We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation that is used for wireless communication simulation in 3D indoor environments. Our approach uses a modified conditional Generative Adversarial Network (GAN) that incorporates encoded geometry and transmitter location while adhering to the electromagnetic propagation theory. The overall physically-inspired learning is able to predict the power distribution in 3D scenes, which is represented using heatmaps. We evaluated our method on 15 complex 3D indoor environments, with 4 additional scenarios later included in the results, showcasing the generalizability of the model across diverse conditions. Our overall accuracy is comparable to ray tracing-based EM simulation, as evidenced by lower mean squared error values. Furthermore, our GAN-based method drastically reduces the computation time, achieving a 5X speedup on complex benchmarks. In practice, it can compute the signal strength in a few milliseconds on any location in 3D indoor environments. We also present a large dataset of 3D models and EM ray tracing-simulated heatmaps. To the best of our knowledge, EM-GANSim is the first real-time algorithm for EM simulation in complex 3D indoor environments. We plan to release the code and the dataset.

Medium- and long-term trading and spot trading are two essential parts of electricity market. The interplay between these market segments is essential: medium- and long-term trading manage risks while spot trading provides real-time price signals that enhance resource allocation. This paper proposes a multistage joint simulation model that integrates both trading mechanisms and exams their combined effects on market operations. Mathematical models for bilateral trading, matching trading, and listing trading are developed and analyzed. A spot trading clearing model is also constructed. The expression of comprehensive price of whole electricity market is formulated. With IEEE 14-bus test system, the study evaluates how the interaction between medium- and long-term trading and spot trading influences overall market prices, the effectiveness of the proposed model is illustrated.

We present a novel algorithm that enhances the accuracy of electromagnetic field simulations in indoor environments by incorporating the Uniform Geometrical Theory of Diffraction (UTD) for surface diffraction. This additional diffraction phenomenology is important for the design of modern wireless systems and allows us to capture the effects of more complex scene geometries. Central to our methodology is the Dynamic Coherence-Based EM Ray Tracing Simulator (DCEM), and we augment that formulation with smooth surface UTD and present techniques to efficiently compute the ray paths. We validate our additions by comparing to analytical solutions of a sphere, method of moments solutions from FEKO, and ray-traced indoor scenes from WinProp. Our algorithm improves shadow region predicted powers by about 5 dB compared to our previous work, and captures nuanced field effects beyond shadow boundaries. We highlight the performance on different indoor scenes and observe 60% faster computation time over WinProp.

Radio applications are increasingly being used in urban environments for cellular radio systems and safety applications that use vehicle-vehicle, and vehicle-to-infrastructure. We present a novel ray tracing-based radio propagation algorithm that can handle large urban scenes with hundreds or thousands of dynamic objects and receivers. Our approach is based on the use of coherence-based techniques that exploit spatial and temporal coherence for efficient wireless propagation and radio network planning. Our formulation also utilizes channel coherence which is used to determine the effectiveness of the propagation model within a certain time in dynamically generated paths; and spatial consistency which is used to estimate the similarity and accuracy of changes in a dynamic environment with varying propagation models and blocking obstacles. We highlight the performance of our simulator in large urban traffic scenes with an area of 2 * 2km(2) and more than 10,000 users and devices. We evaluate the accuracy by comparing the results with discrete model simulations performed using WinProp. In practice, our approach scales linearly with the area of the urban environment and the number of dynamic obstacles or receivers.

5G applications have become increasingly popular in recent years as the spread of fifth-generation (5G) network deployment has grown. For vehicular networks, mmWave band signals have been well studied and used for communication and sensing. In this work, we propose a new dynamic ray tracing algorithm that exploits spatial and temporal coherence. We evaluate the performance by comparing the results on typical vehicular communication scenarios with GEMV^2, which uses a combination of deterministic and stochastic models, and WinProp, which utilizes the deterministic model for simulations with given environment information. We also compare the performance of our algorithm on complex, urban models and observe a reduction in computation time by 36% compared to GEMV^2 and by 30% compared to WinProp, while maintaining similar prediction accuracy.

A pyrene-containing polyimide (p-PI) was synthesized from a diamine monomer and 4,4 & PRIME;-(Hexa-fluoroisopropylidene)diphthalic anhydride via one step polycondensation procedure. The polymer possessed good solubility due to the large rigid non-planar conjugated side groups. Therefore, the fluorescence test paper could be produced by a convenient method. The "turn-on" fluorescent response of the test paper to Fe3+ and Pb2+ was observed. In addition, this polymer possessed excellent thermal and thermooxidative stability. The onset decomposition temperatures (Td) of p-PI in O2 and N2 atmosphere were 431 and 442 degrees C, respectively. There was a good linear relationship (R2 = 0.9943) of the p-PI film between fluorescence intensity and temperature from 298 to 408K. This polymer could be considered as fluorescence chemosensor for detecting metal ions and a tem-perature sensor for monitoring environment in the future.

This paper proposes an on-orbit software reconfiguration method for a general signal processing platform based on SOC chip. The SOC-based reconfiguration technology can be flexibly switched or modified, depending upon different signal processing algorithms or front-end functions. It also provides rapidly on-orbit response ability, which depending on user's requirements. This method performs segment allocation for the configuration program partition and cache program partition, based on the Xilinx ZYNQ series SOC chip system architecture. And it separates the ARM0, ARM1, and FPGA uplink injection software program through the look-up table, then reprograms the bootloader which generates automatically by compiler. Finally, it completes the injection software storage and reconfiguration through the above operations. This method provides an autonomous controllable and functional scalable basis for the spacecraft software defined radio systems in the future.

BACKGROUND For improving the permeability and selectivity of adsorption separation membranes, membrane materials, structural morphology and separation technology must continue to be investigated. Development of a membrane that is environmentally friendly, cost-effective and with high filter precision is an important direction in current research. RESULTS The air pressure spraying method was used to prepare an activated carbon (AC)-cellulose acetate/chitosan (CA/CS) composite membrane. The CA/CS fiber composite membrane was prepared as substrate layer, and AC was loaded on the surface of the CA/CS composite membrane as a functional adsorption layer. When the spraying pressure was 0.01 MPa and the mass ratio of AC/CS was 2:1, a composite membrane with even and compact morphology could be obtained. Both the compact AC functional layer and complete CA/CS substrate layer endowed the AC-CA/CS composite membrane with an excellent water filtration property. At low feeding pressure, the permeate flux and rejection rate of bisphenol A (BPA) could reach 9.27 x 10(3)L m(-2)h(-1)and 98.31%, respectively. Meanwhile, the adsorption capacities of the membrane were investigated. It tended to equilibrate quickly in 2 min for dyes and BPA. The equilibrium adsorption capacities of the membrane for acid blue, acid yellow and BPA were 175.80, 164.0815 and 79.58 mg g(-1), respectively. The adsorption process conformed to pseudo-second-order kinetics and the Langmuir monolayer adsorption model. CONCLUSIONS This work could provide a practical and effective feasibility for membrane separation technology. (c) 2020 Society of Chemical Industry (SCI)

The pilot project of low-carbon cities is an important effort to align China's national goals for climate change governance with local governments' low-carbon behavior. The purpose of such pilot project is to promote low-carbon development and encourage policy innovation, but the role of nested structure has not been fully appreciated. Based on the survey data of low-carbon pilot cities, this paper finds that nested structure has a positive impact on policy innovation and discusses the impact on policy innovation from three dimensions: the nested pilot, the nested policy and the nested department. Heckman selection Model and Possion Model are used to make an empirical analysis of the factors influencing policy innovation and used a case study to explore the mechanism. The results show that the nested structure has a positive effect on policy innovation in pilot projects of low-carbon cities with weak incentives and weak constraints. Specifically, the participation of local governments in low-carbon related pilot projects, the number of low-carbon policies introduced by local governments, and the participation of local government departments in the process of low-carbon governance have a positive impact on policy innovation. Nested structure promotes policy innovation mainly through coordination mechanism and funding mechanism.

During market activities, participants in electricity market may suffer from risks like price risk and bear great losses, which will eventually lead to a higher possibility of default in market transactions. Thus, for the market operator, it is essential to implement risk control on market transactions. A risk control mechanism based on margin requirement is proposed in this paper, which aims to minimize the adverse effect of these risk events. The risk of participants in long-term and spot transactions are assessed based on different theories, that is, the risks of long-term transactions are limited by the daily price limit, and the risk of spot transactions should include the whole settlement process. Then, the margin requirements are calculated based on these risk assessments. Simulation studies show that through the risk control mechanism, the market operator is able to cover a market participant's risk with its margin.

Four different contents of pyrene group were introduced in polyimide (PI) backbone via one step polycondensation procedure. The chemical structure, thermal property, process ability, and photophysical performance of polymers were characterized and analyzed. The obtained PIs exhibited high glass-transition temperatures from 270 to 285 degrees C, and weight remainder rates of PIs were all above 50% at 800 degrees C in N-2. Fluorescence detection of metal ions was taken by adding Cu2+, Pb2+, Cr3+, Cd2+, Fe3+, Co2+, and Ni2+ to polymer solutions. It was interesting to found that the polymer exhibited significant luminescence emission "turn-on" response to Cu2+ and fluorescence emission quenching to Fe3+. Furthermore, the uniform morphologies of pyrene-containing PI fibers were prepared via electrospinning technique for investigating their optical properties in solid state. The fluorescent images and optical photographs indicated that the luminescence stability of polymers in fiber solid state was decided by both the chromophore in macromolecular chain and effective draft of polymer chains during spinning process. This study provided a facility strategy for controlling the morphology structure of materials and keeping the PL emission intensity in flexible devices. (C) 2020 The Author(s). Published by Elsevier B.V.

Each household in the smart community may have distributed wind power/distributed photovoltaic/distributed energy storage and other power generation resources. With the help of advanced sensing, communication, and computing technologies in the smart community, the cooperative power consumption of smart community households can be realized, thus the efficient use of distributed resources in the smart community and the improvement of power utilization efficiency can be realized. The establishment of a smart community household cooperative power optimization model has quantified the benefit of the efficient utilization of distributed power generation resources brought about by the cooperation of household electricity consumption. Based on the coalitional game theory, the proportional allocation method, the nucleolus allocation method, the Shapley value method and the bargaining allocation method are used to allocate the cooperative benefit of household electricity consumption. Finally, an example is used to verify the effectiveness and rationality of the cooperative electricity benefit allocation method.

In recent years, China's power market reform has been continuously promoted, and reasonable settlement of trading behavior is an important cornerstone for ensuring the orderly development of the power market. The paper analyzes the data risks faced in the power market settlement, and proposes the method of using DS evidence theory to identify the data risks in the settlement process through multi-evidence fusion decision. The PCA and ICA construct statistics are used as evidence sources. After multiple evidences are combined, abnormal data is identified through reasonable decision-making methods. In addition, based on the calculation results of evidence theory, the paper puts forward the risk processing method of settlement data, and proposes a reconstruction method based on polynomial fitting and historical correlation for the abnormal data with low reliability. The effectiveness of the proposed method is demonstrated by an example. The reconstruction method can obtain more accurate data and can effectively reduce the settlement risk of the power market.

针对目前电力市场价格信号不清晰、竞争不充分等问题,基于挂牌交易机制,提出了一种深度博弈的发用电侧双向挂牌竞价模式,通过信息公示、电厂挂牌用户摘牌、用户挂牌电厂摘牌三个阶段,供需双方在价格接受范围内分别进行挂牌、摘牌,并按摘牌电价出清.该竞价模式不仅能激励供需双方合理报价,还实现交易量价的有机配合,降低了交易风险,扩大了成交电量,提高了资源优化配置的效率.云南电力市场的运营经验证实了该竞价模式的合理性、有效性和实用性.

Because of the intermittent, volatility and randomness features of wind power, the flexibility of integrated energy system and the energy conversion ability between power system and natural gas system is an essential condition to consume large scale wind power. Since CHP (combined heat and power) unit and GF (gas furnace) are widely used and P2G (power to gas) technology is introduced, it had a great effect on different types of energy services, including electricity, gas, and heat. This paper puts forward an integrated energy system expansion planning model considering the coupling of power system, gas system and thermal system during the planning period. Based on the curve of load duration and wind intensities, by using scenario method to illustrate the uncertainty of wind power and load, then a mathematical model for dynamic coupling between gas and electricity of integrated energy planning that can consider wind power, P2G and CHP unit at the same time is built. The model minimize the investment cost and operation cost during planning period, which is constrained by operation safety and coupling relationship of power system and gas system, determining the optimal planning scheme for wind turbines, coal-fired units, transmission lines, gas transmission lines, CHP, GF, P2G. In order to achieve a balance between efficiency and precision, a series of linearization method is adopted to solve the model. Simplified mathematical model is a mixed integer linear programming model, which is programmed on GAMS software and call on CPLEX solver to solve the model. 6 nodes gas and electricity coupling test system is utilized to analyze and compare the cases. Case studies validate the effectiveness and superiority of the model in the paper.

In view of unclear price signal,lack of competition and other issues in electricity market,an independent listing bidding model is put forward based on listing trade mechanism.In this mechanism,both supply and demand sides list and delist in price range through three stages,including information publicity,power plant listing and users' delisting,and users' listing and power plant delisting.Then electricity prices are cleared according to the delisting price and cumulative uplper limit of delisting power is not constrained.By optimizing transaction order in the two trading stages,public electricity of market participants are considered as bidding transaction volume limits.Transaction flow chart and market clearing calculation method are also given in the paper.Not only can the bidding model stimulate reasonable offer of both supply and demand sides,but also it can realize organic cooperation of trading price and amount,reduce trading risk,expand trading power and improve efficiency of resource allocation.Operational experience of Yunnan electricity power market confirms rationality,validity and practicability of the bidding model.

To achieve a reliable communication link, a robust radio frequency (RF) communication system should be designed for the ability to predict the system performance in the intended environment prior to the network deployment is critical. In this paper, a comprehensive method to analyze the capacity of a ground-to-air/air-to-air communication RF links in both urban and rural areas is presented. Communication link analysis performed using a systems-level method provides a better understanding of the ground-to-air/air-to-air communication link, and moreover, the propagation model can be used for other system designs in similar scenarios.

Aiming at the problems such as unclear price signals and insufficient competition in the current power market, a deep game bidding model for supply and demand sides is proposed based on the listing trading mechanism. Supply and demand sides both list and delist within the limits of price through three stages including information publicity, power plants listing and power consumers delisting, power consumers listing and power plants delisting, then electricity prices are cleared according to the delisting price. The accumulated delisting capacity limit is unconstrained.By optimizing the order of transactions in the two trading phases, the publicity volume of market participants is taken as the upper limit of the transaction volume of the entire two-way listing transaction. Flow charts of the transaction organization and the market clearing calculation method are also given. Not only can the bidding model stimulate the reasonable offer of both supply and demand, but also it can realize the organic cooperation of trading price and amount, reduce trading risk, expand trading power and improve the efficiency of resource allocation. The operation of the Yunnan electricity market has confirmed the rationality, effectiveness and practicability of the bidding model.

Schisandrin B (Sch B) has received much attention owing to its various biological activities. Schisandrin B exists as a racemate in "wuweizi", a traditional Chinese medicine in China. In the present study, a novel chiral LC-MS/MS method was developed for enantioselective separation and determination of Schisandrin B in rat plasma. The plasma samples were prepared by liquid-liquid extraction (LLE). Schisandrol B was used as internal standard. Chiral separation was obtained on a Chiralpak IC column using 0.1% (v/v) formic acid in mixture of methanol and water (90:10, v/v) as a mobile phase. Parameters including the selectivity, linearity, precision, accuracy, extraction recovery, matrix effect and stability were evaluated. The method described here is simple and reproducible. The lower limit of quantification of 5.0 ng/mL for each Sch B enantiomer permits the use of the method in investigating the stereoselective pharmacokinetics of Sch B. Following racemic Sch B and "wuweizi" extracts, the area under the curve of (8R, 8'S)-Sch B was statistically higher than the one of (85, 8' R)-Sch B, with a ratio of 1.16-1.40 in three cases. This study firstly reports the development and validation of enantioselective behavior of Sch B in vivo, and provides a reference for clinical practice and encourages further research into Sch B enantioselective metabolism and drug interactions. (C) 2018 Published by Elsevier B.V.

Due to the progressive expansion of public mobile networks and the dramatic growth of the number of wireless users in recent years, researchers are motivated to study the radio propagation in urban environments and develop reliable and fast path loss prediction models. During last decades, different types of propagation models are developed for urban scenario path loss predictions such as the Hata model and the COST 231 model. In this paper, the path loss prediction model is thoroughly investigated using machine learning approaches. Different non-linear feature selection methods are deployed and investigated to reduce the computational complexity. The simulation results are provided to demonstratethe validity of the machine learning based path loss prediction engine, which can correctly determine the signal propagation in a wireless urban setting.