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The prolonged training time of the neural network (NN) has sparked considerable debate regarding their application in the field of optimization. The purpose of this paper is to explore the beneficial assistance of NN-based alternative models in inductance design, with a particular focus on multi-objective optimization and uncertainty analysis processes.

Under Gaussian-distributed manufacturing errors, this study predicts error intervals for Pareto points and select robust solutions with minimal error margins. Furthermore, this study establishes correlations between manufacturing errors and inductance value discrepancies, offering a practical means of determining permissible manufacturing errors tailored to varying accuracy requirements.

The NN-assisted methods are demonstrated to offer a substantial time advantage in multi-objective optimization compared to conventional approaches, particularly in scenarios where the trained NN is repeatedly used. Also, NN models allow for extensive data-driven uncertainty quantification, which is challenging for traditional methods.

Three objectives including saturation current are considered in the multi-optimization, and the time advantages of the NN are thoroughly discussed by comparing scenarios involving single optimization, multiple optimizations, bi-objective optimization and tri-objective optimization. This study proposes direct error interval prediction on the Pareto front, using extensive data to predict the response of the Pareto front to random errors following a Gaussian distribution. This approach circumvents the compromises inherent in constrained robust optimization for inductance design and allows for a direct assessment of robustness that can be applied to account for manufacturing errors with complex distributions.

This study aims to examine the role of supplier selection and supplier monitoring in public procurement efficiency in terms of cost reduction in Tanzania.

A structured questionnaire was used to collect cross-sectional survey data from 179 public procuring entities in Tanzania. Structural equation modelling (SEM) was used to analyse the collected data.

The findings revealed that supplier selection and supplier monitoring are positive and significant predictors of public procurement efficiency in terms of cost reduction.

This study was conducted in Tanzanian public procurement contexts, so generalisations should be made with caution. Also, this study collected cross-sectional data; other studies may consider longitudinal data.

This study provides procurement practitioners with insights into selecting the proper suppliers and embracing supplier monitoring to achieve procurement efficiency in terms of cost reduction.

This study examines the effects of supplier selection and supplier monitoring on procurement cost reduction as a measure of public procurement efficiency in the Tanzanian context. Consequently, it provides empirical evidence of supplier management practices in the public procurement context.

Decentralization has profound implications for many health systems. This study investigates the effect of health system decentralization in Organization for Economic Co-operation and Development (OECD) countries on public health security capacity and health service satisfaction.

Multiple linear regression analyses were employed for variables related to the level of health security capacity and satisfaction with the healthcare system while controlling for all socio-demographic variables from the European Social Survey, including over 44,000 respondents from 25 OECD countries. The Health Systems in Transition series of countries were used for assessing the decentralization level.

The result of multiple linear regression analyses showed that the level of decentralization in health systems was significantly associated with higher health security capacity (ß-coefficient 3.722, 95% confidence interval (CI) [3.536 3.908]; p=<0.001) and health service satisfaction (ß-coefficient 1.463, 95% CI [1.389 1.536]; p=<0.001) in the study. Countries with a higher level of decentralization in health policy tasks and areas were significantly likely to have higher health services satisfaction, whereas this satisfaction had a significant negative relation with the lower level of decentralization status of secondary/tertiary care services in OECD countries (ß-coefficient −5.250, 95% CI [−5.757–4.743]; p = 0.001).

This study contributes to a better understanding of the extent to which decentralization of health services affects public health safety capacity and satisfaction with health services, whereas the level of decentralization in OECD countries varies considerably. Overall, the findings highlight the importance of public health security and satisfaction with health care delivery in assessing the effects of decentralization in health services.