2 and 3-dimensional Hamiltonians with Shape Invariance Symmetry

Via a special dimensional reduction, that is, Fourier transforming over one of the coordinates of Casimir operator of su(2) Lie algebra and 4-oscillator Hamiltonian, we have obtained 2 and 3 dimensional Hamiltonian with shape invariance symmetry. Using this symmetry we have obtained their eigenspectrum. In the mean time we show equivalence of shape invariance symmetry and Lie algebraic symmetry of these Hamiltonians.Comment: 24 Page

SURVEY THE MUTATION OF FGB (BETA FIBRINOGEN) AND FV (FACTOR V LEIDEN), FACTOR XIII AND FACTOR II (PROTHROMBIN), IN PATIENTS WITH RECURRENT ABORTIONS ALONG WITH NORMAL KARYOTYPE

Some pregnancies are abnormal in human genetically and end with the spontaneous abortion, which is the most common problem of pregnancy. The recurrent abortions are often referred to as multifactorial disease that one of which is thrombosis. The thrombosis in placenta capillaries seems to disturb the blood circulation between the mother and the fetus and eventually lead to abortion. Recently, studies have shown that genetic basis for thrombophilia relates with recurrent abortion. The aim of this study is the survey of G1691A and G4070A mutations in the Factor V gene, -455G>A mutation in the gen of XIII factor, G103T mutation in Beta fibrinogen and A20210G mutation in the thrombin gene. The samples were collected from 60 patients referred to Tehran Imam Khomeini hospital .DNA was extracted from patients' blood samples by multiple PCR simultaneously containing different mutations were duplicated then the existence of mutation was evaluated by the strip technique. The genes mutation of G1691A in Factor V, G4070A in Factor V, G103T in Beta fibrinogen, -455G>A in the XIII factor and G20210A were identified 6.6, 45, 36, 40 and 3.3 respectively. Studies on the other population showed that frequency of examined mutations varies with other communities. Anyway, more samples are required in order to obtain more accurate statistics related to the frequency of mutations

The importance of Guilan silk and its economic significance at Safavid period in Caspian region

Guilan province is located in north of Iran. It plays an important natural, economic and strategic role in Iranian history due to its geographical location between the Caspian Sea and the Alborz Mountain. This particular characteristic of Guilan has made it one of the most important political centers in its history and in the history of its struggles against the central government of Iran. Iran neighborhood with Russia and Caucasia during the Safavid Dynasty has added to Guilan economic significance. Guilan silk in the Safavid era made Iran well known as far as most of traders and tourists and orientalists who traveled to Iran have pointed to it in their writings. In this study, the importance of this strategic merchandise are investigated and discussed from the viewpoint of economics, income creation, commerce, tolls, customs as well as its export, taxes, products and the commercial routes

Relationship between stuttering severity in children and their mother's speaking rate

Context and Objective: Stuttering is a complex disease that influences occupational, social, academic and emotional achievements. The aim of this study was to correlate the stuttering severity index with speaking rates of mothers and children. Design and Setting: Cross-sectional study, at the child rehabilitation clinics of Tehran city. Methods: 35 pairs of mothers and their children who stuttered were studied. There were 29 boys and six girls, of mean age 8.5 years (range: 5.1-12.0). Speech samples from the mother-child pairs were audiotaped for approximately 15 minutes, until a reciprocal verbal enteraction had been obtained. This sample was then analyzed in accordance with a stuttering severity index test and speaking rate parameters. Results: The research results outlined a significant relationship between the mothers' speaking rate and their children stuttering severity. Conclusion: The results suggest that the mothers' speaking rate should be incorporated in the assessment and treatment of stuttering. Copyright © 2008, Associação Paulista de Medicina

Quantum phase transition to unconventional multi-orbital superfluidity in optical lattices

Orbital physics plays a significant role for a vast number of important phenomena in complex condensed matter systems such as high-T$_c$ superconductivity and unconventional magnetism. In contrast, phenomena in superfluids -- especially in ultracold quantum gases -- are commonly well described by the lowest orbital and a real order parameter. Here, we report on the observation of a novel multi-orbital superfluid phase with a {\it complex} order parameter in binary spin mixtures. In this unconventional superfluid, the local phase angle of the complex order parameter is continuously twisted between neighboring lattice sites. The nature of this twisted superfluid quantum phase is an interaction-induced admixture of the p-orbital favored by the graphene-like band structure of the hexagonal optical lattice used in the experiment. We observe a second-order quantum phase transition between the normal superfluid (NSF) and the twisted superfluid phase (TSF) which is accompanied by a symmetry breaking in momentum space. The experimental results are consistent with calculated phase diagrams and reveal fundamentally new aspects of orbital superfluidity in quantum gas mixtures. Our studies might bridge the gap between conventional superfluidity and complex phenomena of orbital physics.Comment: 5 pages, 4 figure

Pattern Functional Dependencies for Data Cleaning

Patterns (or regex-based expressions) are widely used to constrain the format of a domain (or a column), e.g., a Year column should contain only four digits, and thus a value like "1980-" might be a typo. Moreover, integrity constraints (ICs) defined over multiple columns, such as (conditional) functional dependencies and denial constraints, e.g., a ZIP code uniquely determines a city in the UK, have been widely used in data cleaning. However, a promising, but not yet explored, direction is to combine regex- and IC-based theories to capture data dependencies involving partial attribute values. For example, in an employee ID such as"F-9-107", "F" is sufficient to determine the finance department. Inspired by the above observation, we propose a novel class of ICs, called pattern functional dependencies (PFDs), to model fine-grained data dependencies gleaned from partial attribute values. These dependencies cannot be modeled using traditional ICs, such as (conditional) functional dependencies, which work on entire attribute values. We also present a set of axioms for the inference of PFDs, analogous to Armstrong's axioms for FDs, and study the complexity of consistency and implication analysis of PFDs. Moreover, we devise an effective algorithm to automatically discover PFDs even in the presence of errors in the data. Our extensive experiments on 15 real-world datasets show that our approach can effectively discover valid and useful PFDs over dirty data, which can then be used to detect data errors that are hard to capture by other types of ICs

Land subsidence susceptibility mapping in South Korea using machine learning algorithms

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results

A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811)

Modified spin-wave theory with ordering vector optimization I: frustrated bosons on the spatially anisotropic triangular lattice

We investigate a system of frustrated hardcore bosons, modeled by an XY antiferromagnet on the spatially anisotropic triangular lattice, using Takahashi's modified spin-wave (MSW) theory. In particular we implement ordering vector optimization on the ordered reference state of MSW theory, which leads to significant improvement of the theory and accounts for quantum corrections to the classically ordered state. The MSW results at zero temperature compare favorably to exact diagonalization (ED) and projected entangled-pair state (PEPS) calculations. The resulting zero-temperature phase diagram includes a 1D quasi-ordered phase, a 2D Neel ordered phase, and a 2D spiraling ordered phase. We have strong indications that the various ordered or quasi-ordered phases are separated by spin-liquid phases with short-range correlations, in analogy to what has been predicted for the Heisenberg model on the same lattice. Within MSW theory we also explore the finite-temperature phase diagram. We find that the zero-temperature long-range-ordered phases turn into quasi-ordered phases (up to a Berezinskii-Kosterlitz-Thouless temperature), while zero-temperature quasi-ordered phases become short-range correlated at finite temperature. These results show that modified spin-wave theory is very well suited for describing ordered and quasi-ordered phases of frustrated XY spins (or, equivalently, of frustrated lattice bosons) both at zero and finite temperatures. While MSW theory, just as other theoretical methods, cannot describe spin-liquid phases, its breakdown provides a fast method for singling out Hamiltonians which may feature these intriguing quantum phases. We thus suggest a tool for guiding our search for interesting systems whose properties are necessarily studied with a physical quantum simulator.Comment: 40 pages, 16 figure