A COMPARATIVE STUDY OF HEURISTIC OPTIMIZATION ALGORITHMS

Heuristic optimization algorithms are of great importance for reaching solutions to various real world problems. These algorithms have a wide range of applications such as cost reduction, artificial intelligence, and medicine. By the term cost, one could imply that that cost is associated with, for instance, the value of a function of several independent variables. Often, when dealing with engineering problems, we want to minimize the value of a function in order to achieve an optimum, or to maximize another parameter which increases with a decrease in the cost (the value of this function). The heuristic cost reduction algorithms work by finding the optimum values of the independent variables for which the value of the function (the “cost”) is the minimum. There is an abundance of heuristic cost reduction algorithms to choose from. We will start with a discussion of various optimization algorithms such as Memetic algorithms, force-directed placement, and evolution-based algorithms. Following this initial discussion, we will take up the working of three algorithms and implement the same in MATLAB. The focus of this report is to provide detailed information on the working of three different heuristic optimization algorithms, and conclude with a comparative study on the performance of these algorithms when implemented in MATLAB. In this report, the three algorithms we will take in to consideration will be the non-adaptive simulated annealing algorithm, the adaptive simulated annealing algorithm, and random restart hill climbing algorithm. The algorithms are heuristic in nature, that is, the solution these achieve may not be the best of all the solutions but provide a means to reach a quick solution that may be a reasonably good solution without taking an indefinite time to implement

The Effect of Risk Return Analysis of Pharmaceutical Companies on Indian Stock Market

Each individual endeavors to stop his/her well deserved funds in different venture roads relying on his/her targets. Among the different venture options, securities exchange is viewed as a standout amongst the most compensating roads of speculation. At the point when the normal return is high, the hazard related with such return is additionally high. So before putting resources into value advertise one should come to realize the hazard return attributes of those stocks and those ventures in which he/she plans to contribute. In this viewpoint, an examination has been embraced to break down the hazard return relationship of chose organizations in pharmaceutical industry of Indian securities exchange. The pharmaceutical business of India positions third on the planet regarding volume and fourteenth as far as esteem. The business is said to be the ideal part for parcel of speculators. The financial specialists must know about the hazard and return associated with the speculation. This investigation encourages the potential financial specialists to settle on educated and reasonable venture choice. The example time of this examination is five years from 2013 to 2018. The investigation has endeavored to discover the hazard return attributes of chosen 10 pharmaceutical organizations in Indian securities exchange. The information has been gathered and examined utilizing MS exceed expectations. The examination inferred that from the chose pharmaceutical organizations Sun Pharmaceutical Industries Ltd gives exceptional yield yet the market danger of the offers is much high. So the value offers of Divi's Laboratories Ltd are increasingly great to potential speculators since it gives exceptional yield and the hazard related with those offers less

A Value-Focused Thinking Approach to Assessing Community Resilience

Disaster or community resilience Divide resilience into 4 or 5 categories (e.g., social, infrastructure, economic, information) Indicators or measures gathered at a state or regional level (20-40 measures) Normalize indicators on a 0-1 scale Aggregate indicators through a weighted linear additive equation (often equal weights) Examples: Cutter et al. 2008, Berke et al. 2012, 2014, Frazier et al. 2013, Linkov et al. 201

Research versus practice in quality improvement? Understanding how we can bridge the gap

The gap between implementers and researchers of quality improvement (QI) has hampered the degree and speed of change needed to reduce avoidable suffering and harm in health care. Underlying causes of this gap include differences in goals and incentives, preferred methodologies, level and types of evidence prioritized and targeted audiences. The Salzburg Global Seminar on 'Better Health Care: How do we learn about improvement?' brought together researchers, policy makers, funders, implementers, evaluators from low-, middle- and high-income countries to explore how to increase the impact of QI. In this paper, we describe some of the reasons for this gap and offer suggestions to better bridge the chasm between researchers and implementers. Effectively bridging this gap can increase the generalizability of QI interventions, accelerate the spread of effective approaches while also strengthening the local work of implementers. Increasing the effectiveness of research and work in the field will support the knowledge translation needed to achieve quality Universal Health Coverage and the Sustainable Development Goals.Fil: Hirschhorn, Lisa R.. Northwestern University; Estados UnidosFil: Ramaswamy, Rohit. University of North Carolina; Estados UnidosFil: Devnani, Mahesh. Post Graduate Institute of Medical Education & Research; IndiaFil: Wandersman, Abraham. University Of South Carolina; Estados UnidosFil: Simpson, Lisa A.. Academy Health; Estados UnidosFil: Garcia Elorrio, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Efectividad Clínica y Sanitaria; Argentin

Building Robust Transcriptomes with Master Splicing Factors

Coherent splicing networks arise from many discrete splicing decisions regulated in unison. Here, we examine the properties of robust, context-specific splicing networks. We propose that a subset of key splicing regulators, or “master splicing factors,” respond to environmental cues to establish and maintain tissue transcriptomes during development.United States. Public Health Service (RO1-GM34277)United States. Public Health Service (R01-CA133404)United States. Public Health Service (U54-CA112967)National Cancer Institute (U.S.) (P30-CA14051