Eco-sectarianism: From ecological disasters to sectarian violence in Syria

YesThis study introduces ‘eco-sectarianism’, which is a new concept that explains the relationship between sectarian violence and environmental pressures in divided societies in the Middle East. Against the backdrop of climate change, ‘eco-sectarianism’ poses a challenge to many fragmented and unequal societies where the sense of national consciousness is weak and nation-building projects are incomplete. This paper draws attention to the links between politicisation of sub-national identities and emerging ecological challenges in Syria

a New Scientific Paradigm of Information and Knowledge Development in National Statistical Systems

Ashofteh, A., & Bravo, J. M. (2021). Data Science Training for Official Statistics: a New Scientific Paradigm of Information and Knowledge Development in National Statistical Systems. Statistical Journal of the IAOS, 37(3), 771 – 789. https://doi.org/10.3233/SJI-210841The ability to incorporate new and Big Data sources and to benefit from emerging technologies such as Web Technologies, Remote Data Collection methods, User Experience Platforms, and Trusted Smart Statistics will become increasingly important in producing and disseminating official statistics. The skills and competencies required to automate, analyse, and optimize such complex systems are often not part of the traditional skill set of most National Statistical Offices. The adoption of these technologies requires new knowledge, methodologies and the upgrading of the quality assurance framework, technology, security, privacy, and legal matters. However, there are methodological challenges and discussions among scholars about the diverse methodical confinement and the wide array of skills and competencies considered relevant for those working with big data at NSOs. This paper develops a Data Science Model for Official Statistics (DSMOS), graphically summarizing the role of data science in statistical business processes. The model combines data science, existing scientific paradigms, and trusted smart statistics, and develops around a restricted number of constructs. We considered a combination of statistical engineering, data engineering, data analysis, software engineering and soft skills such as statistical thinking, statistical literacy and specific knowledge of official statistics and dissemination of official statistics products as key requirements of data science in official statistics. We then analyse and discuss the educational requirements of the proposed model, clarifying their contribution, interactions, and current and future importance in official statistics. The DSMOS was validated through a quantitative method, using a survey addressed to experts working at the European statistical systems. The empirical results show that the core competencies considered relevant for the DSMOS include acquisition and processing capabilities related to Statistics, high-frequency data, spatial data, Big Data, and microdata/nano-data, in addition to problem-solving skills, Spatio-temporal modelling, machine learning, programming with R and SAS software, Data visualisation using novel technologies, Data and statistical literacy, Ethics in Official Statistics, New data methodologies, New data quality tools, standards and frameworks for official statistics. Some disadvantages and vulnerabilities are also addressed in the paper.publishersversionpublishe

A study on the quality of novel coronavirus (COVID-19) official datasets

Ashofteh, A., & Bravo, J. M. (2020). A study on the quality of novel coronavirus (COVID-19) official datasets. Statistical Journal of the IAOS, 36(2), 291-301. https://doi.org/10.3233/SJI-200674Policy makers depend on complex epidemiological models that are compelled to be robust, realistic, defendable and consistent with all relevant available data disclosed by official authorities which is deemed to have the highest quality standards. This paper analyses and compares the quality of official datasets available for COVID-19. We used comparative statistical analysis to evaluate the accuracy of data collection by a national (Chinese Center for Disease Control and Prevention) and two international (World Health Organization; European Centre for Disease Prevention and Control) organisations based on the value of systematic measurement errors. We combined excel files, text mining techniques and manual data entries to extract the COVID-19 data from official reports and to generate an accurate profile for comparisons. The findings show noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories, raising data comparability concerns and pointing to the need for better coordination and harmonized statistical methods. The study offers a COVID-19 combined dataset and dashboard with minimum systematic measurement errors, and valuable insights into the potential problems in using databanks without carefully examining the metadata and additional documentation that describe the overall context of data.publishersversionpublishe

Ensemble Methods for Consumer Price Inflation Forecasting

Inflation forecasting is one of the central issues in micro and macroeconomics. Standard forecasting methods tend to follow a winner-take-all approach by which, for each time series, a single believed to be the best method is chosen from a pool of competing models. This paper investigates the predictive accuracy of a metalearning strategy called Arbitrated Dynamic Ensemble (ADE) in inflation forecasting using United States data. The findings show that: i) the SARIMA model exhibits the best average rank relative to ADE and competing state-of-the-art model combination and metalearning methods; ii) the ADE methodology presents a better average rank compared to widely used model combination approaches, including the original Arbitrating approach, Stacking, Simple averaging, Fixed Share, or weighted adaptive combination of experts; iii) the ADE approach benefits from combining the base-learners as opposed to selecting the best forecasting model or using all experts; iv) the method is sensitive to the aggregation (weighting) mechanism

A Non-Parametric-Based Computationally Efficient Approach for Credit Scoring

This research aimed at the case of credit scoring in risk management and presented the novel method for credit scoring to be used for default prediction. This study uses Kruskal-Wallis non-parametric statistic to form a computationally efficient credit-scoring model based on artificial neural network to study the impact on modelling performance. The findings show that new credit scoring methodology represents reasonable coefficient of determination and low false negative rate. It is computationally less expensive with high accuracy (AUC=0.99). Because of the recent respective of continued credit/behavior scoring, our study suggests to use this credit score for non-traditional data sources such as mobile phone data to study and reveal changes of client’s behavior during the time. This is the first study that develops a non-parametric credit scoring, which is able to reselect effective features for continued credit evaluation and weighted out by their level of contribution with a good diagnostic ability

A non-parametric-based computationally efficient approach for credit scoring

Ashofteh, A., & Bravo, J. M. (2019). A non-parametric-based computationally efficient approach for credit scoring. In Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao 2019: 19ª Conferencia da Associacao Portuguesa de Sistemas de Informacao, CAPSI 2019 - 19th Conference of the Portuguese Association for Information Systems, CAPSI 2019; Lisboa; Portugal; 11 October 2019 through 12 October 2019 (pp. 19). (Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao).This research aimed at the case of credit scoring in risk management and presented the novel method for credit scoring to be used for default prediction. This study uses Kruskal-Wallis non-parametric statistic to form a computationally efficient credit-scoring model based on artificial neural network to study the impact on modelling performance. The findings show that new credit scoring methodology represents reasonable coefficient of determination and low false negative rate. It is computationally less expensive with high accuracy (AUC=0.99). Because of the recent respective of continued credit/behavior scoring, our study suggests to use this credit score for non-traditional data sources such as mobile phone data to study and reveal changes of client’s behavior during the time. This is the first study that develops a non-parametric credit scoring, which is able to reselect effective features for continued credit evaluation and weighted out by their level of contribution with a good diagnostic ability.publishersversionpublishe

Magnetostatic waves in metallic rectangular waveguides filled with uniaxial negative permeability media

The propagation characteristics of magneto-quasistatic waves, more commonly, known as magnetostatic waves in a long, metallic rectangular waveguide filled with a metamaterial slab are comprehensively investigated. The metamaterial slab consists of split-ring resonators as an anisotropic uniaxial medium with transversal negative effective permeability. Some analytical relations and numerical validations on the characteristics of these waves are presented. The results include the dispersion relations, mode patterns (field distributions) that can be supported by such media and their corresponding cutoff frequencies, group velocities, power flows, and storage energies of magnetostatic waves. The findings from the present research study can be advantageous to advance the synthesis and development of negative permeability materials with peculiar features in guiding structures.Comment: 9 pages, 6 figure

A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic

Forecasting model selection and model combination are the two contending approaches in the time series forecasting literature. Ensemble learning is useful for addressing a given predictive task by different predictive models when direct mapping from inputs to outputs is inaccurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, we build each model with a specific holdout and make the ensemble model of time series with a dynamic selection approach. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series of reported respiratory disease deaths to show the amount of improvement in predictive performance of excess mortality. Then we compare the forecasting outcome of our model with the corresponding total deaths of COVID-19 for selected countries

Multi-partite entanglement and quantum phase transition in the one-, two-, and three-dimensional transverse field Ising model

In this paper we consider the quantum phase transition in the Ising model in the presence of a transverse field in one, two and three dimensions from a multi-partite entanglement point of view. Using \emph{exact} numerical solutions, we are able to study such systems up to 25 qubits. The Meyer-Wallach measure of global entanglement is used to study the critical behavior of this model. The transition we consider is between a symmetric GHZ-like state to a paramagnetic product-state. We find that global entanglement serves as a good indicator of quantum phase transition with interesting scaling behavior. We use finite-size scaling to extract the critical point as well as some critical exponents for the one and two dimensional models. Our results indicate that such multi-partite measure of global entanglement shows universal features regardless of dimension $d$. Our results also provides evidence that multi-partite entanglement is better suited for the study of quantum phase transitions than the much studied bi-partite measures.Comment: 7 pages, 8 Figures. To appear in Physical Review