Approximation of circular arcs by Bézier curves

AbstractFor the circular arc of angle 0<α<π we present the explicit form of the best GC3 quartic approximation and the best GC2 quartic approximations of various types, and give the explicit form of the Hausdorff distance between the circular arc and the approximate Bézier curves for each case. We also show the existence of the GC4 quintic approximations to the arc, and find the explicit form of the best GC3 quintic approximation in certain constraints and their distances from the arc. All approximations we construct in this paper have the optimal order of approximation, twice of the degree of approximate Bézier curves

East Asian Financial and Monetary Cooperation and Its Prospect : Beyond the CMI

This paper examines the ASEAN+3 cooperation of regional financial safety nets, and reviews the regional monetary issues of a single currency and currency competition in East Asia. We point out potential systemic risks in East Asia and the importance of regional surveillance. ASEAN+3 regional surveillance should move forward to the stronger measures of peer review and peer pressure, and make the AMRO a well-resourced professional surveillance secretariat to create capacity to apply independent conditionality. To this effective surveillance mechanism, we propose to establish the Board of Coordination to support the ASEAN+3 ERPD by confirming its decision or remitting the relevant case to the ASEAN+3 ERPD and providing possible legal consultation. The institution building of the CMIM secretariat will accelerate the establishment of a regional monetary institution, e.g. an Asian Monetary Fund. The current crisis provides sufficient incentives for East Asian economies to pursue internationalization of their currencies, and it would open the possibility towards a single currency in East Asia.Regional financial safety nets, surveillance mechanism, CMIM, ABMI

Developing Early Risk Detection and Preparedness System with Risk Analysis and Contingency Plan

PresentationWhen the natural or human-made disasters, such as hurricanes, floods, tornadoes, wildfires and gas leaks, threaten a populated area, mass casualties and property losses may be followed. To avoid, minimize or eliminate the risks for public safety, a well-organized early risk detection and preparedness system is needed in order to save lives and minimize losses. To make this early detection system efficient yet effective, a mobile app, risk preparedness aid, was developed. This aid system can communicate with sensors, location information, and disaster management server. The aid was designed using the concepts of location based service and risk management and it includes gas leak detection, warning and emergency evacuation procedure with routing. Based on the identified risks and preparing procedure, various contingency plans were developed. The contingency plans should be very clear so that it is easy for public and employee to follow. Because each system has unique infrastructure its contingency plan must be unique. This paper also shows an evacuation process in the form of a flowchart for ease of use in the event of an emergency

Identification of protein functions using a machine-learning approach based on sequence-derived properties

<p>Abstract</p> <p>Background</p> <p>Predicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities.</p> <p>Results</p> <p>A highly accurate prediction method capable of identifying protein function, based solely on protein sequence properties, is described. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions and determines the combination of protein sequence features that best characterises protein function. Thirty-three features that represent subtle differences in local regions and full regions of the protein sequences were introduced. On the basis of 484 features extracted solely from the protein sequence, models were built to predict the functions of 11 different proteins from a broad range of cellular components, molecular functions, and biological processes. The accuracy of protein function prediction using random forests with feature selection ranged from 94.23% to 100%. The local sequence information was found to have a broad range of applicability in predicting protein function.</p> <p>Conclusion</p> <p>We present an accurate prediction method using a machine-learning approach based solely on protein sequence properties. The primary contribution of this paper is to propose new <it>PNPRD </it>features representing global and/or local differences in sequences, based on positively and/or negatively charged residues, to assist in predicting protein function. In addition, we identified a compact and useful feature subset for predicting the function of various proteins. Our results indicate that sequence-based classifiers can provide good results among a broad range of proteins, that the proposed features are useful in predicting several functions, and that the combination of our and traditional features may support the creation of a discriminative feature set for specific protein functions.</p