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The joinpoint regression model (JRM) is used to describe trend changes in many applications and relies on the detection of joinpoints (changepoints). However, the existing joinpoint detection methods, namely, the grid search (GS)-based methods, are computationally demanding, and hence, the maximum number of computable joinpoints is limited. Herein, we developed a genetic algorithm-based joinpoint (GAJP) model in which an explicitly decoupled computing procedure for optimization and regression is used to embed a binary genetic algorithm into the JRM for optimal joinpoint detection. The combinations of joinpoints were represented as binary chromosomes, and genetic operations were performed to determine the optimum solution by minimizing the fitness function, the Bayesian information criterion (BIC) and BIC3 . The accuracy and computational performance of the GAJP model were evaluated via intensive simulation studies and compared with those of the GS-based methods using BIC, BIC3 , and permutation test. The proposed method showed an outstanding computational efficiency in detecting multiple joinpoints. Finally, the suitability of the GAJP model for the analysis of cancer incidence trends was demonstrated by applying this model to data on the incidence of colorectal cancer in the United States from 1975 to 2016 from the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Thus, the GAJP model was concluded to be practically feasible to detect multiple joinpoints up to the number of grids without requirement to preassign the number of joinpoints and be easily extendable to cancer trend analysis utilizing large datasets. [ABSTRACT FROM AUTHOR]

The current Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network is approaching 20 years of age, leading researchers to begin exploring new opportunities for a next-generation network in the United States. With a vast list of requirements for a new weather radar network, research has provided various approaches to the design and fabrication of such a network. Additionally, new weather radar networks in other countries, as well as networks on smaller scales, must balance a large number of variables in order to operate in the most effective way possible. To offer network designers an objective analysis tool for such decisions, a coverage optimization technique, utilizing a genetic algorithm with a focus on low-level coverage, is presented. Optimization is achieved using a variety of variables and methods, including the use of climatology, population density, and attenuation due to average precipitation conditions. A method to account for terrain blockage in mountainous regions is also presented. Various combinations of multifrequency radar networks are explored, and results are presented in the form of a coverage-based cost-benefit analysis, with considerations for total network lifetime cost. [ABSTRACT FROM AUTHOR]

Homeland security challenges civil engineers to develop solutions to protect critical infrastructure systems from intentional attacks. The water infrastructure system is one of eight critical infrastructures defined in the report by the President’s Commission on Critical Infrastructure Protection (PDD 1998). This paper offers a model for an operational response strategy to mitigate consequences of intentional physical attacks on the water infrastructure system. An intentional physical attack occurs when a water infrastructure facility is nonfunctional because of physical destruction of the facility by individuals or groups with malicious intentions. Three indices are presented in this paper to measure the consequences of intentional physical attacks: (a) the degree of the disruption of critical infrastructure services, (b) economic loss, and (c) the number of people affected. Then, a multiobjective genetic algorithm is used to find solutions to affect minimum consequences among these indicators. Evaluation of the model using two water infrastructure networks indicates that the genetic algorithms can successfully identify Pareto-optimal solutions to reduce the negative consequences of different attack scenarios. These solutions represent a pool of strategies to be incorporated in emergency plans. In addition, the modified nondominated sorting genetic algorithms (NSGA-II) developed in this study can significantly improve the quality of the solution early in the evolution process. This can be very critical when the time available to obtain mitigation plans is very limited. [ABSTRACT FROM AUTHOR]

Objectives: Menu planning is an important part of personalized lifestyle counseling. The paper describes the results of an automated menu generator (MenuGene) of the web-based lifestyle counseling system Cordelia that provides personalized advice to prevent cardiovascular diseases.Methods: The menu generator uses genetic algorithms to prepare weekly menus for web users. The objectives are derived from personal medical data collected via forms in Cordelia, combined with general nutritional guidelines. The weekly menu is modeled as a multilevel structure.Results: Results show that the genetic algorithm-based method succeeds in planning dietary menus that satisfy strict numerical constraints on every nutritional level (meal, daily basis, weekly basis). The rule-based assessment proved capable of manipulating the mean occurrence of the nutritional components thus providing a method for adjusting the variety and harmony of the menu plans.Conclusions: By splitting the problem into well determined sub-problems, weekly menu plans that satisfy nutritional constraints and have well assorted components can be generated with the same method that is for daily and meal plan generation. [ABSTRACT FROM AUTHOR]

The article reports on the study conducted dynamical frustration in the U.S. Research shows that dynamical frustration is used in various applications including genetic algorithms, computers, the immune system, the brain, protein folding, the stock market and systems that evolve and adopt points to the well-defined concept of frustration as a unifying theme. Research show that a system without dynamical frustration will either settle to equilibrium or grow without bounds and the restoring force always opposes the direction of the displacement thus producing endless oscillations.