Transport properties in Simplified Double Exchange model

Transport properties of the manganites by the double-exchange mechanism are considered. The system is modeled by a simplified double-exchange model, i.e. the Hund coupling of the itinerant electron spins and local spins is simplified to the Ising-type one. The transport properties such as the electronic resistivity, the thermal conductivity, and the thermal power are calculated by using Dynamical mean-field theory. The transport quantities obtained qualitatively reproduce the ones observed in the manganites. The results suggest that the Simplified double exchange model underlies the key properties of the manganites.Comment: 5 pages, 5 eps figure

Competition between Kondo and RKKY correlations in the presence of strong randomness

We propose that competition between Kondo and magnetic correlations results in a novel universality class for heavy fermion quantum criticality in the presence of strong randomness. Starting from an Anderson lattice model with disorder, we derive an effective local field theory in the dynamical mean-field theory (DMFT) approximation, where randomness is introduced into both hybridization and Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions. Performing the saddle-point analysis in the U(1) slave-boson representation, we reveal its phase diagram which shows a quantum phase transition from a spin liquid state to a local Fermi liquid phase. In contrast with the clean limit of the Anderson lattice model, the effective hybridization given by holon condensation turns out to vanish, resulting from the zero mean value of the hybridization coupling constant. However, we show that the holon density becomes finite when variance of hybridization is sufficiently larger than that of the RKKY coupling, giving rise to the Kondo effect. On the other hand, when the variance of hybridization becomes smaller than that of the RKKY coupling, the Kondo effect disappears, resulting in a fully symmetric paramagnetic state, adiabatically connected with the spin liquid state of the disordered Heisenberg model. .....

Dynamic Physical-Layer Secured Link in a Mobile MIMO VLC System

This paper proposes a novel approach to provide a privately secured multiple-input and multiple-output visible light communication (VLC) in the mobility conditions. In the proposed system, a private secured VLC link is adaptively allocated to a mobile user all the time thanks to the movement tracking assistance by a camera-based detection system. The generation of the dynamic location-based scrambling matrix will be introduced providing a secured communication zone within a full normal coverage illumination area. An extensive range of numerical evaluation and practical experiments is carried out to demonstrate and evaluate the proposed system performance in different environment configurations including the mobility, camera resolutions, link range, and environment light intensity. We demonstrate that the proposed system is fully capable of securely steering the information with respect to a receiver location with a high level of reliability

One-pot preparation of alumina-modified polysulfone-graphene oxide nanocomposite membrane for separation of emulsion-oil from wastewater

In recent years, polysulfone-based nanocomposite membranes have been widely used for contaminated water treatment because they comprise properties such as high thermal stability and chemical resistance. In this study, a polysulfone (PSf) nanocomposite membrane was fabricated using the wet-phase inversion method with the fusion of graphene oxide (GO) and alumina (Al2O3) nanoparticles. We also showed that GO-Al2O3 nanoparticles were synthesised successfully by using a one-pot hydrothermal method. The nanocomposite membranes were characterised by Fourier transform infrared (FT-IR), scanning electron microscopy (SEM), nitrogen adsorption-desorption isotherms, energy-dispersive X-ray spectroscopy (EDX), thermogravimetric analysis (TGA), and water contact angle. The loading of GO and Al2O3 was investigated to improve the hydrophilic and oil rejection of the matrix membrane. It was shown that by using 1.5 wt.% GO-Al2O3 loaded in polysulfone, ~74% volume of oil was separated from the oil/water emulsion at 0.87 bar and 30 min. This figure was higher than that of the process using the unmodified membrane (PSf/GO) at the same conditions, in which only ~60% volume of oil was separated. The pH, oil/water emulsion concentration, separation time, and irreversible fouling coefficient (FRw) were also investigated. The obtained results suggested that the GO-Al2O3 nanoparticles loaded in the polysulfone membrane might have potential use in oily wastewater treatment applications

Multichannel Photon Counting Lidar Measurements Using USB-based Digital Storage Oscilloscope

We present a simple method of making multichannel photon counting measurements of weak lidar signal from large ranges, using commonly available USB-based digital storage oscilloscopes. The single photon pulses from compact photomultiplier tubes are amplified and stretched so that the pulses are large and broad enough to be sampled efficiently by the USB oscilloscopes. A software interface written in Labview is then used to count the number of photon pulses in each of the prescribed time bins to form the histogram of LIDAR signal. This method presents a flexible alternative to the modular multichannel scalers and facilitate the development of sensitive lidar systems

Charge-ordered ferromagnetic phase in manganites

A mechanism for charge-ordered ferromagnetic phase in manganites is proposed. The mechanism is based on the double exchange in the presence of diagonal disorder. It is modeled by a combination of the Ising double-exchange and the Falicov-Kimball model. Within the dynamical mean-field theory the charge and spin correlation function are explicitely calculated. It is shown that the system exhibits two successive phase transitions. The first one is the ferromagnetic phase transition, and the second one is a charge ordering. As a result a charge-ordered ferromagnetic phase is stabilized at low temperature.Comment: To appear in Phys. Rev.

An active learning framework for duplicate detection in SaaS platforms

With the rapid growth of users’ data in SaaS (Software-as-a-service) platforms using micro-services, it becomes essential to detect duplicated entities for ensuring the integrity and consistency of data in many companies and businesses (primarily multinational corporations). Due to the large volume of databases today, the expected duplicate detection algorithms need to be not only accurate but also practical, which means that it can release the detection results as fast as possible for a given request. Among existing algorithms for the deduplicate detection problem, using Siamese neural networks with the triplet loss has become one of the robust ways to measure the similarity of two entities (texts, paragraphs, or documents) for identifying all possible duplicated items. In this paper, we first propose a practical framework for building a duplicate detection system in a SaaS platform. Second, we present a new active learning schema for training and updating duplicate detection algorithms. In this schema, we not only allow the crowd to provide more annotated data for enhancing the chosen learning model but also use the Siamese neural networks as well as the triplet loss to construct an efficient model for the problem. Finally, we design a user interface of our proposed deduplicate detection system, which can easily apply for empirical applications in different companies

Duplicate identification algorithms in SaaS platforms

Existing duplicate records is one of the most common issues in many Software-as-as-Service (SaaS) platforms. In this paper, we study the duplicate identification problem in one specific SaaS platform related to quality and compliance management by using the address information. We interpret all typical mistakes from users that can generate the existent duplicated organizations in a given dataset, collected from the SaaS platform. Also, we create another set by crawling location data from Open Address (US Zone). We compare different methods, including Bag-of-words (using Cosine Distance), Record Linkage Toolkits, and Siamese Neural Networks using the triplet loss, in terms of precision, recall, and F1-score. The experimental results show that using Siamese Neural Networks can achieve a better performance in comparison with other techniques. We plan to publish our Open Address dataset and all implementation codes to facilitate further research in the related fields

Blood pressure screening during the May Measurement Month 2017 programme in Vietnam-South-East Asia and Australasia.

Elevated blood pressure (BP) is a growing burden worldwide, leading to over 10 million deaths each year. May Measurement Month (MMM) is a global initiative aimed at raising awareness of high BP and to act as a temporary solution to the lack of screening programmes worldwide. Our aim was to screen for hypertension (HTN) and cardiovascular risk factors in people aged ≥18 years in the community, thereby define the proportion of subjects with elevated BP and assess the awareness and the effectiveness of its treatment. An opportunistic cross-sectional survey of volunteers aged ≥18 years was carried out in May 2017. Blood pressure measurement, the definition of HTN and statistical analysis followed the standard MMM protocol. From May 2017 to June 2017, through 10 cities/provinces in Vietnam, 10 993 individuals with mean age 49.1 ± 16.2 years were screened during MMM17. After multiple imputation, 3154 (28.7%) had HTN. Of individuals not receiving antihypertensive medication, 1509 (16.1%) were hypertensive. Of individuals receiving antihypertensive medication, 620 (37.7%) had uncontrolled BP. Raised BP was also associated with additional risk factors including smoking, alcohol, overweight-obesity, and diabetes. May Measurement Month 17 was the largest BP screening campaign ever undertaken in Vietnam. Undiagnosed and uncontrolled HTN in Vietnam remains a substantial health problem. Local campaigns applying standardized methods such as MMM17, will be highly useful to screen for the significant number of individuals with raised BP and increase the awareness of HTN

Spectral functions in itinerant electron systems with geometrical frustration

The Hubbard model with geometrical frustration is investigated in a metallic phase close to half-filling. We calculate the single particle spectral function for the triangular lattice within dynamical cluster approximation, which is further combined with non-crossing approximation and fluctuation exchange approximation to treat the resulting cluster Anderson model. It is shown that frustration due to non-local correlations suppresses short-range antiferromagnetic fluctuations and thereby assists the formation of heavy quasi-particles near half-filling.Comment: 4 pages, 5 eps figure