Impact of Thermal Fluctuations on Logarithmic Corrected Massive Gravity Charged Black Hole

We investigate the influence of the first-order correction of entropy caused by thermal quantum fluctuations on the thermodynamics of a logarithmic corrected charged black hole in massive gravity. For this black hole, we explore the thermodynamic quantities, such as entropy, Helmholtz free energy, internal energy, enthalpy, Gibbs free energy and specific heat. We discuss the influence of the topology of the event horizon, dimensions and nonlinearity parameter on the local and global stability of the black hole. As a result, it is found that the holographic dual parameter vanishes. This means that the thermal corrections have no significant role to disturb the holographic duality of the logarithmic charged black hole in massive gravity, although the thermal corrections have a substantial impact on the thermodynamic quantities in the high-energy limit and the stability conditions of black holes.     

Bibliometric Analysis on Supercritical CO2 Power Cycles for Concentrating Solar Power Applications

In recent years, supercritical CO₂ power cycles have received a large amount of interest due to their exceptional theoretical conversion efficiency above 50%, which is leading a revolution in power cycle research. Furthermore, this high efficiency can be achieved at a moderate temperature level, thus suiting concentrating solar power (CSP) applications, which are seen as a core business within supercritical technologies. In this context, numerous studies have been published, creating the need for a thorough analysis to identify research areas of interest and the main researchers in the field. In this work, a bibliometric analysis of supercritical CO₂ for CSP applications was undertaken considering all indexed publications within the Web of Science between 1990 and 2020. The main researchers and areas of interest were identified through network mapping and text mining techniques, thus providing the reader with an unbiased overview of sCO₂ research activities. The results of the review were compared with the most recent research projects and programs on sCO₂ for CSP applications. It was found that popular research areas in this topic are related to optimization and thermodynamics analysis, which reflects the significance of power cycle configuration and working conditions. Growing interest in medium temperature applications and the design of sCO₂ heat exchangers was also identified through density visualization maps and confirmed by a review of research projects.     

An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.     

Equality and Freedom as Uncertainty in Groups

In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group’s optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a “sweet spot” in the capacity of a normal model in each agent’s decentralized optimization, and that this “sweet spot” corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent’s capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a “sweet surface” in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets.     

Age of Information of Parallel Server Systems with Energy Harvesting

Motivated by current communication networks in which users can choose different transmission channels to operate and also by the recent growth of renewable energy sources, we study the average Age of Information of a status update system that is formed by two parallel homogeneous servers and such that there is an energy source that feeds the system following a random process. An update, after getting service, is delivered to the monitor if there is energy in a battery. However, if the battery is empty, the status update is lost. We allow preemption of updates in service and we assume Poisson generation times of status updates and exponential service times. We show that the average Age of Information can be characterized by solving a system with eight linear equations. Then, we show that, when the arrival rate to both servers is large, the average Age of Information is one divided by the sum of the service rates of the servers. We also perform a numerical analysis to compare the performance of our model with that of a single server with energy harvesting and to study in detail the aforementioned convergence result.     

Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.     

Analytical Expressions for Ising Models on High Dimensional Lattices

We use an m-vicinity method to examine Ising models on hypercube lattices of high dimensions d≥3. This method is applicable for both short-range and long-range interactions. We introduce a small parameter, which determines whether the method can be used when calculating the free energy. When we account for interaction with the nearest neighbors only, the value of this parameter depends on the dimension of the lattice d. We obtain an expression for the critical temperature in terms of the interaction constants that is in a good agreement with the results of computer simulations. For d=5,6,7, our theoretical estimates match the numerical results both qualitatively and quantitatively. For d=3,4, our method is sufficiently accurate for the calculation of the critical temperatures; however, it predicts a finite jump of the heat capacity at the critical point. In the case of the three-dimensional lattice (d=3), this contradicts the commonly accepted ideas of the type of the singularity at the critical point. For the four-dimensional lattice (d=4), the character of the singularity is under current discussion. For the dimensions d=1, 2 the m-vicinity method is not applicable.     

SN-分子离子的势能函数和光谱常数研究

采用耦合簇CCSD(T)方法结合系列相关一致基组aug-cc-pVXZ (X=D,T,Q,5) 对基态SN^-分子离子(X³∑^-) 进行了结构优化和单点能扫描计算. 用四种方法进行基组外推得到该体系的平衡核间距R_e=0.15852 nm, 谐振频率ω_e=948.05 cm^-1, 离解能D_e=3.934 eV均与实验数据符合得很好. 对单点能扫描结果用9参数Murrell-Sorbie势能函数进行了最小二乘拟合, 得到了体系的解析势能函数表达式, 并进一步推导出了体系的力常数和光谱常数. 拟合所得势能曲线准确地再现了SN^-分子离子的离解能和平衡结构特征. 通过求解核运动的径向薛定谔方程, 得到了无转动SN^- (X³∑^-) 的全部振动态, 并进一步计算出了各振动能级相应的分子常数. 与实验结果及其他理论研究结果的对比表明, 本文关于SN^-分子离子平衡常数和光谱常数的计算结果达到了较高的精度, 能为进一步的实验探测和理论研究提供参考依据. 关键词： 势能曲线 解析势能函数 光谱常数 振动能级     

近玻尔速度Ne2+离子穿过碳膜引起的电子发射

本文测量了入射能为2–25 keV/u的Ne²⁺离子穿过不同厚度碳膜诱导的前向、后向 (分别对应出射表面和入射表面) 电子发射产额. 实验中通过改变炮弹离子的能量, 系统的研究了势能沉积、电子能损以及反冲原子对前向、后向电子发射产额的贡献. 结果表明, 离子的势能沉积只对后向电子发射有贡献, 前向、后向电子发射产额分别与Ne²⁺离子在薄膜出射、入射表面的电子能损近似成正比关系, 其中电子能损很低 (对应于离子能量很低) 的时候, 反冲原子对电子发射的贡献不能忽略. 关键词： 近玻尔速度 电子发射 电子能损 反冲原子     

The lowest electronic states of Ne2 +, Ar2 + and Kr2 +: comparison of theory and experiment

Non-relativistic configuration interaction (CI) ab initio calculations using large basis sets have been carried out to determine the potential curves of the first electronic states of Ne₂ ⁺, Ar₂ ⁺ and Kr₂ ⁺. The spin—orbit interaction was treated assuming that the spin—orbit coupling constant is independent of the internuclear separation (R). For Ar₂ ⁺, calculated dissociation energies and equilibrium separations are in good agreement with experimental results. The calculations for Ne₂ ⁺ suggest that the lowest vibrational level of the I(1/2u) ground state observed by threshold photoelectron spectroscopy by Hall et al. [1995, J. Phys. B: At. molec. opt. Phys., 28, 2435] and assigned to either ν = 0 or ν = 2 actually corresponds to ν = 4. The calculations also predict the I(1/2g) state of Ne₂ ⁺ and Ar₂ ⁺ to possess a double-well potential and that of Kr₂ ⁺ to be repulsive at short range and to only possess a single shallow well at large internuclear separation. The ab initio calculations provide an explanation for the observation made by Yoshii et al. [2002, J. chem. Phys., 117, 1517] that Kr₂ ⁺ and Xe₂ ⁺ dissociate after photoemission from the II(1/2u) state to the I(1/2g) state whereas Ar₂ ⁺ does not.