Framework for the usage of data from real-time indoor localization systems to derive inputs for manufacturing simulation

Discrete event simulation is becoming increasingly important in the planning and operation of complex manufacturing systems. A major problem with today’s approach to manufacturing simulation studies is the collection and processing of data from heterogeneous sources, because the data is often of poor quality and does not contain all the necessary information for a simulation. This work introduces a framework that uses a real-time indoor localization systems (RTILS) as a central main data harmonizer, that is designed to feed production data into a manufacturing simulation from a single source of truth. It is shown, based on different data quality dimensions, how this contributes to a better overall data quality in manufacturing simulation. Furthermore, a detailed overview on which simulation inputs can be derived from the RTILS data is given

Using Component-based Software Synthesis and Constraint Solving to generate Sets of Manufacturing Simulation Models

There is a high degree of flexibility in the design of production systems when it comes to the selection and configuration of machines. Simulation supports this complex decision process. However, modeling various configurations in a simulation environment is very time-consuming. We present a framework that includes component-based software synthesis to generate the set of all possible simulation models for the respective planning case. From the set, feasible solutions for a simulation study are then selected using constraint solving methods. We evaluate our approach using a practical example from sheet metal production

Sheet-Metal Production Scheduling Using AlphaGo Zero

This work investigates the applicability of a reinforcement learning (RL) approach, specifically AlphaGo Zero (AZ), for optimizing sheet-metal (SM) production schedules with respect to tardiness and material waste. SM production scheduling is a complex job shop scheduling problem (JSSP) with dynamic operation times, routing flexibility and supplementary constraints. SM production systems are capable of processing a large number of highly heterogeneous jobs simultaneously. While very large relative to the JSSP literature, the SM-JSSP instances investigated in this work are small relative to the SM production reality. Given the high dimensionality of the SM-JSSP, computation of an optimal schedule is not tractable. Simple heuristic solutions often deliver bad results. We use AZ to selectively search the solution space. To this end, a single player AZ version is pretrained using supervised learning on schedules generated by a heuristic, fine-tuned using RL and evaluated through comparison with a heuristic baseline and Monte Carlo Tree Search. It will be shown that AZ outperforms the other approaches. The work’s scientific contribution is twofold: On the one hand, a novel scheduling problem is formalized such that it can be tackled using RL approaches. On the other hand, it is proved that AZ can be successfully modified to provide a solution for the problem at hand, whereby a new line of research into real-world applications of AZ is opened

Empirical characteristic functions-based estimation and distance correlation for locally stationary processes

In this paper, we propose a kernel-type estimator for the local characteristic function of locally stationary processes. Under weak moment conditions, we prove joint asymptotic normality for local empirical characteristic functions. For time-varying linear processes, we establish a central limit theorem under the assumption of finite absolute first moments of the process. Additionally, we prove weak convergence of the local empirical characteristic process. We apply our asymptotic results to parameter estimation. Furthermore, by extending the notion of distance correlation of Szekely, Rizzo and Bakirov (2007) to locally stationary processes, we are able to provide asymptotic theory for local empirical distance correlations. Finally, we provide a simulation study on minimum distance estimation for a-stable distributions and illustrate the pairwise dependence structure over time of log returns of German stock prices via local empirical distance correlations

Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test)

Background In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. Methods The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. Results Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. Conclusions Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients

Boundary spanning at the science–policy interface: the practitioners’ perspectives

Cultivating a more dynamic relationship between science and policy is essential for responding to complex social challenges such as sustainability. One approach to doing so is to “span the boundaries” between science and decision making and create a more comprehensive and inclusive knowledge exchange process. The exact definition and role of boundary spanning, however, can be nebulous. Indeed, boundary spanning often gets conflated and confused with other approaches to connecting science and policy, such as science communication, applied science, and advocacy, which can hinder progress in the field of boundary spanning. To help overcome this, in this perspective, we present the outcomes from a recent workshop of boundary-spanning practitioners gathered to (1) articulate a definition of what it means to work at this interface (“boundary spanning”) and the types of activities it encompasses; (2) present a value proposition of these efforts to build better relationships between science and policy; and (3) identify opportunities to more effectively mainstream boundary-spanning activities. Drawing on our collective experiences, we suggest that boundary spanning has the potential to increase the efficiency by which useful research is produced, foster the capacity to absorb new evidence and perspectives into sustainability decision-making, enhance research relevance for societal challenges, and open new policy windows. We provide examples from our work that illustrate this potential. By offering these propositions for the value of boundary spanning, we hope to encourage a more robust discussion of how to achieve evidence-informed decision-making for sustainability.Support for the workshop was provided by Margaret A. Cargill Philanthropies and The Pew Charitable Trusts. PFEA is supported by the U.K. Natural Environment Research Council (NE/N005457/1)

Generating FAIR research data in experimental tribology

Solutions for the generation of FAIR (Findable, Accessible, Interoperable, and Reusable) data and metadata in experimental tribology are currently lacking. Nonetheless, FAIR data production is a promising path for implementing scalable data science techniques in tribology, which can lead to a deeper understanding of the phenomena that govern friction and wear. Missing community-wide data standards, and the reliance on custom workflows and equipment are some of the main challenges when it comes to adopting FAIR data practices. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building, and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability, and reusability of experimental tribological data beyond typical publication practices

Digitalisering af undervisningen i almen patologi

Patologi er læren om sygdommes manifestationer i væv og celler, herunder sygdommes årsager og mekanismer, og er en central del af undervisningen på en lang række studier om menneskers og dyrs sygdomme. Imod slutningen af 00’erne kom de første præparatscannere til Danmark, og for faget almen patologi på Københavns Universitet blev dette udnyttet til at forbedre og modernisere undervisningen. Faget baserer nu sin undervisning på digitalisering og anvendelse af digitale medier, og både undervisning og eksamen i vævs- og celleforandringer foregår nu med digitale hjælpemidler. I løbet af de sidste fem år er alle vævspræparater digitaliserede, og sideløbende hermed er den elektroniske platform for faget blevet udbygget. Fagets egne forelæsninger videooptages og streames, forelæsninger udført af internationalt anerkendte undervisere på udenlandske universiteter transmitteres live, og en patologi-blog øger studerendes adgang til underviserne. Til selvstudium udvikles multiple choice-tests baseret på mikroskopipræparater samt korte filmklip, der gennemgår mikroskopiforandringerne. Digitale spørgeskemaer har vist, at disse tiltag har forbedret undervisningen og øget studentertilfredsheden ganske betydeligt. Denne oversigtsartikel vil præsentere de digitale tiltag og deres betydning for undervisningen i patologi på Københavns Universitet