Monetary Targeting in Pakistan: A Skeptical Note

The objective of this study is to evaluate monetary targeting strategy in Pakistan by testing the Quantity Theory of Money and the income velocity of money stated by Monetarists and the endogenous money hypothesis postulated by the Post Keynesians. Our tests on the Pakistani data covering about thirty years reveal that the quantity theory is an inadequate explanation of inflation, income velocity of money is unstable, and money is endogenous. These results suggest rethinking on monetary targeting strategy in Pakistan.Monetary Targeting, QTM, Income Velocity of Money, Endogenous Money

A simple state-based prognostic model for filter clogging

In today's maintenance planning, fuel filters are replaced or cleaned on a regular basis. Monitoring and implementation of prognostics on filtration system have the potential to avoid costs and increase safety. Prognostics is a fundamental technology within Integrated Vehicle Health Management (IVHM). Prognostic models can be categorised into three major categories: 1) Physics-based models 2) Data-driven models 3) Experience-based models. One of the challenges in the progression of the clogging filter failure is the inability to observe the natural clogging filter failure due to time constraint. This paper presents a simple solution to collect data for a clogging filter failure. Also, it represents a simple state-based prognostic with duration information (SSPD) method that aims to detect and forecast clogging of filter in a laboratory based fuel rig system. The progression of the clogging filter failure is created unnaturally. The degradation level is divided into several groups. Each group is defined as a state in the failure progression of clogging filter. Then, the data is collected to create the clogging filter progression states unnaturally. The SSPD method consists of three steps: clustering, clustering evaluation, and remaining useful life (RUL) estimation. Prognosis results show that the SSPD method is able to predicate the RUL of the clogging filter accurately

Gauge theories for fluids in 2+1 dimensions through master actions

Two actions which are functionals of different variables but describing the same dynamical system can be shown to possess the same origin by constructing a master action which generates both of them. We first present the master action which produces an action depending on the fluid variables and a gauge theory action whose equations of motion are equivalent to the incompressible fluid Euler equations in $2+1$ dimensions. We then introduce the master action generating the actions which on shell provide the linearized shallow water equations. One of them is a functional of the variables of the shallow water and the other one is the Maxwell-Chern-Simons gauge theory action.The maps between gauge vector fields and fluid variables are obtained for both of the systems. We employ them to derive the corresponding gauge theory solutions of the Hopfion and the coastal Kelvin wave solutions.Comment: 8 page

Development and Implementation of Artificial Intelligence in Colonoscopy

INTRODUCTION: There have been rapid advances in artificial intelligence (AI) applied to colonoscopy recently, particularly in tasks such as computer aided polyp detection (CADe). However, there has been limited clinical adoption. The aims of this thesis were to develop a CADe system to improve clinical value and identify major barriers to implementation. METHODS: A CADe algorithm was developed and evaluated on a high-risk dataset enriched with subtle lesions including a high proportion of advanced lesions. Performance was compared with endoscopists in a video study. An eye-tracking study was performed to investigate perceptual errors. An online survey was used to evaluate endoscopist opinions on different aspects of CADe designs for integration into clinical workflow. A video study was also performed to evaluate inter-observer variation in the perception of simulated CADe false positives. An international study was conducted to prioritise research priorities for the implementation of AI in colonoscopy using a modified Delphi method. RESULTS: The CADe algorithm demonstrated a high sensitivity for the detection of flat neoplasia, sessile serrated lesions and advanced polyps in an enriched video dataset. The algorithm detected significantly more subtle polyps than endoscopists. Eye tracking studies demonstrated that cognitive errors accounted for the majority of perceptual errors rather than gaze errors. The online survey demonstrated that there was a significant difference in the perception of visual markers, simulated false positives and their interference with regular workflow. The top ten research priorities for implementation were grouped into five major themes including clinical trial design, technological developments, integration into endoscopy workflow, data and regulatory approvals. CONCLUSIONS: Novel findings and insights into key areas where the development of AI in colonoscopy could be improved to provide further value have been identified. Current major barriers to AI implementation in routine practice are prioritised for future research