Democratic neutrino mass matrix from generalized Fridberg-Lee model with the perturbative solar mass splitting

We propose a phenomenological model of the Dirac neutrino mass matrix based on the Fridberg-Lee neutrino mass model at a special point. In this case, the Fridberg-Lee model reduces to the Democratic mass matrix with the $S_3$ permutation family symmetry. The Democratic mass matrix has an experimentally unfavored degenerate mass spectrum on the base of tribimaximal mixing matrix. We rescue the model to find a nondegenerate mass spectrum by adding the breaking mass term as preserving the twisted Fridberg-Lee symmetry. The tribimaximal mixing matrix can be also realized. Exact tribimaximal mixing leads to $\theta_{13}=0$. However, the results from Daya Bay and RENO experiments have established a nonzero value for $\theta_{13}$. Keeping the leading behavior of $U$ as tribimaximal, we use Broken Democratic neutrino mass model. We characterize a perturbation mass matrix which is responsible for a nonzero $\theta_{13}$ along with CP violation, besides the solar neutrino mass splitting has been resulted from it. We consider this work in two stages: In the first stage, we obtain the perturbation mass matrix with real components which breaks softly the $\mu-\tau$ symmetry and this leads to a nonzero value for $\theta_{13}$. In the second stage, we extend the perturbation mass matrix to a complex symmetric matrix which leads to CP violation. Therefore obtain a realistic neutrino mixing matrix with $\theta_{23}=45^\circ$. We obtain the solar mass splitting, the ordering of the neutrino masses is inverted. Using only two sets of the experimental data, we can fix all of the parameters of mass matrix and predict the masses of neutrinos and phases. These predictions include the following: $m_{1}\approx(4.82-4.93)10^{-2}eV$, $|m_2|\approx(4.90-5.01)10^{-2} eV$, $m_3\approx0$ and, $\phi\approx(0.687^\circ-10.31^\circ)$ as the origin of the Majorana phases.Comment: arXiv admin note: text overlap with arXiv:0811.0905, arXiv:1204.5619, arXiv:hep-ph/0511108 by other authors. substantial text overlap with arXiv:1505.04296, arXiv:1211.438

The design of worm gear sets

A method is presented for designing worm gear sets to meet torque multiplication requirements. First, the fundamentals of worm gear design are discussed, covering worm gear set nomenclature, kinematics and proportions, force analysis, and stress analysis. Then, a suggested design method is discussed, explaining how to take a worm gear set application, and specify a complete worm gear set design. The discussions are limited to cylindrical worm gear sets that have a 90 deg shaft angle between the worm and the mating gear

Lightweight Blockchain Framework for Location-aware Peer-to-Peer Energy Trading

Peer-to-Peer (P2P) energy trading can facilitate integration of a large number of small-scale producers and consumers into energy markets. Decentralized management of these new market participants is challenging in terms of market settlement, participant reputation and consideration of grid constraints. This paper proposes a blockchain-enabled framework for P2P energy trading among producer and consumer agents in a smart grid. A fully decentralized market settlement mechanism is designed, which does not rely on a centralized entity to settle the market and encourages producers and consumers to negotiate on energy trading with their nearby agents truthfully. To this end, the electrical distance of agents is considered in the pricing mechanism to encourage agents to trade with their neighboring agents. In addition, a reputation factor is considered for each agent, reflecting its past performance in delivering the committed energy. Before starting the negotiation, agents select their trading partners based on their preferences over the reputation and proximity of the trading partners. An Anonymous Proof of Location (A-PoL) algorithm is proposed that allows agents to prove their location without revealing their real identity. The practicality of the proposed framework is illustrated through several case studies, and its security and privacy are analyzed in detail

Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.Comment: arXiv admin note: substantial text overlap with arXiv:1503.0625