Seismic risk in the city of Al Hoceima (north of Morocco) using the vulnerability index method, applied in Risk-UE project

The final publication is available at Springer via http://dx.doi.org/10.1007/s11069-016-2566-8Al Hoceima is one of the most seismic active regions in north of Morocco. It is demonstrated by the large seismic episodes reported in seismic catalogs and research studies. However, seismic risk is relatively high due to vulnerable buildings that are either old or don’t respect seismic standards. Our aim is to present a study about seismic risk and seismic scenarios for the city of Al Hoceima. The seismic vulnerability of the existing residential buildings was evaluated using the vulnerability index method (Risk-UE). It was chosen to be adapted and applied to the Moroccan constructions for its practicality and simple methodology. A visual inspection of 1102 buildings was carried out to assess the vulnerability factors. As for seismic hazard, it was evaluated in terms of macroseismic intensity for two scenarios (a deterministic and probabilistic scenario). The maps of seismic risk are represented by direct damage on buildings, damage to population and economic cost. According to the results, the main vulnerability index of the city is equal to 0.49 and the seismic risk is estimated as Slight (main damage grade equal to 0.9 for the deterministic scenario and 0.7 for the probabilistic scenario). However, Moderate to heavy damage is expected in areas located in the newer extensions, in both the east and west of the city. Important economic losses and damage to the population are expected in these areas as well. The maps elaborated can be a potential guide to the decision making in the field of seismic risk prevention and mitigation strategies in Al Hoceima.Peer ReviewedPostprint (author's final draft

Dynamic Ecology in GNU Octave

Ekin Akoglu is a marine biologist and has expertise in ecological modelling with emphasis on trophodynamic and end-to-end ecosystem models. He carries out research on the effects of climate change, trophic competition and fisheries on fish stocks and marine ecosystems. He is currently employed as an assistant professor in the Institute of Marine Sciences at Middle East Technical University, Turkey. Kevin J Flynn is a plankton physiologist who has combined laboratory and modelling studies in his teaching and research work over 4 decades. He has a particular interest in developing simulation models to guide experiment design and to enthuse the next generation of marine scientists in plankton dynamics and ecophysiology. He has authored, or co-authored, over 175 papers, and also authored the book Dynamic Ecology upon which this work was developed. He currently works at the Plymouth Marine Laboratory, UK

EwE-F 1.0: an implementation of Ecopath with Ecosim in Fortran 95/2003 for coupling and integration with other models

Abstract. Societal and scientific challenges foster the implementation of the ecosystem approach to marine ecosystem analysis and management, which is a comprehensive means of integrating the direct and indirect effects of multiple stressors on the different components of ecosystems, from physical to chemical and biological and from viruses to fishes and marine mammals. Ecopath with Ecosim (EwE) is a widely used software package, which offers capability for a dynamic description of the multiple interactions occurring within a food web, and, potentially, a crucial component of an integrated platform supporting the ecosystem approach. However, being written for the Microsoft .NET framework, seamless integration of this code with Fortran-based physical and/or biogeochemical oceanographic models is technically not straightforward. In this work we release a re-coding of EwE in Fortran (EwE-F). We believe that the availability of a Fortran version of EwE is an important step towards setting up coupled/integrated modelling schemes utilising this widely adopted software because it (i) increases portability of the EwE models and (ii) provides additional flexibility towards integrating EwE with Fortran-based modelling schemes. Furthermore, EwE-F might help modellers using the Fortran programming language to get close to the EwE approach. In the present work, first fundamentals of EwE-F are introduced, followed by validation of EwE-F against standard EwE utilising sample models. Afterwards, an end-to-end (E2E) ecological representation of the Gulf of Trieste (northern Adriatic Sea) ecosystem is presented as an example of online two-way coupling between an EwE-F food web model and a biogeochemical model. Finally, the possibilities that having EwE-F opens up are discussed

An exposure-effect approach for evaluating ecosystem-wide risks from human activities

Ecosystem-based management (EBM) is promoted as the solution for sustainable use. An ecosystem-wide assessment methodology is therefore required. In this paper, we present an approach to assess the risk to ecosystem components from human activities common to marine and coastal ecosystems. We build on: (i) a linkage framework that describes how human activities can impact the ecosystem through pressures, and (ii) a qualitative expert judgement assessment of impact chains describing the exposure and sensitivity of ecological components to those activities. Using case study examples applied at European regional sea scale, we evaluate the risk of an adverse ecological impact from current human activities to a suite of ecological components and, once impacted, the time required for recovery to pre-impact conditions should those activities subside. Grouping impact chains by sectors, pressure type, or ecological components enabled impact risks and recovery times to be identified, supporting resource managers in their efforts to prioritize threats for management, identify most at-risk components, and generate time frames for ecosystem recovery

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services

On defining rules for cancer data fabrication

Funding: This research is partially funded by the Data Lab, and the EU H2020 project Serums: Securing Medical Data in Smart Patient-Centric Healthcare Systems (grant 826278).Data is essential for machine learning projects, and data accuracy is crucial for being able to trust the results obtained from the associated machine learning models. Previously, we have developed machine learning models for predicting the treatment outcome for breast cancer patients that have undergone chemotherapy, and developed a monitoring system for their treatment timeline showing interactively the options and associated predictions. Available cancer datasets, such as the one used earlier, are often too small to obtain significant results, and make it difficult to explore ways to improve the predictive capability of the models further. In this paper, we explore an alternative to enhance our datasets through synthetic data generation. From our original dataset, we extract rules to generate fabricated data that capture the different characteristics inherent in the dataset. Additional rules can be used to capture general medical knowledge. We show how to formulate rules for our cancer treatment data, and use the IBM solver to obtain a corresponding synthetic dataset. We discuss challenges for future work.Postprin

Reducing Controversy by Connecting Opposing Views

Peer reviewe

Discovering Polarized Communities in Signed Networks

Signed networks contain edge annotations to indicate whether each interaction is friendly (positive edge) or antagonistic (negative edge). The model is simple but powerful and it can capture novel and interesting structural properties of real-world phenomena. The analysis of signed networks has many applications from modeling discussions in social media, to mining user reviews, and to recommending products in e-commerce sites. In this paper we consider the problem of discovering polarized communities in signed networks. In particular, we search for two communities (subsets of the network vertices) where within communities there are mostly positive edges while across communities there are mostly negative edges. We formulate this novel problem as a "discrete eigenvector" problem, which we show to be NP-hard. We then develop two intuitive spectral algorithms: one deterministic, and one randomized with quality guarantee $\sqrt{n}$ (where $n$ is the number of vertices in the graph), tight up to constant factors. We validate our algorithms against non-trivial baselines on real-world signed networks. Our experiments confirm that our algorithms produce higher quality solutions, are much faster and can scale to much larger networks than the baselines, and are able to detect ground-truth polarized communities

Comparative Prognostic Accuracy of Clinical and Inflammation- or Nutrition-Based Scores in Older Adults with Community-Acquired Pneumonia

Merve Eksioglu,1 Burcu Azapoglu Kaymak,1 Ebru Unal Akoglu,1 Selman Faruk Akyıldız,1 Ramazan Sivil,2 Tuba Cimilli Ozturk1 1Department of Emergency Medicine, University of Health Sciences, Fatih Sultan Mehmet Education and Research Hospital, Istanbul, Turkey; 2Department of Emergency Medicine, University of Health Sciences, Antalya Education and Research Hospital, Antalya, TurkeyCorrespondence: Merve Eksioglu, Department of Emergency Medicine, University of Health Sciences, Fatih Sultan Mehmet Education and Research Hospital, Hastane Sokak No: 1/9 İçerenköy, Ataşehir, Istanbul, 34752, Turkey, Tel +90 216 578 30 00; +90 505 295 36 87, Email [email protected]: This study aimed to assess the prognostic accuracy of the Glasgow Prognostic Score (GPS), modified Glasgow Prognostic Score (mGPS), and C-reactive protein/albumin ratio (CAR) in predicting 30-day mortality and intensive care unit (ICU) admission compared with the Pneumonia Severity Index (PSI) and CURB-65 in older adults with community-acquired pneumonia (CAP).Patients and Methods: This retrospective, single-center cohort study was conducted in a tertiary emergency department. Patients aged ≥ 65 years with CAP were included. Exclusion criteria were hospital- or ventilator-associated pneumonia, pneumonia mimics, and immunocompromised status. GPS and mGPS were calculated using CRP > 10 mg/L and albumin < 35 g/L. ROC and logistic regression analyses were performed.Results: A total of 349 patients (mean age: 77.96 ± 8.42 years; 52.7% men) were included. The 30-day mortality and ICU admission rates were 19.5% and 27.2%, respectively. For predicting mortality, the GPS showed an AUC of 0.753 (95% CI: 0.690– 0.816), sensitivity of 75.0%, specificity of 73.3%, PPV of 43.9%, and NPV of 92.4%. mGPS had an AUC of 0.747 (95% CI: 0.679– 0.814), sensitivity 77.9%, specificity 73.3%, PPV 45.2%, and NPV 93.2%. The CAR yielded an AUC of 0.677 (95% CI: 0.604– 0.751), sensitivity of 82.4%, specificity of 45.6%, PPV of 29.5%, and NPV of 91.4%. For ICU admission, the AUCs were 0.770 (GPS), 0.757 (mGPS), and 0.676 (CAR). The PSI demonstrated the highest predictive accuracy (AUC: 0.884 for mortality, 0.919 for ICU admission), followed by CURB-65 (AUC: 0.848 and 0.879, respectively). Independent predictors of 30-day mortality included acute confusion, lower PaO2/FiO2 ratio, low systolic blood pressure, reduced hemoglobin levels, and Alzheimer’s disease or dementia.Conclusion: The PSI and CURB-65 demonstrated superior prognostic accuracy. GPS and mGPS showed moderate performance, whereas CAR exhibited the lowest overall discriminative ability for both outcomes.Keywords: geriatric emergency care, community-acquired pneumonia, prognostic scores, pneumonia severity index, PSI, glasgow prognostic score, GPS, C-reactive protein to albumin ratio, CA