Continuous estimates on the earthquake early warning magnitude by use of the near-field acceleration records

In this article, the seismic records of Japan＇s Kik-net are selected to measure the acceleration, displacement, and effective peak acceleration of each seismic record within a certain time after P wave, then a continuous estimation is given on earthquake early warning magnitude through statistical analysis method, and Wenchuan earthquake record is utilized to check the method. The results show that the reliability of earthquake early warning magnitude continuously increases with the increase of the seismic information, the biggest residual happens if the acceleration is adopted to fit earthquake magnitude, which may be caused by rich high-frequency components and large dispersion of peak value in acceleration record, the influence caused by the high-frequency components can be effectively reduced if the effective peak acceleration and peak displacement is adopted, it is estimated that the dispersion of earthquake magnitude obviously reduces, but it is easy for peak displacement to be affected by long-period drifting. In various components, the residual enlargement phenomenon at vertical direction is almost unobvious, thus it is recommended in this article that the effective peak acceleration at vertical direction is preferred to estimate earthquake early warning magnitude. Through adopting Wenchuan strong earthquake record to check the method mentioned in this article, it is found that this method can be used to quickly, stably, and accurately estimate the early warning magnitude of this earthquake, which shows that this method is completely applicable for earthquake early warning.

Earthquake magnitude estimation using the s_c and P_d method for earthquake early warning systems

Earthquake early warning （EEW） systems are one of the most effective ways to reduce earthquake disaster. Earthquake magnitude estimation is one of the most important and also the most difficult parts of the entire EEW system. In this paper, based on 142 earthquake events and 253 seismic records that were recorded by the KiK-net in Japan, and aftershocks of the large Wenchuan earthquake in Sichuan, we obtained earthquake magnitude estimation relationships using the τe and Pa methods. The standard variances of magnitude calculation of these two formulas are ±0.65 and ±0.56, respectively. The Pd value can also be used to estimate the peak ground motion of velocity, then warning information can be released to the public rapidly, according to the estimation results. In order to insure the stability and reliability of magnitude estimation results, we propose a compatibility test according to the natures of these two parameters. The reliability of the early warning information is significantly improved though this test.

Spectroscopic and molecular docking studies on the binding mechanism of Mobic and lipase

Under simulated physiological conditions (pH=7.40), the interaction between non-steroidal anti-inflammatory drug Mobic and lipase was studied by fluorescence spectra, ultraviolet absorption spectra, circular dichroism spectra and computer simulation technique. The experimental results showed that Mobic could quench the fluorescence of lipase by static quenching, and the binding site number is about 1. According to F¨orster’s theory of non-radiation energy transfer, the binding distance between Mobic and lipase was obtained, r<7 nm, which indicated that there was non-radiation energy transfer in the system. The thermodynamic parameters were obtained from van’t Hoff equation, Gibbs free energy -G<0, indicating that the reaction between them was spontaneous,-H<0,-S>0, indicating that hydrophobic force played a major role in the formation of Mobic and lipase complex. The results of synchronous fluorescence spectra, UV spectra and circular dichroism spectra showed that Mobic changed the conformation of lipase. The molecular docking results showed that the binding position of Mobic was close to the active center, indicating that Mobic could change the microenvironment of amino acid residues at the active center of lipase catalysis. The results of docking showed that there was hydrogen bond between Mobic and lipase, so the interaction between Mobic and lipase was driven by hydrophobic interaction and hydrogen bond.

Probabilistic earthquake early warning times in Fujian Province

Earthquake early warning （EEW） systems are a new and effective way to mitigate the damage associated with earthquakes. A prototype EEW system is currently being constructed in the Fujian Province, a region along the Southeast coast of China. It is anticipated that the system will be completed in time to be tested at the end of this year （2013）. In order to evaluate how much advanced warning the EEW system will be able to provide different cities in Fujian, we established an EEW information release scheme based on the seismic monitoring stations distributed in the region. Based on this scheme, we selected 71 historical earthquakes. We then obtained the delineation of the region＇s potential seismic source data in order to estimate the highest potential seismic intensities for each city as well as the EEW system warning times. For most of the Fujian Province, EEW alarms would sound several seconds prior to the arrival of the destructive wave. This window of time gives city inhabitants the opportunity to take protective measures before the full intensity of the earthquake strikes.

Comparison of two earthquake early warning location methods

According to earthquake catalog records of Fujian Seismic Network, the Tnow method and the fourstation continuous location method put forward by Jin Xing are inspected by using P-wave arrival information of the first four stations in each earthquake. It shows that the fourstation continuous location method can locate more seismic events than the Tnow method. By analyzing the results, it is concluded that the reason for this is that the Tnow method makes use of information from stations without being triggered, while some stations failed to be reflected in earthquake catalog because of discontinuous records or unclear records of seismic phases. For seismic events whose location results can be given, there is no obvious difference in location results of the two methods and positioning deviation of most seismic events is also not significant. For earthquakes outside the network, the positioning deviation may amplify as the epicentral distance enlarges, which may relate to the situation that the seismic stations are centered on one side of epicenter and the opening angle between seismic stations used for location and epicenter is small.

Progress of the earthquake early warning system in Fujian, China

In this article, we systematically introduce the atest progress of the earthquake early warning （EEW） ;ystem in Fujian, China. We focus on the following key echnologies and methods： continuous earthquake location md its error evaluation; magnitude estimation; reliability udgment of EEW system information; use of doubleparameter principle in EEW system information release hreshold; real-time estimation of seismic intensity and available time for target areas; seismic-monitoring network and data sharing platform; EEW system information ; elease and receiving platform; software test platform; and est results statistical analysis. Based on strong ground notion data received in the mainshock of the Wenchuan earthquake, the EEW system developed by the above algorithm is simulated online, and the results show that the ;ystem can reduce earthquake hazards effectively. In lddition, we analyzed four earthquake cases with magniude greater than 5.5 processed by our EEW system since he online-testing that was started one year ago, and results ndicate that our system can effectively reduce earthquake lazards and have high practical significance.

Spectroscopic study on the mechanism of meloxicam and α-amylase

In order to explore the mechanism of action of meloxicam and α-amylase. The interaction between the rheumatoid arthritis drug meloxicam and α-amylase was studied by fluorescence spectroscopy, synchronous fluorescence spectroscopy and molecular docking under the experimental conditions of pH=6.80. The results showed that meloxicam was able to effectively quench the endogenous fluorescence of α-amylase in a static quenching form a 1:1 complex and change the conformation of α-amylase. Thermodynamic results indicated that the main type of meloxicam and α-amylase system was hydrophobic interaction. Molecular docking indicated that the binding system had hydrogen bonds in addition to hydrophobic interaction and meloxicam was surrounded by the active amino acid residues Trp13 and Trp263 of α-amylase, which changed the microenvironment of amino acid residues at the active center of α-amylase. By establishing the binding model, it can be seen that the protein binding rate W(B) of meloxicam to -amylase was 2.76%-41.79% under the experimental conditions. The results showed that the binding of meloxicam to α-amylase had an effect on the number of free -amylase. The drug binding rate W(Q) of the system was 2.76%-1.67%, which indicated that the combination of α-amylase and meloxicam would not affect the efficacy of meloxicam.

Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data

Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks(ADNs)to facilitate integration of distributed renewable generation.Due to unavailability of network topology and line impedance in many distribution networks,physical model-based methods may not be applicable to their operations.To tackle this challenge,some studies have proposed constraint learning,which replicates physical models by training a neural network to evaluate feasibility of a decision(i.e.,whether a decision satisfies all critical constraints or not).To ensure accuracy of this trained neural network,training set should contain sufficient feasible and infeasible samples.However,since ADNs are mostly operated in a normal status,only very few historical samples are infeasible.Thus,the historical dataset is highly imbalanced,which poses a significant obstacle to neural network training.To address this issue,we propose an enhanced constraint learning method.First,it leverages constraint learning to train a neural network as surrogate of ADN's model.Then,it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical dataset.By incorporating historical and synthetic samples into the training set,we can significantly improve accuracy of neural network.Furthermore,we establish a trust region to constrain and thereafter enhance reliability of the solution.Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.

Stochastic Unit Commitment with High-Penetration Offshore Wind Power Generation in Typhoon Scenarios

To tackle the energy crisis and climate change,wind farms are being heavily invested in across the world.In China's coastal areas,there are abundant wind resources and numerous offshore wind farms are being constructed.The secure operation of these wind farms may suffer from typhoons,and researchers have studied power system operation and resilience enhancement in typhoon scenarios.However,the intricate movement of a typhoon makes it challenging to evaluate its spatial-temporal impacts.Most published papers only consider predefined typhoon trajectories neglecting uncertainties.To address this challenge,this study proposes a stochastic unit commitment model that incorporates high-penetration offshore wind power generation in typhoon scenarios.It adopts a data-driven method to describe the uncertainties of typhoon trajectories and considers the realistic anti-typhoon mode in offshore wind farms.A two-stage stochastic unit commitment model is designed to enhance power system resilience in typhoon scenarios.We formulate the model into a mixed-integer linear programming problem and then solve it based on the computationally-efficient progressive hedging algorithm(PHA).Finally,numerical experiments validate the effectiveness of the proposed method.

重组人鼻病毒3C融合蛋白酶的安全性研究

为了评价重组人鼻病毒3C融合蛋白酶(GST-3C)的安全性,本工作通过单次给药毒性试验和免疫毒性试验对GST-3C的毒性进行了研究。设置一个给药组,准备雌雄ICR小鼠各10只,采用最大给药量法,分别尾静脉注射GST-3C,单次给药最大给药剂量为25.25 mg/kg;实验另设一个生理盐水对照组。给药一次,观察ICR小鼠的中毒症状、中毒程度、性质、恢复情况和死亡等,并在观察期结束对动物及时进行解剖,观察GST-3C对小鼠脏器的影响。对免疫毒性进行试验,50只小鼠随机分为5组(溶剂对照组、环磷酰胺阳性对照组、含佐剂GST-3C低剂量组、含佐剂GST-3C高剂量组和不含佐剂GST-3C对照组),通过迟发型超敏反应方法,计算脾指数、胸腺指数和肿胀度,观察GST-3C对迟发超敏反应的影响;40只小鼠随机分为4组(溶剂对照组、含佐剂GST-3C低剂量组、含佐剂GST-3C高剂量组和不含佐剂GST-3C对照组),通过溶血空斑试验方法,对溶血空斑计数,评价其对小鼠体液免疫功能影响。单次给药毒性试验结果表明,和生理盐水对照组相比,实验组小鼠体重差异并不明显,给药组动物脏器均未发现异常表现情况。在本次试验条件下,ICR小鼠静脉注射GST-3C单次给药的最大耐受剂量(MTD)大于25.25 mg/kg。免疫毒性试验结果表明,给药期间,与溶剂对照组比较,GST-3C给药各组动物体重未见显著降低,动物笼旁观察未见与给药相关的异常变化,迟发超敏试验和溶血空斑试验结果分别显示GST-3C对动物的细胞免疫功能和体液免疫功能均无抑制作用,为评价GST-3C的安全性提供了依据。

Truncated Strategy Based Dynamic Network Pricing for Energy Storage

With the increasing penetration of local renewable energy and flexible demand,the system demand is more unpredictable and causes network overloading,resulting in costly system investment.Although the energy storage(ES)helps reduce the system peak power flow,the incentive for ES operation is not sufficient to reflect its value on the system investment deferral resulting from its operation.This paper designs a dynamic pricing signal for ES based on the truncated strategy under robust operation corresponding to the network charge reduction.Firstly,the operation strategy is designed for ES to reduce the total network investment cost considering the uncertainties of flexible load and renewable energy.These nodal uncertainties are converted into branch power flow uncertainties by the cumulant and Gram-Charlier expansion strategy.Then,a time of use(ToU)pricing scheme is designed to guide the ES operation reflecting its impact on network investment based on the longrun investment cost(LRIC)pricing scheme.The proposed To U LRIC method allocates the investment costs averagely to network users over the potential curtailment periods,which connects the ES operation with network investment.The curtailment amount and the distribution of power flow are assessed by the truncated strategy considering the impact of uncertainties.As demonstrated in a Grid Supply Point(GSP)distribution network in the UK,the network charges at the peak time reduce more than 20%with ES operation.The proposed method is cost-reflective and ensures the fairness and efficiency of the pricing signal for ES.

Optimal transmission switching to eliminate voltage violations during light-load periods using decomposition approach

With the rapid load increase in some countries such as China, power grids are becoming more strongly interconnected, and the differences between peak and valley loads are also increasing. As a result, some bulk power systems are facing high voltage limit violations during light-load periods. This paper proposes to utilize transmission switching(TS) to eliminate voltage violations. The TS problem is formed as a mixed-integer nonlinear program(MINLP) with AC power flow constraints and binary variables. The proposed MINLP problem is non-deterministic polynomial hard.To efficiently solve the problem, a decomposition approach is developed. This approach decomposes the original problem into a mixedinteger linear programming master problem and an AC optimal power flow slave problem that is used to check the AC feasibility. Prevention of islanding is also taken into consideration to ensure the feasibility of the TS results.The modified IEEE 39-bus and IEEE 57-bus test systems are used to demonstrate the applicability and effectiveness of the proposed method.

Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management

This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household appliances.Model-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV generation.However,discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous actions.Thus,a mixed deep reinforcement learning(MDRL)algorithm is proposed,which integrates deep Q-learning(DQL)algorithm and deep deterministic policy gradient(DDPG)algorithm.The DQL algorithm deals with discrete actions,while the DDPG algorithm handles continuous actions.The MDRL algorithm learns optimal strategy by trialand-error interactions with the environment.However,unsafe actions,which violate system constraints,can give rise to great cost.To handle such problem,a safe-MDRL algorithm is further proposed.Simulation studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy management.The proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test dataset.Moreover,the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.

Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

Time-efficient Strategic Power Dispatch for District Cooling Systems Considering Evolution of Cooling Load Uncertainties

District cooling system(DCS)provides centralized chilled water to multiple buildings for air conditioning with high energy-efficiency and operational flexibility.It is one of the most popular cooling systems for large buildings in modern cities and an important demand response source for power systems.In order to enhance its energy efficiency and utilize its flexibility,strategic operation is indispensable.However,finding an optimal policy for DCS operation is a challenging task because of the high inter-connectivity among components.The evolution of cooling load uncertainties further increases the difficulties.This paper addresses the aforementioned challenges by proposing a novel optimal power dispatch model for DCS.The proposed model optimizes water temperature and mass flow rates simultaneously to improve the energy efficiency as much as possible.It also explicitly describes the uncertainty accumulation and propagation.Chance-constrained programming is employed to guarantee the cooling service quality.We further propose a more timeefficient formulation to overcome the computational intractability caused by the non-smooth and non-convex constraints.Numerical experiments based on a real DCS confirm that a time-efficient formulation can save about half of solution time with negligible cost increase.