Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. Our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product

Top research productivity and its persistence. A survival time analysis for a panel of Belgian scientists.

The paper contributes to the debate on cumulative advantage effects in academic research by examining top performance in research and its persistence over time, using a panel dataset comprising the publications of biomedical and exact scientists at the KU Leuven in the period 1992-2001. We study the selection of researchers into productivity categories and analyze how they switch between these categories over time. About 25% achieves top performance at least once, while 5% is persistently top. Analyzing the hazard to first and subsequent top performance shows strong support for an accumulative process. Rank, gender, hierarchical position and past performance are highly significant explanatory factors.Economics of science; Effects; Factors; Hazard models; Performance; Persistence; Processes; Productivity; Research; Research productivity; Researchers; Scientists; Selection; Studies; Time;

Survey of the literature on innovation and economic performance

Despite very strong differences in their treatment of technological change in economic theory, both the neoclassical and the more Schumpetarian (and evolutionary) economic approaches often assume that market selection rewards the most innovative firms. However, despite such strong assumptions, empirical evidence on whether innovative firms perform better than non-innovative firms remains inconclusive. If innovators do not grow more, does this imply that market selection fails? And does the different impact of innovation on industrial performance (measured by firm growth and profitability) and financial performance (measured by market value and stock returns) signal differences in how industrial and financial markets react to firm level efforts around innovation? This discussion paper reviews the literature on the interaction between innovation and economic/financial performance, and outlines the way that work within FINNOV Work Package 2 (SELECTION), Co-Evolution of Industry Dynamics and Financial Dynamics, will contribute to better understanding this interaction

On the linear quadratic data-driven control

The classical approach for solving control problems is model based: first a model representation is derived from given data of the plant and then a control law is synthesized using the model and the control specifications. We present an alternative approach that circumvents the explicit identification of a model representation. The considered control problem is finite horizon linear quadratic tracking. The results are derived assuming exact data and the optimal trajectory is constructed off-line

Succeeding with Smart People Initiatives: Difficulties and Preconditions for Smart City Initiatives that Target Citizens

Smart City is a paradigm for the development of urban spaces through the implementation of state-of-the-art ICT. There are two main approaches when developing Smart Cities: top-down and bottom-up. Based on the bottom-up approach, the concepts of Smart People and Smart Communities have emerged as dimensions of the Smart City, advocating for the engagement of citizens in Smart People initiatives. The aim of this research is both to find the types of Smart People initiatives and to identify their difficulties and preconditions for success. However, such initiatives that aim to (1) leverage the citizens intellectually and (2) use citizens as a source of input for ideas and innovation, are understudied. Therefore, this research proposes a concentrated framework of Smart People initiatives from an extensive literature review. On one hand, this framework contributes with a common ground and vocabulary that facilitates the dialogue within and between practitioners and academia. On the other hand, the identification of difficulties and preconditions guides the academia and practitioners in how to successfully account for citizens in the Smart City. From the literature review and the conduct of case studies of five European cities, participation came out as the key difficulty across both types of Smart People initiatives and cases, closely followed by awareness, motivation and complexity

Digital Preservation, Archival Science and Methodological Foundations for Digital Libraries

Digital libraries, whether commercial, public or personal, lie at the heart of the information society. Yet, research into their long‐term viability and the meaningful accessibility of their contents remains in its infancy. In general, as we have pointed out elsewhere, ‘after more than twenty years of research in digital curation and preservation the actual theories, methods and technologies that can either foster or ensure digital longevity remain startlingly limited.’ Research led by DigitalPreservationEurope (DPE) and the Digital Preservation Cluster of DELOS has allowed us to refine the key research challenges – theoretical, methodological and technological – that need attention by researchers in digital libraries during the coming five to ten years, if we are to ensure that the materials held in our emerging digital libraries are to remain sustainable, authentic, accessible and understandable over time. Building on this work and taking the theoretical framework of archival science as bedrock, this paper investigates digital preservation and its foundational role if digital libraries are to have long‐term viability at the centre of the global information society.

The role of resilience in individual innovation

Organisations in today‘s changing environment face significant challenges, requiring continual innovation. A critical factor in their response may be employees‘ resilience, the ability to apply high levels of effort and persistence while initiating, promoting and applying new ideas. However, despite growing evidence of the value of many positive psychological characteristics in organisational behaviour, the role of resilience in individual innovation has received little attention in the literature. This thesis describes two studies of this issue. First, current perspectives and definitions of resilience were reviewed, revealing a need for an improved definition, a re-examination of its dimensions and a new measure. A new construct based in the positive psychology framework is proposed. Unlike previous studies viewing resilience as recovery from adversity, in the present view adversity is an opportunity for employees to grow as a person. This distinction between ‗survival‘ and ‗growth‘ perspectives can be traced back to humanistic psychology. A measure of this new construct was developed, building on existing measures, and tested on 167 managers from large organisations in Indonesia. Exploratory factor analysis revealed two dimensions to the new construct: developmental persistency, a combination of perseverance and commitment to growth, and positive emotion. Study 2 validated the results of Study 1 and assessed the causal model linking resilience to innovative behaviour using 241 managers from companies and industries comparable to Study 1. Confirmatory factor analysis using two-step structural equation modelling showed two primary findings. First, construct validity was demonstrated by the factor analysis results and by correlations with related constructs. The correlation between developmental persistency and positive emotion was moderate, and the reliability of each construct was reasonably acceptable. Second, factor analysis confirmed that Janssen‘s (2000) measure of innovative behaviour is better treated as multidimensional – comprising idea generation, idea promotion and idea implementation rather than unidimensional. Finally, the causal relationships between the dimensions of resilience and the dimensions of innovative behaviour were positive, as hypothesised. Four paths had moderately large and statistically significant coefficients: from developmental persistency to idea implementation and idea promotion, and from positive emotion to idea promotion and idea generation. Two paths had low and insignificant coefficients: from developmental persistency to idea generation and from positive emotion to idea implementation. In light of these findings, suggestions for future research are presented and theoretical and practical implications, including interventions to increase employees‘ resilience, are explored

Autopoietic organization of firm: an illustration for the construction industry

Generally poor productivity, delays, low profitability and exceeded budgets are Common problems in modern construction management, however it seems that a basic obstacle lies far deeper in the understanding of a firm's fundamental mission, its existence. The main objective of this paper therefore is to examine the operational living of a construction firm and by doing that to reveal the key problem or the solution for a construction firm - its organization. A firm as a social system in which interactions between its constitutive components (employees) are surordinated to its maintenance (keeping a system alive) is an autopoietic social system. Two domains of external perturbations are uncovered to which a construction firm has to adapt (market driven and project driven perturbations). Constructed conceptual model of an autopoietic organization is based upon two necessary and sufficient operational domains that a firm has to create in order to become an autopoietic, adaptive social system. The first one is a domain of interactions between employees and other operationally external systems, which is representing an idea-generating domain of interactions. The second is employee's autonomous operational domain, which embodies employee's autonomy and individuality and represents a necessary condition for the establishment of an idea-generating domain. Finally, it is recognized that interactions within these four domains keep a construction firm alive

Expertise Profiling in Evolving Knowledgecuration Platforms

Expertise modeling has been the subject of extensiveresearch in two main disciplines: Information Retrieval (IR) andSocial Network Analysis (SNA). Both IR and SNA approachesbuild the expertise model through a document-centric approachproviding a macro-perspective on the knowledge emerging fromlarge corpus of static documents. With the emergence of the Webof Data there has been a significant shift from static to evolvingdocuments, through micro-contributions. Thus, the existingmacro-perspective is no longer sufficient to track the evolution ofboth knowledge and expertise. In this paper we present acomprehensive, domain-agnostic model for expertise profiling inthe context of dynamic, living documents and evolving knowledgebases. We showcase its application in the biomedical domain andanalyze its performance using two manually created datasets