Adaptive water demand forecasting for near real-time management of smart water distribution systems

This paper presents a novel methodology to perform adaptive Water Demand Forecasting (WDF) for up to 24h ahead with the aim to support near real-time operational management of smart Water Distribution Systems (WDSs). The novel WDF methodology is exclusively based on the analysis of water demand time series (i.e., demand signals) and makes use of Evolutionary Artificial Neural Networks (EANNs). It is implemented in a fully automated, data-driven and self-learning Demand Forecasting System (DFS) that is readily transferable to practice. The main characteristics of the DFS are: (a) continuous adaptability to ever changing water demand patterns and (b) generic and seamless applicability to different demand signals. The DFS enables applying two alternative WDF approaches. In the first approach, multiple EANN models are used in parallel to separately forecast demands for different hours of the day. In the second approach, a single EANN model with a fixed forecast horizon (i.e., 1h) is used in a recursive fashion to forecast demands. Both approaches have been tested and verified on a real-life WDS in the United Kingdom (UK). The results obtained illustrate that, regardless of the WDF approach used, the novel methodology allows accurate forecasts to be generated thereby demonstrating the potential to yield substantial improvements to the state-of-the-art in near real-time WDS management. The results obtained also demonstrate that the multiple-EANN-models approach slightly outperforms the single-EANN-model approach in terms of WDF accuracy. The single-EANN-model approach, however, still enables achieving good WDF performance and may be a preferred option in engineering practice as it is easier to setup/implement. © 2014 Elsevier Ltd.UK Engineering and Physical Sciences Research Counci

New technological devices for the assessment of systemic inflammation in the primary prevention of cardiovascular disease.

PURPOSE OF REVIEW: The prediction of cardiovascular disease (CVD) events is of strategic importance for the primary prevention of one of the big killers in the world. Predictive models have a history of decades, but still the desired accuracy is not reached by any of the existing models. The inclusion of inflammatory factors in the models did not increase their accuracy. In this review, we discuss the possible reasons for that failure and we propose a paradigm shift. RECENT FINDINGS: Systemic inflammation is a very volatile phenomenon. The blood concentration of inflammatory biomarkers may change considerably in one individual with a timescale of seconds. Sudden changes in environmental conditions can trigger rapid modifications in the inflammatory profile of an individual. In routine clinical practice, the blood tests for inflammation are carried out at one point in time, not in standard environmental conditions, and are therefore inadequate. SUMMARY: We have to direct CVD research toward the understanding of the synchronic relationship between external environmental conditions and internal physiological reactions. CVD risk assessment must be carried out by using continuous real-time monitoring of external and internal parameters together, something that may become possible with the advent of new technological devices

Monetary theory and electronic money : reflections on the Kenyan experience

This article uses a class of models of money and the payments system to inform an analysis of "mobile banking" in the context of the rapid expansion of M-PESA, a new technology in Kenya that allows payments via mobile phones (even without any access to a bank account), and currently reaches close to 38 percent of Kenyan adults. The separation of households and firms in space and time suggests, in theory, from various separate models, a number of implications. These include (i) the potential gain, under some circumstances, from allowing net e-money credit creation, (ii) the impact that the associated enhancement of credit markets can have on monetary policy and on the real economy, (iii) the roles that e-money could play not only in credit but also in insurance, unrelated to its payment function, (iv) the potential role for an activist monetary policy and e-money management, (v) the role of e-money as a circulating private debt and as a store of value though with potential coordination problems associated with achieving balanced security transformation, (vi) the potential welfare losses from insisting on continuous net clearing of cash and e-money and the difficulty, in any event, of achieving this in practice, and (vii) the management of shortages in the context of fixed rates of exchange of e-money for cash. We provide some summary statistics from data collected on M-PESA agents and users that are reminiscent of the environments of the models and that support some of these implications. Other implications of the models suggest reforms to enhance the system's efficiency.Monetary policy ; Inflation (Finance) ; Financial institutions ; Payment systems

Dynamic Walking: Toward Agile and Efficient Bipedal Robots

Dynamic walking on bipedal robots has evolved from an idea in science fiction to a practical reality. This is due to continued progress in three key areas: a mathematical understanding of locomotion, the computational ability to encode this mathematics through optimization, and the hardware capable of realizing this understanding in practice. In this context, this review article outlines the end-to-end process of methods which have proven effective in the literature for achieving dynamic walking on bipedal robots. We begin by introducing mathematical models of locomotion, from reduced order models that capture essential walking behaviors to hybrid dynamical systems that encode the full order continuous dynamics along with discrete footstrike dynamics. These models form the basis for gait generation via (nonlinear) optimization problems. Finally, models and their generated gaits merge in the context of real-time control, wherein walking behaviors are translated to hardware. The concepts presented are illustrated throughout in simulation, and experimental instantiation on multiple walking platforms are highlighted to demonstrate the ability to realize dynamic walking on bipedal robots that is agile and efficient

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects