Study of decentralised decision models in distributed environments

Many of today's complex systems require effective decision making within uncertain distributed environments. The central theme of the thesis considers the systematic analysis for the representation of decision making organisations. The basic concept of stochastic learning automata provides a framework for modelling decision making in complex systems. Models of interactive decision making are discussed, which result from interconnecting decision makers in both synchronous and sequential configurations. The concepts and viewpoints from learning theory and game theory are used to explain the behaviour of these structures. This work is then extended by presenting a quantitative framework based on Petri Net theory. This formalism provides a powerful means for capturing the information flow in the decision-making process and demonstrating the explicit interactions between decision makers. Additionally, it is also used for the description and analysis of systems that axe characterised as being concurrent, asynchronous, distributed, parallel and/ or stochastic activities. The thesis discusses the limitations of each modelling framework. The thesis proposes an extension to the existing methodologies by presenting a new class of Petri Nets. This extension has resulted in a novel structure which has the additional feature of an embedded stochastic learning automata. An application of this approach to a realistic decision problem demonstrates the impact that the use of an artificial intelligence technique embedded within Petri Nets can have on the performance of decision models

Machine Learning for Cardiovascular Disease Risk Assessment: A Systematic Review

Accurate diagnosis and early detection of heart disease can help save lives because it is the primary cause of mortality. If a forecast is inaccurate, patients could potentially suffer significant harm. Today, it is challenging to predict and identify heart disease. 24 hour monitoring is not practical due to the extensive equipment and time required. Heart disease treatments can be both expensive and challenging. In order to obtain the data from databases and use this information to successfully forecast cardiac illness, a variety of data mining techniques and machine learning algorithms are now accessible. We have used every technique to put the heart disease prognosis into practise. The algorithms used in SVM, NAIVE BAYER, REGRESSION, KNN, ADABOOST, DECISION TREE, and XG-BOOST And Voting Ensemble Method

Optimal Design of Hydraulic Disc Brake for Magnetorheological (MR) Application

This paper aims to provide a new design considering compressive force application in the MR fluid andimprove its braking torque by optimizing it. According to the current study, compressing the MR region will increase braking torque compared to no compression. The area covered by an existing model of the conventional disc brake is taken into consideration for the unique design of the MR brake to operate in shear and compression mode, and the required compression given by the hydraulic pressure similar to a conventional disc brake. The suggested MR brake’s structural layout is presented. The Herschel-Bulkley shear thinning model’s mathematical expression for the torque equation for the compression and shear modes is provided. An analytical magnetic circuit is done for the proposed design for determining the relationship between applied current and magnetic field strength as a function of the geometrical and material attributes of the MR brake. Simulation is done on COMSOL software with the help of an AC/DC module, considering the non-linear relationship between the magnetic field and magnetic flux. Simulation results of braking torque achieved with the varying current are determined. The graph displays the braking torque for current in the compression plus shear mode as well as shear mode. After that, optimization is done on the proposed model for optimal design parameters. For optimization, we adopt the most popular Genetic Algorithm (GA) method. Optimization aims to increase the braking torque capacity of the MR brake for the given volume

Systematic Development and Test-Retest Reliability of The Electronic Instrumental Activities of Daily Living Satisfaction Assessment (EISA) Outcome Measure

Assessment of the level of satisfaction with completing Instrumental Activities of Daily Living (IADLs) using accessible Information Communication Technology (ICT) or Electronic Assistive Devices (EAD) is critical for enabling high quality of life and community participation for people with disabilities (PWD). Currently there are no reliable and valid outcome measures that have been specifically designed for assessing level of satisfaction with completing IADLs using EAD. In this dissertation study, the Electronic Instrumental activities of daily living Satisfaction Assessment (EISA) self-report outcome measure was developed to fill this void. The EISA research study had the following specific aims: (1) identify common functional tasks that all people use ICT to complete; (2) review the literature to identify any existing outcome measures for EAD; (3) develop and establish content validity at acceptable levels; and (4) establish test-retest reliability and internal consistency at acceptable levels. The EISA research study was sub-divided into 4 studies. Study 1, reviewed the literature, to assess, common functional tasks, that all people, with or without disabilities, use ICT to complete. Study 2, reviewed the literature, to identify any existing outcome measures for EAD. This study had three phases: phase 1 reviewed relevant databases to identify any self-report outcome measures for EAD; phase 2 reviewed the National Institutes of Health (NIH) Patient Reported Outcome (PRO) measures; and phase 3 reviewed the literature to identify any self-report IADL measures. Study 3 involved content validation using expert clinicians and EAD users, as domain experts. Study 4 covered establishment of test-retest reliability and internal consistency at acceptable levels. Using the Scale Content Validity Index (SCVI) Average method, the content validity of the EISA, was SCVI = 0.91. Reliability was assessed by conducting a repeated-measures cohort study (n = 84) using the Qualtrics on-line research platform. Both test-retest reliability (Rs = .81) and internal consistency (Cronbach's alpha = 0.88) of EISA were found to be acceptable. The study results indicate that the EISA-Version 1.0 is a reliable and stable tool for assessing the functional performance of individuals who use or need EAD interventions

Environmental Conditions and the Fertility Intentions of Utahns

One of the most important decisions a person may make in their lifetime is whether to have children. Many factors shape fertility intentions and outcomes. A better understanding of individual reproductive intentions can shed light on current fertility patterns, enable more accurate population projections1-2 and planning efforts, and improve our ability to address environmental drivers and implications

General Formula for the Momentum Imparted to Test Particles in Arbitrary Spacetimes

Ehlers and Kundt have provided an approximate procedure to demonstrate that gravitational waves impart momentum to test particles. This was extended to cylindrical gravitational waves by Weber and Wheeler. Here a general, exact, formula for the momentum imparted to test particles in arbitrary spacetimes is presented.Comment: 6 page