The factors affecting the student’s decision to continue to rent accommodation in Thu Duc district

The authors have carried out a study to determine and evaluate the impact of the factors affecting students' decisions to continue renting accommodation in Thu Duc District. The research model is built on Maslow's theory of demand hierarchy, Mankiw's theory of choice in consumption, Le Bon's theory of crowd psychology, Hoang Huu Phe and Wakely's theory of position and quality. The study is conducted through 2 phases: Qualitative research and quantitative research with the answer sheets of 668 students. Preliminary research results with responses from 30 students conducted by the direct interview method show that all students agree 7 factors are affecting the decision to continue renting. The primary research was performed with answer sheets of 668 students, all valid votes were filtered, coded, and then analyzed for the reliability of Cronbach's Alpha, EFA method, Logit analysis method, correct forecast rate of the Logit model, Logit model defect test, and Logit model conformity test. Logit's analysis results show that there are 4 factors affecting the decision to continue renting, including (1) Social relations, (2) Facilities, (3) Environment, (4) Price. The results show the needs and concerns of students in Thu Duc District when making the decision to continue renting, through which the authors give recommendations to improve the quality of accommodation for homeowners and student awareness.

A hybrid predictive methodology for head checks in railway infrastructure

This paper presents a hybrid method to assess the rail health with the focus on a specific type of rail surface defect called head check. The proposed method uses physics-based and data-driven models in order to model defect initiation and defect evolution on a rail for a given rail traffic tonnage. Ultrasonic (US) and Eddy Current (EC) defect detection measurements are used to provide Infrastructure Managers (IMs) with insight in the current rail condition. The defect initiation results obtained from the first part of the hybrid method which consists of the physics-based model is successfully validated with the EC measurements. Furthermore, the US and EC measurements are utilized to derive a data-driven model for defect evolution. Finally, a set of robust and predictive Key Performance Indicators (KPIs) are proposed to quantify the future condition of the rail based on different characteristics of rail health resulting from the defect initiation and defect evolution analysis.

Particle-like behavior of defects near a defect line in 2D Ising model: Defect–antidefect pair production and interaction

The aim of this work is to investigate Casimir effect in a system comprising of a defect line along with isolated defects (vacancies) in 2D Ising model. We have found out that the interaction energy has a decaying exponent with distance between defects. We are interested in an analogy between Casimir behavior of this defect structure and quantum field theory. The simplest deformation of a defect line (a defect’s position change) can be treated as defect–“antidefect” pair creation. Single defect is attracted to a defect line. By means of Monte Carlo simulation, the energy of pair creation and Casimir interaction potential are calculated. The interaction turned out that a Yukawa potential turns to the Coulomb’s one at phase transition point.

Control Quality of Composite Materials by Using Subminiature Eddy Current Transducers

Based eddy current transducer (ECT), a probe has been designed to research composite materials. Defects inspection of composite materials is performed to determine the following standard defects: defect of the metallic and (or) polymer layer uniformity. The subminiature ECT of the original design is used as a sensor in this device, it is made according to a differential scheme of switching on of the coils of a transformer ECT and allowing to localize the control area up to 0.1-0.5 mm. The measurement procedure allowing one to detect defects in composite materials with a high accuracy is described. The sensor was tested on the composite material consisting of paper or low-density polyethylene and aluminum layers in which the model defect was placed. The dependences of the ECT signal on the defect in this structure are given. The determined dependence of electrical conductivity of composite materials on model defects make it possible to carry out defects inspection of composite materials.

Formulation of Model Defects Suitable for the Resonance Regime

A method to account for model deficiencies in nuclear data evaluations in the resonance regime is proposed. The method follows the ideas of Schnabel and coworkers and relies on Gaussian processes with a novel problemadapted ansatz for the covariance matrix of model uncertainties extending the formalism to the energy region of resonances. The method was used to evaluate a set of schematic but realistic neutron reaction data generated by an R-matrix code and a well defined model defect. Using the extended ansatz for model defects the Bayesian evaluation successfully recovered the built-in model defect in size and structure thus demonstrating the applicability of the method.

A first sketch: Construction of model defect priors inspired by dynamic time warping

Model defects are known to cause biased nuclear data evaluations if they are not taken into account in the evaluation procedure. We suggest a method to construct prior distributions for model defects for reaction models using neighboring isotopes of 56Fe as an example. A model defect is usually a function of energy and describes the difference between the model prediction and the truth. Of course, neither the truth nor the model defect are accessible. A Gaussian process (GP) enables to define a probability distribution on possible shapes of a model defect by referring to intuitively understandable concepts such as smoothness and the expected magnitude of the defect. Standard specifications of GPs impose a typical length-scale and amplitude valid for the whole energy range, which is often not justified, e.g., when the model covers both the resonance and statistical range. In this contribution, we show how a GP with energy-dependent length-scales and amplitudes can be constructed from available experimental data. The proposed construction is inspired by a technique called dynamic time warping used, e.g., for speech recognition. We demonstrate the feasibility of the data-driven determination of model defects by inferring a model defect of the nuclear models code TALYS for (n,p) reactions of isotopes with charge number between 20 and 30. The newly introduced GP parametrization besides its potential to improve evaluations for reactor relevant isotopes, such as 56Fe, may also help to better understand the performance of nuclear models in the future.

Bayesian optimization of generalized data

Direct application of Bayes' theorem to generalized data yields a posterior probability distribution function (PDF) that is a product of a prior PDF of generalized data and a likelihood function, where generalized data consists of model parameters, measured data, and model defect data. The prior PDF of generalized data is defined by prior expectation values and a prior covariance matrix of generalized data that naturally includes covariance between any two components of generalized data. A set of constraints imposed on the posterior expectation values and covariances of generalized data via a given model is formally solved by the method of Lagrange multipliers. Posterior expectation values of the constraints and their covariance matrix are conventionally set to zero, leading to a likelihood function that is a Dirac delta function of the constraining equation. It is shown that setting constraints to values other than zero is analogous to introducing a model defect. Since posterior expectation values of any function of generalized data are integrals of that function over all generalized data weighted by the posterior PDF, all elements of generalized data may be viewed as nuisance parameters marginalized by this integration. One simple form of posterior PDF is obtained when the prior PDF and the likelihood function are normal PDFs. For linear models without a defect this PDF becomes equivalent to constrained least squares (CLS) method, that is, the χ2 minimization method.

A Software Defect Prediction Model during the Test Period

Effective defect prediction is an important topic in software engineering. This paper studies multiple defect prediction models and proposes a defect prediction model during the test period for organic project. This model is based on the analysis of project defect data and refer to Rayleigh model. Defect prediction model plays an important role in the analysis of software quality, rationally allocating resources of software test, improving the efficiency of software test. This paper selected representative software defect data to apply this model, which has been shown to improve project performance.