Association between serum bisphenol A concentration and incident risk of hypertension

BackgroundPrevious studies have shown that bisphenol A exposure is associated with the risk of hypertension; however, most of them are cross-sectional and the conclusions are not consistent. ObjectiveTo evaluate the association between bisphenol A exposure and the incident risk of hypertension. MethodsBased on a nested case-control design involving 1990 subjects derived from the Dongfeng-Tongji cohort, a total of 1080 subjects were included in this study after excluding 887 hypertensive cases at baseline and 23 subjects with missing blood pressure data in follow-up visits. Epidemiological information was collected through questionnaire survey, and serum bisphenol A concentration was detected by high performance liquid chromatography tandem mass spectrometry. Logistic regression model was used to analyze the potential association between serum bisphenol A level and the risk of hypertension incidence, and linear regression model was used to analyze the association between serum bisphenol A level and blood pressure changes between baseline and follow-up. ResultsThe average age of the 1 080 participants was (62.03±7.45) years, of which 41.1% were male. During the follow-up period, a total of 477 (44.2%) developed hypertension. The median serum concentration of bisphenol A in the total population was 3.15 μg·L−1, and the baseline bisphenol A concentration in the new case group (3.24 μg·L−1) was higher than that in the control group (2.98 μg·L−1) (P0.05). ConclusionBisphenol A exposure is positively associated with the risk of hypertension

Accelerating the SCE-UA Global Optimization Method Based on Multi-Core CPU and Many-Core GPU

The famous global optimization SCE-UA method, which has been widely used in the field of environmental model parameter calibration, is an effective and robust method. However, the SCE-UA method has a high computational load which prohibits the application of SCE-UA to high dimensional and complex problems. In recent years, the hardware of computer, such as multi-core CPUs and many-core GPUs, improves significantly. These much more powerful new hardware and their software ecosystems provide an opportunity to accelerate the SCE-UA method. In this paper, we proposed two parallel SCE-UA methods and implemented them on Intel multi-core CPU and NVIDIA many-core GPU by OpenMP and CUDA Fortran, respectively. The Griewank benchmark function was adopted in this paper to test and compare the performances of the serial and parallel SCE-UA methods. According to the results of the comparison, some useful advises were given to direct how to properly use the parallel SCE-UA methods

An analysis of the key safety technologies for natural gas hydrate exploitation

Natural Gas Hydrate (NGH) is a high combustion efficiency clean energy and its reserve is twice as that of natural gas and petroleum, so NGH is the potential resource which could overcome the increasing energy assumption. One of the essential aspects during the exploitation of NGH is to avoid risk, and here in this work, we summarized the relevant management experience to study the critical safety risk in the exploitation of natural gas hydrate. The problems that must be resolved during NGH exploitation were identified through the research on the comparison of the characteristics of conventional gas hydrate mining methods and potential drilling engineering risks and stratum damages in the processes of exploitation. Combined with typical case analysis of gas hydrate mining, it is concluded that the key for safe NGH exploitation is the changes of stratum stress caused by hydrate decomposition; and all safety management experiences should be based on steady drilling and reasonable exploitation to prevent environment, equipment, persons and other aspects damages from layering and stress changes.Cited as: Yang, Y., He, Y., Zheng, Q. An analysis of the key safety technologies for natural gas hydrate exploitation. Advances in Geo-Energy Research, 2017, 1(2): 100-104, doi: 10.26804/ager.2017.02.0

A novel location algorithm for power quality disturbance source using chain table and matrix operation

To determine the power quality disturbance (PQD) source in distribution network, a novel three-stage automatic location algorithm based on chain table and matrix operation is proposed. Firstly, fast pre-judgment of feeder sub-region of PQD is achieved according to chain table. Secondly, preliminary judgment of the PQD source is realized based on definition and operation of several critical describing matrices, which can be automatically generated with chain table and computer programming. Thirdly, accurate location algorithm is implemented using virtual power quality monitor(PQM) and extended matrices. A case study demonstrated that the proposed algorithm can realize location of PQD sources automatically and has many advantages such as small computational complexity and suitable for computer programming. © 2011 Praise Worthy Prize S.r.l. - All rights reserved

A Fault-Tolerant Location Approach for Transient Voltage Disturbance Source Based on Information Fusion

This paper proposed a fault-tolerant approach based on information fusion (IF) to automatically locate the transient voltage disturbance source (TVDS) in smart distribution grids. We first defined three credibility factors that will influence the reliability of the direction-judgments at each power quality monitor (PQM). Then we proposed two rules of influence and a verification factor for the distributed generation (DG) integration. Based on the two sets of direction-judgment criteria, a novel decision-making method with fault tolerance based on the IF theory is proposed for automatic location of the TVDS. Three critical schemes, including credibility fusion, conflict weakening, and correction for DG integration, have been integrated in the proposed fusion method, followed by a reliability evaluation of the location results. The proposed approach was validated on the IEEE 13-node test feeder, and the TVDS location results demonstrated the effectiveness and fault tolerance of the IF based approach

Online energy flow control for residential microgrids with URGs: An event-driven approach

Energy flow control (EFC) of residential microgrids (RMGs) equipped with renewable generations (RGs) is an essential component for the future smart grid that contributes to enhance renewable energy consumption and reduce cost. Different from most existing papers that devote to offline EFC to against the uncertainties caused by RGs and local load demand in RMGs, this paper focuses on online EFC framework for achieving optimal operations of a RMG. This framework is based on an event-driven approach that maximizes RGs utilization and maintains supply-demand balance considering the schedulable ability of active loads and the uncertainties of RMG. An event-driven EFC architecture for RMG is developed, and the events analysis are presented. Based on this architecture, the state machine is adopted to trigger the execution of the online EFC. Furthermore, an online algorithm is designed for communal energy server platform to determine scheduling plans for active loads. Finally, the performance analysis of the online algorithm is evaluated. Simulation results illustrate the basic characteristics and the advantages of the proposed approach

Toward Optimal Risk-Averse Configuration for HESS with CGANs-Based PV Scenario Generation

In this paper, an optimal risk-averse configuration framework for hybrid energy storage system (HESS) is proposed in planning utility-scale photovoltaic (PV) plants with conditional generative adversarial networks (CGANs)-based PV scenario generation. Other than most existing economy-based methods, we focus on frequency-based method to size a battery-supercapacitor HESS for mitigating the PV generation fluctuations in two time scales. We explore for the first time the potential of CGANs to generate sufficient PV scenarios through learning for experimental data preparation. For satisfying the fluctuation restrictions strictly, a data-driven frequency-based batteries optimization is developed, combining the flexible low-pass filter with wavelet package transform to guide the behaviors of both battery and supercapacitor for every individual scenario. Moreover, to hedge against risk exposure imposed by uncertain PV resource, we employ conditional value-at-risk to perform the optimal risk-averse configuration to meet the fluctuation mitigating requirements and minimize the expected configuration as well as the risk. Case studies are provided to verify the reasonableness and the efficiency of the proposed method

An event-driven ADR approach for residential energy resources in microgrids with uncertainties

In this paper, we present an event-driven automatic demand response (EADR) framework for online operation of residential microgrids (RMGs). Our framework involves comprehensive analysis of the schedulable ability (SA) for residential energy resources (RERs), and the uncertainties of both sides are also considered. Specifically, we first construct an EADR architecture to minimize the total operation cost and maintain supply-demand balance, and the event analyses are provided. Then, we define the concept of SA for RERs and also establish an SA evaluation system taking the real-time and historical information of the RERs into account. On these bases, SA-based interactions between RERs and EADR system and state machine are introduced to trigger the execution of the online EADR in a close-loop way. Furthermore, to implement this framework in practice, we functionally decompose the overall EADR architecture into event manager, EADR server, and a group of local controllers, and also design distributed online algorithms for each of them. The key idea of the designed algorithms is to operate the RMG simulating its real-life dynamics. Numerical simulations on a practical RMG verify the effectiveness of the proposed approach

Automated Demand Response Framework in ELNs: Decentralized Scheduling and Smart Contract

Blockchain technique, with the novelties of decentralization, smart contract, security and cooperative autonomy, is expected to play great effects on promoting the development of energy local networks (ELNs). This paper presents an automated demand response (ADR) framework for decentralized scheduling and secure peer-to-peer (P2P) trading among energy storage systems in ELNs. Different from most existing works that trade electricity over long distances and through complex meshes, this proposed work performs decentralized and automated demand response through energy sharing of P2P executors. We explore for the first time the benefits of a promising blockchain to conduct the overall ADR framework and increase the P2P trading security. To achieve decentralized scheduling without relying on a central entity, a price-incentive noncooperative game theoretic model is introduced to produce equilibrium solutions for energy storage systems. Moreover, we develop a schedulable ability evaluation system to match trading pairs involving buying and selling nodes. On this basis, a state-machine-driven smart contract mechanism is built to realize P2P trading without reliance on a trusted third party. To illustrate the implementation details of the ADR method, a distributed algorithm is designed. Case studies are provided to verify the effectiveness of the proposed method