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A modified matrix adaptation evolution strategy with restarts for constrained real-world problems
(2020)
In combination with successful constraint handling techniques, a Matrix Adaptation Evolution Strategy (MA-ES) variant (the εMAg-ES) turned out to be a competitive algorithm on the constrained optimization problems proposed for the CEC 2018 competition on constrained single objective real-parameter optimization. A subsequent analysis points to additional potential in terms of robustness and solution quality. The consideration of a restart scheme and adjustments in the constraint handling techniques put this into effect and simplify the configuration. The resulting BP-εMAg-ES algorithm is applied to the constrained problems proposed for the IEEE CEC 2020 competition on Real-World Single-Objective Constrained optimization. The novel MA-ES variant realizes improvements over the original εMAg-ES in terms of feasibility and effectiveness on many of the real-world benchmarks. The BP-εMAg-ES realizes a feasibility rate of 100% on 44 out of 57 real-world problems and improves the best-known solution in 5 cases.
In this paper, we consider the question of data aggregation using the practical example of emissions data for economic activities for the sustainability assessment of regional bank clients. Given the current scarcity of company-specific emission data, an approximation relies on using available public data. These data are reported in different standards in different sources. To determine a mapping between the different standards, an adaptation to the Covariance Matrix Self-Adaptation Evolution Strategy is proposed. The obtained results show that high-quality mappings are found. Nevertheless, our approach is transferable to other data compatibility problems. These can be found in the merging of emissions data for other countries, or in bridging the gap between completely different data sets.
Recent developments in the area of Natural Language Processing (NLP) increasingly allow for the extension of such techniques to hitherto unidentified areas of application. This paper deals with the application of state-of-the-art NLP techniques to the domain of Product Safety Risk Assessment (PSRA). PSRA is concerned with the quantification of the risks a user is exposed to during product use. The use case arises from an important process of maintaining due diligence towards the customers of the company OMICRON electronics GmbH.
The paper proposes an approach to evaluate the consistency of human-made risk assessments that are proposed by potentially changing expert panels. Along the stages of this NLP-based approach, multiple insights into the PSRA process allow for an improved understanding of the related risk distribution within the product portfolio of the company. The findings aim at making the current process more transparent as well as at automating repetitive tasks. The results of this paper can be regarded as a first step to support domain experts in the risk assessment process.
In 2021, a prominent Austria dairy producer suffered from an IT attack and was completely paralysed. Without clearly defined mitigation measures in place, major disruptions were caused alongside the whole supply chain, including logistics service providers, governmental food safety bodies, as well as retailers (i.e., supermarkets and convenience stores). In this paper, we ask the question how digitisation and digital transformation impact IT security, especially when considering the complex company ecosystems of food production and food supply chains in Austria. The problem statement stems from a gap in knowledge of key differences in approaches towards IT security, resilience, risk management and especially business interfaces between food suppliers, supermarkets, distributors, logistics and other service providers. In order to answer related research questions, firstly, the authors conduct literature research, and highlight common guidelines and standardisation as well as look at state-based recommendations for critical infrastructure. In a second step, the paper describes a quantitative and qualitative survey with Austrian food companies (producers and retailers) which is described in detail in the paper. A description of recommended measures for the industry, further steps, as well as an outlook conclude the paper.
Creating a schedule to perform certain actions in a realworld environment typically involves multiple types of uncertainties. To create a plan which is robust towards uncertainties, it must stay flexible while attempting to be reliable and as close to optimal as possible. A plan is reliable if an adjustment to accommodate for a new requirement causes only a few disruptions. The system needs to be able to adapt to the schedule if unforeseen circumstances make planned actions impossible, or if an unlikely event would enable the system to follow a better path. To handle uncertainties, the used methods need to be dynamic and adaptive. The planning algorithms must be able to re-schedule planned actions and need to adapt the previously created plan to accommodate new requirements without causing critical disruptions to other required actions.
To create a map of an unknown area, autonomous robots must follow a strategy to explore the area without knowing the optimal paths to reduce the time needed to map the whole area. To reduce the time to accomplish this task, multiple robots can work together to create a map in a more efficient way. However, without proper coordination, the time a team of autonomous robots needs to explore the unknown area can exceed the time needed by a single robot. To counteract the challenges, a shared infrastructure is needed which extracts useful information for the individual robots out of the shared information of all robots so the exploration can be coordinated. These measures introduce new challenges to the system, concerning the load of the communication infrastructure as well as the overall task of exploring and mapping becoming dependent on the correct communication and robustness of the shared team infrastructure. Therefore, the amount of communication and dependency of each individual robot of the rest of the other robots of the team must be reduced to ensure that the robots can continue working even if the communication with the shared infrastructure fails.
Small and medium-sized enterprises often face resource deficits and there- fore depend on cooperating with other actors to stay innovative in a competitive environment. Establishing and maintaining actual co-creation and service inter- action strategies however is challenging. A reason for this is the complexity of finding methodologies and tools to create valuable outcome and the lack of knowledge of collaboration toolsets, also in virtual environments. This paper introduces an Innovation-Method-Framework consisting of innovation methods for increased service interaction and value co-creation among service stakeholders. Also, toolsets for the framework’s practical application are provided.
Blood flow and ventilatory flow strongly influence the concentrations of volatile organic compounds (VOCs) in exhaled breath. The physicochemical properties of a compound (e.g., water solubility) additionally determine if the concentration of the compound in breath reflects the alveolar concentration, the concentration in the upper airways, or a mixture of both. Mathematical modeling based on mass balance equations helps to understand how measured breath concentrations are related to their corresponding blood concentrations and physiological parameters, such as metabolic rates and endogenous production rates. In addition, the influence of inhaled compounds on their exhaled concentrations can be quantified and appropriate correction formulas can be derived. Isoprene and acetone, two endogenous VOCs with very different water solubility, have been modeled to explain the essential features of their behavior in breath. This chapter introduces the theory of physiological modeling of exhaled VOCs, with examples of isoprene and acetone.
Towards a high productivity automatic analysis framework for classification. An initial study
(2013)
Post-operative isoflurane has been observed to be present in the end-tidal breath of patients who have undergone major surgery, for several weeks after the surgical procedures. A major new noncontrolled, non-randomized, and open-label approved study will recruit patients undergoing various surgeries under different inhalation anaesthetics, with two key objectives, namely to record the washout characteristics following surgery, and to investigate the influence of a patient’s health and the duration and type of surgery on elimination. In preparation for this breath study using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS), it is important to identify first the analytical product ions that need to be monitored and under what operating conditions. In this first paper of this new research programme, we present extensive PTR-TOF-MS studies of three major
anaesthetics used worldwide, desflurane (CF3CHFOCHF2), sevoflurane ((CF3)2CHOCH2F), and isoflurane (CF3CHClOCHF2) and a fourth one, which is used less extensively, enflurane (CHF2OCF2CHFCl), but is of interest because it is an isomer of isoflurane. Product ions are identified as a function of reduced electric field (E/N) over the range of approximately 80 Td to 210 Td, and the effects of operating the drift tube under ‘normal’ or ‘humid’ conditions on the intensities of the product ions are presented. To aid in the analyses, density functional theory (DFT) calculations of the proton affinities and the gas-phase basicities of the anaesthetics have been determined. Calculated energies for the ion-molecule reaction pathways leading to key product ions, identified as ideal for monitoring the inhalation anaesthetics in breath with a high sensitivity and selectivity, are also presented.
Towards a strategic management framework for engineering of organizational robustness and resilience
(2020)
Purpose: Although there is an apparent potential in using data for advanced services in manufacturing environments, SMEs are reluctant to share data with their ecosystem partners, which prevents them from leveraging this potential. Therefore, the purpose of this paper is to analyse the reasons behind these resistances. The argumentation paves the way for elaborating countermeasures that are adequate for the specific situation and the typical capabilities of SMEs.
Design/Methodology/Approach: The analysis is based on literature research and in-depth interviews with management representatives of 15 companies in manufacturing service ecosystems. Half of these are manufacturers and the other half technology or service providers for manufacturers. They are SMEs or partly larger companies operating in structures that are typical for SMEs.
Findings: Data sharing hurdles are investigated in the five dimensions, 1. quantifying the value of data, 2. willingness to share data and trust, 3. organizational culture and mindset, 4. legal aspects, and 5. security and privacy. The ability to quantify the value of data is a necessary but not sufficient precondition for data sharing, which must be enabled by adequate measures in the other four dimensions.
Originality/Value: The findings of this empirical study and the solution approach provide an SME-specific framework to analyze hurdles that must be overcome for sharing data in an ecosystem.
Manufacturing SMEs can apply the framework to overcome the hurdles by specific insights and solution approaches. Furthermore, the analysis illustrates the future research direction of the project towards a comprehensive solution approach for data sharing in a manufacturing ecosystem.