Integrating statistical and nonstatistical audit evidence using belief functions: A case of variable sampling

The main purpose of this article is to show how one can integrate statistical and nonstatistical items of evidence in the belief function framework. First, we use the properties of consonant belief functions to define the belief that the true mean of a variable lies in a given interval when a statistical test is performed for the variable. Second, we use the above definition to determine the sample size for a statistical test when a desired level of belief is needed from the sample. Third, we determine the level of belief that the true mean lies in a given interval when a statistical test yields certain values for the sample mean and the standard deviation of the mean for the variable. Finally, we use the auditing situation to illustrate the process of integrating statistical and nonstatistical items evidence. © 1994 John Wiley & Sons, Inc.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

A novel sine augmented scaled sine cosine algorithm for frequency control issues of a hybrid distributed two-area power system

Neural Computing and Applications - In this research article, a novel approach is proposed by considering the sine augmented scaled sine cosine (SAS-SCA) Algorithm for the load frequency control of...     

I-convergence in probabilistic n-normed space

In this article we introduce the notion of I-Cauchy sequence and I-convergent sequence in probabilistic n-normed space. The concept of I*-Cauchy sequence and I*-convergence in probabilistic n-normed space are also introduced and some of their properties related to these notions have been established.     

A balanced solution of a fuzzy soft set based decision making problem in medical science

The purpose of this paper is two folded. Firstly, the concept of mean potentiality approach (MPA) has been developed and an algorithm based on this new approach has been proposed to get a balanced solution of a fuzzy soft set based decision making problem. Secondly, a parameter reduction procedure based on relational algebra with the help of the balanced algorithm of mean potentiality approach has been used to reduce the choice parameter set in the parlance of fuzzy soft set theory and it is justified to the problems of diagnosis of a disease from the myriad of symptoms from medical science. Moreover the feasibility of this proposed method is demonstrated by comparing with Analytical Hierarchy Process (AHP), Naive Bayes classification method and Feng's method.     

Particle distribution control in cast aluminium alloy-mica composites

A suspension of mica particles (40m diameter and 3.7 thick) obtained in a mechanically stirred Al-4 wt % Cu-1.5 wt % Mg melt was poured and solidified in a variety of moulds under different heat flow configurations. The resulting cast structure showed a non-uniform distribution of dispersed mica particles with mica-depleted and segregated zones due to their flotation before and during solidification. The experimentally observed profiles of mica-free regions deviate significantly from those computed on the basis of Stokes's law and freezing-time computations. In relatively thick castings, segregation of mica could be minimized by using low pouring temperatures and/or side as well as bottom chilling. It was found, however, that thin castings (12.5 mm) could easily be produced with a homogeneous distribution of mica particles.     

Physiological Imaging Methods for Evaluating Response to Immunotherapies in Glioblastomas

Glioblastoma (GBM) is the most malignant brain tumor in adults, with a dismal prognosis despite aggressive multi-modal therapy. Immunotherapy is currently being evaluated as an alternate treatment modality for recurrent GBMs in clinical trials. These immunotherapeutic approaches harness the patient’s immune response to fight and eliminate tumor cells. Standard MR imaging is not adequate for response assessment to immunotherapy in GBM patients even after using refined response assessment criteria secondary to amplified immune response. Thus, there is an urgent need for the development of effective and alternative neuroimaging techniques for accurate response assessment. To this end, some groups have reported the potential of diffusion and perfusion MR imaging and amino acid-based positron emission tomography techniques in evaluating treatment response to different immunotherapeutic regimens in GBMs. The main goal of these techniques is to provide definitive metrics of treatment response at earlier time points for making informed decisions on future therapeutic interventions. This review provides an overview of available immunotherapeutic approaches used to treat GBMs. It discusses the limitations of conventional imaging and potential utilities of physiologic imaging techniques in the response assessment to immunotherapies. It also describes challenges associated with these imaging methods and potential solutions to avoid them.     

Microstructure and Properties of Spark Plasma-Sintered ZrO2–ZrB2 Nanoceramic Composites

In a recent work, ¹ we have reported the optimization of the spark plasma sintering (SPS) parameters to obtain dense nanostructured 3Y-TZP ceramics. Following this, the present work attempts to answer some specific issues: (a) whether ZrO₂-based composites with ZrB₂ reinforcements can be densified under the optimal SPS conditions for TZP matrix densification (b) whether improved hardness can be obtained in the composites, when 30 vol% ZrB₂ is incorporated and (c) whether the toughness can be tailored by varying the ZrO₂–matrix stabilization as well as retaining finer ZrO₂ grains. In the present contribution, the SPS experiments are carried out at 1200°C for 5 min under vacuum at a heating rate of 600 K/min. The SPS processing route enables retaining of the finer t -ZrO₂ grains (100–300 nm) and the ZrO₂–ZrB₂ composite developed exhibits optimum hardness up to 14 GPa. Careful analysis of the indentation data provides a range of toughness values in the composites (up to 11 MPa·m^1/2), based on Y₂O₃ stabilization in the ZrO₂ matrix. The influence of varying yttria content, t -ZrO₂ transformability, and microstructure on the properties obtained is discussed. In addition to active contribution from the transformation-toughening mechanism, crack deflection by hard second phase brings about appreciable increment in the toughness of the nanocomposites.     

Experimental design in soap manufacturing for optimization of fuzzified process capability index

In manufacturing industries, the quality of a product depends on the combined effect of multiple input variables working singly or together and therefore attention has been given on process capability indices to shift from single to multivariate domain. In case of multivariable domain the capability to incorporate uncertainties at the time of decision making becomes necessary. Fuzzy system is introduced to take care of this requirement. In this article the process parameters of soap manufacturing industries have been analyzed. The process capability is determined using Fuzzy Inference System rule editor based on a set of justified if then statements as applicable for the process. The data has been collected in linguistic form to derive its process capability, using a set of justified rules and the effect of each factor has been determined using Design of Experiments (DoE) and analysis of variance technique (ANOVA) for improving the soap quality from perspective of its softness. This article ventures to propose a new methodology by integrating Fuzzy with DoE providing better result followed by DoE and Fuzzy Inference system in isolation.     

Pressure drop for single and two‐phase flow of non‐newtonian liquids in helical coils

New experimental results on pressure loss for the single and two‐phase gas‐liquid flow with non‐Newtonian liquids in helical coils are reported. For a constant value of the curvature ratio, the value of the helix angle of the coils is varied from 2.56° to 9.37°. For single phase flow, the effect of helix angle on pressure loss is found to be negligible in laminar flow regime but pressure loss increases with the increasing value of helix angle in turbulent flow conditions. On the other hand, for the two‐phase flow, the well‐known Lockhart‐Martinelli method correlates the present results for all values of helix angle (2.56‐9.37°) satisfactorily under turbulent/laminar and turbulent/turbulent conditions over the following ranges of variables as: 0.57 ≤ n′ ≤ 1; Re′ < 4000; Re_l < 4000; Re_g < 8000; 8 ≤ x ≤ 1000 and 0.2 ≤ De′ ≤ 1000.