Spin g Factors and Neutrino Magnetic Moment of Elementary Fermions

The spin magnetic moments and spin g factors (gs = -2) of electron, muon and tau are explained based on the electric charges (EC) and lepton charges (LC) in terms of the three-dimensional quantized space model. The spin g factors of electron, muon and tau are gs = -2 which is the sum of the EC g factor (gEC = -1) and the LC g factor (gLC = -1). The spin g factor (gs = -2) of the electron is predicted by the Dirac’s equation. The orbit g factors of electron, muon and tau are gL = gEC = -1 from the EC g factor (gEC = -1) without the contribution of the LC g factor (gLC = -1). The spin g factors of the elementary fermions are calculated from the equation of gs = gEC + gLC + gCC where gEC = EC/|EC|, gLC = LC/|LC| and gCC = CC/|CC|. For example, the spin g factors of the neutrinos and dark matters are gs = -1. The spin g factors of the u and d quarks are gs = 0 and gs = -2, respectively. The g factor problem of neutrinos with the non-zero LC charges are solved by the LC Coulomb force of Fc(LC) ≈0. It is, for the first time, proposed that the binary motion (fluctuations) of the mEC and mLC masses for the electron, muon and tau leptons make the anomalous g factor. This binary motion could be originated from the virtual particle processes including the photons. Also, the weak force (beta) decay is closely related to the binary motion of the mEC and mLC for the electron, muon and tau leptons.

Torque density maximization of vernier machine by using series compensation

This paper deals with the design of SPM vernier machine with high slots/pole/phase q, to get higher torque density as well as improved power factor by applying the concept of series compensation. The torque density of a vernier machine can be enhanced by increasing the number of slots/pole/phase q, but as q increases the drastic increase in the reactance makes the power factor even worse. Therefore, it is general to choose low q, even if higher torque can be obtained with higher q. In this study, the idea of series compensation is applied to get vernier machine with high q without the low power factor problem. Series compensation is performed by supplying the desired reactive power to the machine from an additional inverter with a floating capacitor. To validate the theoretical analyses, three SPM vernier machines with different q (1 ∼ 3) are designed and then analyzed by using FEM.

Adept smart meters using homomorphic encryption based on factor problem over groups

Purpose The purpose of this paper is to replace electronic meters with smart meters. Smart meters will provide high resolution real-time end-user power consumption data for utilities to better monitor and control the system, which is used for end users to better manage their energy usage and bills. By using smart meters, we can reduce the errors and also minimize human intervention in processing information in an efficient way. So that time will be reduced for organization functionalities and accuracy will be increase. Design/methodology/approach The authors propose a new cryptosystem based on factor problem over non commutative groups. They tried to use this cryptosystem in the field of electricity. They extend and transform this projected cryptosystem to design expert smart meters based on homomorphic encryption with factor problem. Findings In these smart meters, a monitoring system is integrated that preserves customer’s privacy by homomorphically accumulating the consumption of all n members of a specific domain. Originality/value This expert smart meter system has a proficient linear O(n) communication cost and is proven to protect customer's privacy even in the presence of a corrupted substation and some malicious smart meters.

Q-Table compression for reinforcement learning

Reinforcement learning (RL) algorithms are often used to compute agents capable of acting in environments without prior knowledge of the environment dynamics. However, these algorithms struggle to converge in environments with large branching factors and their large resulting state-spaces. In this work, we develop an approach to compress the number of entries in a Q-value table using a deep auto-encoder. We develop a set of techniques to mitigate the large branching factor problem. We present the application of such techniques in the scenario of a real-time strategy (RTS) game, where both state space and branching factor are a problem. We empirically evaluate an implementation of the technique to control agents in an RTS game scenario where classical RL fails and provide a number of possible avenues of further work on this problem.

Derivation, Replication, and Validity Analyses of a Screener for the Behavioral Assessment of Executive Functions in Young Adults

Ecologically valid indicators of executive functions are designed to capture dysfunction not easily measured in a lab setting. Here, we present two studies on the development and validity analyses of a behavioral screener for executive functions among young adults. In Study 1, we derived a four-factor (problem solving, attentional control, behavioral control, and emotional control) behavioral screener using a sample of 765 individuals. We used invariance analyses to evaluate the screener’s measurement reliability across sex. In Study 2, we replicated the screener derivation analyses using an independent sample of 197 undergraduates. To further examine the screener’s validity, we evaluated it against a well-known executive functions rating scale. The four-factor model was supported in both samples and analyses provided support for this screener as a valid and reliable measure for everyday executive functions among young adults.

Identification of Loads on Shield Tunneling Machines Based on PSO-SVM Method

The effective control of loads acting on shield tunneling machines is vital for the equipment operating security. The identification of shield machine loads is a non-linear multi-factor problem. This article proposes an identification method for shield machine loads by using the Particle Swarm Optimization (PSO) based Support Vector Machine (SVM). A PSO-SVM identification model is developed based on on-site data of a subway project. This model implements multi-parameter input related to loads, which reflects the mapping from loads to geological parameters and operating parameters during tunneling. The comparison between identification results and the on-site data verify the accuracy of the PSO-SVM method. This work can offer helpful references for the loads control.