A role delineation study for the SME

The purpose of this research was to perform a role delineation study to validate and prioritize the competency areas included in the body of knowledge developed by SME/AME/Shingo for their three levels of certification examinations in lean manufacturing. A modified Delphi technique was used to gather data and describe what experts in the field consider important for candidates to know and become certified in the discipline of lean manufacturing. Seventy-six Delphi panel experts were selected to serve on the Delphi panel, based on their experience, expertise, and commitment. The study incorporated a Web-based pre-Delphi study followed by three rounds of Delphi questionnaire iterations in both mail and electronic format. A hybrid quantitative and qualitative research design was used for this study in which the Delphi experts were asked to rate the importance of competency areas for testing at each level of lean certification using a 5-point Likert scale and provide additional comments. A convergence of opinion on the competency areas obtained from the Delphi study provided a basis for validating the body of knowledge. A combined grand average of the mean rating of importance and yes percent rating for inclusion was utilized to determine the number of items to be included under each major domain for the Bronze, Silver, and Gold levels of lean certification examinations. The results of the study indicated a need for modifications in the body of knowledge, change in percentage of importance to five major domains under each certification level, and inclusion of a few additional competency areas

Exploring the impact of global agricultural production and trade on infectious disease risks

Agriculture is considered a nexus issue on which the future of global sustainability, health and the environment depend. Although, the environmental impact of agriculture is well established, the potential human health impacts of agriculture are less well understood and quantified, despite their potential to hinder or undermine global health and development efforts. In this thesis, using gold standard methods from the medical sciences, epidemiology, and industrial ecology, I explore the impacts of agricultural land use and trade on infectious diseases risks. Through conducting a systematic review and meta-analysis, I quantify the association between occupational or residential exposure to agricultural land uses and being infected with a pathogen using Southeast Asia as a focal model system (Chapter 2). I further extend these evidence synthesis methods to other geographical regions and integrate meta-analytic estimates with burden estimation methods and input-output analysis to calculate the global human infectious disease impacts of agricultural production and trade (Chapter 3). To address the possibility of spatial autocorrelation and confounding within agriculture-disease relationships, I focus on childhood malaria in sub-Saharan Africa as a case study. Here, I assess the relationships between agricultural land use and malaria whilst controlling for socio-economic and environmental confounders using hierarchical modelling (Chapter 4). Finally, I summarise the main findings of my research, synthesise the added value of the research conducted and highlight future research opportunities (Chapter 5). To combat agricultural land use and trade induced infectious disease risks, governments must acknowledge and address the human health impacts involved with the production of agricultural commodities. The findings from this thesis provide decision makers with a number of impactful recommendations on how public health, development, economic and environmental practitioners can jointly respond to mitigate the negative health impacts of agricultural production and trade. This can aid governments in securing co-benefits and mitigating trade-offs when trying to achieve multiple sustainable development goals simultaneously.Open Acces

Activation Function: Key to Cloning from Human Learning to Deep Learning

Maneuvering a steady on-road obstacle at high speed involves taking multiple decisions in split seconds. An inaccurate decision may result in crash. One of the key decision that needs to be taken is can the on-road steady obstacle be surpassed. The model learns to clone the drivers behavior of maneuvering a non-surpass-able obstacle and pass through a surpass-able obstacle. No data with labels of 201C;surpass-able201D; and 201C;non-surpass-able201D; was provided during training. We have development an array of test cases to verify the robustness of CNN models used in autonomous driving. Experimenting between activation functions and dropouts the model achieves an accuracy of 87.33% and run time of 4478 seconds with input of only 4881 images (training + testing). The model is trained for limited on-road steady obstacles. This paper provides a unique method to verify the robustness of CNN models for obstacle mitigation in autonomous vehicles

Which Is the Best Parametric Statistical Method For Analyzing Delphi Data?

This study compares the three parametric statistical methods: coefficient of variation, Pearson correlation coefficient, and F-test to obtain reliability in a Delphi study that involved more than 100 participants. The results of this study indicated that coefficient of variation was the best procedure to obtain reliability in such a study