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Global Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds., Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347

International audience; The effectiveness factor, E f, (fraction of the electrode surface that participates effectively in the investigated reaction) of fast (Fe3+/Fe2+), moderate (Cl2/Cl−) and slow (O2/H2O) redox couples has been estimated using IrO2-based electrodes with different loading. The method of choice was linear sweep voltammetry (measurement of the anodic peak current) for the Fe3+/Fe2+ redox couple and steady-state polarization (determination of the exchange current) for the O2 and Cl2 evolution reactions. The results have shown that the effectiveness factor depends strongly on the kinetics of the investigated redox reaction. For the Fe3+/Fe2+ redox couple, effectiveness factors close to zero (max 4%) have been obtained contrary to the O2 evolution reaction where effectiveness factors close to 100% can be achieved, all being independent of IrO2 loading. For the Cl2 evolution reaction, intermediate values of the effectiveness factor have been found and they decrease strongly, from 100% down to about 60%, with increasing loading.

Summarization: The aim of this special issue on “Regression Methods Based on OR Techniques and Computational Aspects in Management, Economics and Finance” of Annals of Operations Research, is to accomplish a wide dissemination of innovative contributions and applications of regression methods based on OR tools and their combination with computational aspects in real-life problems for the benefit of the community of both researchers and practitioners. Presented on

International audience; This work aims to supplement the realization and validation of a higher-order well-balanced unstructured finite volume (FV) scheme, that has been relatively recently presented, for numerically simulating weakly non-linear weakly dispersive water waves over varying bathymetries. We in-vestigate and develop solution strategies for the sparse linear system that appears during this FV discretisation of a set of extended Boussinesq-type equations on unstructured meshes. The resultant linear system of equations must be solved at each discrete time step as to recover the actual velocity field of the flow and advance in time. The system’s coefficient matrix is sparse, un-symmetric and often ill-conditioned. Its characteristics are affected by physical quantities of the problem to be solved, such as the undisturbed water depth and the mesh topology. To this end, we investigate the application of different well-known iterative techniques, with and without the usage of preconditioners and reordering, for the solution of this sparse linear system. The iiterative methods considered are the GMRES and the BiCGSTAB, three preconditioning techniques, including different ILU factorizations and two different reordering techniques are implemented and discussed. An optimal strategy, in terms of computational efficiency and robustness, is finally proposed which combines the use of the BiCGSTAB method with the ILUT preconditioner and the Reverse Cuthill–McKee reordering.

Summarization: We propose a holistic evaluation framework that banks could adopt when screening corporate entities. The framework is based on the development of a composite indicator of social, environmental and financial performance. This integrated view of evaluation is conceptually aligned with ISO standards and empirical proposals of academics and market practitioners in defining inclusive performance. We complement those proposals with a methodological framework that permits incorporation of a plethora of viewpoints in the evaluation process, reflecting the expectations of the various stakeholders in the environment of a bank. We further enhance the evaluation process with analytics of a more detailed hierarchical view of performance, fine-tuning of the evaluation process, and provide implications and suggestions to the senior executive team of a bank’s clients. Presented on: European Journal of Operational Research

Purpose The combination of desalination technology with renewable energy sources (RES) provides a sustainable approach for increasing potable water availability without imposing negative environmental effects. This paper aims to present the development of a platform, which is an internet-based tool integrating the design optimization of desalination systems with spatial modeling based on a geographic information system (GIS). Design/methodology/approach The proposed platform assists decision-makers to select the optimal location and configuration of both the energy- and water-related subsystems of desalination plants that are power-supplied by RES, such that the lifetime cost of the overall desalination plant is minimized. It enables to optimize the desalination plant site selection and sizing with various hybrid power supply (solar, wind, wave and electrical grid power systems) and desalination technologies combinations, while simultaneously exploiting spatial technologies in an internet-based GIS platform. Findings A pilot study for the optimal design of stand-alone and grid-connected desalination plants powered by RES is presented, which demonstrates the functionality and features of the proposed platform. It is also shown that a grid-connected desalination plant designed by the proposed software design tool exhibits significantly lower lifetime installation and maintenance costs compared to its stand-alone counterpart. Originality/value The proposed platform combines technological, scientific and industrial knowledge with information about societal/political conditions and geo-spatial technologies in a user-friendly graphical interface. Therefore, it provides a design tool enabling its users to secure water supply in a sustainable and economically viable manner.

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3.

Summarization: Previous literature is rather inconclusive concerning the impact of state ownership on banks. We report that its overall impact is not monotonic as it has so far been implicitly assumed, and that it depends on a contemporaneous conflicting impact on risk and financial performance. This suggests the existence of an optimal level, which we investigate by comparing the relative “overall performance” and efficiency of the institutions. We show that a minimal presence, as opposed to no state ownership can improve performance and efficiency, reduce the likelihood of a bailout, while it is less costly compared to capital injections. Presented on: Annals of Operations Research

International audience

International audience

International audience

International audience; This study details new geochemical analysis from an outcrop in Crete to improve understanding of the hydrocarbon potential of the southern margin of the Hellenic Arc along the continental convergent zone of the central Mediterranean Ridge. Seventeen samples were collected from the Late Miocene sub-salt sedimentary succession of Plouti section in central Crete and were studied in terms of their organic geochemical features using Rock-Eval VI pyrolysis. Results of this investigation revealed intervals with sufficient organic material of good enough quality and quantity to be considered as potential source rocks. The obtained data generally present poor to fair and/or good in some cases hydrocarbon generation potential. The TOC values range from 0.03 to 1.99%, with an average fair (2.1 mg HC/g rock) hydrocarbon potential. A Type III kerogen was identified, indicating a terrestrial origin organic matter. T max and Production Index values suggest that the most promising parts of the section (organic-rich sediments) are immature with respect to oil generation and have not experienced high temperature during burial. Overall, the present study offers the opportunity to advance our understanding on the hydrocarbon potential onshore Crete and further investigate hydrocarbon prospectivity in the adjoining area, and particularly, the Greek part of the Mediterranean Ridge, a region with crucial economic and strategic importance.

International audience; In recent years, fake news detection has been an emerging research area. In this paper, we put forward a novel statistical approach for the generation of feature vectors to describe a document. Our so-called class label frequency distance (clfd), is shown experimentally to provide an effective way for boosting the performance of machine learning methods. Specifically, our experiments, carried out in the fake news detection domain, verify that efficient traditional machine learning methods that use our vectorization approach, consistently outperform deep learning methods that use word embeddings for small and medium sized datasets, while the results are comparable for large datasets. In addition, we demonstrate that a novel hybrid method that utilizes both a clfd-boosted logistic regression classifier and a deep learning one, clearly outperforms deep learning methods even in large datasets.

Summarization: Intrapreneurship has long been recognised as a way to develop competitive advantages. This is particularly evident in small and medium-sized enterprises (SMEs), so the intrapreneurial orientation (IO) of these companies should be evaluated from multiple perspectives. Research on this topic involves a wide variety of elements that not only have a direct impact on intrapreneurial behaviour but also change over time. Nonetheless, the development of a holistic system for evaluating IO in SMEs appears to be of great interest to academics and practitioners. Based on the epistemological convictions embodied in multiple criteria decision analysis (MCDA), this study sought to combine cognitive mapping techniques and the Choquet integral (CI) to develop a new IO evaluation system for SMEs. To this end, a panel of experienced decision makers with expertise in the subject under analysis was recruited. The results of a practical application of the proposed evaluation system and composite index were validated both by the panel members and a board member of the Entrepreneurship and Innovation Department of the Portuguese Institute for the Support of Small and Medium-Sized Enterprises and Innovation (in Portuguese, Instituto de Apoio às Pequenas e Médias Empresas e à Inovação (IAPMEI)). The contributions and limitations of the proposed dual methodology are also discussed. Presented on: Journal of the Operational Research Society

International audience

Summarization: This paper presents a novel approach to forecasting the success of a newly launched service in tourism by using a hybrid intelligence system called the Adaptive Neuro Fuzzy Inference System (ANFIS). Recent studies have addressed the problem of modeling the success of a newly launched service by using different methods including artificial intelligence and model-based approaches. The ANFIS combines both the learning capabilities of a neural network and the reasoning capabilities of fuzzy logic to give enhanced forecasting capabilities, as compared to using a single methodology alone. Data collected through a questionnaire that concerns the variables of developing a new service in tourism have been used as inputs to the model. A new technique that is achieved by using a method that cycles through all the inputs and builds ANFIS models has been used for input reduction and input selection. The final model has been trained by leaving out a part of the data. The model was then evaluated by the data that were left out. The forecasting accuracy of the ANFIS model is evaluated by calculating well-known performance measures. The results have shown that ANFIS provides a prudent way to capture uncertainty in relationships among input variables and output variables to forecast the successful launch of a new tourism service. A comparative analysis with other methodologies confirms the superiority of the proposed approach. Presented on: European Journal of Operational Research

Summarization: Environmental sustainability has become a major goal of public policies throughout the world, as environmental resources are continually eliminated. Climate change mitigation involves decarbonizing electricity generation as the most important energy system-wide change, with a vital role for energy efficiency. The utilization and development of Renewable Energy Sources (RES) constitutes the most critical measure to safeguard energy security, strengthen environmental protection, and tackle climate change. RES are favored as a means of reducing the use of fossil fuels. The market of RES is continuously developing and promoted through the Internet. Renewable energy is, by definition, sustainable and clean. Moreover, the market of RES offers the opportunity to tackle the increasing depletion of fossil resources and the associated environmental impacts. The paper aims to present RES and study the energy policy in EU and Greece. Furthermore, the status of the renewable energy market in Greece and especially in the Prefecture of Thessaloniki is presented. The Internet research retrieved 30 enterprises that promote renewable energy in the Prefecture of Thessaloniki, which were analyzed according to 18 characteristics representing internet adoption. The multicriteria method used for total ranking is PROMETHEE II. According to PROMETHEE II findings, the ‘superior’ websites can be used to form a conceptual content model while designing an enhanced website for a RES enterprise. The results show that these enterprises in the Prefecture of Thessaloniki must adjust to the new ‘information era’ and aim to become more effective and efficient while creating their autonomous website. Furthermore, most enterprises with their own websites in this sector are generally still in the initial adoption phase and they should further mature in the next phases of e-commerce adoption. Presented on: Technological Forecasting and Social Change

Bacteria are frequently studied due to their involvement in pollutants transformation processes during wastewater treatment. In this study, the treatment efficiency, bacteria densities and their vertical profile were investigated in pilot-scale vertical flow constructed wetlands (VFCW) planted with different plant species under a tropical climate in west Africa. Five beds were planted with local plant species, i.e., Andropogon gayanus, Chrysopogon zizanioides, Echinochloa pyramidalis, Pennisetum purpureum and Tripsacum laxum, while one bed remained unplanted. These species have been rarely used in CWs while some (e.g., T. laxum) are tested for the first time. After a 7-month trial, bacteria densities were measured in substrate samples separated into six layers along the bed depth. Plants presence enhanced the bacterial density and VFCW efficiency; the removal rates of organic matter (90.9–95.9%; COD and 95.2–98.5%; BOD5), nitrogen (74.3–84%; TN and 76–84%; NH4-N) and phosphorus (77.4–96.9%; PO4-P) were higher by 5.9–24.1% compared to the control bed, providing an overall excellent treatment performance for a single-stage VFCW system. Small numbers of anaerobic bacteria were obtained in the VFCWs, explaining the low-to-zero NO3-N removal, except for the VFCWs with T. laxum and P. purpureum. Aerobic bacteria decreased from the upper to bottom layers from 17.4 to 0.1 × 106 CFU/g in the planted beds, while anaerobic bacteria increased from 0.1 to 2.1 × 106 CFU/g. Anaerobic bacteria were more abundant in the unplanted than in the planted beds. The total bacteria count was dominated by aerobic bacteria, and decreased from the surface towards the bottom. Overall, the VFCW with P. purpureum demonstrated the highest efficiency, indicating that this design is an effective and sustainable nature-based solution for wastewater treatment in a tropical climate.

Summarization: Understanding the Mediterranean rangelands degradation trends is a key element of mitigating their vulnerability and enhancing their resilience. Climate change and its inherent effects on mean temperature and the precipitation variability can regulate the magnitude, frequency and duration of droughts and aridity with a profound effect on ecosystem productivity. Here we investigate the effects of climate change to project the development of vegetation in the Mediterranean rangelands by (i) estimating the relative Standardized Precipitation Index and a modification of the United Nations Environment Programme Aridity Index to classify climate variability, and (ii) modelling vegetation response to climate using the Food and Agriculture Organisation crop–water production function. Climate model data are obtained from nine general circulation models under Relative Concentration Pathways 2.6 and 8.5 of the fifth phase of the Coupled Model Intercomparison Project. After correcting climate model data for biases, results for two 40-year future study periods are compared with the baseline period 1961–2000 within a domain that includes the European Mediterranean. We show that a gradual but robust increase of aridity and drought frequency is estimated for most of the Mediterranean region, impacting rangeland vegetation yields. Projected drought and aridity disturbances may well represent permanent shifts to a warmer and more frequently dry status. This alternative stability of climatic pressure lies outside the limits of ecosystem resilience and may indicate that in some cases vegetation will either adapt to the new conditions or be succeeded by more water-stress tolerant species. Results raise concerns about the fate of the Mediterranean rangelands and the effectiveness of mitigation measures. Copyright © 2017 John Wiley & Sons, Ltd. Παρουσιάστηκε στο: Land Degradation and Development

Summarization: This study uses fixed-effect regressions estimated with heteroskedasticity-consistent standard errors to investigate the effect of international diversification on corporate cash holding behavior of French-listed firms during economic downturns. The findings show that internationally diversified firms are less inclined to save cash out of their cash flows than their undiversified counterparts. However, during economic downturns, the relationship shifts and shows that international diversification is positively associated with the propensity of firms to save cash out of their cash flows. The negative relationship between international diversification and the propensity of firms to save cash out of their cash flows suggests that risk-reducing effects coupled with easy access to external finance prevail over the high agency costs and information asymmetry associated with international companies. However, during economic slumps, this relationship becomes positive, highlighting a significant influence of the financial crisis on internationally diversified firms relative to their stand-alone counterparts. Thus, this study should provide useful insights for academics, practitioners as well as financial regulators. Παρουσιάστηκε στο: Journal of Economic Behavior and Organization

International audience

Summarization: Automated valuation models are widely used in real estate to provide estimates for property prices. Such models are typically developed through regression approaches. This study presents a comparative analysis about the performance of parametric and non-parametric regression techniques for developing reliable automated valuation models for residential properties. Different approaches are explored to incorporate spatial effects into the valuation process, covering both global and locally weighted models. The analysis is based on a large sample of properties from Greece during the period 2012–2016. The results demonstrate that linear regression models developed with a weighted spatial (local) scheme provide the best results, outperforming machine learning approaches and models that do not consider spatial effects. Presented on: Annals of Operations Research

Summarization: This paper examines banking efficiency using recent data from PIGS countries (i.e., Portugal, Italy, Greece and Spain), which suffer from debt problems. We employ a two-stage approach based on the effect of several items of balance sheets on cash flows and data envelopment analysis (DEA). More specifically, we extend previous studies by giving attention to the deposit dilemma. The reported results show that the choice of inputs and outputs does matter in the case of European banking efficiency. Although the role of deposits is controversial, we find that deposits may be an output variable, owing to liquidity issues that play a major role in the efficiency of PIGS' banking sector. We also report that the DEA model with deposits as an output variable generates efficiency scores that fall between periods. These results are helpful to bank managers and financial analysts dealing with efficiency modelling. Presented on

Summarization: Family business growth is crucial not only for the long-term sustainability of this type of firm but also for economic growth as a whole. However, family businesses face internal and external obstacles that hamper their healthy, sustainable growth. Analyses of family business growth are critical to overcoming these barriers. By combining fuzzy cognitive mapping techniques and the system dynamics approach, this study sought to create an analysis model that allows for a more holistic perspective on determinants of family business growth, their cause-and-effect relationships, and thus their long-term behavior. The model developed is a complete, transparent, and realistic tool that enables decision makers to make better and more informed decisions. This study was grounded in the insights provided by a panel of experts in family business, who shared their knowledge and experience over the course of two group sessions. The results of the static and dynamic analyses carried out were validated by the head of department and a board member of the Southern Business Support Center of Portugal’s Institute for the Support of Small and Medium-sized Enterprises and Innovation (IAPMEI). This research’s contributions are also discussed, and suggestions are made for future research. Presented on: Annals of Operations Research

Summarization: This study quantifies the effects of the Fed’s quantitative easing (QE) and tapering programs’ announcements on professionals’ consensus forecasts of U.S. macroeconomic and financial variables at different forecast horizons. The results of a vector autoregression (VAR) analysis show that the first QE (QE1) program is more effective in terms of significantly affecting the variability of near and medium term forecasts on GDP, inflation and short-term interest rates. This is not the case for these variables of long forecast horizons across all QE/tapering announcements, the forecasts of U.S. currency and long-term rates present significant short-lived responses, while the tapering displays a dominant effect on the volatility of long-term rates across long-term forecast horizons. A dynamic correlation analysis among different horizon forecasts also reveals that the Fed successfully anchor inflation and real economic growth expectations during the expansionary policy (QE) periods. Additional findings show the anchoring of the expectations across different horizons on short-term rates, as opposed to long-term rates, during the QE1 program. During the contractionary (tapering) period, the decrease in the correlations among different horizons for the short-term rates’ forecasts is a sign that the Fed increases the range of possible outcomes and highlights a signal of a monetary policy change. Παρουσιάστηκε στο: Journal of International Financial Markets, Institutions and Money

International audience

Summarization: This study focused on some of the most important aspects of ethics in the healthcare industry and examined the relevance of the system created to evaluate ethical healthcare practices in the public sector. The research included developing a conceptual model representing variables that influence ethics in public healthcare services in Portugal, using fuzzy cognitive mapping to facilitate the identification of the most significant variables. This method is a well-established decision problem-structuring tool that allows experts to define cause-and-effect relationships between variables and make decisions. The insights obtained were generated in face-to-face group meetings with experts in the public healthcare sector, showing that professional profiles, management, team relationships, external factors, equity principles, and respect are among the most relevant determinants of ethical healthcare. That is, these criteria are the most commonly applied standards in analyses of ethical practices in public healthcare services. The resulting assessment framework and its applicability were validated by both the panel of experts and the chairperson of the board of directors of S. Francisco Xavier Hospital in Lisbon, Portugal. The advantages and limitations of the analysis developed in this study are also discussed. Presented on: Journal of Multi-Criteria Decision Analysis

International audience

Summarization: Although many empirical studies have focused on R & D performance models for markets globally, the available financial information for R & D expenditure is limited. In other words, can we assume that the reported accounting information for R & D investment is adequate and valuable? This study empirically investigates the effect of R & D reported information on the value relevance of the accounting information of firms’ financial statements. Specifically, using Ohlson’s equation, it is examined whether changes in stock prices are explained better when R & D factors are included in models, in conjunction with changes in book value and abnormal earnings. We focus on listed firms on the Athens Stock Exchange in order to explore whether R & D expenses are value relevant, in a market which has been affected for a long period by the global economic crisis of 2007. In our findings, we observe that the reported R & D expenses do not have any significant influence on the investors’ choices, in contrast to expectations based on the prior literature. Moreover, the panel data analysis employed in the paper overcomes common methodological problems (such as autocorrelation, multicollinearity, and heteroscedasticity) and allows the estimation of unbiased and efficient estimators. Παρουσιάστηκε στο: Mathematics

The development of sustainable energy systems is pivotal in addressing climate change, but is also a complex and multifaceted task that should take into consideration a wide range of technological and socio-economic issues. The energy trilemma concept acknowledges this complexity and emphasizes the need to achieve a balance among three main dimensions: energy security, energy equity, and environmental sustainability. This study provides a systematic treatment of the energy trilemma at the country level. A novel multicriteria assessment framework is employed to evaluate the related performance of countries. Such an evaluation provides useful results for policy making, as it enables the examination of the status of each country and the challenges that it faces in achieving energy sustainability. The obtained empirical results are analyzed over time as well as considering the characteristics of the countries.

International audience

International audience

Nanoscale quantum optics explores quantum phenomena in nanophotonics systems for advancing fundamental knowledge in nano and quantum optics and for harnessing the laws of quantum physics in the development of new photonics-based technologies. Here, we review recent progress in the field with emphasis on four main research areas: Generation, detection, manipulation and storage of quantum states of light at the nanoscale, Nonlinearities and ultrafast processes in nanostructured media, Nanoscale quantum coherence, Cooperative effects, correlations and many-body physics tailored by strongly confined optical fields. The focus is both on basic developments and technological implications, especially for what concerns information and communication technology, sensing and metrology, and energy efficiency., 45 pages, 16 figures

Part 2: Poster Papers; International audience; Underwater optical wireless communications (UOWC) have gained attention due to the advantages they offer that can greatly alter the performance of a link. But such systems are affected by absorption and non predictable factors that may decrease the availability and the reliability of the system. In this work, the bit error rate (BER) of an underwater optical link with heterodyne detection modulated with M-QAM technique in presence of turbulence and pointing errors will be investigated. This investigation will lead to a closed form mathematical expression for the BER as a function of link parameters, that is very important in order to deploy an effective link. Various numerical results will also be presented for the reliability of the system.

We design an observer for ODE-PDE cascades where the ODE is nonlinear of strict-feedback structure and the PDE is a linear and of parabolic type. The observer provides online estimates of the (finite-dimensional) ODE state vector and the (infinite-dimensional) state of the PDE, based only on sampled boundary measurements. A design that simultaneously addresses nonlinear ODEs and boundary measurement sampling is the paper’s key contribution. Our observer design combines the backstepping design approach and the high-gain observer methodology and our analysis employs a Lyapunov–Krasovskii type functional to establish exponential convergence. Our sufficient conditions for convergence involve the maximum sampling interval and the PDE domain length.

Purely vertical 4H-SiC JFETs have been modeled by using three different approaches: the analytical model, the finite element model and the compact model. The results of the modeling have been compared with experimental results on a series of fabricated self-aligned devices with two different channel lengths (0.3 and 1.1μm) and various channel widths (1.5, 2, 2.5, 3, 4 and 5 μm). For all the considered models I-V and C-V characteristics could be satisfactorily simulated.

Summarization: Droughts can have strong environmental and socio-economic impacts in the Mediterranean region, in particular for countries relying on rain-fed agricultural production, but also in areas in which irrigation plays an important role and in which natural vegetation has been modified or is subject to water stress. The purpose of this review is to provide an assessment of the complexity of the drought phenomenon in the Mediterranean region and present various perspectives on drought in the present and under future climate change scenarios. The projections of various model experiments on future climate change scenarios strongly agree on an increased frequency and severity of droughts in the Mediterranean basin. Nevertheless, given the complexity of the phenomenon, with different types of droughts and complex interrelated impacts, significant future uncertainties remain. For example, uncertainties are stronger for hydrological droughts than meteorological droughts due to human influences and water withdrawal. Significant drought impacts are expected in the future, in particular for developing countries in the southern and eastern parts of the Mediterranean basin. To improve the resilience and adaptive capacities of societies and environments faced with drought, we aim to provide an overview of the key issues in research on climate change impacts on droughts, with a specific focus on the Mediterranean region, in order to: i) redefine more meaningful drought metrics tailored to the Mediterranean context, ii) better take into account vegetation and its feedback on droughts, iii) improve the modelling and forecasting of drought events through remote sensing and land surface models, and iv) promote a more integrated vision of droughts taking into account both water availability and water use. This overview reflects the complexity of the problem and the need to combine scientific research with adaptation solutions to deal with drought in the future. Presented on

International audience; This study analyses the profitability of firms participating in the European Union Emissions Trading Scheme during the period from 2006 to 2014, covering the three phases of the scheme. The analysis covers a large dataset from 19 European Union countries and with five different modelling specifications. The examined models use firm-specific attributes, country-level data about the economic environment and energy-related characteristics. In particular, the influences of time/firm/country characteristics on profitability are examined by performing cross-classified multilevel modelling. The empirical results show that both economic and energy-related variables significantly influence firms' profitability. Measures such as reducing environmental impacts (verified emissions and allowances allocated) or increasing energy efficiency should be taken into consideration in decision-making for the firm's profitability improvement.

Purpose The optimization of investment portfolios is a topic of major importance in financial decision making, with many relevant models available in the relevant literature. The purpose of this paper is to perform a thorough comparative assessment of different bi-objective models as well as multi-objective one, in terms of the performance and robustness of the whole set of Pareto optimal portfolios. Design/methodology/approach In this study, three bi-objective models are considered (mean-variance (MV), mean absolute deviation, conditional value-at-risk (CVaR)), as well as a multi-objective model. An extensive comparison is performed using data from the Standard and Poor’s 500 index, over the period 2005–2016, through a rolling-window testing scheme. The results are analyzed using novel performance indicators representing the deviations between historical (estimated) efficient frontiers, actual out-of-sample efficient frontiers and realized out-of-sample portfolio results. Findings The obtained results indicate that the well-known MV model provides quite robust results compared to other bi-objective optimization models. On the other hand, the CVaR model appears to be the least robust model. The multi-objective approach offers results which are well balanced and quite competitive against simpler bi-objective models, in terms of out-of-sample performance. Originality/value This is the first comparative study of portfolio optimization models that examines the performance of the whole set of efficient portfolios, proposing analytical ways to assess their stability and robustness over time. Moreover, an extensive out-of-sample testing of a multi-objective portfolio optimization model is performed, through a rolling-window scheme, in contrast static results in prior works. The insights derived from the obtained results could be used to design improved and more robust portfolio optimization models, focusing on a multi-objective setting.

Summarization: The level of modeling sophistication in financial services has increased considerably over the years. Nowadays, the complexity of financial problems and the vast amount of data require an engineering approach based on analytical modeling tools for planning, decision making, reporting, and supervisory control. This article provides an overview of the main financial applications of computational and data analytics approaches, focusing on the coverage of the recent developments and trends. The overview covers different methodological tools and their uses in areas, such as portfolio management, credit analysis, banking, and insurance. Presented on

International audience; In this paper we propose a novel Deep Reinforcement Learning (DRL) algorithm that uses the concept of “action-dependent state features”, and exploits it to approximate the Q-values locally, employing a deep neural network with parallel Long Short Term Memory (LSTM) components, each one responsible for computing an action-related Q-value. As such, all computations occur simultaneously, and there is no need to employ “target” networks and experience replay, which are techniques regularly used in the DRL literature. Moreover, our algorithm does not require previous training experiences, but trains itself online during game play. We tested our approach in the Settlers Of Catan multi-player strategic board game. Our results confirm the effectiveness of our approach, since it outperforms several competitors, including the state-of-the-art jSettler heuristic algorithm devised for this particular domain.

Summarization: Quality of life and well-being is a topic of major importance for public policy making and urban planning decisions. However, these concepts entail complex multiple socio-economic aspects, and are ill-defined. Quantitative assessments of quality of life based on composite indicators have become quite popular in academic research and practice. This study presents the application of an approach that combines ideas from multiple criteria decision making and efficiency analysis to the assessment of quality of life at the municipality level. A dataset involving French municipalities in 2012 is used for the analysis based on a rich set of key performance indicators that cover different aspects of quality of life conditions for citizens. The relationship of quality of life performance with the financial strength of the municipalities is also explored. Παρουσιάστηκε στο: Annals of Operations Research

International audience

International audience; Near-eye (VR/AR) displays suffer from technical, interaction as well as visual quality issues which hinder their commercial potential. This tutorial will deliver an overview of cutting-edge VR/AR display technologies, focusing on technical, interaction and perceptual issues which, if solved, will drive the next generation of display technologies. The most recent advancements in near-eye displays will be presented providing (i) correct accommodation cues, (ii) near-eye varifocal AR, (iii) high dynamic range rendition, (iv) gaze-aware capabilities, either predictive or based on eye-tracking as well as (v) motion-awareness. Future avenues for academic and industrial research related to the next generation of AR/VR display technologies will be analyzed.

Summarization: Finance is a popular field for applied and methodological research involving multiple criteria decision aiding (MCDA) techniques. In this study we present an up-to-date bibliographic survey of the contributions of MCDA in financial decision making, focusing on the developments during the past decade. The survey covers all main areas of financial modeling as well as the different methodological approaches in MCDA and its connections with other analytical fields. On the basis of the survey results, we discuss the contributions of MCDA in different areas of financial decision making and identify established and emerging research topics, as well as future opportunities and challenges. Presented on: European Journal of Operational Research

International audience

International audience

Summarization: Since the early applications of operations research and management science techniques in corporate financial management, financial decision support has evolved to a multi-disciplinary field combing different analytical approaches and technologies for supporting the decision-making process for financial problems faced by firms, organizations, and individuals. This paper provides an overview of the nature of financial decision support and its contributions, covering past developments and advances, as well as current trends and emerging topics on methodological, application, and implementation issues. Παρουσιάστηκε στο: EURO Journal on Decision Processes

An extension and numerical approximation of the shear shallow water equations model recently proposed in [21] is considered in this work. The model equations are able to describe the oscillatory nature of turbulent hydraulic jumps and as such correct the deficiency of the classical shallow water equations in describing such phenomena. The model equations, orig- inally developed for horizontal flow or flows occurring over small constant slopes, are straight- forwardly extended here for modeling flows over non-constant slopes and numerically solved by a second-order well-balanced finite volume scheme. Further, a new set of exact solutions to the extended model equations are derived and several numerical tests are performed to validate the numerical scheme and its ability to predict the oscillatory nature of hydraulic jumps under different conditions.