Wormhole phase in the RST model

We show that the RST model describing the exactly soluble black hole model can have a dynamical wormhole solution along with an appropriate boundary condition. The necessary exotic matter which is usually negative energy density is remarkably produced by the quantization of the infalling matter fields. Then the asymptotic geometry in the past is two-dimensional anti-de Sitter(AdS$_2$), which implies the exotic matter is negative. As time goes on, the wormhole eventually evolves into the black hole and its Hawking radiation appears. The throat of the static RST wormhole is lower-bounded but in the presence of infalling matter it collapses to a black hole.Comment: v1. REVTeX3, 12 pages and 1 figure; v2. JHEP3, 10 pages and 1 figure, version published in JHE

Microstructure of GaN Grown on (111) Si by MOCVD

Gallium nitride was grown on (111) Si by MOCVD by depositing an AIN buffer at 108O"C and then GaN at 1060 {degrees}C. The 2.2pm layer cracked along {1-100} planes upon cooling to room temperature, but remained adherent. We were able to examine the microstructure of material between cracks with TEM. The character and arrangement of dislocation are much like those of GaN grown on Al{sub 2}O{sub 3}: -2/3 pure edge and - 1/3 mixed (edge + screw), arranged in boundaries around domains of GaN that are slightly disoriented with respect to neighboring material. The 30 nm AIN buffer is continuous, indicating that AIN wets the Si, in contrast to GaN on Al{sub 2}O{sub 3}

Online EV charging controlled by reinforcement learning with experience replay

The extensive penetration of distributed energy resources (DERs), particularly electric vehicles (EVs), creates a huge challenge for the distribution grids due to the limited capacity. An approach for smart charging might alleviate this issue, but most of the optimization algorithms has been developed so far under an assumption of knowing the future, or combining it with complicated forecasting models. In this paper we propose to use reinforcement learning (RL) with replaying past experience to optimally operate an EV charger. We also introduce explorative rewards for better adjusting to environment changes. The reinforcement learning agent controls the charger’s power of consumption to optimize expenses and prevent lines and transformers from being overloaded. The simulations were carried out in the IEEE 13 bus test feeder with the load profile data coming from the residential area. To simulate the real availability of data, an agent is trained with only the transformer current and the local charger’s state, like state of the charge (SOC) and timestamp. Several algorithms, namely Q-learning, SARSA, Dyna-Q and Dyna-Q+ are tested to select the best one to utilize in the stochastic environment and low frequency of data streaming

Introducing user preferences for peer-to-peer electricity trading through stochastic multi-objective optimization

Peer-to-peer electricity markets are dedicated markets that enable the direct participation of small electricity end-users in energy trading activities. They are seen as a promising alternative that can empower end-users and accelerate the energy transition, by researchers, business developers, and legislators. Moreover, they can include environmental, social, or altruistic preferences that are relevant to end-users, in addition to the economic perspective. Such preferences are sometimes included in the modeling of P2P markets in the existing literature, but the assumptions behind them are rarely validated in practice. To investigate the desired attributes and preferences of end-users to participate in P2P markets, an online survey including a discrete choice experiment was conducted in The Netherlands The results of the survey are used to design a P2P electricity market with product differentiation. The participants in the market are residential end-users that are equipped with a home energy management system that can control some of the household appliances and automate the decision-making process for participation in the market. To facilitate this, a multi-objective stochastic optimization model is presented that incorporates results from the discrete choice experiment and real smart-meter measurements. The case study results demonstrate user preferences’ influence on market outcomes.</p

Improving Clustering-Based Forecasting of Aggregated Distribution Transformer Loadings With Gradient Boosting and Feature Selection

Load forecasting is more important than ever to enable new monitor and control functionalities of distribution networks aiming to mitigate the impact of the energy transition. Load forecasting at medium voltage (MV) level is becoming more challenging, because these load profiles become more stochastic due to the increasing penetration of photovoltaic (PV) generation in distribution networks. This work combines medium to low voltage (MV/LV) transformer loadings measured with advanced metering infrastructure (AMI) and machine learning (ML) algorithms to propose a new clustering based day-ahead aggregated load forecasting approach. This four-step approach improves the day-ahead load forecast of a city. First, MV/LV transformer loadings are clustered based on the shape of their load pattern. Second, a gradient boosting algorithm is used to forecast the load of each cluster and calculate the related feature importance. Third, feature selection is applied to improve the forecast accuracy of each cluster. Finally, the day-ahead load forecast of all clusters are aggregated. The case study presented uses 519 measured MV/LV transformer loadings in a city to perform 30 day-ahead load forecasts. Compared against the day-ahead aggregated load forecast without clustering, the average normalized root mean squared error (NRMSE) reduced 12.7 %, the average mean absolute percentage error (MAPE) reduced 18.2 % and the average Pearson Correlation Coefficient (PCC) increased 0.37 %. The 95 % confidence interval of the difference between the average NRMSE, MAPE and PCC without clustering and with the proposed method indicates a statistically significant improvement in accuracy

A "Littlest Higgs" Model with Custodial SU(2) Symmetry

In this note, a ``littlest higgs'' model is presented which has an approximate custodial SU(2) symmetry. The model is based on the coset space $SO(9)/(SO(5)\times SO(4))$. The light pseudo-goldstone bosons of the theory include a {\it single} higgs doublet below a TeV and a set of three $SU(2)_W$ triplets and an electroweak singlet in the TeV range. All of these scalars obtain approximately custodial SU(2) preserving vacuum expectation values. This model addresses a defect in the earlier $SO(5)\times SU(2)\times U(1)$ moose model, with the only extra complication being an extended top sector. Some of the precision electroweak observables are computed and do not deviate appreciably from Standard Model predictions. In an S-T oblique analysis, the dominant non-Standard Model contributions are the extended top sector and higgs doublet contributions. In conclusion, a wide range of higgs masses is allowed in a large region of parameter space consistent with naturalness, where large higgs masses requires some mild custodial SU(2) violation from the extended top sector.Comment: 22 pages + 8 figures; JHEP style, added references and extra discussion on size of T contributions, as well as some other minor clarifications. Version to appear in JHE

Helicobacter pylori-derived extracellular vesicles increased in the gastric juices of gastric adenocarcinoma patients and induced inflammation mainly via specific targeting of gastric epithelial cells

Evidence indicates that Helicobacter pylori is the causative agent of chronic gastritis and perhaps gastric malignancy. Extracellular vesicles (EVs) play an important role in the evolutional process of malignancy due to their genetic material cargo. We aimed to evaluate the clinical significance and biological mechanism of H. pylori EVs on the pathogenesis of gastric malignancy. We performed 16S rDNA-based metagenomic analysis of gastric juices either from endoscopic or surgical patients. From each sample of gastric juices, the bacteria and EVs were isolated. We evaluated the role of H. pylori EVs on the development of gastric inflammation in vitro and in vivo. IVIS spectrum and confocal microscopy were used to examine the distribution of EVs. The metagenomic analyses of the bacteria and EVs showed that Helicobacter and Streptococcus are the two major bacterial genera, and they were significantly increased in abundance in gastric cancer (GC) patients. H. pylori EVs are spherical and contain CagA and VacA. They can induce the production of tumor necrosis factor-��, interleukin (IL)-6 and IL-1�� by macrophages, and IL-8 by gastric epithelial cells. Also, EVs induce the expression of interferon gamma, IL-17 and EV-specific immunoglobulin Gs in vivo in mice. EVs were shown to infiltrate and remain in the mouse stomach for an extended time. H. pylori EVs, which are abundant in the gastric juices of GC patients, can induce inflammation and possibly cancer in the stomach, mainly via the production of inflammatory mediators from gastric epithelial cells after selective uptake by the cells. ? 2017 KSBMB. All rights reserved.115Ysciescopuskc

Practice-Oriented Optimization of Distribution Network Planning Using Metaheuristic Algorithms

Distribution network operators require more advanced planning tools to deal with the challenges of future network planning. An appropriate planning and optimization tool can identify which option for network extension should be selected from available alternatives. However, many optimization approaches described in the literature are quite theoretical and do not yield results that are practically relevant and feasible. In this paper, a distribution network planning approach is proposed which meets requirements originating from network planning practice to guarantee realistic outcomes. This approach uses a state-of-the-art evolutionary algorithm: Gene-pool Optimal Mixing Evolutionary Algorithm. The performance of this algorithm, as well as the proposed model, is demonstrated using a real-world case study

Review of Recent Developments in Technical Control Approaches for Voltage and Congestion Management in Distribution Networks

The increasing installation of distributed energy resources in residential households is causing frequent voltage and congestion issues in low- and medium-voltage electrical networks. To defer or avoid the costly and complicated grid expansion, technical, pricing-based, and market-based approaches have been proposed in the literature. These approaches can help distribution system operators (DSOs) exploit flexible resources to manage their grids. This study focuses on technical control approaches, which are easier to implement, and provides an up-to-date review of their developments in modeling, solution approaches, and innovative applications facilitating indirect control from DSOs. Challenges and future research directions are also discussed