Insar Maps of Land Subsidence and Sea Level Scenarios to Quantify the Flood Inundation Risk in Coastal Cities: The Case of Singapore

Global mean sea level rise associated with global warming has a major impact on coastal areas and represents one of the significant natural hazards. The Asia-Pacific region, which has the highest concentration of human population in the world, represents one of the larger areas on Earth being threatened by the rise of sea level. Recent studies indicate a global sea level of 3.2 mm/yr as measured from 20 years of satellite altimetry. The combined effect of sea level rise and local land subsidence, can be overwhelming for coastal areas. The Synthetic Aperture Radar (SAR) interferometry technique is used to process a time series of TerraSAR-X images and estimate the land subsidence in the urban area of Singapore. Interferometric SAR (InSAR) measurements are merged to the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 sea-level rise scenarios to identify projected inundated areas and provide a map of flood vulnerability. Subsiding rates larger than 5 mm/year are found near the shore on the low flat land, associated to areas recently reclaimed or built. The projected flooded map of Singapore are provided for different sea-level rise scenarios. In this study, we show that local land subsidence can increase the flood vulnerability caused by sea level rise by 2100 projections. This can represent an increase of 25% in the flood area in the central area of Singapore for the RCP4.5 scenario

Dynamic Updating of Working Memory Resources for Visual Objects

Recent neurophysiological and imaging studies have investigated how neural representations underlying working memory (WM) are dynamically updated for objects presented sequentially. Although such studies implicate information encoded in oscillatory activity across distributed brain networks, interpretation of findings depends crucially on the underlying conceptual model of how memory resources are distributed.Here, we quantify the fidelity of human memory for sequences of colored stimuli of different orientation. The precision with which each orientation was recalled declined with increases in total memory load, but also depended on when in the sequence it appeared. When one item was prioritized, its recall was enhanced, but with corresponding decrements in precision for other objects. Comparison with the same number of items presented simultaneously revealed an additional performance cost for sequential display that could not be explained by temporal decay. Memory precision was lower for sequential compared with simultaneous presentation, even when each item in the sequence was presented at a different location.Importantly, stochastic modeling established this cost for sequential display was due to misbinding object features (color and orientation). These results support the view that WM resources can be dynamically and flexibly updated as new items have to be stored, but redistribution of resources with the addition of new items is associated with misbinding object features, providing important constraints and a framework for interpreting neural data

Forecast Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives

The impact of depression at preconception on pregnancy planning and unmet need for contraception in the first postpartum year: a cohort study from rural Malawi

BACKGROUND: The impact of depression on women's use of contraception and degree of pregnancy planning in low-income settings has been poorly researched. Our study aims to explore if symptoms of depression at preconception are associated with unplanned pregnancy and nonuse of contraception at the point of conception and in the postpartum period. METHODS: Population-based cohort of 4244 pregnant women in rural Malawi were recruited in 2013 and were followed up at 28 days, 6 months and 12 months postpartum. Women were asked about symptoms of depression in the year before pregnancy and assessed for depression symptoms at antenatal interview using the Self-Reporting Questionnaire-20, degree of pregnancy planning using the London Measure of Unplanned Pregnancy and use of contraception at conception and the three time points postpartum. RESULTS: Of the 3986 women who completed the antenatal interview, 553 (13.9%) reported depressive symptoms in the year before pregnancy and 907 (22.8%) showed current high depression symptoms. History of depression in the year before pregnancy was associated with inconsistent use of contraception at the time of conception [adjusted relative risk (adjRR) 1.52; 95% confidence interval (1.24-1.86)] and higher risk of unplanned [adjRR 2.18 (1.73-2.76)] or ambivalent [adj RR 1.75 (1.36-2.26)] pregnancy. At 28 days post-partum it was also associated with no use of contraception despite no desire for a further pregnancy [adjRR 1.49 (1.13-1.97)] as well as reduced use of modern contraceptives [adj RR 0.74 (0.58-0.96)]. These results remained significant after adjusting for socio-demographic factors known to impact on women's access and use of family planning services, high depression symptoms at antenatal interview as well as disclosure of interpersonal violence. Although directions and magnitudes of effect were similar at six and 12 months, these relationships were not statistically significant. CONCLUSIONS: Depression in the year before pregnancy impacts on women's use of contraception at conception and in the early postpartum period. This places these women at risk of unplanned pregnancies in this high fertility, high unmet need for contraception cohort of women in rural Malawi. Our results call for higher integration of mental health care into family planning services and for a focus on early postnatal contraception

Power system flexibility improvement with a focus on demand response and wind power variability

Unpredictable system component contingencies have imposed vulnerability on power systems, which are under high renewables penetration nowadays. Intermittent nature of renewable energy sources has made this unpredictability even worse than before and calls for excellent adaptability. This paper proposes a flexible security-constrained structure to meet the superior flexibility by coordination of generation and demand sides. In the suggested model, demand-side flexibility is enabled via an optimum real-time (RT) pricing program, while the commitment of conventional units through providing up and down operational reserves improves the flexibility of supply-side. The behaviour of two types of customers is characterized to define an accurate model of demand response and the effect of customers' preferences on the optimal operation of power networks. Conclusively, the proposed model optimizes RT prices in the face of contingency events as well as wind power penetration. System operators together with customers could benefit from the proposed method to schedule generation and consumption units reliably.fi=vertaisarvioitu|en=peerReviewed

Resiliency assessment of the distribution system considering smart homes equipped with electrical energy storage, distributed generation and plug-in hybrid electric vehicles

This paper presents a novel method for resiliency assessment of the distribution system considering smart homes' arbitrage strategies in the day-ahead and real-time markets. The main contribution of this paper is that the impacts of smart homes' arbitrage strategy on the resilient operation of the distribution system are explored. The optimal commitment of smart homes in external shock conditions is another contribution of this paper. An arbitrage index is proposed to explore the impacts of this process on the system costs and resiliency of the system. A two-level optimization process is proposed for day-ahead and real-time markets. At the first stage of the first level, the optimal bidding strategies of smart homes are estimated for the day-ahead market. Then, the database is updated and the optimal bidding strategies of smart homes for real-time horizon are assessed in the second stage of the first level problem. At the first stage of the second level problem, the optimal day-ahead scheduling of the distribution system is performed considering the arbitrage and resiliency indices. At the second stage of the second level, the distribution system optimal scheduling is carried out for the real-time horizon. Finally, at the third stage of the second level, if an external shock is detected, the optimization process determines the optimal dispatch of system resources. The proposed method is assessed for the 33-bus and 123-bus IEEE test systems. The proposed framework reduced the expected values of aggregated costs of 33-bus and 123-bus systems by about 62.14 % and 32.06 % for the real-time horizon concerning the cases in which the smart homes performed arbitrage strategies. Furthermore, the average values of the locational marginal price of 33-bus and 123-bus systems were reduced by about 59.38 % and 63.98 % concerning the case that the proposed method was not implemented.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Cost-efficient Deployment of Storage Unit in Residential Energy Systems

With the mushrooming of distributed renewable generation, energy storage unit (ESU) is becoming increasingly important in residential energy systems. This letter proposes a fractional programming model to determine the optimal power and energy capacities of residential ESUs, aiming at minimizing the ratio between the reduced electricity tariff and the investment cost of ESU, ensuring the minimal payback time. A decomposition algorithm is developed to solve the fractional program based on convex optimization; the subproblem is a dual convex quadratic program which provides cutting planes, and the master problem comes down to a small linear program after variable transformations. Compared to the widely used cost-minimum method, the proposed model is cost-efficient: it enjoys a higher rate of return which is usually welcomed by smaller consumers.© 2020 Institute of Electrical and Electronics Engineersfi=vertaisarvioimaton|en=nonPeerReviewed

Zero Energy Building by Multi-Carrier Energy Systems including Hydro, Wind, Solar and Hydrogen

This paper proposes a unified solution to address the energy issues in net zero energy building (ZEB), as a new contribution to earlier studies. The multi carrier energy system including hydro-wind-solar-hydrogen-methane-carbon dioxide-thermal energies is integrated and modeled in ZEB. The electrical sector is supplied by hydro-wind-solar, combined heat and power, and pumped hydro storage. The purpose is to minimize the released CO2 to the atmosphere while all the electrical-thermal load demands are successfully supplied following events and disruptions. The model improves the energy resilience and minimizes the environmental pollutions simultaneously. The results demonstrate that the developed model reduces the CO2 pollution by about 33451 kg per year. The model is a resilient energy system that can handle all failures of components and supply both the thermal and electrical loads following events. The model can efficiently handle 26% increment in the electrical loads and 110% increment in the thermal loads.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of one week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. © 2011 Elsevier Ltd. All rights reserved