Exemplary Scenario Research Articles

Cost-effectiveness of future lockdown policies against the COVID-19 pandemic.

Aim: While the European Union (EU) has approved several COVID-19 vaccines, new variants of concern may be able to escape immunity. The purpose of this study is to project the cost-effectiveness of future lockdown policies in conjunction with a variant-adapted vaccine booster. The exemplary scenario foresees a 25% decline in the vaccine protection against severe disease. Methods: A decision model was constructed using, for example, information on age-specific fatality rates, intensive care unit (ICU) costs and outcomes, and herd protection threshold. The costs and benefits of a future lockdown strategy were determined from a societal viewpoint under three future scenarios—a booster shot’s efficacy of 0%, 50%, and 95%. Results: The cost-effectiveness ratio of a lockdown policy in conjunction with a booster dose with 95% efficacy is €44,214 per life year gained. A lockdown is cost-effective when the probability of approving a booster dose with 95% efficacy is at least 48% (76% when considering uncertainty in input factors). Conclusion: In this exemplary scenario, a future lockdown policy appears to be cost-effective if the probability of approving a variant-adapted vaccine booster with an efficacy of 95% is at least 48%.

Black Lives Matter to the NBA: The Impact of Sport Fanship and Political Affiliation on the Perception of the NBA’s Racial Justice Initiatives During the 2020 Playoff Bubble

The NBA’s (National Basketball Association’s) racial justice initiatives during the 2020 Playoff Bubble are considered an act of corporate social advocacy and provide an exemplary scenario to explore this intersection of sport and politics. Based on this observation, the purpose of this study was to explore how one’s level of identification with the NBA and his/her identification with a political party can impact one’s perception of the NBA’s racial justice initiatives. Specifically, the researchers wanted to determine if outrage toward the NBA and one’s perception of the NBA’s reputation is influenced more by one’s political identity or one’s fanship for the league. A survey was conducted using a national convenience sample of 518 participants recruited through Amazon Mechanical Turk. Results showed that while both fanship and political identity had effects on one’s outrage toward the NBA and one’s perceived reputation of the NBA, the stronger factor differed between Democrats and Republicans.

Approach for Data-driven Controlling of Construction Projects using the example of Claim Management

Current methods for controlling of construction projects are characterized by manual processes, which are failure-prone, tedious and for that time-consuming and cost-intensive. Furthermore, an additional challenge is caused by the absent dynamic in conventional controlling processes, which is of high importance to avoid damages and ensure a proactive management of a construction project. As construction projects are highly complex due to various influences which imply an enormous amount of information and are designated by dynamic processes, conventional controlling methods are insufficient and not able to meet these requirements. Therefore, current data-driven developments which can deal with an enormous amount of information are indispensable for a sufficient controlling of complex and dynamic projects. Accordingly, within this paper an approach for a data-driven controlling solution is presented, in which common data environments, databases and business intelligence applications are combined to ensure an efficient and proactive controlling. To investigate the advantages of business intelligence solutions for data-driven controlling the exemplary scenario of claim management controlling is used. It is shown that the user is able to speed up the processes and secure a proactive controlling by using a consistent database throughout the project. To achieve these targets an automatic data orchestration and visualization of the claim offers is exploited. Consequently, the claim manager is able to predict developments, identify risk potential, investigate KPIs or take actions to minimize negative effects e.g., time effort and costs. The requirements for these purposes are considered in a recommendation for action differentiated regarding the individual elements of a socio-technical system (human, organization, technology). The outlook includes an overview of potential technologies to ensure real-time analytics to shorten reaction and damage times related to the claims by immediately initiating countermeasures.

Towards an Efficient, Comprehensive Value Stream Planning in Production Networks

Globally operating companies’ production networks usually consist of multiple sites, each having its local value stream. These value streams need to be interlinked to fulfill customers’ orders. Major challenges arise during strategic network design of the resulting comprehensive value stream within the production network. Adjustments to the network structure, uncertain planning data, e.g., uncertain key performance indicator (KPI) values, and planning experts optimizing their local value stream lead to inefficiencies like high total inventories. This article presents a planning approach for an efficient, comprehensive value stream planning to address the aforementioned problem. Using a discrete-event simulation model, parametrized by uncertain planning data, simulation experiments were executed to generate simulation data using a data farming approach. Subsequently, this data was used to apply data mining analysis aiming for data-based planning decisions for production network operations. Exemplary scenarios for the steps of the approach show an initial applicability of the planning approach and future research areas were derived.

Political Decision Making in the COVID-19 Pandemic: The Case of Germany from the Perspective of Risk Management.

The COVID-19 pandemic is permanently changing modern social and economic coexistence. Most governments have declared infection control to be their top priority while citizens face great restrictions on their civil rights. A pandemic is an exemplary scenario in which political actors must decide about future, and thus uncertain, events. This paper tries to present a tool well established in the field of entrepreneurial and management decision making which could also be a first benchmark for political decisions. Our approach builds on the standard epidemiological SEIR model in combination with simulation techniques used in risk management. By our case study we want to demonstrate the opportunities that risk management techniques, especially risk analyses using Monte Carlo simulation, can provide to policy makers in general, and in a public health crisis in particular. Hence, our case study can be used as a framework for political decision making under incomplete information and uncertainty. Overall, we want to point out that a health policy that aims to provide comprehensive protection against infection should also be based on economic criteria. This is without prejudice to the integration of ethical considerations in the final political decision.

Radio Channel Capacity with Directivity Control of Antenna Beams in Multipath Propagation Environment.

The basic technology that will determine the expansion of the technical capabilities of fifth generation cellular systems is a massive multiple-input-multiple-output. Therefore, assessing the influence of the antenna beam orientations on the radio channel capacity is very significant. In this case, the effects of mismatching the antenna beam directions are crucial. In this paper, the methodology for evaluating changes in the received signal power level due to beam misalignment for the transmitting and receiving antenna systems is presented. The quantitative assessment of this issue is presented based on simulation studies carried out for an exemplary propagation scenario. For non-line-of-sight (NLOS) conditions, it is shown that the optimal selection of the transmitting and receiving beam directions may ensure an increase in the level of the received signal by several decibels in relation to the coaxial position of the beams. The developed methodology makes it possible to analyze changes in the radio channel capacity versus the signal-to-noise ratio and distance between the transmitter and receiver at optimal and coaxial orientations of antenna beams for various propagation scenarios, considering NLOS conditions. In the paper, the influence of the directional antenna use and their direction choices on the channel capacity versus SNR and the distance between the transmitter and receiver is shown.

Open set task augmentation facilitates generalization of deep neural networks trained on small data sets

Many application scenarios for image recognition require learning of deep networks from small sample sizes in the order of a few hundred samples per class. Then, avoiding overfitting is critical. Common techniques to address overfitting are transfer learning, reduction of model complexity and artificial enrichment of the available data by, e.g., data augmentation. A key idea proposed in this paper is to incorporate additional samples into the training that do not belong to the classes of the target task. This can be accomplished by formulating the original classification task as an open set classification task. While the original closed set classification task is not altered at inference time, the recast as open set classification task enables the inclusion of additional data during training. Hence, the original closed set classification task is augmented with an open set task during training. We therefore call the proposed approach open set task augmentation. In order to integrate additional task-unrelated samples into the training, we employ the entropic open set loss originally proposed for open set classification tasks and also show that similar results can be obtained with a modified sum of squared errors loss function. Learning with the proposed approach benefits from the integration of additional “unknown” samples, which are often available, e.g., from open data sets, and can then be easily integrated into the learning process. We show that this open set task augmentation can improve model performance even when these additional samples are rather few or far from the domain of the target task. The proposed approach is demonstrated on two exemplary scenarios based on subsets of the ImageNet and Food-101 data sets as well as with several network architectures and two loss functions. We further shed light on the impact of the entropic open set loss on the internal representations formed by the networks. Open set task augmentation is particularly valuable when no additional data from the target classes are available—a scenario often faced in practice.

Modeling solute transport and mixing in heterogeneous porous media under turbulent flow conditions

We develop and test a modeling approach to quantify turbulence-driven solute transport and mixing in porous media. Our approach addresses two key elements: (a) the spatial variability of the effective diffusion coefficient which is typically documented in the presence of a sediment–fluid interface and (b) the need to provide a model that can yield the complete distribution of the concentration probability density function, not being limited only to the mean concentration value and thus fully addressing solute mixing. Our work is motivated by the importance of solute transport processes in the hyporheic zone, which can have strong implications in natural attenuation of pollutants. Our approach combines Lagrangian schemes to address transport and mixing in the presence of spatial variability of effective diffusion. An exemplary scenario we consider targets a setup constituted by a homogeneous (fully saturated) porous medium underlying a clear water column where turbulent flow is generated. Solute concentration histories obtained through a model based solely on diffusive transport are benchmarked against an analytical solution. These are then compared against the results obtained by modeling the combined effects of diffusion and mixing. A rigorous sensitivity analysis is performed to evaluate the influence of model parameters on solute concentrations and mixing, the latter being quantified in terms of the scalar dissipation rate.

Product design lifecycle information model (PDLIM)

In response to rapidly changing market and customer needs, product design and development (PDD) is evolving into a human-centred and data-driven design paradigm. The design environment gets more open often involving crowdsourcing and the design process becomes more complex, considering product family design along product whole lifecycle development, and needing more data support. Therefore, it is critical to effectively capture, share, and manage design-related information in such a complex design environment. From this perspective, it is a prerequisite to have a proper product design lifecycle information model (PDLIM) to guide information gathering, sharing and management. To the best of our knowledge, currently, there lacks such a PDLIM to support effective PDD, though digital twin (DT) technology shows a great potential of supporting product lifecycle information collection and management. In this paper, the overall structure of the proposed PDLIM is firstly developed to frame in all main product lifecycle stages and the corresponding key phases for structurally capturing and storing necessary data along a product lifecycle. Secondly, key design information items against the main product lifecycle stages and their corresponding key phases are explored from literature reviews and case study analyses. Thirdly, the necessity of the identified information items in the PDLIM is qualitatively evaluated by two case studies. Finally, the PDLIM is further evaluated by applying formal object-role modelling (ORM) to demonstrate how design information items are used and interacted in exemplary design interaction scenarios, and to approve that it can be formally described and managed as an information model. The evaluation results show that the PDLIM is feasible to be adapted in a crowdsourcing-combined PDD process for supporting design management, reviewing, quality control, and next round product redesign and improvement.

Performance Evaluation of Hybrid Crowdsensing and Fixed Sensor Systems for Event Detection in Urban Environments.

Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.

Determining service provider and transport system related effects of ridesourcing services by simulation within the travel demand model mobiTopp

PurposeRidesourcing services have become popular recently and play a crucial role in Mobility as a Service (MaaS) offers. With their increasing importance, the need arises to integrate them into travel demand models to investigate transport system-related effects. As strong interdependencies between different people’s choices exist, microscopic and agent-based model approaches are especially suitable for their simulation.MethodThis paper presents the integration of shared and non-shared ridesourcing services (i.e., ride-hailing and ride-pooling) into the agent-based travel demand model mobiTopp. We include a simple vehicle allocation and fleet control component and extend the mode choice by the ridesourcing service. Thus, ridesourcing is integrated into the decision-making processes on an agent’s level, based on the system’s specific current performance, considering current waiting times and detours, among other data.Results and DiscussionIn this paper, we analyze the results concerning provider-related figures such as the number of bookings, trip times, and occupation rates, as well as effects on other travel modes. We performed simulation runs in an exemplary scenario with several variations with up to 1600 vehicles for the city of Stuttgart, Germany. This extension for mobiTopp provides insights into interdependencies between ridesourcing services and other travel modes and may help design and regulate ridesourcing services.

Fault Maintenance and Repair Application Based on Augmented Reality Technology in Electrical Distribution Systems

This study presents an AR technology-based software that will work on mobile platforms and the pilot application of this software to solve the possible problems that may be experienced other than routine maintenance activities on protection/control panels and devices in SCADA centers. AR is essentially an information technology that provides the ability to present digital data and images in the same environment as our real world through its visualization, instruction/guidance, and interaction capabilities. Object recognition methods, necessary software, and infrastructure requirements are mentioned in this study in order to benefit from these advantages of AR technology correctly. Different scenarios have been created by using AR for possible failures in automation panels in a distribution center with a telemetry system. Finally, an exemplary scenario in the study is realized in a distribution center located in the SEDAŞ area of responsibility.

Modeling learning in knowledge space theory through bivariate Markov processes

Bivariate Markov processes (BMPs) described by Ephraim and Mark (2012) consist of a pair of stochastic processes in the continuous time, one observable and the other latent, that are jointly Markov. In the present article, the navigation behavior and the learning process of a user of a web-based tutoring system are jointly modeled as BMPs constrained by assumptions that are coherent with the concepts of competence-based knowledge space theory. Such constraints are expressed as formal assumptions about the web-based system and about the nature of the learning process. Scenarios are considered where the observed process is the navigation of an individual through the pages of an intelligent tutoring system, whereas the latent learning process consists of transitions among states in a competence structure. The approach seems to be rather general and flexible in modeling learning scenarios with different assumptions. As an example, BMP models are developed for some exemplary scenarios. Maximum likelihood parameter estimation via expectation–maximization algorithm is presented. The results of a simulation study showed that the parameter values are well-recovered by the estimation algorithm. The results of the application of a bivariate Markov model to the real data of students navigating the intelligent tutoring system Stat-Knowlab showed that the proposed approach provides useful insight into students’ learning processes.

Design of a district heating roadmap for Hamburg

This paper presents a methodology for the development of a transformation strategy for large-scale district heating systems (DHSs). Using this methodology, a roadmap for Hamburg’s municipal DHS is derived with an exemplary scenario for a climate-neutral heat supply in 2050. The city of Hamburg is currently in the process of transforming its municipal DHS towards climate-neutrality until 2050. So far, a blueprint for the development of such a long-term strategy does not exist. The presented scenario is based on an analysis of the future heat demand and of the regional renewable heat potentials. In the resulting generation mix, the expected heat demand in 2050 can mainly be supplied by industrial surplus heat, large heat pumps and biomass. To meet the city’s climate goals, fossil fuels will have to phase out and even waste incineration needs to be reduced drastically. After the application to the DHS of Hamburg, the methodology and results are transformed into a general approach for municipal heat planning that can support the transfer to other cities. Most importantly, strategic heat planning should be integrated into municipal administration to guarantee a flexible planning process. The paper also reveals shortcomings of currently available methodologies and tools for strategic heat planning, such as spatially resolved forecasts of the heat demand and regional potentials for renewable heat sources, which should be tackled in future scientific research projects.

Impurity in a Fermi gas under non-Hermitian spin–orbit coupling

We study the fate of an impurity in a two-component, non-interacting Fermi gas under a non- Hermitian spin-orbit coupling (SOC) which is generated by dissipative Raman lasers. While SOC mixes the two spin species in the Fermi gas thus modifies the single-particle dispersions, we consider the case where the impurity only interacts with one of the spin species. As a result, spectral properties of the impurity constitute an ideal probe to the dissipative Fermi gas in the background. In particular, we show that dissipation destabilizes polarons in favor of molecular formation, consistent with previous few-body studies. The dissipative nature of the Fermi gas further leads to broadened peaks in the inverse radio-frequency spectra for both the attractive and repulsive polaron branches, which could serve as signals for experimental observation. Our results provides an exemplary scenario where the interplay of non-Hermiticity and interaction can be probed.

Inter-Beam Co-Channel Downlink and Uplink Interference for 5G New Radio in mm-Wave Bands.

This paper presents a methodology for assessing co-channel interference that arises in multi-beam transmitting and receiving antennas used in fifth-generation (5G) systems. This evaluation is essential for minimizing spectral resources, which allows for using the same frequency bands in angularly separated antenna beams of a 5G-based station (gNodeB). In the developed methodology, a multi-ellipsoidal propagation model (MPM) provides a mapping of the multipath propagation phenomenon and considers the directivity of antenna beams. To demonstrate the designation procedure of interference level we use simulation tests. For exemplary scenarios in downlink and uplink, we showed changes in a signal-to-interference ratio versus a separation angle between the serving (useful) and interfering beams and the distance between the gNodeB and user equipment. This evaluation is the basis for determining the minimum separation angle for which an acceptable interference level is ensured. The analysis was carried out for the lower millimeter-wave band, which is planned to use in 5G micro-cells base stations.

Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways

Future automated vehicles will have to meet the challenge of anticipating the intentions of other road users in order to plan their own behavior without compromising safety and efficiency of the surrounding road traffic. Therefore, the research area of cooperative driving deals with maneuver-planning algorithms that enable vehicles to behave cooperatively in interactive traffic scenarios. To prove the functionality of these algorithms, single test scenarios are used in the current body of literature. The use of a single, exemplary scenario bears the risk that the presented approach only works in the presented scenario and thus no general statement can be made about the performance of the algorithm. Furthermore, there is a risk that fictitious traffic scenarios may be solved which do not occur in reality. Therefore, we present a procedure for generating test scenarios based on real-world traffic datasets that require cooperation of at least one of the involved vehicles and thus are challenging from the perspective of cooperation. This procedure is applied to a large highway traffic dataset, resulting in a test scenario catalog that allows a comprehensive performance evaluation. The extracted scenarios are clustered according to the cooperative actions used to solve the respective scenario, which enables a more detailed understanding of the underlying cooperative mechanisms. In order to serve as a basis for making comparisons between different behavior planners and thus contribute to the development of future maneuver planning algorithms, a tool to extract the test scenarios from the used traffic dataset is made publicly available.

From Visual Spectrum to Millimeter Wave: A Broad Spectrum of Solutions for Food Inspection

The consequences of food adulteration can be far reaching. In the past, inexpensive adulterants were used to inflate different products, leading to severe health issues. Contamination of food has many causes and can be physical (plant stems in tea), chemical (melamine in infant formula), or biological (bacterial contamination). Employing suitable sensor systems along the production process is a requirement for food safety. In this article, different approaches to food inspection are illustrated, and exemplary scenarios outline the potential of different sensor systems along the spectrum.

Intelligent User Assistance for Automated Data Mining Method Selection

In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step. However, these experts are not always available and data mining novices may be required to perform the task. While there are several research efforts for automated method selection as a means of support, only a few approaches consider the particularities of problems expressed in the natural and domain-specific language of the novice. The study proposes the design of an intelligent assistance system that takes problem descriptions articulated in natural language as an input and offers advice regarding the most suitable class of data mining methods. Following a design science research approach, the paper (i) outlines the problem setting with an exemplary scenario from industrial practice, (ii) derives design requirements, (iii) develops design principles and proposes design features, (iv) develops and implements the IT artifact using several methods such as embeddings, keyword extractions, topic models, and text classifiers, (v) demonstrates and evaluates the implemented prototype based on different classification pipelines, and (vi) discusses the results’ practical and theoretical contributions. The best performing classification pipelines show high accuracies when applied to validation data and are capable of creating a suitable mapping that exceeds the performance of joint novice assessments and simpler means of text mining. The research provides a promising foundation for further enhancements, either as a stand-alone intelligent assistance system or as an add-on to already existing data science and analytics platforms.

Standard for acoustic metadata for passive acoustic monitoring

Passive acoustic monitoring often relies on data collected with various instrumentation, by multiple research groups in different locations, under diverse conditions, and with a variety of data analysis methods. To make the output comparable and to preserve data for long time-series analysis, metadata needs to be reported in a standardized way. This will enable broader metadata studies and the sharing of data in repositories, which will allow for cross fertilization of analyses and hence cost savings for new projects. The Acoustical Society of America Working Group S3-SC1-WG7 “Acoustic Metadata for Passive Acoustic Monitoring” is in the process of defining this standard. We are building upon schemata developed for the metadata database software “Tethys” [Roch et al., Ecol. Inf. 31, 122–126 (2016)]. Instrument deployments and on- or off-effort detections of acoustic signals were defined within this manuscript. The current deployment schema is being implemented in the National Center for Environmental Information Passive Acoustic Monitoring Archive. We have expanded the vocabulary to include mobile platforms, vector sensors, localizations, and calibrations. Exemplary scenarios were developed to test the comprehensiveness of the current standard definitions for these frameworks. We also consider extensibility, making sure that the standard is flexible enough to support new ideas.Passive acoustic monitoring often relies on data collected with various instrumentation, by multiple research groups in different locations, under diverse conditions, and with a variety of data analysis methods. To make the output comparable and to preserve data for long time-series analysis, metadata needs to be reported in a standardized way. This will enable broader metadata studies and the sharing of data in repositories, which will allow for cross fertilization of analyses and hence cost savings for new projects. The Acoustical Society of America Working Group S3-SC1-WG7 “Acoustic Metadata for Passive Acoustic Monitoring” is in the process of defining this standard. We are building upon schemata developed for the metadata database software “Tethys” [Roch et al., Ecol. Inf. 31, 122–126 (2016)]. Instrument deployments and on- or off-effort detections of acoustic signals were defined within this manuscript. The current deployment schema is being implemented in the National Center for Environmental Infor...