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Disasters and other emergency events have complex effects on human systems, particularly if the events are severe or prolonged. When these types of events happen in rural communities, the resources of the local public health, healthcare, and emergency response organizations can be quickly depleted or overwhelmed. Planning for emergencies can help to mitigate their impact. Model-based systems engineering (MBSE) methods, including computer simulations, can provide insight on how best to prepare for these events and to explore the effects of varying approaches and resource utilization. In order to best apply these methods for improving disaster management in rural settings, a synthesis of the current body of evidence in this field is needed. The objective of this scoping review is to provide a descriptive overview of the application of computer simulation based on MBSE approaches to disaster preparedness and response for rural healthcare systems.

The aim of this article is to provide an overview of greenhouse gas emission reduction potentials for 2030 based on the assessment of detailed sectoral studies. The overview updates a previous assessment that dates back more than ten years. We find a total emission reduction potential of 30&ndash, 36 GtCO2e compared to a current-policies baseline of 61 GtCO2e. The energy production and conversion sector is responsible for about one third of this potential and the agriculture, buildings, forestry, industry, and transport sectors all contribute substantially to the total potential. The potential for 2030 is enough to bridge the gap towards emissions pathways that are compatible with a maximum global temperature rise of 1.5&ndash, 2 &deg, C compared to preindustrial levels.

This research has been conducted within the field of industrial engineering and computer science. Emphasis in this study has been on machine learning and to what extent it can be used to predict the recipients of individual awards in sports, based on the data behind their performances. For this work, different machine learning algorithms have been used and evaluated to predict the outcome of the “most valuable player” award in the National Basketball Association. Quantitative data have been gathered from almost 20 seasons to build a base for the machine learning fitting. The selection of features and tuning of hyperparameters have been optimized to improve the predictions. The algorithms have been gauged and compared in different ways to find out how to maximize the accuracy. The conclusion of the study was that the results can be predicted with an R2-score higher than 80% and that each method that was tested predicted more than half of the MVPs. Denna studie har utförts inom områdena industriell ekonomi och datateknik. Fokus har legat på maskininlärning och med vilken precision det kan användas för att förutspå mottagare av idrottsutmärkelser, baserat på deras individuella prestationer. I arbetet har olika maskininlärningsalgoritmer använts och utvärderats för att förutsäga vem som utses till den mest värdefulla spelaren i National Basketball Association. Kvantitativa data har samlats in från nästan 20 säsonger för att lägga grunden för träningen av algoritmerna. Features har valts ut och hyperparametrar har ställts in för att optimera förutsägelserna. Algoritmerna har sedan utvärderats och jämförts för att kunna maximera precisionen. Arbetet resulterade i förutsägelser med R2-scores på över 80% samt att varje metod förutsåg fler än hälften av alla MVPs.

Background: experimentation is widely adopted within industry. Many large organisations have invested in their own infrastructure to be able to run experiments server-side. Objective: we wanted to investigate why organisations switch to server-side experimen- tation and which challenges they encounter in doing so. Method: a qualitative virtual discussion based on a survey was conducted. Twelve Dutch organisations, represented by twelve interviewees, participated in the study. Results: organisations switch to server- side experimentation to run higher quality, more advanced and cheaper experiments. Challenges organisations face are: a shortage of development resources, no standardized process and a lack of a culture of experimentation. Conclusions: this is the first study that explores why organisations from The Netherlands in transition to continuous experimentation choose for investing in server-side experimentation infrastructure, and the challenges they encounter.