Optimal Stratification of Univariate Populations via stratify R Package

Stratification reduces the variance of sample estimates for population parameters by creating homogeneous strata. Often, surveyors stratify the population using the most convenient variables such as age, sex, region, etc. Such convenient methods often do not produce internally homogeneous strata, hence, the precision of the estimates of the variables of interest could be further improved. This paper introduces an R-package called ’stratifyR’ whereby it proposes a method for optimal stratification of survey populations for a univariate study variable that follows a particular distribution estimated from a data set that is available to the surveyor. The stratification problem is formulated as a mathematical programming problem and solved by using a dynamic programming technique. Methods for several distributions such as uniform, weibull, gamma, normal, lognormal, exponential, right-triangular, cauchy and pareto are presented. The package is able to construct optimal stratification boundaries (OSB) and calculate optimal sample sizes (OSS) under Neyman allocation. Several examples, using simulated data, are presented to illustrate the stratified designs that can be constructed with the proposed methodology. Results reveal that the proposed method computes OSB that are precise and comparable to the established methods. All the calculations presented in this paper were carried out using the stratifyR package that will be made available on CRAN

Readiness and Perception of Pacific Students to Mobile Phones for Higher Education

The emergence and advancement of Information Communication Technologies have transformed facilitation and content delivery in higher education worldwide, the Pacific region being no exception. The extensive use of mobile phones in the Pacific, especially with the student-aged populace, is gradually creating a niche for mobile learning in the education landscape. However, there is a growing concern on the effectiveness of this innovative intervention keeping in mind the digital intelligence of Pacific students. This paper explores the readiness and perception of the Pacific students using mobile devices for learning. An online questionnaire was used to collect data for this exploratory research. While the results revealed the student’s willingness and positive perception to leverage on mobile phones for learning in higher education, their readiness for the mobile-driven education is rather dependent on various factors which are explored in detail. The paper concludes with recommendations for the higher education institutes and education ministries in the Pacific region

Future Armour Materials and Technologies for Combat Platforms

The ultimate goal of armour research is to create better armour for battle worthy combat plat forms such as main battle tanks, infantry combat vehicles and light combat vehicles. In each of these applications, the main aim boils down to one of the two things; either reduce the weight without sacrificing protection or enhance the performance at same or even reduced weight. In practice, these ambitions can be fulfilled only if we have with us, appropriate improved armour materials, advanced and innovative technologies and also improved designs, which enable us to use them for creating next generation armour modules. Armour systems have progressed through improvements in metallic, ceramic and lightweight (low areal density) composite materials. Similarly, the advances in development of explosive reactive armour (ERA) and non-explosive reactive armour (NERA) have generated efficient armour system against contemporary high explosive antitank ammunition and missile threats for the armoured vehicles. Yet, to achieve armour performance exceeding that of the current light combat vehicles and main battle tanks, further advancements in armour materials, systems, and survivability technologies are required for new vehicular systems that weigh significantly less than the present combat platforms. Various approaches and advancements in the metallic and composite armour materials, ERA and NERA systems to improve the survivability of armoured vehicles in the futuristic multi-spectral battlefield scenarios are described

stratifyR: Optimal Stratification of Univariate Populations

This R package implements the stratification of univariate populations under stratified sampling designs using the method of Khan et al. (2002, 2008, 2015). It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions

Optimal stratification in stratified designs using weibull - distributed auxiliary information

Sampling has evolved into a universally accepted approach for gathering information and data mining as it is widely accepted that a reasonably modest-sized sample can sufficiently characterize a much larger population. In stratified sampling designs, the whole population is divided into homogeneous strata in order to achieve higher precision in the estimation. This paper proposes an efficient method of constructing optimum stratum boundaries (OSB) and determining optimum sample size (OSS) for the survey variable. The survey variable may not be available in practice since the variable of interest is unavailable prior to conducting the survey. Thus, the method is based on the auxiliary variable which is usually readily available from past surveys. To illustrate the application as an example using a real data, the auxiliary variable considered for this problem follows Weibull distribution. The stratification problem is formulated as a Mathematical Programming Problem (MPP) that seeks minimization of the variance of the estimated population parameter under Neyman allocation. The solution procedure employs the dynamic programming technique, which results in substantial gains in the precision of the estimates of the population characteristics

On the temperature dependence of the symmetry energy

We perform large-scale shell model Monte Carlo (SMMC) calculations for many nuclei in the mass range A=56-65 in the complete pfg_{9/2}d_{5/2} model space using an effective quadrupole-quadrupole+pairing residual interaction. Our calculations are performed at finite temperatures between T=0.33-2 MeV. Our main focus is the temperature dependence of the symmetry energy which we determine from the energy differences between various isobaric pairs with the same pairing structure and at different temperatures. Our SMMC studies are consistent with an increase of the symmetry energy with temperature. We also investigate possible consequences for core-collapse supernovae events

High frequency impedance based fault location in distribution system with DGs

Distributed generations (DGs) in the distribution systems are connected into the buses using power electronic converters. During fault, it is challenging to provide a constant impedance model for DGs in the system frequency due to the variable converter control strategies. System frequency impedance measurement based fault locations can be influenced by the converters’ fault behaviour. This study addresses this problem by proposing a wide-area high-frequency impedance comparison based fault location technique. The high-frequency impedance model of DG is provided. Based on the constant DG impedance model in high-frequency range, the faulted line sections can be distinguished by comparing the measured impedance differences without requiring the exact distribution system parameters. Simulation results show that the proposed wide-area transient measurements based fault location method can provide accurate faulted sections in the distribution systems with DGs regardless of the load and DG output variations, measurement noise, unbalanced loads and islanding operations