Generalized Transmuted Family of Distributions: Properties and Applications

We introduce and study general mathematical properties of a new generator of continuous distributions with two extra parameters called the Generalized Transmuted Family of Distributions. We investigate the shapes and present some special models. The new density function can be expressed as a linear combination of exponentiated densities in terms of the same baseline distribution. We obtain explicit expressions for the ordinary and incomplete moments and generating function, Bonferroni and Lorenz curves, asymptotic distribution of the extreme values, Shannon and R´enyi entropies and order statistics, which hold for any baseline model. Further, we introduce a bivariate extension of the new family. We discuss the different methods of estimation of the model parameters and illustrate the potential application of the model via real data. A brief simulation for evaluating Maximum likelihood estimator is done. Finally certain characterziations of our model are presented

CMB anisotropies seen by an off-center observer in a spherically symmetric inhomogeneous universe

The current authors have previously shown that inhomogeneous, but spherically symmetric universe models containing only matter can yield a very good fit to the SNIa data and the position of the first CMB peak. In this work we examine how far away from the center of inhomogeneity the observer can be located in these models and still fit the data well. Furthermore, we investigate whether such an off-center location can explain the observed alignment of the lowest multipoles of the CMB map. We find that the observer has to be located within a radius of 15 Mpc from the center for the induced dipole to be less than that observed by the COBE satellite. But for such small displacements from the center, the induced quadru- and octopoles turn out to be insufficiently large to explain the alignment.Comment: 8 pages (REVTeX4), 7 figures; v2: minor changes, matches published versio

(R1239) A New Type II Half Logistic-G family of Distributions with Properties, Regression Models, System Reliability and Applications

This study proposes a new family of distributions based on the half logistic distribution. With the new family, the baseline distributions gain flexibility through additional shape parameters. The important statistical properties of the proposed family are derived. A new generalization of the Weibull distribution is used to introduce a location-scale regression model for the censored response variable. The utility of the introduced models is demonstrated in survival analysis and estimation of the system reliability. Three data sets are analyzed. According to the empirical results, it is observed that the proposed family gives better results than other existing models

A Hydrodynamic Study of Benzyl Alcohol Oxidation in a Micro-Packed Bed Reactor

The various flow regimes prevalent during gold-palladium catalyzed benzyl alcohol oxidation in a micro-packed bed reactor and their influence on reaction performance are identified. The reaction is studied in a 300μm deep x 600μm wide silicon-glass micro-structured reactor packed with 65μm catalyst particles at a temperature of 120°C, pressure of 1 bar (g), using pure oxygen and neat benzyl alcohol as the feed. Significant improvement in the conversion and selectivity to the main product, benzaldehyde, is observed with increasing gas flowrate and decreasing liquid flowrate, which coincides with a change in the flow pattern from “liquid-dominated slug” (segregated regions of liquid and gas slugs) to “gas-continuous trickle” (thin film coated catalyst particles with gas flowing through the voids). The latter flow regime results in enhanced external mass transfer due to an increase in the available interfacial area and shorter diffusional distances. Results show selectivity up to 81% at a catalyst space time of 76 gcatgalc -1.s, outperforming a conventional batch laboratory reactor

Robot-Bone Attachment Device for Robot-Assisted Percutaneous Bone Fragment Manipulation

The treatment of joint-fractures is a common task in orthopaedic surgery causing considerable health costs and patient disabilities. Percutaneous techniques have been developed to mitigate the problems related to open surgery (e.g. soft tissue damage), although their application to joint-fractures is limited by the sub-optimal intra-operative imaging (2D-fluoroscopy) and by the high forces involved. Our earlier research toward improving percutaneous reduction of intra-articular fractures has resulted in the creation of a robotic system prototype, i.e. RAFS (Robot-Assisted Fracture Surgery) system. We propose a robot-bone attachment device for percutaneous bone manipulation, which can be anchored to the bone fragment through one small incision, ensuring the required stability and reducing the “biological cost” of the procedure. The device has been evaluated through the reduction of 9 distal femur fractures on human cadavers using the RAFS system

Type II General Exponential Class of Distributions

In this paper, a new class of continuous distributions with two extra positive parameters is introduced and is called the Type II General Exponential (TIIGE) distribution. Some special models are presented. Asymptotics, explicit expressions for the ordinary and incomplete moments, moment residual life, reversed residual life, quantile and generating functions and stress-strengh reliability function are derived. Characterizations of this family are obtained based on truncated moments, hazard function, conditional expectation of certain functions of the random variable are obtained. The performance of the maximum likelihood estimators in terms of biases, mean squared errors and confidence interval length is examined by means of a simulation study. Two real data sets are used to illustrate the application of the proposed class

RAFS: A computer-assisted robotic system for minimally invasive joint fracture surgery, based on pre- and intra-operative imaging

The integration of minimally invasive robotic assistance and image-guidance can have positive impact on joint fracture surgery, providing a better clinical outcome with respect to the current open procedure. In this paper, a new design of the RAFS surgical system is presented. The redesign of the robotic system and its integration with a novel 3D navigation system through a new clinical workflow, overcomes the drawbacks of the earlier prototype. This makes the RAFS surgical system more suitable to clinical scenarios in the operating theatre. System accuracy and effectiveness are successfully demonstrated through laboratory trials and preliminary cadaveric trials. The experimental results demonstrate that the system allows the surgeon to reduce a 2-fragment distal femur fracture in a cadaveric specimen, with a reduction accuracy of up to 0.85 mm and 2.2°. Preliminary cadaveric trials also provided a positive and favorable outcome pointing to the usability and safety of the RAFS system in the operating theatre, potentially enhancing the capacity of joint fracture surgeries

The Odd Log-Logistic Gompertz Lifetime Distribution: Properties and Applications

In this paper, we introduce a new three-parameter generalized version of the Gompertz model called the odd log-logistic Gompertz (OLLGo) distribution. It includes some well-known lifetime distributions such as Gompertz (Go) and odd log-logistic exponential (OLLE) as special sub-models. This new distribution is quite flexible and can be used effectively in modeling survival data and reliability problems. It can have a decreasing, increasing and bathtub-shaped failure rate function depending on its parameters. Some mathematical properties of the new distribution, such as closed-form expressions for the density, cumulative distribution, hazard rate function, the kth order moment, moment generating function and the quantile measure are provided. We discuss maximum likeli- hood estimation of the OLLGo parameters as well as three other estimation methods from one observed sample. The flexibility and usefulness of the new distribution is illustrated by means of application to a real data set