Lara Waltersmann

Bio: Lara Waltersmann is an academic researcher from Fraunhofer Society. The author has contributed to research in topics: Sustainability & Manufacturing. The author has an hindex of 4, co-authored 9 publications receiving 45 citations.

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Abstract: Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of the current AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

Lithium Ion Batteries

Abstract: Phase entropy as a function of lithium filling in the layered oxide S = KB ln(Ω) , Boltzman configuration entropy With Ω the configuration multiplicity (number of ways the inserted lithium cations could be arranged in the host lattice). Under the reasonable assumption of indistinguishable lithium cations, the configuration multiplicity of N lithium cations into the host material with Ns effective total number of sites follows the combinatorial equation below: Notes by MIT Student (and MZB)

human Development Index

Abstract: How many countries are included in the 2013 HDI? The 2013 HDI covers 187 countries, the same number as in 2012 and 2011. Maintaining the same number of is the result of intensified efforts by the Human Development Report Office (HDRO) to work with international data providers and national statistical agencies to obtain required development indicators for the HDI which had been unavailable for some countries in previous years. For a full explanation of the results and methodology of the 2013HDI and other indexes in the 2014 Human Development Report, please see the Technical Notes 1-5. What does the HDI tell us? The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. For example, Malaysia has GNI per capita higher than Chile but life expectancy at birth is about 5 years shorter, mean years of schooling is shorter and expected years of schooling is 2.5 years shorter resulting in Chile having a much higher HDI value than the Malaysia. These striking contrasts can stimulate debate about government policy priorities. Did the HDI rankings change for many countries in 2013? Based on the consistent data series that were available on 15 November 2013, there are few countries with changed ranks between 2012 and 2013. The HDI values for 2012 and 2013 are given in Table 1 of Statistical Annex. The HDI trends since 1980 are given in Table 2. In this table we also provide the change in ranks between 2008 and 2013. We advise users of the HDR not to compare the results from different Reports, but to use the consistent data given in Table 2 of the latest report. The consistent data are based on the latest data revisions and are obtained using the same methodology. The effect of change in achievements (improvement or declining) in human development indicators of

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Abstract: Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

Digital Twins From Smart Manufacturing to Smart Cities: A Survey

Abstract: Digital twins are quickly becoming a popular tool in several domains, taking advantage of recent advancements in the Internet of Things, Machine Learning and Big Data, while being used by both the industry sector and the research community. In this paper, we review the current research landscape as regards digital twins in the field of smart cities, while also attempting to draw parallels with the application of digital twins in Industry 4.0. Although digital twins have received considerable attention in the Industrial Internet of Things domain, their utilization in smart cities has not been as popular thus far. We discuss here the open challenges in the field and argue that digital twins in smart cities should be treated differently and be considered as cyber-physical “systems of systems”, due to the vastly different system size, complexity and requirements, when compared to other recent applications of digital twins. We also argue that researchers should utilize established tools and methods of the smart city community, such as co-creation, to better handle the specificities of this domain in practice.