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The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages, such as SPARQL, can support rich log analyses by leveraging semantic relationships between objects in heterogeneous log streams. Constructing, materializing, and maintaining centralized log knowledge graphs, however, poses significant challenges. To tackle this issue, we propose VloGraph—a distributed and virtualized alternative to centralized log knowledge graph construction. The proposed approach does not involve any a priori parsing, aggregation, and processing of log data, but dynamically constructs a virtual log KG from heterogeneous raw log sources across multiple hosts. To explore the feasibility of this approach, we developed a prototype and demonstrate its applicability to three scenarios. Furthermore, we evaluate the approach in various experimental settings with multiple heterogeneous log sources and machines; the encouraging results from this evaluation suggest that the approach can enable efficient graph-based ad-hoc log analyses in federated settings.

SummaryIn view of the recent revival of interest in circadian biology and circadian epidemiology at the Medical University of Vienna, it seems appropriate to highlight the rich and pioneering history of circadian research in Austria. Among the forefathers of circadian research in Vienna are Otto Marburg (1874–1948), who discovered important elements of the pineal gland physiology, Robert Hofstätter (1883–1970), who used pineal gland extract in obstetrics/gynecology, and Paul Engel (1907–1997), who discovered that the pineal gland was controlled by light. More recently, Vera Lapin (1920–2007) showed that surgical removal of the pineal gland increased tumor growth, while Franz Waldhauser (*1946) investigated melatonin in conjunction with night work. Michael Kundi (*1950) and his team conducted among the first studies demonstrating differences in rhythms of night workers and early evidence for health impairments among them. Furthermore, Vienna-born Erhard Haus (1926–2013) pioneered the discovery of the role and importance of melatonin in relation to numerous diseases. This rich pioneering contribution of scientists in Vienna or with roots in Vienna is continued today by a new generation of chronobiologists, epidemiologists and clinicians in Vienna whose new insights contribute to the rapidly developing field of circadian rhythms research. Current topics and contributions relate to the impact of circadian rhythm disruption on health, and the application of chronotherapeutic approaches in clinical and preventive settings.

In Production Systems Engineering (PSE), domain experts aim at reusing partial system designs implemented as Industry 4.0 assets and software. However, the knowledge on assets is often scattered across engineering artifacts from multiple disciplines and domain experts, making it difficult to find reusable assets and map them to requirements. In this paper, we (i) identify challenges and requirements for the representation of reuse knowledge in PSE, based on the results of a domain analysis in automotive manufacturing; (ii) refine the Industry 4.0 Asset Network (I4AN) meta-model that integrates multi-disciplinary dependencies between the assets; (iii) introduce the I4AN reference model that exposes recurring patterns; and (iv) present basic and applied patterns for reuse in PSE that aim at improving reuse efficiency and lowering risks. We evaluate the I4AN reference model and patterns with reuse scenarios in a feasibility study in automotive manufacturing. The study results indicate that the I4AN reference model and patterns satisfy the elicited requirements and enable PSE domain experts to identify patterns for reuse and sufficiently complete sets of reusable assets in their contexts.

Purpose. Agile Production Systems Engineering (PSE) is characterised by parallel and iterative engineering of several disciplines. This multi-view engineering requires capabilities for tracing changes to support configuration management of PSE assets. Yet, traditional model transformation approaches in PSE do not preserve local views and hierarchies on concepts of PSE assets, such as plans and configurations. Thus, tracing multi-view changes to PSE assets is challenging. Method. Following the Design Science approach, we (i) elicit requirements for tracing multi-view changes to PSE assets from a domain analysis in automotive manufacturing; (ii) introduce and evaluate the Traceable Multi-view Model Transformation (TMvMT) process; and (iii) propose the TMvMT pipeline architecture to provide traceable model integration capabilities for agile PSE. Results. In a feasibility study on robot cell models, we evaluate the TMvMT process and architecture regarding the requirements for traceability compared to traditional approaches. Conclusion. The proposed TMvMT approach provides traceability of changes in multi-view modelling as a basis through the separation of modelling transformation steps and provision of clear input and output artefacts to achieve traceable configuration management and validation of system designs for production system assets in agile PSE.

Background The NMDA receptor (NMDAR) plays a key role in the central nervous system, e.g., for synaptic transmission. While synaptic NMDARs are thought to have protective characteristics, activation of extrasynaptic NMDARs might trigger excitotoxic processes linked to neuropsychiatric disorders. Since extrasynaptic NMDARs are typically GluN2B-enriched, the subunit is an interesting target for drug development and treatment monitoring. Recently, the novel GluN2B-specific PET radioligand (R)-[11C]Me-NB1 was investigated in rodents and for the first time successfully translated to humans. To assess whether (R)-[11C]Me-NB1 is a valuable radioligand for (repeated) clinical applications, we evaluated its safety, biodistribution and dosimetry. Methods Four healthy subjects (two females, two males) underwent one whole-body PET/MR measurement lasting for more than 120 min. The GluN2B-specific radioligand (R)-[11C]Me-NB1 was administered simultaneously with the PET start. Subjects were measured in nine passes and six bed positions from head to mid-thigh. Regions of interest was anatomically defined for the brain, thyroid, lungs, heart wall, spleen, stomach contents, pancreas, liver, kidneys, bone marrow and urinary bladder contents, using both PET and MR images. Time-integrated activity coefficients were estimated to calculate organ equivalent dose coefficients and the effective dose coefficient. Additionally, standardized uptake values (SUV) were computed to visualize the biodistribution. Results Administration of the radioligand was safe without adverse events. The organs with the highest uptake were the urinary bladder, spleen and pancreas. Organ equivalent dose coefficients were higher in female in almost all organs, except for the urinary bladder of male. The effective dose coefficient was 6.0 µSv/MBq. Conclusion The GluN2B-specific radioligand (R)-[11C]Me-NB1 was well-tolerated without reported side effects. Effective dose was estimated to 1.8 mSv when using 300 MBq of presented radioligand. The critical organ was the urinary bladder. Due to the low effective dose coefficient of this radioligand, longitudinal studies for drug development and treatment monitoring of neuropsychiatric disorders including neurodegenerative diseases are possible., EJNMMI Research, 12 (1), ISSN:2191-219X

Background Epigenetic modifications like DNA methylation are understood as an intermediary between environmental factors and neurobiology. Cerebral monoamine oxidase A (MAO-A) levels are altered in depression, as are DNA methylation levels within the MAOA gene, particularly in the promoter/exon I/intron I region. An effect of MAOA methylation on peripheral protein expression was shown, but the extent to which methylation affects brain MAO-A levels is not fully understood. Methods Here, the influence of MAOA promoter/exon I/intron I region DNA methylation on global MAO-A distribution volume (VT), an index of MAO-A density, was assessed via [11C]harmine positron emission tomography in 22 patients (14 females) suffering from seasonal affective disorder and 30 healthy controls (17 females). Results No significant influence of MAOA DNA methylation on global MAO-A VT was found, despite correction for health status, sex, season, and MAOA variable number of tandem repeat genotype. However, season affected average methylation in women, with higher levels in spring and summer (Puncorr = .03). We thus did not find evidence for an effect of MAOA DNA methylation on brain MAO-A VT. Conclusions In contrast to a previous study demonstrating an effect of methylation of a MAOA promoter region located further 5’ on brain MAO-A, MAOA methylation of the region assessed here appears to affect brain protein levels to a limited extent at most. The observed effect of season on methylation levels is in accordance with extensive evidence for seasonal effects within the serotonergic system. Clinicaltrials.gov Identifier NCT02582398 (https://clinicaltrials.gov/ct2/show/NCT02582398).

The verification of Extended Entity Relationship (EER) diagrams and other conceptual models that capture the design of information systems is crucial to ensure reliable systems. To scale up verification processes to larger groups of experts, Human Computation techniques were used focusing primarily on closed tasks, which constrain the number and variety of reported defects in favor of easy aggregation of derived judgements. To address this limitation of closed tasks, in this paper, we investigate EER verification (as instance of a broader family of model verification problems) with open tasks to extend the range of collected results. We also address the challenge of aggregating results of open tasks by proposing a follow-up HC task for defect validation. We evaluate our approach for HC-based EER Verification with open tasks in a set of experiments conducted with junior developers and show that (1) open tasks allow collecting a variety of insights that go beyond a manually built gold standard while still leading to good performance (F1=60%) and (2) HC-based validation can be reliably used for validating the results of open tasks (F1=84% compared to expert validation).

Epigenetic modifications, such as DNA methylation, are understood as an intermediary between environmental factors affecting disease risk and pathophysiologic changes to brain structure and function. Cerebral monoamine oxidase A (MAO-A) levels are altered in depression, as are DNA methylation levels within the MAOA gene, particularly in the promoter / exon I / intron I region. An effect of MAOA methylation on peripheral protein expression was shown, but the extent to which methylation affects brain MAO-A levels is not fully understood. Here, the influence of average and CpG site-specific MAOA promoter / exon I / intron I region DNA methylation on global MAO-A distribution volume (VT), an index of MAO-A density, was assessed via [11C]harmine positron emission tomography in 22 patients suffering from winter-type seasonal affective disorder and 30 healthy controls. No significant influence of MAOA DNA methylation on global MAO-A VT was found, despite correction for health status (patients vs. controls), sex, season (methylation analysis in spring / summer vs. fall / winter) and MAOA variable number of tandem repeat genotype (VNTR; high vs. low expression groups). However, in female subjects, season affected average DNA methylation, with higher levels in spring and summer (puncorr = 0.03). We thus did not find evidence for an effect of MAOA DNA methylation on brain MAO-A VT. In contrast to a previous study that demonstrated an effect of the methylation of a MAOA promoter region located further 5’ on brain MAO-A, in the present study MAOA methylation appears to affect brain protein levels to a limited extent. The observed effect of season on methylation levels is in accordance with extensive evidence for seasonal effects within the serotonergic system.Clinicaltrials.gov IdentifierNCT02582398EUDAMED NumberCIV-AT-13-01-009583

The NMDA receptor (NMDAR) plays a key role in the central nervous system, e.g., for synaptic transmission. While synaptic NMDARs are thought to have protective characteristics, activation of extrasynaptic NMDARs might trigger excitotoxic processes linked to neuropsychiatric disorders. Since extrasynaptic NMDARs are typically GluN2B-enriched, the subunit is an interesting target for drug development and treatment monitoring. Recently, the novel GluN2B-specific PET radioligand (R)-[Four healthy subjects (two females, two males) underwent one whole-body PET/MR measurement lasting for more than 120 min. The GluN2B-specific radioligand (R)-[Administration of the radioligand was safe without adverse events. The organs with the highest uptake were the urinary bladder, spleen and pancreas. Organ equivalent dose coefficients were higher in female in almost all organs, except for the urinary bladder of male. The effective dose coefficient was 6.0 µSv/MBq.The GluN2B-specific radioligand (R)-[

Background Measures to reduce the spread of the SARS-CoV-2 virus have an impact on the mental health of the general population. Drug prescription rates can be used as a surrogate marker to estimate help seeking and health parameters of a population. The aim of this study was to compare psychopharmacologic drug prescriptions in Austria from the start of the pandemic in 2020 over time and with the previous year and to investigate the impact of the COVID-19 lockdowns in 2020. Methods Data from the three largest public health insurances in Austria, covering over 98% of the general population, were analyzed. A total of 1,365,294 patients with a prescription of a psychopharmacologic drug in the months March to December in 2019 and 2020 were selected. Results There was no significant change in prescribed defined daily doses (DDDs) during the lockdowns. However, there was a stockpiling effect before and at the beginning of lockdown 1. The number of new patients initiating psychopharmacologic treatment was significantly reduced during lockdown 1 but not during lockdown 2. Conclusions The first COVID-19 lockdown in 2020 functioned as a barrier for new psychiatric patients seeking help, whereas the patients with ongoing treatments did not have significant problems. These results have to be taken into account for future planning, but follow-up studies are needed, as our results could be indicative of a change in the effect of the protective measures on the utilization of the healthcare system over time.

In Cyber-Physical Production System (CPPS) engineering, domain experts design assets like products, production processes, and resources. However, the representation of dependencies between assets in heterogeneous engineering artifacts, like system plans, models, and tool data, is insufficient to coordinate engineering activities, like changes to shared asset properties, as it increases the risk of unplanned rework and project delay. While Industry 4.0 (I4.0) assets, defined in RAMI 4.0, allow representing multi-disciplinary views on assets, it remains open how to leverage this capability to coordinate changes in CPPS engineering. In this paper, we introduce the Product-Process-Resource Asset Network (PAN) coordination artifact, a knowledge graph based on I4.0 assets. We argue that the PAN coordination artifact fosters an explicit representation of change dependencies and engineering knowledge, improving capabilities for coordinating changes efficiently. In a feasibility study, we investigate the coordination capabilities of a PAN that represents change dependencies on a typical robot work cell in automotive manufacturing. Results show that the PAN provides effective capabilities for identifying risky assets for re-validation after changes.

Background: Bright light therapy (BLT) has been used as a treatment for seasonal affective disorder (SAD) for over 30 years. This meta-analysis was aimed to assess the efficacy of BLT in the treatment of SAD in adults. Method: We performed a systematic literature search including randomized, single- or double-blind clinical trials investigating BLT (≥1,000 lx, light box or light visor) against dim light (≤400 lx) or sham/low-density negative ion generators as placebo. Only first-period data were used from crossover trials. The primary outcome was the post-treatment depression score measured by validated scales, and the secondary outcome was the rate of response to treatment. Results: A total of 19 studies finally met our predefined inclusion criteria. BLT was superior over placebo with a standardized mean difference of –0.37 (95% CI: –0.63 to –0.12) for depression ratings (18 studies, 610 patients) and a risk ratio of 1.42 (95% CI: 1.08–1.85) for response to active treatment (16 studies, 559 patients). We found no evidence for a publication bias, but moderate heterogeneity of the studies and a moderate-to-high risk of bias. Conclusions: BLT can be regarded as an effective treatment for SAD, but the available evidence stems from methodologically heterogeneous studies with small-to-medium sample sizes, necessitating larger high-quality clinical trials.

The testing of automation applications has become a crucial pillar of every production systems engineering (PSE) project with the proliferation of cyber-physical systems (CPSs). In light of new attack vectors against CPSs, caused, inter alia, by increased connectivity, security aspects must be considered throughout the PSE process. In this context, software testing represents a critical activity, as a lack of adequate security mechanisms puts a variety of valuable assets (e.g., system configurations and production details) at risk of information theft and sabotage. Thus, organizations must analyze the security of their software testing process on a regular basis in order to counter these threats. Yet, due to the required security knowledge or budget constraints for security-related expenses, these undertakings may be destined to fail. In this work, we present a framework that supports the semi-automated security analysis of an organization’s software testing process for industrial automation software. This framework is based on the VDI/VDE 2182 guideline and integrates an ontological approach to model the necessary background knowledge, including, e.g., data flows, assets, entities, threats, and countermeasures. The framework comprises a default model of the testing process, which users can adapt so that the target of inspection accurately reflects their software testing environment. In particular, the testing process considered for creating the default model is based on best practices observed at a major system integrator, aligned with the ISO/IEC/IEEE 29119 series of software testing standards. Moreover, we developed a tool that enables the automatic generation of attack–defense trees from such formal models of the organization’s software testing process. We demonstrate how the proposed framework can be applied to a generic software testing process to answer essential questions in conducting a security risk analysis. The results of the exemplary security analysis provide guidance, should raise awareness in the industrial domain, and support effective, yet cost- and time-efficient security analyses. Finally, we evaluate the presented framework by performing a comprehensive comparison of suitable security analysis tools.

The aim of the present study was to estimate the number of patients with a seasonal prescription pattern of antidepressants, which might be taken as a surrogate marker for medicated patients with seasonal affective disorder (SAD). Furthermore, we examined the time course of sick leaves for patients with seasonal and non-seasonal prescriptions of antidepressants. A retrospective analysis of prescription data of all patients insured by the Sickness Fund Burgenland (BGKK) between 2005 and 2016 was performed. Patients with treatment initiation of an antidepressant in the last and first quarter of the year for at least two consecutive years were selected (SAD-med). Patients with continuation treatment in the third quarter and patients with initiation of antidepressant medication in the second and third quarter of the year were excluded. The mean yearly prescription rate for antidepressants was 9.6% in the insured population. 3.0% of patients treated with antidepressants and 0.9% of insured cases satisfied the definition of SAD-med. The mean number of yearly sick leave days was similar for SAD-med patients and those with non-seasonal prescriptions. Time series analysis showed that sick leaves in SAD-med were influenced by seasonal fluctuations for several years after the first antidepressant prescription. Our study sheds light on antidepressant prescription and sick leave patterns in the general population. Compared to the prevalence of SAD, the estimated rate of SAD-med is substantial. Sick leaves appear to be closely linked to antidepressant prescriptions, and show a characteristic time course before and after the initial prescription.

During engineering and operation of flexible robot-based production systems meeting the Industry 4.0 (I40) paradigm, engineers require guidance to analyze and resolve issues that may disturb the production process. Challenging issues stem from causes in several heterogeneous engineering disciplines. Unfortunately, current risk mitigation guidelines frequently focus on isolated components and not on risks of the entire system. This fragmented guidance is hard to apply for engineers who are not aware of existing dependencies between components of different types. Therefore, risks in the engineering and operation of I40 components are hard to identify and mitigate. In this paper, we propose the Industry 4.0 Asset-based Risk Mitigation (I4ARM) approach, providing knowledge for efficient root cause analysis to non-experts based on (a) a minimal model for knowledge representation as an I40 asset network with cause-effect annotations and (b) the I4ARM method for model building and risk mitigation with structured guidance. We build on the I40 asset network concept, cause-effect analysis, and decision trees to enable efficient and effective risk mitigation with structured guidance. I4ARM facilitates for engineers (a) defining an Industry 4.0 asset network and relationships, (b) identifying risks, and (c) supporting risk mitigation. We conceptually evaluate I4ARM for a real-world I40 use case. The results showed that the I40 Asset Network with cause-effect relationships and decision trees is usable and useful both for experienced and novice engineers to efficiently and systematically mitigate risks in I40 environments.

An essential core function of one's cognitive flexibility is the use of acquired knowledge and skills to adapt to ongoing environmental changes. Animal models have highlighted the influence serotonin has on neuroplasticity. These effects have been predominantly demonstrated during emotional relearning which is theorized as a possible model for depression. However, translation of these mechanisms is in its infancy. To this end, we assessed changes in effective connectivity at rest and during associative learning as a proxy of neuroplastic changes in healthy volunteers. 76 participants underwent 6 weeks of emotional or non-emotional (re)learning (face-matching or Chinese character–German noun matching). During relearning participants either self-administered 10 mg/day of the selective serotonin reuptake inhibitor (SSRI) escitalopram or placebo in a double-blind design. Associative learning tasks, resting-state and structural images were recorded before and after both learning phases (day 1, 21 and 42). Escitalopram intake modulated relearning changes in a network encompassing the right insula, anterior cingulate cortex and right angular gyrus. Here, the process of relearning during SSRI intake showed a greater decrease in effective connectivity from the right insula to both the anterior cingulate cortex and right angular gyrus, with increases in the opposite direction when compared to placebo. In contrast, intrinsic connections and those at resting-state were only marginally affected by escitalopram. Further investigation of gray matter volume changes in these functionally active regions revealed no significant SSRI-induced structural changes. These findings indicate that the right insula plays a central role in the process of relearning and SSRIs further potentiate this effect. In sum, we demonstrated that SSRIs amplify learning-induced effective connections rather than affecting the intrinsic task connectivity or that of resting-state.

BackgroundNeuroplastic processes are influenced by selective serotonergic reuptake inhibitors, while learning in conjunction with the administration of serotonergic agents alters white matter microstructure in humans. The goal of this double-blind, placebo-controlled imaging study was to investigate the influence of escitalopram on white matter plasticity during (re)learning.MethodsSeventy-one healthy individuals (age = 25.6±5.0, 43 females) underwent 3 diffusion magnetic resonance imaging sessions: at baseline, after 3-weeks of associative learning (emotional/non-emotional content) and after relearning shuffled associations for an additional 3 weeks. During the relearning phase, subjects received daily escitalopram 10 mg or placebo orally. Statistical analysis was performed with statistical parametric mapping (SPM) and using sandwich estimator.ResultsA three-way and two-way rmANOVA was conducted to analyze the effects of escitalopram on AD, FA, MD and RD during the learning and relearning period. We found no significant three-way or two-way interactions for each DTI metrics (pFDR > 0.05), thus neither after 3 nor after 6 weeks we found significant changes in white matter microstructure.ConclusionWe examined neither an effect of escitalopram nor learning (or relearning) interventions on different DTI metrics. The duration and intensity of study interventions (i.e. administration of escitalopram and learning as the relearning task) might have been chosen insufficiently to induce detectable alterations. Previous studies examining the effects of SSRIs on white matter tracts in humans are underrepresented, but do mainly yield towards non-significant findings. The results implicate that escitalopram does not impact white matter microstructures in healthy subjects.

Background Seasonal affective disorder (SAD) is a seasonal pattern of recurrent depressive episodes that is often treated with second-generation antidepressants (SGAs), light therapy, or psychotherapy. Objectives To assess the efficacy and safety of second-generation antidepressants (SGAs) for the treatment of seasonal affective disorder (SAD) in adults in comparison with placebo, light therapy, other SGAs, or psychotherapy. Search methods This is an update of an earlier review first published in 2011. We searched the Cochrane Central Register of Controlled Trials (CENTRAL; 2020, Issue 1) in the Cochrane Library (all years), Ovid MEDLINE, Embase, and PsycINFO (2011 to January 2020), together with the Cochrane Common Mental Disorders Controlled Trials Register (CCMDCTR) (all available years), for reports of randomised controlled trials (RCTs). We hand searched the reference lists of all included studies and other systematic reviews. We searched ClinicalTrials.gov for unpublished/ongoing trials. We ran a separate update search for reports of adverse events in the Ovid databases. SELECTION CRITERIA: For efficacy we included RCTs of SGAs compared with other SGAs, placebo, light therapy, or psychotherapy in adult participants with SAD. For adverse events we also included non-randomised studies. Data collection and analysis Two review authors independently screened abstracts and full-text publications against the inclusion criteria. Data extraction and 'Risk of bias' assessment were conducted individually. We pooled data for meta-analysis where the participant groups were similar, and the studies assessed the same treatments with the same comparator and had similar definitions of outcome measures over a similar duration of treatment. Main results In this update we identified no new RCT on the effectiveness of SGAs in SAD patients. We included 2 additional single-arm observational studies that reported on adverse events of SGAs. For efficacy we included three RCTs of between five and eight weeks' duration with a total of 204 participants. For adverse events we included two RCTs and five observational (non-randomised) studies of five to eight weeks' duration with a total of 249 participants. All participants met the DSM (Diagnostic and Statistical Manual of Mental Disorders) criteria for SAD. The average age ranged from 34 to 42 years, and the majority of participants were female (66% to 100%). Results from one trial with 68 participants showed that fluoxetine (20/36) was numerically superior to placebo (11/32) in achieving clinical response; however, the confidence interval (CI) included both a potential benefit as well as no benefit of fluoxetine (risk ratio (RR) 1.62, 95% CI 0.92 to 2.83, very low-certainty evidence). The number of adverse events was similar in both groups (very low-certainty evidence). Two trials involving a total of 136 participants compared fluoxetine versus light therapy. Meta-analysis showed fluoxetine and light therapy to be approximately equal in treating seasonal depression: RR of response 0.98 (95% CI 0.77 to 1.24, low-certainty evidence), RR of remission 0.81 (95% CI 0.39 to 1.71, very low-certainty evidence). The number of adverse events was similar in both groups (low-certainty evidence). We did not identify any eligible study comparing SGA with another SGA or with psychotherapy. Two RCTs and five non-randomised studies reported adverse event data on a total of 249 participants who received bupropion, fluoxetine, escitalopram, duloxetine, nefazodone, reboxetine, light therapy, or placebo. We were only able to obtain crude rates of adverse events, therefore caution is advised regarding interpretation of this information. Between 0% and 100% of participants who received an SGA suffered an adverse event, and between 0% and 25% of participants withdrew from the study due to adverse events. Authors' conclusions Evidence for the effectiveness of SGAs is limited to one small trial of fluoxetine compared with placebo showing a non-significant effect in favour of fluoxetine, and two small trials comparing fluoxetine against light therapy suggesting equivalence between the two interventions. The lack of available evidence precluded us from drawing any overall conclusions on the use of SGAs for SAD. Further, larger RCTs are required to expand and strengthen the evidence base on this topic, and should also include comparisons with psychotherapy and other SGAs. Data on adverse events were sparse, and a comparative analysis was not possible. The data we obtained on adverse events is therefore not robust, and our confidence in the data is limited. Overall, up to 25% of participants treated with SGAs for SAD withdrew from the study early due to adverse events.

The regulation of hydro power plants is influenced by amongst others reservoir capacity and the knowledge about how it is influenced by changes in in- and out?ow. The hydro power reservoir Aurdalsfjord has a small capacity compared to the inflow, a narrowing between inflow in the north and outflow in the south and has implemented strict governmental restriction. The paper will present a reservoir model that was developed in order to investigate the water ?ow and water level conditions in Aurdalsfjord. The water level in each cell is described with a mass balance, while the flow between two cells is described with a momentum balance. The balance equations are formulated as DAEs. The model is developed in Modelica which allows it to be used in connection with models of a wide variety of other components that are present in a hydro power system. The model will be integrated in the open-source hydro power library OpenHPL developed at USN. The Modelica model shows promising results. For Aurdalsfjord it needed to be adjusted with non-physical parameters. The reservoir model should be implemented for a less complex shaped reservoir in order to see if the challenges are related to the Modelica implementation or Aurdalsfjords shape.

Industrially relevant syngas (15 % CO, 15% H

Together with many success stories, promises such as the increase in production speed and the improvement in stakeholders' collaboration have contributed to making agile a transformation in the software industry in which many companies want to take part. However, driven either by a natural and expected evolution or by contextual factors that challenge the adoption of agile methods as prescribed by their creator(s), software processes in practice mutate into hybrids over time. Are these still agile? In this article, we investigate the question: what makes a software development method agile? We present an empirical study grounded in a large-scale international survey that aims to identify software development methods and practices that improve or tame agility. Based on 556 data points, we analyze the perceived degree of agility in the implementation of standard project disciplines and its relation to used development methods and practices. Our findings suggest that only a small number of participants operate their projects in a purely traditional or agile manner (under 15%). That said, most project disciplines and most practices show a clear trend towards increasing degrees of agility. Compared to the methods used to develop software, the selection of practices has a stronger effect on the degree of agility of a given discipline. Finally, there are no methods or practices that explicitly guarantee or prevent agility. We conclude that agility cannot be defined solely at the process level. Additional factors need to be taken into account when trying to implement or improve agility in a software company. Finally, we discuss the field of software process-related research in the light of our findings and present a roadmap for future research., Comment: Journal paper, 17 pages, 14 figures

Background. In Production Systems Engineering (PSE) models, which describe plants, represent different views on several engineering disciplines (such as mechanical, electrical and software engineering) and may contain up to 10,000s of instance elements, such as concepts, attributes and relationships. Validating these models requires an integrated multi-model view and the domain expertise of human experts related to individual views. Unfortunately, the heterogeneity of disciplines, tools, and data formats makes it hard to provide a technology-independent multi-model view. Aim. In this paper, we aim at improving Multi-Model Reviewing (MMR) capabilities of domain experts based on selected model visualisation methods and mechanisms. Method. We (a) derive requirements for graph-based visualisation to facilitate reviewing multi-disciplinary models; (b) introduce the MMR approach to visualise engineering models for review as hierarchical and linked structures; (c) design an MMR software prototype; and (d) evaluate the prototype based on tasks derived from real-world PSE use cases. For evaluation purposes we compare capabilities of the MMR prototype and a text-based model editor. Results. The MMR prototype enabled performing the evaluation tasks in most cases considerable faster than the standard text-based model editor. Conclusion. The promising results of the MMR approach in the evaluation context warrant empirical studies with a wider range of domain experts and use cases on the usability and usefulness of the MMR approach in practice.

Animal studies using selective serotonin reuptake inhibitors (SSRIs) and learning paradigms have demonstrated that serotonin is important for flexibility in executive functions and learning. SSRIs might facilitate relearning through neuroplastic processes and thus exert their clinical effects in psychiatric diseases where cognitive functioning is affected. However, translation of these mechanisms to humans is missing. In this randomized placebo-controlled trial, we assessed functional brain activation during learning and memory retrieval in healthy volunteers performing associative learning tasks aiming to translate facilitated relearning by SSRIs. To this extent, seventy-six participants underwent three MRI scanning sessions: (1) at baseline, (2) after three weeks of daily associative learning and subsequent retrieval (face-matching or Chinese character–noun matching) and (3) after three weeks of relearning under escitalopram (10 mg/day) or placebo. Associative learning and retrieval tasks were performed during each functional MRI (fMRI) session. Statistical modeling was done using a repeated-measures ANOVA, to test for content-by-treatment-by-time interaction effects. During the learning task, a significant substance-by-time interaction was found in the right insula showing a greater deactivation in the SSRI cohort after 21 days of relearning compared to the learning phase. In the retrieval task, there was a significant content-by-time interaction in the left angular gyrus (AG) with an increased activation in face-matching compared to Chinese-character matching for both learning and relearning phases. A further substance-by-time interaction was found in task performance after 21 days of relearning, indicating a greater decrease of performance in the placebo group. Our findings that escitalopram modulate insula activation demonstrates successful translation of relearning as a mechanism of SSRIs in human. Furthermore, we show that the left AG is an active component of correct memory retrieval, which coincides with previous literature. We extend the function of this region by demonstrating its activation is not only stimulus dependent but also time constrained. Finally, we were able to show that escitalopram aids in relearning, irrespective of content.

Production Systems Engineering (PSE) networks create engineering results represented by interdependent engineering data models. A key aspect of engineering result quality is the consistency achieved in and among the discipline-specific data models. However, the heterogeneity of discipline-specific data models makes it hard to represent and evaluate consistency across engineering disciplines. In this paper, we introduce PSE-MECon - a method for Modelling and Evaluating Consistency rules in PSE environments across disciplines. We describe a meta-model for representing consistency rules in a multi-disciplinary data model and demonstrate the viability of the meta-model by representing dependencies in AutomationML as a foundation for consistency checks for engineering data logistics in multi-disciplinary engineering networks.

In Cyber-Physical Production System (CPPS) engineering for discrete manufacturing, the definition of test cases is vital to ensure correct behavior of production processes and to test risky cases. Unfortunately, the definition of test cases requires know-how both from the CPPS engineering domain and on software test automation, and is time-consuming. In this paper, we investigate how domain experts can efficiently derive test cases for an assembly process step from process preconditions concerning product properties. We introduce the Test Case Derivation for PPR Models (TCD4PPR) method building on the Formalised Process Description and best practices from software testing. We evaluate the TCD4PPR method with an illustrative use case from industry in a feasibility study with domain experts at a large production systems engineering company for discrete manufacturing. The main result was that the domain experts found the TCD4PPR method efficient, usable, and useful. The evaluation results indicate that investing reasonable effort into modeling Product, Process, Resource (PPR) knowledge with preconditions can considerably reduce risks of untested production process behavior.

Das privat geführte Zirkusarchiv Winkler in Berlin gilt als die derzeit größte Sammlung von Printmaterialien zu Artistik und Schaustellerwesen im deutschsprachigen Raum. Das Ehepaar Winkler sieht die Aufgabe des Archivs darin, diesen Bereich der Kulturgeschichte mit Dokumenten aus dessen Geschichte und Gegenwart zu bewahren und für die Forschung und künstlerische Auseinandersetzung bereit zu stellen. Neben eigenen Publikationen werden wissenschaftliche Arbeiten von Studierenden und Forschern unterstützt sowie künstlerische Produktionen und Ausstellungen.

Due to the increasing use of renewable energy sources, and to counter the effects of fossil fuels, renewable dispatchable hydro power can be used for balancing load and generation from intermittent sources (solar and wind). During higher percentage change in load acceptance or rejection in the intermittent grid, the operations of surge tanks are crucial in terms of water mass oscillation and water hammer pressure, and to avoid wear and tear in actuators and other equipment, such as hydro turbines. Surge tanks are broadly classified as open types, with access to open air, and closed types, with a closed volume of pressurized air. Closed surge tanks are considered to have a more flexible operation in terms of suppressing water mass oscillation and water hammer pressure. In this paper, a mechanistic model of an air cushion surge tank (ACST) for hydro power plants is developed based on the ordinary differential equations (ODEs) for mass and momentum balances. The developed mechanistic model of the ACST is a feature extension to an existing open-source hydro power library—OpenHPL. The developed model is validated with experimental data from the Torpa hydro power plant (HPP) in Norway. Results show that the air friction inside the ACST is negligible as compared to the water friction. The results also indicate that a hydro power plant with an ACST is a potential candidate as a flexible hydro power in an interconnected power system grid supplied with intermittent energy sources. Conclusions are drawn based on the simulation results from hydraulic performance of the ACST.

Production system operators need support for collecting and pre-processing data on production systems consisting of several system components, as foundation for optimization and defect detection. Traditional approaches based on hard-coded programming of such run-time data collection systems take time and effort, and require both domain and technology knowledge. In this article, we introduce the AML-RTDC approach, which combines the strengths of AutomationML (AML) data modeling and model-driven engineering, to reduce the manual effort for realizing the run-time data collection (RTDC) system. We evaluate the feasibility of the AML-RTDC approach with a demonstration case about a lab-sized production system and a use case based on real-world requirements.

Conceptual domain models, such as taxonomies, knowledge graphs or Extended Entity Relationship (EER) diagrams are core to all information systems. The task of verifying the correctness of these models is of high interest to the knowledge and software engineering communities and attracted the first solution approaches using human computation. Yet, since these solutions are published within the boundaries of their communities, there is a lack of concerted work on this topic. As a first step to alleviate this status quo, we formalize the problem of verifying conceptual models and propose a generic approach (VeriCoM) to solve it with human computation techniques. We show how VeriCoM was applied in a software engineering use case focusing on verifying the correctness of an EER diagram against a system specification document. An evaluation of VeriCoM in a series of four workshops within one controlled experiment performed with a crowd of semi-experts lead to the identification of a set of defects with precision of 73% and a recall from a Gold Standard defect set of 63%.

Objective: Therapeutic sleep deprivation (SD) is a nonpharmacological treatment that is used most often for depression. The aim of this study was to examine the pattern of use of SD in psychiatric ...

Background Comprehensive description of ketamine’s molecular binding profile becomes increasingly pressing as use in real-life patient cohorts widens. Animal studies attribute a significant role in the substance’s antidepressant effects to the serotonergic system. The serotonin transporter is a highly relevant target in this context, because it is central to depressive pathophysiology and treatment. This is, to our knowledge, the first study investigating ketamine’s serotonin transporter binding in vivo in humans. Methods Twelve healthy subjects were assessed twice using [11C]DASB positron emission tomography. A total of 0.50 mg/kg bodyweight ketamine was administered once i.v. prior to the second positron emission tomography scan. Ketamine plasma levels were determined during positron emission tomography. Serotonin transporter nondisplaceable binding potential was computed using a reference region model, and occupancy was calculated for 4 serotonin transporter-rich regions (caudate, putamen, thalamus, midbrain) and a whole-brain region of interest. Results After administration of the routine antidepressant dose, ketamine showed

Empirical software engineering (ESE) focuses on gathering evidence through measurements and experiments involving humans and software systems (software products, processes, and resources). While empirical studies often include considerable human effort for study planning, execution, and data analysis, human computation (HC) methods, such as crowdsourcing, are increasingly used to address human input intensive tasks in software engineering and beyond. Therefore, in this chapter, we explore the use of HC techniques to support ESE experiments. We address researchers from both research communities and provide (1) introductory notions into both fields, (2) an analysis of ESE experiment requirements and HC capabilities that could match those, and (3) a concrete example of an ESE experiment that compares the effects of using HC in software inspection with respect to a traditional inspection process preformed using pen and paper. Our focus is on software inspection for detecting defects in software engineering models (namely, extended entity relationship models). This chapter will enable ESE researchers to apply HC in their work and HC researchers to explore ESE as a new application area to further improve their methods and tools.