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Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystem

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystem

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystem

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystem

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystem

This paper investigates the shift in crowdsourcing towards self-managed enterprises of crowdworkers (SMECs), diverging from traditional platform-controlled models. It reviews the literature to understand the foundational aspects of this shift, focusing on identifying key factors that may explain the rise of SMECs, particularly concerning power dynamics and tensions between Online Labor Platforms (OLPs) and crowdworkers. The study aims to guide future research and inform policy and platform development, emphasizing the importance of fair labor practices in this evolving landscape., Comment: 12 pages, 5 figures, ICEIS 2024 - 2024 International Conference on Enterprise Information Systems

Widespread adoption of AI for medical decision making is still hindered due to ethical and safety-related concerns. For AI-based decision support systems in healthcare settings it is paramount to be reliable and trustworthy. Common deep learning approaches, however, have the tendency towards overconfidence under data shift. Such inappropriate extrapolation beyond evidence-based scenarios may have dire consequences. This highlights the importance of reliable estimation of local uncertainty and its communication to the end user. While stochastic neural networks have been heralded as a potential solution to these issues, this study investigates their actual reliability in clinical applications. We centered our analysis on the exemplary use case of mortality prediction for ICU hospitalizations using EHR from MIMIC3 study. For predictions on the EHR time series, Encoder-Only Transformer models were employed. Stochasticity of model functions was achieved by incorporating common methods such as Bayesian neural network layers and model ensembles. Our models achieve state of the art performance in terms of discrimination performance (AUC ROC: 0.868+-0.011, AUC PR: 0.554+-0.034) and calibration on the mortality prediction benchmark. However, epistemic uncertainty is critically underestimated by the selected stochastic deep learning methods. A heuristic proof for the responsible collapse of the posterior distribution is provided. Our findings reveal the inadequacy of commonly used stochastic deep learning approaches to reliably recognize OoD samples. In both methods, unsubstantiated model confidence is not prevented due to strongly biased functional posteriors, rendering them inappropriate for reliable clinical decision support. This highlights the need for approaches with more strictly enforced or inherent distance-awareness to known data points, e.g., using kernel-based techniques., Comment: Keywords: probabilistic inference, uncertainty estimation, uncertainty quantification, epistemic uncertainty, clinical prognosis, electronic health records

Cyber Physical Systems (CPS) enable new kinds of applications as well as significant improvements of existing ones in numerous different application domains. A major trait of upcoming CPS is an increasing degree of automation up to the point of autonomy, as there is a huge potential for economic success as well as for ecologic and societal improvements. However, to unlock the full potential of such (cooperative and automated) CPS, we first need to overcome several significant engineering challenges, where safety assurance is a particularly important one. Unfortunately, established safety assurance methods and standards do not live up to this task, as they have been designed with closed and less complex systems in mind. This paper structures safety assurance challenges of cooperative automated CPS, provides an overview on our vision of dynamic risk management and describes already existing building blocks.

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystem

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystem

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystem

With the increasing prevalence of security incidents, the adoption of deception-based defense strategies has become pivotal in cyber security. This work addresses the challenge of scalability in designing honeytokens, a key component of such defense mechanisms. The manual creation of honeytokens is a tedious task. Although automated generators exists, they often lack versatility, being specialized for specific types of honeytokens, and heavily rely on suitable training datasets. To overcome these limitations, this work systematically investigates the approach of utilizing Large Language Models (LLMs) to create a variety of honeytokens. Out of the seven different honeytoken types created in this work, such as configuration files, databases, and log files, two were used to evaluate the optimal prompt. The generation of robots.txt files and honeywords was used to systematically test 210 different prompt structures, based on 16 prompt building blocks. Furthermore, all honeytokens were tested across different state-of-the-art LLMs to assess the varying performance of different models. Prompts performing optimally on one LLMs do not necessarily generalize well to another. Honeywords generated by GPT-3.5 were found to be less distinguishable from real passwords compared to previous methods of automated honeyword generation. Overall, the findings of this work demonstrate that generic LLMs are capable of creating a wide array of honeytokens using the presented prompt structures., Comment: 12 pages

In the field of network security, with the ongoing arms race between attackers, seeking new vulnerabilities to bypass defense mechanisms and defenders reinforcing their prevention, detection and response strategies, the novel concept of cyber deception has emerged. Starting from the well-known example of honeypots, many other deception strategies have been developed such as honeytokens and moving target defense, all sharing the objective of creating uncertainty for attackers and increasing the chance for the attacker of making mistakes. In this paper a methodology to evaluate the effectiveness of honeypots and moving target defense in a network is presented. This methodology allows to quantitatively measure the effectiveness in a simulation environment, allowing to make recommendations on how many honeypots to deploy and on how quickly network addresses have to be mutated to effectively disrupt an attack in multiple network and attacker configurations. With this optimum, attacks can be detected and slowed down with a minimal resource and configuration overhead. With the provided methodology, the optimal number of honeypots to be deployed and the optimal network address mutation interval can be determined. Furthermore, this work provides guidance on how to optimally deploy and configure them with respect to the attacker model and several network parameters.

The zeitgeist of the digital era has been dominated by an expanding integration of Artificial Intelligence~(AI) in a plethora of applications across various domains. With this expansion, however, questions of the safety and reliability of these methods come have become more relevant than ever. Consequently, a run-time ML model safety system has been developed to ensure the model's operation within the intended context, especially in applications whose environments are greatly variable such as Autonomous Vehicles~(AVs). SafeML is a model-agnostic approach for performing such monitoring, using distance measures based on statistical testing of the training and operational datasets; comparing them to a predetermined threshold, returning a binary value whether the model should be trusted in the context of the observed data or be deemed unreliable. Although a systematic framework exists for this approach, its performance is hindered by: (1) a dependency on a number of design parameters that directly affect the selection of a safety threshold and therefore likely affect its robustness, (2) an inherent assumption of certain distributions for the training and operational sets, as well as (3) a high computational complexity for relatively large sets. This work addresses these limitations by changing the binary decision to a continuous metric. Furthermore, all data distribution assumptions are made obsolete by implementing non-parametric approaches, and the computational speed increased by introducing a new distance measure based on the Empirical Characteristics Functions~(ECF).

Many individual genetic risk loci have been associated with multiple common human diseases. However, the molecular basis of this pleiotropy often remains unclear. We present an integrative approach to reveal the molecular mechanism underlying the PROCR locus, associated with lower coronary artery disease (CAD) risk but higher venous thromboembolism (VTE) risk. We identify PROCR-p.Ser219Gly as the likely causal variant at the locus and protein C as a causal factor. Using genetic analyses, human recall-by-genotype and in vitro experimentation, we demonstrate that PROCR-219Gly increases plasma levels of (activated) protein C through endothelial protein C receptor (EPCR) ectodomain shedding in endothelial cells, attenuating leukocyte-endothelial cell adhesion and vascular inflammation. We also associate PROCR-219Gly with an increased pro-thrombotic state via coagulation factor VII, a ligand of EPCR. Our study, which links PROCR-219Gly to CAD through anti-inflammatory mechanisms and to VTE through pro-thrombotic mechanisms, provides a framework to reveal the mechanisms underlying similar cross-phenotype associations.

Artificial Intelligence (AI) increasingly shows its potential to outperform predicate logic algorithms and human control alike. In automatically deriving a system model, AI algorithms learn relations in data that are not detectable for humans. This great strength, however, also makes use of AI methods dubious. The more complex a model, the more difficult it is for a human to understand the reasoning for the decisions. As currently, fully automated AI algorithms are sparse, every algorithm has to provide a reasoning for human operators. For data engineers, metrics such as accuracy and sensitivity are sufficient. However, if models are interacting with non-experts, explanations have to be understandable. This work provides an extensive survey of literature on this topic, which, to a large part, consists of other surveys. The findings are mapped to ways of explaining decisions and reasons for explaining decisions. It shows that the heterogeneity of reasons and methods of and for explainability lead to individual explanatory frameworks.

The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a variety of applications. However, safety assurance is a key barrier to widespread usage, especially given the unpredictable operational and environmental factors experienced by UAVs, which are hard to capture solely at design-time. This paper proposes a new reliability modeling approach called SafeDrones to help address this issue by enabling runtime reliability and risk assessment of UAVs. It is a prototype instantiation of the Executable Digital Dependable Identity (EDDI) concept, which aims to create a model-based solution for real-time, data-driven dependability assurance for multi-robot systems. By providing real-time reliability estimates, SafeDrones allows UAVs to update their missions accordingly in an adaptive manner.

Machine Learning~(ML) has provided promising results in recent years across different applications and domains. However, in many cases, qualities such as reliability or even safety need to be ensured. To this end, one important aspect is to determine whether or not ML components are deployed in situations that are appropriate for their application scope. For components whose environments are open and variable, for instance those found in autonomous vehicles, it is therefore important to monitor their operational situation to determine its distance from the ML components' trained scope. If that distance is deemed too great, the application may choose to consider the ML component outcome unreliable and switch to alternatives, e.g. using human operator input instead. SafeML is a model-agnostic approach for performing such monitoring, using distance measures based on statistical testing of the training and operational datasets. Limitations in setting SafeML up properly include the lack of a systematic approach for determining, for a given application, how many operational samples are needed to yield reliable distance information as well as to determine an appropriate distance threshold. In this work, we address these limitations by providing a practical approach and demonstrate its use in a well known traffic sign recognition problem, and on an example using the CARLA open-source automotive simulator.

Virtual Reality (VR) has the potential to support mobile knowledge workers by complementing traditional input devices with a large three-dimensional output space and spatial input. Previous research on supporting VR knowledge work explored domains such as text entry using physical keyboards and spreadsheet interaction using combined pen and touch input. Inspired by such work, this paper probes the VR design space for authoring presentations in mobile settings. We propose PoVRPoint -- a set of tools coupling pen- and touch-based editing of presentations on mobile devices, such as tablets, with the interaction capabilities afforded by VR. We study the utility of extended display space to, for example, assist users in identifying target slides, supporting spatial manipulation of objects on a slide, creating animations, and facilitating arrangements of multiple, possibly occluded, shapes. Among other things, our results indicate that 1) the wide field of view afforded by VR results in significantly faster target slide identification times compared to a tablet-only interface for visually salient targets; and 2) the three-dimensional view in VR enables significantly faster object reordering in the presence of occlusion compared to two baseline interfaces. A user study further confirmed that the interaction techniques were found to be usable and enjoyable., Comment: IEEE VR 2022; to appear in IEEE transactions on visualization and computer graphics, 2022

Reliability estimation of Machine Learning (ML) models is becoming a crucial subject. This is particularly the case when such \mbox{models} are deployed in safety-critical applications, as the decisions based on model predictions can result in hazardous situations. In this regard, recent research has proposed methods to achieve safe, \mbox{dependable}, and reliable ML systems. One such method consists of detecting and analyzing distributional shift, and then measuring how such systems respond to these shifts. This was proposed in earlier work in SafeML. This work focuses on the use of SafeML for time series data, and on reliability and robustness estimation of ML-forecasting methods using statistical distance measures. To this end, distance measures based on the Empirical Cumulative Distribution Function (ECDF) proposed in SafeML are explored to measure Statistical-Distance Dissimilarity (SDD) across time series. We then propose SDD-based Reliability Estimate (StaDRe) and SDD-based Robustness (StaDRo) measures. With the help of a clustering technique, the similarity between the statistical properties of data seen during training and the forecasts is identified. The proposed method is capable of providing a link between dataset SDD and Key Performance Indicators (KPIs) of the ML models., Comment: This paper has been submitted for 5th International Workshop on Artificial Intelligence Safety Engineering (WAISE2022)

This editorial provides an overview of ten scientific articles published as the Special paper selection in Thermal Science. The papers were selected from almost six hundred contributions, presented at the 16th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES 2021), held on October 10-15, 2021 in Dubrovnik, Croatia. The topics covered in the Special paper selection include economics of electricity markets, nuclear technology, repowering of the coal-based power plant, hybrid renewable energy system, sustainable biomass handling and conversion, post-combustion emissions control, and efficient cooling technology. The editorial also emphasised the papers recently published in the Special Issues of leading scientific journals dedicated to the series of SDEWES Conferences.

This paper resolves some puzzles regarding Spinoza’s appropriations and rejections of various aspects of Bacon’s methodology, and uses these solutions to resolve some long-standing puzzles concerning Spinoza’s modus operandi in the TTP. We argue first that, appearances to contrary, Spinoza takes a consistent line in his assessment of Bacon’s epistemic approach. We argue that Spinoza follows Bacon in grounding his overall epistemic method in a “historiola mentis” (a brief account or history of the mind), and that differences between Spinoza’s and Bacon’s respective historiola mentis can explain Spinoza’s embrace of this inductive method for his interpretation of Scripture in the TTP, as well as his general abandonment of Bacon’s inductive method in the metaphysical investigation of the Ethics. In short, we argue that the “historiola mentis” constructed by Bacon depicts the intellect as an error-prone faculty that needs be continuously restrained by observation and experimentation—a depiction which motivates Bacon’s reformed inductive empiricism. Spinoza accepts this depiction in regard to a subset of the mind’s ideas—the ideas of the imagination, and hence sees the inductive method as suitable for interpreting Scripture. But contra Bacon, Spinoza’s “historiola mentis” also shows that the human mind includes a subset of ideas that yield true, certain knowledge of things “infinite” and sub specie aeternitatis. Spinoza finds these “intellectual” ideas to be quite useful for systematic metaphysics, but of limited use for interpreting historical texts like Scripture.

Lysosome-related organelles play evolutionarily conserved roles in zinc storage, but mechanisms that control zinc flow in and out are not well understood. In C. elegans intestinal cells, the CDF-2 transporter stores zinc in these organelles during excess. Here we identify ZIPT-2.3 as the transporter that releases zinc during deficiency. The expression levels of CDF-2 and ZIPT-2.3 are reciprocally regulated in zinc excess and deficiency, establishing a fundamental mechanism of homeostasis. Super-resolution microscopy demonstrated these organelles are composed of a spherical acidified compartment and a hemispherical expansion compartment. The expansion compartment inflates during zinc excess and deficiency by vesicle fusion delivering zinc transporters. These results identify an unexpected structural feature of lysosome-related organelles that facilitates rapid transitions in the composition of zinc transporters to mediate homeostasis.

Work schedules in the service sector are routinely unstable and unpredictable, and this unpredictability may have harmful effects on health and economic insecurity. However, because schedule unpredictability often coincides with low wages and other dimensions of poor job quality, the causal effects of unpredictable work schedules are uncertain. Seattle's Secure Scheduling ordinance, enacted in 2017, mandated greater schedule predictability, providing an opportunity to examine the causal relationship between work scheduling and worker health and economic security. We draw on pre- and postintervention survey data from workers in Seattle and comparison cities to estimate the impacts of this law using a difference-in-differences approach. We find that the law had positive impacts on workers' schedule predictability and stability and led to increases in workers' subjective well-being, sleep quality, and economic security. Using the Seattle law as an instrumental variable, we also estimate causal effects of schedule predictability on well-being outcomes. We show that uncertainty about work time has a substantial effect on workers' well-being, particularly their sleep quality and economic security.

Work schedules in the service sector are routinely unstable and unpredictable, and this unpredictability may have harmful effects on health and economic insecurity. However, because schedule unpredictability often coincides with low wages and other dimensions of poor job quality, the causal effects of unpredictable work schedules are uncertain. Seattle's Secure Scheduling ordinance, enacted in 2017, mandated greater schedule predictability, providing an opportunity to examine the causal relationship between work scheduling and worker health and economic security. We draw on pre- and postintervention survey data from workers in Seattle and comparison cities to estimate the impacts of this law using a difference-in-differences approach. We find that the law had positive impacts on workers' schedule predictability and stability and led to increases in workers' subjective well-being, sleep quality, and economic security. Using the Seattle law as an instrumental variable, we also estimate causal effects of schedule predictability on well-being outcomes. We show that uncertainty about work time has a substantial effect on workers' well-being, particularly their sleep quality and economic security.

Visible-light communication is a promising technology for industrial environments. However, a variety of physical effects may influence the communication quality in this potentially harsh environment: Dust and other particles lead to increased attenuation. Artificial light sources and industrial processes, such as grinding and welding, cause optical cross-talk. A multitude of reflective surfaces can lead to fading due to multi-path propagation. These three aspects are experimentally investigated in exemplary manufacturing processes at five different production sites in order to estimate the relative importance and their specific impact on VLC transmission in industrial areas. Spectral measurements demonstrate the presence of interfering light sources, which occupy broad parts of the visible spectrum. They give rise to flickering noise, which comprises a set of frequencies in the electrical domain. The impact of these effects on the communication is analysed with reference to the maximum achievable channel capacity and data rate approximation based on on-off keying is deduced. It is found that cross-talk by environmental and artificial light sources is one of the strongest effects, which influences the optical, but also the electrical spectrum. It is also observed that industrial areas differ strongly and must be categorised according to the manufacturing processes, which can induce quite a variation of dust and attenuation accordingly.

Operational Technology (OT)-networks and -devices, i.e. all components used in industrial environments, were not designed with security in mind. Efficiency and ease of use were the most important design characteristics. However, due to the digitisation of industry, an increasing number of devices and industrial networks is opened up to public networks. This is beneficial for administration and organisation of the industrial environments. However, it also increases the attack surface, providing possible points of entry for an attacker. Originally, breaking into production networks meant to break an Information Technology (IT)-perimeter first, such as a public website, and then to move laterally to Industrial Control Systems (ICSs) to influence the production environment. However, many OT-devices are connected directly to the Internet, which drastically increases the threat of compromise, especially since OT-devices contain several vulnerabilities. In this work, the presence of OT-devices in the Internet is analysed from an attacker's perspective. Publicly available tools, such as the search engine Shodan and vulnerability databases, are employed to find commonly used OT-devices and map vulnerabilities to them. These findings are grouped according to country of origin, manufacturer, and number as well as severity of vulnerability. More than 13000 devices were found, almost all contained at least one vulnerability. European and Northern American countries are by far the most affected ones., Comment: IEEE Internet of Things Journal, May 2021

Cooperative driving systems, such as platooning, rely on communication and information exchange to create situational awareness for each agent. Design and performance of control components are therefore tightly coupled with communication component performance. The information flow between vehicles can significantly affect the dynamics of a platoon. Therefore, both the performance and the stability of a platoon depend not only on the vehicle's controller but also on the information flow Topology (IFT). The IFT can cause limitations for certain platoon properties, i.e., stability and scalability. Cellular Vehicle-To-Everything (C-V2X) has emerged as one of the main communication technologies to support connected and automated vehicle applications. As a result of packet loss, wireless channels create random link interruption and changes in network topologies. In this paper, we model the communication links between vehicles with a first-order Markov model to capture the prevalent time correlations for each link. These models enable performance evaluation through better approximation of communication links during system design stages. Our approach is to use data from experiments to model the Inter-Packet Gap (IPG) using Markov chains and derive transition probability matrices for consecutive IPG states. Training data is collected from high fidelity simulations using models derived based on empirical data for a variety of different vehicle densities and communication rates. Utilizing the IPG models, we analyze the mean-square stability of a platoon of vehicles with the standard consensus protocol tuned for ideal communication and compare the degradation in performance for different scenarios.

An increasing number of consumer-oriented head-mounted displays (HMD) for augmented and virtual reality (AR/VR) are capable of finger and hand tracking. We report on the accuracy of off-the-shelf VR and AR HMDs when used for touch-based tasks such as pointing or drawing. Specifically, we report on the finger tracking accuracy of the VR head-mounted displays Oculus Quest, Vive Pro and the Leap Motion controller, when attached to a VR HMD, as well as the finger tracking accuracy of the AR head-mounted displays Microsoft HoloLens 2 and Magic Leap. We present the results of two experiments in which we compare the accuracy for absolute and relative pointing tasks using both human participants and a robot. The results suggest that HTC Vive has a lower spatial accuracy than the Oculus Quest and Leap Motion and that the Microsoft HoloLens 2 provides higher spatial accuracy than Magic Leap One. These findings can serve as decision support for researchers and practitioners in choosing which systems to use in the future., Comment: To appear in SUI 2021, November 09-10, Virtual Conference

The open and cooperative nature of Cyber-Physical Systems (CPS) poses new challenges in assuring dependability. The DEIS project (Dependability Engineering Innovation for automotive CPS. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 732242, see http://www.deis-project.eu) addresses these challenges by developing technologies that form a science of dependable system integration. In the core of these technologies lies the concept of a Digital Dependability Identity (DDI) of a component or system. DDIs are modular, composable, and executable in the field facilitating (a) efficient synthesis of component and system dependability information over the supply chain and (b) effective evaluation of this information in-the-field for safe and secure composition of highly distributed and autonomous CPS. The paper outlines the DDI concept and opportunities for application in four industrial use cases.

Cyber-Physical Systems (CPS) provide enormous potential for innovation but a precondition for this is that the issue of dependability has been addressed. This paper presents the concept of a Digital Dependability Identity (DDI) of a component or system as foundation for assuring the dependability of CPS. A DDI is an analyzable and potentially executable model of information about the dependability of a component or system. We argue that DDIs must fulfill a number of properties including being universally useful across supply chains, enabling off-line certification of systems where possible, and providing capabilities for in-field certification of safety of CPS. In this paper, we focus on system safety as one integral part of dependability and as a practical demonstration of the concept, we present an initial implementation of DDIs in the form of Conditional Safety Certificates (also known as ConSerts). We explain ConSerts and their practical operationalization based on an illustrative example.

Deception technology has proven to be a sound approach against threats to information systems. Aside from well-established honeypots, decoy elements, also known as honeytokens, are an excellent method to address various types of threats. Decoy elements are causing distraction and uncertainty to an attacker and help detecting malicious activity. Deception is meant to be complementing firewalls and intrusion detection systems. Particularly insider threats may be mitigated with deception methods. While current approaches consider the use of multiple decoy elements as well as context-sensitivity, they do not sufficiently describe a relationship between individual elements. In this work, inter-referencing decoy elements are introduced as a plausible extension to existing deception frameworks, leading attackers along a path of decoy elements. A theoretical foundation is introduced, as well as a stochastic model and a reference implementation. It was found that the proposed system is suitable to enhance current decoy frameworks by adding a further dimension of inter-connectivity and therefore improve intrusion detection and prevention.

Honeypots are a well-studied defensive measure in network security. This work proposes an effective low-cost honeypot that is easy to deploy and maintain. The honeypot introduced in this work is able to handle commands in a non-standard way by blocking them or replying with an insult to the attacker. To determine the most efficient defense strategy, the interaction between attacker and defender is modeled as a Bayesian two-player game. For the empirical analysis, three honeypot instances were deployed, each with a slight variation in its configuration. In total, over 200 distinct sessions were captured, which allows for qualitative evaluation of post-exploitation behavior. The findings show that attackers react to insults and blocked commands in different ways, ranging from ignoring to sending insults themselves. The main contribution of this work lies in the proposed framework, which offers a low-cost alternative to more technically sophisticated and resource-intensive approaches.

Nearly 250,000 workers are employed in the retail and food service sector in the state of Connecticut. Nationally, jobs in the service sector are characterized by low pay and few fringe benefits, and workers employed in the service sector have little control over the days and times that they will work. In addition, many service sector employers across the country rely on just-in-time and on-call scheduling practices designed to minimize labor costs by closely aligning staffing with consumer demand. These practices can introduce a great deal of instability into the lives of workers and their families.

Nearly 250,000 workers are employed in the retail and food service sector in the state of Connecticut. Nationally, jobs in the service sector are characterized by low pay and few fringe benefits, and workers employed in the service sector have little control over the days and times that they will work. In addition, many service sector employers across the country rely on just-in-time and on-call scheduling practices designed to minimize labor costs by closely aligning staffing with consumer demand. These practices can introduce a great deal of instability into the lives of workers and their families.

Virtual Reality (VR) has the potential to transform knowledge work. One advantage of VR knowledge work is that it allows extending 2D displays into the third dimension, enabling new operations, such as selecting overlapping objects or displaying additional layers of information. On the other hand, mobile knowledge workers often work on established mobile devices, such as tablets, limiting interaction with those devices to a small input space. This challenge of a constrained input space is intensified in situations when VR knowledge work is situated in cramped environments, such as airplanes and touchdown spaces. In this paper, we investigate the feasibility of interacting jointly between an immersive VR head-mounted display and a tablet within the context of knowledge work. Specifically, we 1) design, implement and study how to interact with information that reaches beyond a single physical touchscreen in VR; 2) design and evaluate a set of interaction concepts; and 3) build example applications and gather user feedback on those applications., Comment: 10 pages, 8 figures, ISMAR 2020

Virtual Reality (VR) can enhance the display and interaction of mobile knowledge work and in particular, spreadsheet applications. While spreadsheets are widely used yet are challenging to interact with, especially on mobile devices, using them in VR has not been explored in depth. A special uniqueness of the domain is the contrast between the immersive and large display space afforded by VR, contrasted by the very limited interaction space that may be afforded for the information worker on the go, such as an airplane seat or a small work-space. To close this gap, we present a tool-set for enhancing spreadsheet interaction on tablets using immersive VR headsets and pen-based input. This combination opens up many possibilities for enhancing the productivity for spreadsheet interaction. We propose to use the space around and in front of the tablet for enhanced visualization of spreadsheet data and meta-data. For example, extending sheet display beyond the bounds of the physical screen, or easier debugging by uncovering hidden dependencies between sheet's cells. Combining the precise on-screen input of a pen with spatial sensing around the tablet, we propose tools for the efficient creation and editing of spreadsheets functions such as off-the-screen layered menus, visualization of sheets dependencies, and gaze-and-touch-based switching between spreadsheet tabs. We study the feasibility of the proposed tool-set using a video-based online survey and an expert-based assessment of indicative human performance potential., Comment: 10 pages, 11 figures, ISMAR 2020

We study a finite and fixed relative formation of possibly mobile wireless networked nodes. The nodes apply average consensus to agree on a common value like the formation's center. %Performance of consensus in terms of convergence speed is affected by message losses due path loss and interference. We assume framed slotted ALOHA based broadcast communication. Our work has two contributions. First, we analyze outage correlation of random media access in wireless networks under Nakagami fading. Second, the correlation terms are applied to the so called L2-joint spectral and numerical radii to analyze convergence speed of average consensus under wireless broadcast communication. This yields a unified framework for studying joint optimization of control and network parameters for consensus subject to message losses in wireless communications. Exemplary we show in this work how far outage correlation in wireless broadcast communication positively affects convergence speed of average consensus compared to consensus in the uncorrelated case., Comment: This is the extended version of the paper "Analytical Derivation of Outage Correlation in Random Media Access with Application to Average Consensus in Wireless Networks", IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2020

Transformations accompanying shape-instability govern the morphological configuration and distribution of the phases in a microstructure. Owing to the influence of the microstructure on the properties of a material, the stability of three-dimensional rods in a representative polycrystalline system is extensively analysed. A multiphase-field model, which recovers the physical laws and sharp-interface relations, and includes grain boundary diffusion, is adopted to investigate the morphological evolution of the precipitate. Moreover, the efficiency of the numerical approach is ensured by establishing the volume-preserving chemical equilibrium through the incorporation TCFe8 (CALPHAD) data and solving phase-field evolution in the Allen-Cahn framework. The morphological evolution of the rod in the multiphase system exhibits a unique transformation mechanism which is significantly different from the evolution of an isolated finite-structure. It is realised that, in a polycrystalline arrangement, irrespective of the initial rod-size , the shape-change begins with the energy-minimising events at the triple junctions. This early transformation renders a characteristic morphology at the longitudinal ends of the structure, which introduces sufficient driving-force through the curvature-difference for the subsequent morphological changes. The continued mass transfer to the terminations, ultimately, breaks-off the rod into separate entities that are entangled in the grain boundary. With increasing aspect ratio of the rod, it is identified that the source of mass transfer, which turns into the ovulation site, shifts from the centre. This increases the number of fragmentation events and introduces satellite particle. A comprehensive understanding of the transformation kinetics and mechanism governing the morphological evolution of the rods in a polycrystalline system is rendered in this work.

The internet landscape is growing and at the same time becoming more heterogeneous. Services are performed via computers and networks, critical data is stored digitally. This enables freedom for the user, and flexibility for operators. Data is easier to manage and distribute. However, every device connected to a network is potentially susceptible to cyber attacks. Security solutions, such as antivirus software or firewalls, are widely established. However, certain types of attacks cannot be prevented with defensive measures alone. Offensive security describes the practice of security professionals using methods and tools of cyber criminals. This allows them to find vulnerabilities before they become the point of entry in a real attack. Furthermore, following the methods of cyber criminals enables security professionals to adapt to a criminal's point of view and potentially discover attack angles formerly ignored. As cyber criminals often employ freely available security tools, having knowledge about these provides additional insight for professionals. This work categorises and compares tools regarding metrics concerning maintainability, usability and technical details. Generally, several well-established tools are available for the first phases, while phases after the initial breach lack a variety of tools., Comment: Six pages, one figure, this work is a preprint of a paper accepted at the 1st Workshop on Next Generation Networks and Applications (NGNA-2020)

The Great Recession has ruined the finances of millions of families and has had long-lasting impacts on employment. But less is known about its social consequences, about how it affected the intimate lives of the most disadvantaged – and in particular how it affected their fertility. Prior research has found that fertility decisions are often disconnected from economic concerns. In a new paper, I find the opposite: fertility falls in response to severe economic shocks among unmarried and teen women.1 I show that during the Great Recession, unmarried women increased their use of contraceptives and made use of more effective contraceptive methods. My results suggest that the Great Recession decreased fertility with consequences for the society as a whole.

The Great Recession has ruined the finances of millions of families and has had long-lasting impacts on employment. But less is known about its social consequences, about how it affected the intimate lives of the most disadvantaged – and in particular how it affected their fertility. Prior research has found that fertility decisions are often disconnected from economic concerns. In a new paper, I find the opposite: fertility falls in response to severe economic shocks among unmarried and teen women.1 I show that during the Great Recession, unmarried women increased their use of contraceptives and made use of more effective contraceptive methods. My results suggest that the Great Recession decreased fertility with consequences for the society as a whole.

Kinetic Monte Carlo (KMC) simulations are used to study transport of guest molecules in a two-phase medium in which the minority phase forms closed regions. This type of model system resembles compositions of mixed-matrix membranes (MMMs) made of a matrix and imbedded filler particles with different permeabilities. Based on an ideal filler-matrix composite morphology as defined in [H. Vinh-Thang, S. Kaliaguine, Chem. Rev. 113, 4980, 2013], the effects of the filler-particle volume fraction, particle size, shape (aspect ratio), and the spatial particle distribution on gas transport through MMMs are addressed. The results obtained for nonoverlapping and randomly placed spherical filler particles are found to be in good agreement with the analytical models available in the literature and have proven their good accuracy also for thin membranes where finite-size effects are expected to play an appreciable role. Furthermore, the prominent influence of the aspect ratio for nonspherical particles on the effective permeability is shown and scenarios are discussed where the alignment of the asymmetric filler particles give rise to anisotropic transport properties potentially favorable for the performance of MMMs. The KMC approach developed guides the optimal spatial arrangement and orientation of the filler particles for the different strategies to increase the membrane permeability and separation selectivity by transport enhancement or inhibition., ChemE/Catalysis Engineering