extensive quantitative as well as qualitative review on practical and numerical applications. The following research areas are identified: Multimaterial modeling, stress constraints, concrete damage modeling, strut and tie modeling, combined truss-continuum topology optimization, the consideration of multiple load cases, a focus on construction techniques and alternative approaches. Although the number of research papers dealing with the topic of topology optimization in concrete construction is numerous, there are only few that actually realized topology optimized concrete structures. In addition, only a little number of experiments was performed for an objective evaluation of the found geometries so far. Concluding this review, a list of future challenges, like the incorporation of sustainability measurements within the optimization process, is given and thus serves as a guidance for subsequent research.

Alternating optimization of design and stress for stress-constrained topology optimization

Handling stress constraints is an important topic in topology optimization. In this paper, we introduce an interpretation of stresses as optimization variables, leading to an augmented Lagrangian formulation. This formulation takes two sets of optimization variables, i.e., an auxiliary stress variable per element, in addition to a density variable as in conventional density-based approaches. The auxiliary stress is related to the actual stress (i.e., computed by its definition) by an equality constraint. When the equality constraint is strictly satisfied, an upper bound imposed on the auxiliary stress design variable equivalently applies to the actual stress. The equality constraint is incorporated into the objective function as linear and quadratic terms using an augmented Lagrangian form. We further show that this formulation is separable regarding its two sets of variables. This gives rise to an efficient augmented Lagrangian solver known as the alternating direction method of multipliers (ADMM). In each iteration, the density variables, auxiliary stress variables, and Lagrange multipliers are alternatingly updated. The introduction of auxiliary stress variables enlarges the search space. We demonstrate the effectiveness and efficiency of the proposed formulation and solution strategy using simple truss examples and a dozen of continuum structure optimization settings.