Learning our way towards a sustainable agri-food system Three cases from Sweden: Stockholm Farmers market, Ramsjö Community Supported Agriculture and Järna Initiative for Local Production

This research is based on case studies of the Stockholm Farmers Market, Ramsjö Community Supported Agriculture, and Järna Initiative for Local Production. These cases are examples of alternative consumerproducer links in the Swedish agri-food system. An adapted SWOT analysis highlights key strengths, weaknesses, opportunities and constraints in each case from the multiple perspectives of producers, consumers, the organization, as well as the environment and society. Diagrams show where learning opportunities exist in the three systems, and how the structure of the consumer-producer link influences learning processes. Implication assessments consider how each link may affect surrounding ecosystems and social aspects of the agri-food system. A framework for assessing a process of development identifies six components that contribute to agri-food system development. Four key issues are discussed in terms of their potential to significantly affect the development of the agri-food system: the length of the food chain linking producers and consumers, the definition of “local”, learning in the system, and what is really being sold – is it food, or values? Critical research questions are highlighted and recommended for future research

Dynamics and calcium association to the N-terminal regulatory domain of human cardiac troponin C: a multiscale computational study.

Troponin C (TnC) is an important regulatory molecule in cardiomyocytes. Calcium binding to site II in TnC initiates a series of molecular events that result in muscle contraction. The most direct change upon Ca(2+) binding is an opening motion of the molecule that exposes a hydrophobic patch on the surface allowing for Troponin I to bind. Molecular dynamics simulations were used to elucidate the dynamics of this crucial protein in three different states: apo, Ca(2+)-bound, and Ca(2+)-TnI-bound. Dynamics between the states are compared, and the Ca(2+)-bound system is investigated for opening motions. On the basis of the simulations, NMR chemical shifts and order parameters are calculated and compared with experimental observables. Agreement indicates that the simulations sample the relevant dynamics of the system. Brownian dynamics simulations are used to investigate the calcium association of TnC. We find that calcium binding gives rise to correlative motions involving the EF hand and collective motions conducive of formation of the TnI-binding interface. We furthermore indicate the essential role of electrostatic steering in facilitating diffusion-limited binding of Ca(2+)

Wspólnota zysków i strat (societas) jako nucleus wspólnoty politycznej u Aarona Aleksandra Olizarowskiego

Aaron Alexander Olizarovius, lawyer and author of De politica hominum societate (1651), restructured Aristotle’s practical philosophy and based his understanding of the political community on the notion of economic partnership (societas) known in Roman law. These aspects point to the creative nature of the treaty as an important document of the dialogue between the Polish-Lithuanian Commonwealth and Western Europe on just social order.Aaron Aleksander Olizarowski, prawnik i autor De politica hominum societate (1651), reinterpretował strukturę Arystotelesowskiej filozofii praktycznej i oparł swoją koncepcję wspólnoty politycznej na znanym w prawie rzymskim pojęciu „spółki” (societas). W tych zabiegach wyraża się oryginalny charakter całego traktatu, który stanowi ważny dokument dialogu Rzeczypospolitej z Zachodem na temat sprawiedliwego ustroju społecznego

Goal-based Workflow Adaptation for Role-based Resources in the Internet of Things

In recent years, the Internet of Things (IoT) has increasingly received attention from the Business Process Management (BPM) community. The integration of sensors and actuators into Process-Aware Information Systems (PAIS) enables the collection of real-time data about physical properties and the direct manipulation of real-world objects. In a broader sense, IoT-aware workflows provide means for context-aware workflow execution involving virtual and physical entities. However, IoT-aware workflow management imposes new requirements on workflow modeling and execution that are outside the scope of current modeling languages and workflow management systems. Things in the IoT may vanish, appear or stay unknown during workflow execution, which renders their allocation as workflow resources infeasible at design time. Besides, capabilities of Things are often intended to be available only in a particular real-world context at runtime, e.g., a service robot inside a smart home should only operate at full speed, if there are no residents in direct proximity. Such contextual restrictions for the dynamic exposure of resource capabilities are not considered by current approaches in IoT resource management that use services for exposing device functionalities. With this work, we aim at providing the modeling and runtime support for defining such restrictions on workflow resources at design time and enabling the dynamic and context-sensitive runtime allocation of Things as workflow resources. To achieve this goal, we propose contributions to the fields of resource management, i.e., resource perspective, and workflow management in the Internet of Things (IoT), divided into the user perspective representing the workflow modeling phase and the workflow perspective representing the runtime resource allocation phase. In the resource perspective, we propose an ontology for the modeling of Things, Roles, capabilities, physical entities, and their context-sensitive interrelations. The concept of Role is used to define non-exclusive subsets of capabilities of Things. A Thing can play a certain Role only under certain contextual restrictions defined by Semantic Web Rule Language (SWRL) rules. At runtime, the existing relations between the individuals of the ontology represent the current state of interactions between the physical and the cyber world. Through the dynamic activation and deactivation of Roles at runtime, the behavior of a Thing can be adapted to the current physical context. In the user perspective, we allow workflow modelers to define the goal of a workflow activity either by using semantic queries or by specifying high-level goals from a Tropos goal model. The goal-based modeling of workflow activities provides the most flexibility regarding the resource allocation as several leaf goals may fulfill the user specified activity goal. Furthermore, the goal model can include additional Quality of Service (QoS) parameters and the positive or negative contribution of goals towards these parameters. The workflow perspective includes the Semantic Access Layer (SAL) middleware to enable the transformation of activity goals into semantic queries as well as their execution on the ontology for role-based Things. The SAL enables the discovery of fitting Things, their allocation as workflow resources, the invocation of referenced IoT services, and the continuous monitoring of the allocated Things as part of the ontology. We show the feasibility and added value of this work in relation to related approaches by evaluation within several application scenarios in a smart home setting. We compare the fulfillment of quantified criteria for IoT-aware workflow management based on requirements extracted from related research. The evaluation shows, that our approach enables an increase in the context-aware modeling of Things as workflow resources, in the query support for workflow resource allocation, and in the modeling support of activities using Things as workflow resources.:1 Introduction 15 1.1 Background 17 1.2 Motivation 17 1.3 Aim and Objective 19 1.3.1 Research Questions and Scope 19 1.3.2 Research Goals 20 1.4 Contribution 20 1.5 Outline 21 2 Background for Workflows in the IoT 23 2.1 Resource Perspective 24 2.1.1 Internet of Things 24 2.1.2 Context and Role Modeling 27 2.2 User Perspective 37 2.2.1 Goal Modeling 38 2.2.2 Tropos Goal Modeling Language 38 2.3 Workflow Perspective 39 2.3.1 Workflow Concepts 39 2.3.2 Workflow Modeling 40 2.3.3 Internet of Things-aware Workflow Management 43 2.4 Summary 44 3 Requirements Analysis and Approach 45 3.1 Requirements 45 3.1.1 IoT Resource Perspective 46 3.1.2 Workflow Resource Perspective 50 3.1.3 Relation to Research Questions 51 3.2 State of the Art Analysis 53 3.2.1 Fulfillment Criteria 54 3.2.2 IoT-aware workflow management 56 3.3 Discussion 65 3.4 Approach 70 3.4.1 Contribution to IoT-aware workflow management 71 3.5 Summary 73 4 Concept for Adaptive Workflow Activities in the IoT 75 4.1 Resource Perspective 75 4.1.1 Role-based Things 75 4.1.2 Semantic Modeling Concepts 79 4.1.3 SWRL Modeling Concepts 81 4.2 User Perspective 81 4.2.1 Semantic Queries in Workflow Activites 81 4.2.2 Goals for Workflow Activites 81 4.2.3 Mapping from Goals to Semantic Queries 82 4.3 Workflow Perspective 83 4.3.1 Workflow metamodel Extensions 83 4.3.2 Middleware for Dynamic Resource Discovery and Allocation 85 4.4 Summary 86 5 Modeling Adaptive Workflow Activities in the IoT 87 5.1 Resource Perspective 87 5.1.1 Role-based Modeling of Context-sensitive Things 87 5.1.2 Ontology Classes 90 5.1.3 Ontology Object properties 93 5.1.4 Ontology Data properties 99 5.1.5 DL-safe SWRL Rules 100 5.2 Discussion of Role Modeling Features 101 5.3 Example Application Scenario Modeling 102 5.3.1 Resource Perspective 102 5.3.2 User Perspective 105 5.3.3 Workflow Perspective 109 5.4 Summary 113 6 Architecture for Adaptive Workflow Activities in the IoT 115 6.1 Overview of the System Architecture 115 6.2 Specification of System Components 117 6.2.1 Resource Perspective 118 6.2.2 User Perspective 118 6.2.3 Workflow Perspective 118 6.3 Summary 123 7 Implementation of Adaptive Workflow Activities in the IoT 125 7.1 Resource Perspective 125 7.2 Workflow Perspective 125 7.2.1 PROtEUS 125 7.2.2 Semantic Access Layer 127 7.3 User Perspective 128 7.4 Summary 128 8 Evaluation 129 8.1 Goal and Evaluation Approach 129 8.1.1 Definition of Test Cases 130 8.2 Scenario Evaluation 134 8.2.1 Ambient Assisted Living Setting 135 8.2.2 Resource Perspective 135 8.2.3 User Perspective 137 8.2.4 Workflow Perspective 138 8.2.5 Execution of Test Cases 139 8.2.6 Discussion of Results 146 8.3 Performance Evaluation 148 8.3.1 Experimental Setup 148 8.3.2 Discussion of Results 151 8.4 Summary 152 9 Discussion 153 9.1 Comparison of Solution to Research Questions 153 9.2 Extendability of the Solutions 155 9.3 Limitations 156 10 Summary and Future Work 157 10.1 Summary of the Thesis 157 10.2 Future Work 159 Appendix 161 Example Semantic Context Model for IoT-Things 171 T-Box of Ontology for Role-based Things in the IoT 178 A-Box for Example Scenario Model 201 A-Box for Extended Example Scenario Model 21

Do populist-leaning citizens support direct democracy?

Populist parties across Europe often support direct democracy, for example through frequent referendums. Do their voters support these initiatives too and why? Tina Freyburg, Robert Huber and Steffen Mohrenberg distinguish between citizens who support direct democracy as a way of giving power to ‘the people’ and those, known as stealth democrats, who do so out of a scepticism that politicians can be effective. They find that both sets of attitudes independently are associated with support for direct democracy, and argue that the distinction is crucial to furthering the debate about populism in Europe

Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD

Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcomes as central tasks, there is a need for such predictive tools, for example, to anticipate complex treatment courses and to be able to offer appropriate therapy on an individualized basis. This study aimed to develop a first basic model for the anticipation of adverse treatment courses based on prior compulsory admission and/or conviction as simple and easily objectifiable parameters in offender patients with a schizophrenia spectrum disorder (SSD). With a balanced accuracy of 67% and an AUC of 0.72, gradient boosting proved to be the optimal ML algorithm. Antisocial behavior, physical violence against staff, rule breaking, hyperactivity, delusions of grandeur, fewer feelings of guilt, the need for compulsory isolation, cannabis abuse/dependence, a higher dose of antipsychotics (measured by the olanzapine half-life) and an unfavorable legal prognosis emerged as the ten most influential variables out of a dataset with 209 parameters. Our findings could demonstrate an example of the use of ML in the development of an easy-to-use predictive model based on few objectifiable factors. Keywords: model building; machine learning; artificial intelligence; schizophrenia; forensic psychiatry; adverse treatment cours

Altered nutrition behavior during COVID-19 pandemic lockdown in young adults

Purpose. The COVID-19 pandemic and the implemented lockdown strongly impact on everyone’s daily life. Stressful situations are known to alter eating habits and increase the risk for obesity. In our study, we aimed to investigate the effect of the lockdown measures on nutrition behavior among young adults. Methods. In this cross-sectional study, we enrolled 1964 voluntary participants from Bavarian universities. All participants were asked to complete an online questionnaire, semi-quantitatively evaluating the amount and type of food before and during pandemic lockdown. Study subjects were inquired to give information about acquisition and food procurement. The primary outcome was the change in food amount, secondary outcomes included alterations of food composition and procurement. Results. Our study cohort (mean age 23.3 ± 4.0 years, 28.5% male) had a mean body mass index of 22.1 ± 4.5 kg/m2. The overall food amount increased in 31.2% of participants (n = 610) during lockdown and decreased in 16.8% (n = 328). A multinominal regression model revealed that an increased food intake was less likely in male participants (OR, 0.7 [CI 0.6–0.9]) and more likely with increasing BMI (OR, 1.4 [CI 1.3–2.0]), increased sports activity (OR, 1.3 [CI 1.2–1.8]), augmented mental stress (OR 1.4 [1.1–1.7]), and an alteration of alcohol consumption (reduced alcohol amount, OR, 1.4 [CI 1.1–1.7], increased alcohol, OR, 1.9 [CI 1.4–2.5]). Increase in food intake was mainly triggered by consumption of bread (increased in 46.8%, n = 284) and confectionary (increased in 64.4%, n = 389). Conclusion. The COVID-19 pandemic lockdown significantly affected eating habits in young adults. Further investigation to evaluate long-term effects on weight change and comorbidities are warranted

Wigner State and Process Tomography on Near-Term Quantum Devices

We present an experimental scanning-based tomography approach for near-term quantum devices. The underlying method has previously been introduced in an ensemble-based NMR setting. Here we provide a tutorial-style explanation along with suitable software tools to guide experimentalists in its adaptation to near-term pure-state quantum devices. The approach is based on a Wigner-type representation of quantum states and operators. These representations provide a rich visualization of quantum operators using shapes assembled from a linear combination of spherical harmonics. These shapes (called droplets in the following) can be experimentally tomographed by measuring the expectation values of rotated axial tensor operators. We present an experimental framework for implementing the scanning-based tomography technique for circuit-based quantum computers and showcase results from IBM quantum experience. We also present a method for estimating the density and process matrices from experimentally tomographed Wigner functions (droplets). This tomography approach can be directly implemented using the Python-based software package \texttt{DROPStomo}.Comment: Extended supplemental section on temporal averagin

Airway management during ongoing chest compressions-direct vs. video laryngoscopy. A randomised manikin study.

BACKGROUND Tracheal intubation is used for advanced airway management during cardiac arrest, particularly when basic airway techniques cannot ensure adequate ventilation. However, minimizing interruptions of chest compressions is of high priority. Video laryngoscopy has been shown to improve the first-pass success rate for tracheal intubation in emergency airway management. We aimed to compare first-pass success rate and time to successful intubation during uninterrupted chest compression using video laryngoscopy and direct laryngoscopy. METHODS A total of 28 anaesthetists and 28 anaesthesia nurses with varied clinical and anaesthesiological experience were recruited for the study. All participants performed a tracheal intubation on a manikin simulator during ongoing chest compressions by a mechanical resuscitation device. Stratified randomisation (physicians/nurses) was performed, with one group using direct laryngoscopy and the other using video laryngoscopy. RESULTS First-pass success rate was 100% (95% CI: 87.9% - 100.0%) in the video laryngoscopy group and 67.8% (95% CI: 49.3% - 82.1%) in the direct laryngoscopy group [difference: 32.2% (95% CI: 17.8% - 50.8%), p<0.001]. The median time for intubation was 27.5 seconds (IQR: 21.8-31.0 seconds) in the video laryngoscopy group and 30.0 seconds (IQR: 26.5-36.5 seconds) in the direct laryngoscopy group (p = 0.019). CONCLUSION This manikin study on tracheal intubation during ongoing chest compressions demonstrates that video laryngoscopy had a higher first-pass success rate and shorter time to successful intubation compared to direct laryngoscopy. Experience in airway management and professional group were not significant predictors. A clinical randomized controlled trial appears worthwhile