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Real-time measurements of the differences in inhaled and exhaled, unlabeled and fully deuterated acetone concentration levels, at rest and during exercise, have been conducted using proton transfer reaction mass spectrometry. A novel approach to continuously differentiate between the inhaled and exhaled breath acetone concentration signals is used. This leads to unprecedented fine grained data of inhaled and exhaled concentrations. The experimental results obtained are compared with those predicted using a simple three compartment model that theoretically describes the influence of inhaled concentrations on exhaled breath concentrations for volatile organic compounds with high blood:air partition coefficients, and hence is appropriate for acetone. An agreement between the predicted and observed concentrations is obtained. Our results highlight that the influence of the upper airways cannot be neglected for volatiles with high blood:air partition coefficients, i.e. highly water soluble volatiles.
For a given set of banks, how big can losses in bad economic or financial scenarios possibly get, and what are these bad scenarios? These are the two central questions of stress tests for banks and the banking system. Current stress tests select stress scenarios in a way which might leave aside many dangerous scenarios and thus create an illusion of safety; and which might consider highly implausible scenarios and thus trigger a false alarm. We show how to select scenarios systematically for a banking system in a context of multiple credit exposures. We demonstrate the application of our method in an example on the Spanish and Italian residential real estate exposures of European banks. Compared to the EBA 2016 stress test our method produces scenarios which are equally plausible as the EBA stress scenario but yield considerably worse system wide losses.
On the extension of digital ecosystems for SCM and customs with distributed ledger technologies
(2019)
Global supply chains represent the backbone of the modern manufacturing industry. Planning of global supply chains still represents a major hurdle, mainly because of the high complexity and unforeseen disruptions that have to be mastered for meeting the different logistics windows in a globally distributed production environment. Trust in supply chains is an additional challenge. A major – albeit sometimes overlooked - part of Supply Chain Management (SCM) is the management and integration of customs processes, clearing of tariffs, (re-)billing of customers, and fulfilling other legal requirements related to crossing borders, ranging from environmental standards over goods inspection to general paper work. With the exception of work offered by the World Customs Organization (WCO) the issue of customs and blockchain is still underrepresented in research and practice. In this paper, we look at innovations that drive the current ICTenabled SCM research and how these can be combined with smart customs management. After a literature review and introduction to the state-of-the-art, we list potential trust-based innovations for SCM and customs in digital business ecosystems. Based upon the innovations we also describe a requirements analysis of existing distributed ledger technologies (requirements for system layout, system configuration, system governance). A description of the prototype for the Lake Constance region – on which we are currently working – concludes the paper.
With the emergence of the recent Industry 4.0 movement, data integration is now also being driven along the production line, made possible primarily by the use of established concepts of intelligent supply chains, such as the digital avatars. Digital avatars – sometimes also called Digital Twins or more broadly Cyber-Physical Systems (CPS) – are already successfully used in holistic systems for intelligent transport ecosystems, similar to the use of Big Data and artificial intelligence technologies interwoven with modern production and supply chains. The goal of this paper is to describe how data from interwoven, autonomous and intelligent supply chains can be integrated into the diverse data ecosystems of the Industry 4.0, influenced by a multitude of data exchange formats and varied data schemas. In this paper, we describe how a framework for supporting SMEs was established in the Lake Constance region and describe a demonstrator sprung from the framework. The demonstrator project’s goal is to exhibit and compare two different approaches towards optimisation of manufacturing lines. The first approach is based upon static optimisation of production demand, i.e. exact or heuristic algorithms are used to plan and optimise the assignment of orders to individual machines. In the second scenario, we use real-time situational awareness – implemented as digital avatar – to assign local intelligence to jobs and raw materials in order to compare the results to the traditional planning methods of scenario one. The results are generated using event-discrete simulation and are compared to common (heuristic) job scheduling algorithms.
The design and development of smart products and services with data science enabled solutions forms a core topic of the current trend of digitalisation in industry. Enabling skilled staff, employees, and students to use data science in their daily work routine of designing such products and services is a key concern of higher education institutions, including universities, company workshop providers and in further education. The scope and usage scenario of this paper is to assess software modules (‘tools’) for integrated data and analytics as service (DAaaS). The tools are usually driven by machine learning, may be deployed in cloud infrastructures, and are specifically targeted at particular needs of the industrial manufacturing, production, or supply chain sector.
The paper describes existing theories and previous work, namely methods used in didactics, work done for visually designing and using machine learning algorithms (no-code / low- code tools), as well as combinations of these two topics. For tools available on the market, an extended assessment of their suitability for a set of learning scenarios and personas is discussed.
The role of entrepreneurs and intrapreneurs in the current zeitgeist is to drive innovation, re-shape rigid, established processes in business as well as for consumers. They use new viewpoints to pioneer new (business) models which focus on ‘smartness’ rather than the purely monetary and short-sighted models of yesteryear. Fostering and supporting the culture of this current zeitgeist is a mayor challenge for entre- and intrapreneurial support infrastructures, namely startup centres and innovation hubs of universities and other public institutions as well as innovation centres of private companies. Hereby, support may range from access to funding over provision of resources such as offices or computing hardware to coaching in the development of business ideas and strategic roadmaps for product and service deployment. In this paper, we focus on describing the status-quo of afore- mentioned support infrastructures in Vorarlberg and the Lake Constance region, then extend the scope to existing (international) approaches for aiding founders and inno- vators in the development of smart services. An analysis of success stories of the Vorarlberg startup centre ‘startupstube’ and other initiatives including their compar- ison to international counterparts builds the basis for a methodological framework for (service science) coaching in entre- and intrapreneurial support infrastructures. The paper is concluded by the description of a framework for choosing the right methods and tools to create service value in entre-/intrapreneurship based upon tested, proven know-how and for defining support infrastructure needs based upon pre-defined stakeholder and target groups as well as the (industry) sectors of the innovators.
On the integration of intelligent logistics ecosystems in production and industry 4.0 settings
(2017)
A step change is needed in the deployment of renewable energy if the triple challenge of ensuring climate change mitigation, energy security, and energy affordability is to be met. Yet, social acceptance of infrastructure projects and policies remains a key concern. While there has been decades of fruitful research on the social acceptance of wind energy and other renewables, much of the extant research is cross-sectional in nature, failing to capture the important dynamic processes that can make or break renewable energy projects. This paper introduces a Special Issue of Energy Policy which focuses on the neglected topic of the dynamics of social acceptance of renewable energy, drawing on contributions made at an international research conference held in St. Gallen (Switzerland) in June 2022. In addition to introducing these papers and drawing out common themes, we also seek to offer some conceptual clarity on the issue of dynamics in social acceptance, taking into account the influence of time, power, and scale in shaping decision-making processes. We conclude by highlighting a number of avenues of potential future research.
In engineering design, optimization methods are frequently used to improve the initial design of a product. However, the selection of an appropriate method is challenging since many
methods exist, especially for the case of simulation-based optimization. This paper proposes a systematic procedure to support this selection process. Building upon quality function deployment, end-user and design use case requirements can be systematically taken into account via a decision
matrix. The design and construction of the decision matrix are explained in detail. The proposed
procedure is validated by two engineering optimization problems arising within the design of box-type boom cranes. For each problem, the problem statement and the respectively applied optimization methods are explained in detail. The results obtained by optimization validate the use
of optimization approaches within the design process. The application of the decision matrix shows the successful incorporation of customer requirements to the algorithm selection.
Mobility choices - an instrument for precise automatized travel behavior detection & analysis
(2021)
Stress testing is part of today’s bank risk management and often required by the governing regulatory authority. Performing such a stress test with stress scenarios derived from a distribution, instead of pre-defined expert scenarios, results in a systematic approach in which new severe scenarios can be discovered. The required scenario distribution is obtained from historical time series via a Vector-Autoregressive time series model. The worst-case search, i.e. finding the scenario yielding the most severe situation for the bank, can be stated as an optimization problem. The problem itself is a constrained optimization problem in a high-dimensional search space. The constraints are the box constraints on the scenario variables and the plausibility of a scenario.
The latter is expressed by an elliptic constraint. As the evaluation of the stress scenarios is performed with a simulation tool, the optimization problem can be seen as black-box optimization problem. Evolution Strategy, a well-known optimizer for black-box problems, is applied here. The necessary adaptations to the algorithm are explained and a set of different algorithm design choices are investigated. It is shown that a simple box constraint handling method, i.e. setting variables which violate a box constraint to the respective boundary of the feasible domain, in combination with a repair of implausible scenarios provides good results.
Comparison of constraint-handling mechanisms for the (1,λ)-ES on a simple constrained problem
(2016)
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.
Breath analysis offers a non-invasive and rapid diagnostic method for detecting various volatile organic compounds that could be indicators for different diseases, particularly metabolic disorders including type 2 diabetes mellitus. The development of type 2 diabetes mellitus is closely linked to metabolic dysfunction of adipose tissue and adipocytes. However, the VOC profile of human adipocytes has not yet been investigated. Gas chromatography with mass spectrometric detection and head-space needle trap extraction (two-bed Carbopack X/Carboxen 1000 needle traps) were applied to profile VOCs produced and metabolised by human Simpson Golabi Behmel Syndrome adipocytes. In total, sixteen compounds were identified to be related to the metabolism of the cells. Four sulphur compounds (carbon disulphide, dimethyl sulphide, ethyl methyl sulphide and dimethyl disulphide), three heterocyclic compounds (2-ethylfuran, 2-methyl-5-(methyl-thio)-furan, and 2-pentylfuran), two ketones (acetone and 2-pentanone), two hydrocarbons (isoprene and n-heptane) and one ester (ethyl acetate) were produced, and four aldehydes (2-methyl-propanal, butanal, pentanal and hexanal) were found to be consumed by the cells of interest. This study presents the first profile of VOCs formed by human adipocytes, which may reflect the activity of the adipose tissue enzymes and provide evidence of their active role in metabolic regulation. Our data also suggest that a previously reported increase of isoprene and sulphur compounds in diabetic patients may be explained by their production by adipocytes. Moreover, the unique features of this profile, including a high emission of dimethyl sulphide and the production of furan-containing VOCs, increase our knowledge about metabolism in adipose tissue and provide diagnostic potential for future applications.
Blood and breath profiles of volatile organic compounds in patients with end-stage renal disease
(2014)
Stability of selected volatile breath constituents in Tedlar, Kynar and Flexfilm sampling bags
(2013)
Product ion distributions resulting from the primary reactions of H3O+ with nine D-labeled volatile organic compounds and the subsequent sequential reactions with H2O have been determined using a Proton Transfer Reaction Time of Flight Mass Spectrometer (PTR-TOF 8000 (IONICON Analytik GmbH)) at various reduced electric field (E/N) values ranging from 80 up to 150 Td and for two different absolute humidity levels of air sample < 0.1% and 5%. The specific D-labeled compounds used in this study are acetone-d6, toluene-d8, benzene-d6, ethanol-d (C2H5OD), ethanol-d2 (CH3CD2OH), ethanol-d6, 2-propanol-d8, 2-propanol-d3 (CD3CH(OH)CH3), and isoprene-d5 (CH2CHC(CD2)CD3). With the exception of the two 2-propanol compounds, non-dissociative proton transfer is the dominant primary reaction pathway. For 2-propanol-d8 and 2-propanol-d3 the major primary reaction channel involved is dissociative proton transfer. However, unlike their undeuterated counterparts, the primary product ions undergo subsequent deuterium/hydrogen isotope exchange reactions with the ever present water in the drift tube, the extent of which of course depends on the humidity within that tube. This exchange leads to the generation of various isotopologue product ions, the product ion branching percentages of which are also
dependent on the humidity in the drift tube. This results in complex mass spectra and the distribution of product ions leads to issues of reduced sensitivity and accuracy. However, the effect of D/H exchange considerably varies between the compounds under study. In the case of acetone-d6 it is very weak (<1%), because the exchange process is not facile when the deuterium is in the methyl functional group. In comparison, the H3O+/ benzene-d6 (C6D6) reaction and sequential reactions with water result in the production of the isotopologue ions C6Dn(H7-n)+ (where n = 0–6). Changing the value of E/N and/or the humidity in the drift tube considerably affects the amount of the isotope exchange reactions and hence the resulting sequential product ion distributions. An important conclusion of the findings from this work is that care must be taken in the choice of an exogenous deuterated compound for use in breath pharmacokinetic studies using proton transfer reaction mass spectrometry; otherwise the resulting D/H exchange processes impose interpretative problems.
© 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Breath analysis holds great promise for real-time and non-invasive medical diagnosis. Thus, there is a considerable need for simple-in-use and portable analyzers for rapid detection of breath indicators for different diseases in their early stages. Sensor technology meets all of these demands. However, miniaturized breath analyzers require adequate breath sampling methods. In this context, we propose non-contact sampling; namely the collection of breath samples by exhalation from a distance into a miniaturized collector without bringing the mouth into direct contact with the analyzing device. To evaluate this approach different breathing maneuvers have been tested in a real-time regime on a cohort of 23 volunteers using proton transfer reaction mass spectrometry. The breathing maneuvers embraced distinct depths of respiration, exhalation manners, size of the mouth opening and different sampling distances. Two inhalation modes (normal, relaxed breathing and deep breathing) and two exhalation manners (via smaller and wider lips opening) forming four sampling scenarios were selected. A sampling distance of approximately 2 cm was found to be a reasonable trade-off between sample dilution and requirement of no physical contact of the subject with the analyzer. All four scenarios exhibited comparable measurement reproducibility spread of around 10%. For normal, relaxed inspiration both dead-space and end-tidal phases of exhalation lasted approximately 1.5 s for both expiration protocols. Deep inhalation prolongs the end-tidal phase to about 3 s in the case of blowing via a small lips opening, and by 50% when the air is exhaled via a wide one. In conclusion, non-contact breath sampling can be considered as a promising alternative to the existing breath sampling methods, being relatively close to natural spontaneous breathing.