BRST invariance and de Rham-type cohomology of 't Hooft-Polyakov monopole

We exploit the 't Hooft-Polyakov monopole to define closed algebra of the quantum field operators and the BRST charge $Q_{BRST}$. In the first-class configuration of the Dirac quantization, by including the $Q_{BRST}$-exact gauge fixing term and the Faddeev-Popov ghost term, we find the BRST invariant Hamiltonian to investigate the de Rham-type cohomology group structure for the monopole system. The Bogomol'nyi bound is also discussed in terms of the first-class topological charge defined on the extended internal 2-sphere.Comment: 8 page

Heavy-tailed statistics in short-message communication

Short-message (SM) is one of the most frequently used communication channels in the modern society. In this Brief Report, based on the SM communication records provided by some volunteers, we investigate the statistics of SM communication pattern, including the interevent time distributions between two consecutive short messages and two conversations, and the distribution of message number contained by a complete conversation. In the individual level, the current empirical data raises a strong evidence that the human activity pattern, exhibiting a heavy-tailed interevent time distribution, is driven by a non-Poisson nature.Comment: 4 pages, 4 figures and 1 tabl

Performance prediction of the full-scale bardenpho process using a genetic adapted time-delay neural network (GATDNN)

Wastewater treatment systems are characterized by large temporal variability of inflow, variable concentrations of components in the incoming wastewater to the plant, and highly variable biological reactions within the process. The behavior of observed process variables within a wastewater treatment plant (WWTP at a certain time instant is the combined effect of various processes initiated at different moments in the past. This is called a time-delay effect in the system. Due to the nature of strong nonlinear mapping, neural networks provide advantages as a modeling and identification tool over a structure-based model. However, the determination of the architecture of the artificial neural networks (ANNs) and the selection of key input variables with a time delay is not easy. in our research, a genetic adapted time-delay neural network (GATDNN), which is a combination of time-delay neural network(TDNN) and genetic algorithms(GAs), was developed and applied to the full-scale Bardenpho advanced sewage treatment process. In a GATDNN, a three-step modelling procedure was performed: (1) selection of significant input variables to maximise the predictive accuracy for each specific output; (2) finding a suitable network topology for the ANN-based process estimator; (3) sensitivity analysis. The results demonstrate that the modelling technique presented using a GATDNN provides a valuable tool for predicting the outputs with high levels of accuracy and identifying key operating variables. This work will permit the development of a reliable control strategy thus reducing the burden of the process engineer

Low-Complexity Iterative Detection for Orthogonal Time Frequency Space Modulation

We elaborate on the recently proposed orthogonal time frequency space (OTFS) modulation technique, which provides significant advantages over orthogonal frequency division multiplexing (OFDM) in Doppler channels. We first derive the input--output relation describing OTFS modulation and demodulation (mod/demod) for delay--Doppler channels with arbitrary number of paths, with given delay and Doppler values. We then propose a low-complexity message passing (MP) detection algorithm, which is suitable for large-scale OTFS taking advantage of the inherent channel sparsity. Since the fractional Doppler paths (i.e., not exactly aligned with the Doppler taps) produce the inter Doppler interference (IDI), we adapt the MP detection algorithm to compensate for the effect of IDI in order to further improve performance. Simulations results illustrate the superior performance gains of OTFS over OFDM under various channel conditions.Comment: 6 pages, 7 figure

Flavor symmetry breaking effects on SU(3) Skyrmion

We study the massive SU(3) Skyrmion model to investigate the flavor symmetry breaking (FSB) effects on the static properties of the strange baryons in the framework of the rigid rotator quantization scheme combined with the improved Dirac quantization one. Both the chiral symmetry breaking pion mass and FSB kinetic terms are shown to improve $c$ the ratio of the strange-light to light-light interaction strengths and $\bar{c}$ that of the strange-strange to light-light.Comment: 12 pages, latex, no figure

Can we improve the identification of cold homes for targeted home energy-efficiency improvements?

Objective: To investigate the extent to which homes with low indoor-temperatures can be identified from dwelling and household characteristics.Design: Analysis of data from a national survey of dwellings, occupied by low-income households, scheduled for home energy-efficiency improvements. Setting: Five urban areas of England: Birmingham, Liverpool, Manchester, Newcastle and Southampton.Methods: Half-hourly living-room temperatures were recorded for two to four weeks in dwellings over the winter periods November to April 2001-2002 and 2002-2003. Regression of indoor on outdoor temperatures was used to identify cold-homes in which standardized daytime living-room and/ or nighttime bedroom-temperatures were < 16 degrees C (when the outdoor temperature was 5 degrees C). Tabulation and logistic regression were used to examine the extent to which these cold-homes can be identified from dwelling and household characteristics.Results: Overall, 21.0% of dwellings had standardized daytime living-room temperatures < 16 degrees C and 46.4% had standardized nighttime bedroom-temperatures below the same temperature. Standardized indoor-temperatures were influenced by a wide range of household and dwelling characteristics, but most strongly by the energy efficiency (SAP) rating and by standardized heating costs. However, even using these variables, along with other dwelling and household characteristics in a multi-variable prediction model, it would be necessary to target more than half of all dwellings in our sample to ensure at least 80% sensitivity for identifying dwellings with cold living-room temperatures. An even higher proportion would have to be targeted to ensure 80% sensitivity for identifying dwellings with cold-bedroom temperatures.Conclusion: Property and household characteristics provide only limited potential for identifying dwellings where winter indoor temperatures are likely to be low, presumably because of the multiple influences on home heating, including personal choice and behaviour. This suggests that the highly selective targeting of energy-efficiency programmes is difficult to achieve if the primary aim is to identify dwellings with cold-indoor-temperatures. (c) 2006 Published by Elsevier Ltd

Information and Communication Technologies in Learning English as a Foreign Language (EFL): Attitudes of EFL Learners in Vietnam.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Coupling of Josephson current qubits using a connecting loop

We propose a coupling scheme for the three-Josephson junction qubits which uses a connecting loop, but not mutual inductance. Present scheme offers the advantages of a large and tunable level splitting in implementing the controlled-NOT (CNOT) operation. We calculate the switching probabilities of the coupled qubits in the CNOT operations and demonstrate that present CNOT gate can meet the criteria for the fault-tolerant quantum computing. We obtain the coupling strength as a function of the coupling energy of the Josephson junction and the length of the connecting loop which varies with selecting two qubits from the scalable design.Comment: 5 pages with updates, version to appear in Phys. Rev.