H3N - Analysewerkzeuge für hybride Wegewahl in heterogenen, unterbrechungstoleranten Ad-Hoc-Netzen für Rettungskräfte

Rettungskräfte müssen unter widrigen Bedingungen zuverlässig kommunizieren können, um in Rettungseinsätzen effizient arbeiten zu können und somit Leben zu retten. Idealerweise ist dazu ein selbstorganisiertes Ad-Hoc-Netz notwendig, weil die Kommunikationsinfrastruktur ggf. beschädigt oder überlastet sein kann. Um die geforderte Robustheit der Kommunikation auch in Szenarien mit größeren zu überbrückenden Entfernungen zu gewährleisten, werden zusätzlich Mechanismen benötigt, die eine Unterbrechungstoleranz ermöglichen. Verzögerungstolerante Netze (engl. Delay Tolerant Networks, kurz: DTN) stellen solche Mechanismen bereit, erfordern aber zusätzliche Verzögerungen, die für Rettungskommunikation nachteilig sind. Deshalb werden intelligente hybride Wegewahlverfahren benötigt, um die Verzögerung durch DTN-Mechanismen zu begrenzen. Außerdem sollten entsprechende Verfahren heterogene Netze unterstützen. Das ermöglicht zusätzlich eine effizientere Weiterleitung durch die Nutzung von Geräten mit unterschiedlichen Kommunikationstechnologien und damit auch Reichweiten. Um solche Systeme und die dafür benötigten Kommunikationsprotokolle zu entwickeln, werden verschiedene Analysewerkzeuge genutzt. Dazu gehören analytische Modelle, Simulationen und Experimente auf der Zielsystemhardware. Für jede Kategorie gibt es verschiedene Werkzeuge und Frameworks, die sich auf unterschiedliche Aspekte fokussieren. Dadurch unterstützen diese herkömmlichen Analysemethoden jedoch meistens nur einen der oben genannten Punkte, während die Untersuchung von hybriden und/oder heterogenen Ansätzen und Szenarien nicht ohne weiteres möglich ist. Im Falle von Rettungskräften kommt hinzu, dass die charakteristischen Merkmale hinsichtlich der Bewegung der Knoten und des erzeugten Datenverkehrs während eines Einsatzes ebenfalls nicht modelliert werden können. In dieser Arbeit werden deshalb verschiedene Erweiterungen zu existierenden Analysewerkzeugen sowie neue Werkzeuge zur Analyse und Modelle zur Nachbildung realistischer Rettungsmissionen untersucht und entwickelt. Ziel ist es, die Vorteile existierender Werkzeuge miteinander zu kombinieren, um ganzheitliche, realitätsnahe Untersuchungen von hybriden Protokollen für heterogene Netze zu ermöglichen. Die Kombination erfolgt in Form von gezielten Erweiterungen und der Entwicklung ergänzender komplementärer Werkzeuge unter Verwendung existierender Schnittstellen. Erste Ergebnisse unter Verwendung der entwickelten Werkzeuge zeigen Verbesserungspotentiale bei der Verwendung traditioneller Protokolle und erlauben die Bewertung zusätzlicher Maßnahmen, um die Kommunikation zu verbessern. Szenarien zur Kommunikation von Rettungskräften werden dabei als ein Beispiel verwendet, die Tools sind jedoch nicht auf die Analyse dieses Anwendungsfalls beschränkt. Über die reine Analyse verschiedener existierender Ansätze hinaus bildet die entwickelte Evaluationsumgebung eine Grundlage für die Entwicklung und Verifikation von neuartigen hybriden Protokollen für die entsprechenden Systeme.Communication between participating first responders is essential for efficient coordination of rescue missions and thus allowing to save human lives. Ideally, ad hoc-style communication networks are applied to this as the first responders cannot rely on infrastructure-based communication for two reasons. First, the infrastructure could be damaged by the disastrous event or not be available for economic reasons. Second, even if public infrastructure is available and functional, it might be overloaded by users. To guarantee the robustness and reliability requirements of first responders, the Mobile Ad Hoc Networks (MANETs) have to be combined with an approach to mitigate intermittent connectivity due to otherwise limited connectivity. Delay Tolerant Networks (DTNs) provide such a functionality but introduce additional delay which is problematic. Therefore, intelligent hybrid routing approaches are required to limit the delay introduced by DTN mechanisms. Besides that, the approach should be applicable to heterogeneous networks in terms of communication technologies and device capabilities. This is required for cross multi-agency and volunteer communication but also enables the opportunistic exploitation of any given communication option. To evaluate such systems and develop the corresponding communication protocols, various tools for the analysis are available. This includes analytical models, simulations and real-world experiments on target hardware. In each category a wide set of tools is available already. However, each tool is focused on specific aspects usually and thus does not provide methods to analyze hybrid approaches out of the box. Even if the tools are modular and allow an extension, there are often other tools that are better suited for partial aspects of hybrid systems. In addition to this, few tools exist to model the characteristics of first responder networks. Especially the generalized movement during missions and the generated data traffic are difficult to model and integrate into analyses. The focus of this project is therefore to develop selected extensions to existing analysis and simulation tools as well as additional tools and models to realistically capture the characteristics of first responder networks. The goal is to combine the advantages of existing specialized simulation tools to enable thorough evaluations of hybrid protocols for heterogeneous networks based on realistic assumptions. To achieve this, the tools are extended by specifically designing tools that enable the interaction between tools and new tools that complement the existing analysis capabilities. First results obtained via the resulting toolbox clearly indicate further research directions as well as a potential for protocol enhancements. Besides that, the toolbox was used to evaluate various methods to enhance the connectivity between nodes in first responder networks. First responder scenarios are used as an example here. The toolbox itself is however not limited to this use case. In addition to the analysis of existing approaches for hybrid and heterogeneous networks, the developed toolbox provides a base framework for the development and verification of newly developed protocols for such use cases

Towards Robust Communications of Wireless Sensor Networks in Vehicular Environments: A Case Study

Wireless Sensor Networks are popular solutions for various applications in order to reduce the efforts required by traditional cable-based systems. If the system in question is used to monitor and control physical processes, robust and reliable communication services have to be provided by the network. This is especially true for industrial or vehicular applications where the nodes are deployed in environments facing harsh propagation conditions. Usually, the effect of this on the system performance is studied on isolated layers of the OSI-reference model only. Therefore, we present results from a measurement campaign of vehicular environments that try to estimate the achievable robustness with respect to multiple OSI layers. Our results show that even if the link is rather poor, protocols on the upper layers are able to compensate this at the cost of additional delay

Autopercepção corporal de praticantes de exercício físico em academias

A preocupação com a imagem corporal sofre forte influência de diversos fatores socioculturais, muitas vezes refletindo numa autoavaliação negativa em relação à própria imagem corporal. A constante idealização do corpo perfeito tem sido um dos principais motivos para a realização de exercícios físicos em academias, objetivando a redução do peso corporal acompanhado de aumento da massa muscular. Trata-se de uma revisão sobre a insatisfação com a imagem corporal, bem como, sobre o método utilizado para sua avaliação em praticantes de exercícios físicos em academias. A presente pesquisa foi realizada em bases de dados cientificas selecionando artigos nos idiomas português, inglês e espanhol, que avaliassem autopercepção corporal e exercícios físicos em praticantes de academia, entre os anos de 2008 a 2018. Dos 65 estudos selecionados, 55 artigos foram excluídos por não estarem de acordo com o objetivo do estudo, resultando em 10 artigos, que preencheram os critérios de inclusão e foram analisados na íntegra. A prevalência de insatisfação corporal encontrada entre homens e mulheres variou entre 7,8% a 98,8%, dentre os estudos avaliados, e os métodos utilizados para avaliar a percepção corporal, entre os artigos selecionados, foram o Body Shape Questionnare (BSQ), seguido da Escala de Stunkard. Apesar da grande variação, foi verificada uma insatisfação corporal entre praticantes de academia, de ambos os sexos, bastante expressiva. Entretanto, ressalta-se a importância de um instrumento ideal para avaliar a percepção corporal entre os praticantes de academias

In vivo hippocampal subfield volumes in bipolar disorder—A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = −0.20), cornu ammonis (CA)1 (d = −0.18), CA2/3 (d = −0.11), CA4 (d = −0.19), molecular layer (d = −0.21), granule cell layer of dentate gyrus (d = −0.21), hippocampal tail (d = −0.10), subiculum (d = −0.15), presubiculum (d = −0.18), and hippocampal amygdala transition area (d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

10Kin1day: A Bottom-Up Neuroimaging Initiative.

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain