Journal articles: 'Online genetic control'

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Maryuningsih, Yuyun, Topik Hidayat, R. Riandi, and Nuryani Y. Rustaman. "Application of genetic problem base online discussion to improve genetic literacy of prospective teachers." JPBI (Jurnal Pendidikan Biologi Indonesia) 8, no. 1 (2022): 65–76. http://dx.doi.org/10.22219/jpbi.v8i1.19035.

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Genetics is a subject that is quite difficult according to students. Various strategies and methods are used to understand genetics in learning to have genetic literacy. One way of increasing genetic literacy in students is to apply genetic problems based on an online discussion in genetics lectures. The research was conducted to determine the effect of genetic problem-based online discussion on increasing students' genetic literacy. The research design used a pre-posttest control group design. It was carried out experimentally on three treatment groups: the genetic problem base of students, the genetic problem base of educators - students, and the genetic problem base of educators. According to the genetic literacy domain, genetic literacy is measured through multiple-choice tests, including genetic models, meiotic models, and molecular models. Manova analyzed the value of gene literacy, and a post-doc further test was performed to differentiate genetic literacy in the three treatment groups. The results showed that genetic literacy increased in all treatment groups, with the highest increase in the group that applied a genetic problem base focused on student problems.

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Wali, W. A., A. I. Al-Shamma’a, Kadhim H. Hassan, and J. D. Cullen. "Online genetic-ANFIS temperature control for advanced microwave biodiesel reactor." Journal of Process Control 22, no. 7 (2012): 1256–72. http://dx.doi.org/10.1016/j.jprocont.2012.05.013.

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Linkens, D. A., and H. O. Nyongesa. "Genetic algorithms for fuzzy control.2. Online system development and application." IEE Proceedings - Control Theory and Applications 142, no. 3 (1995): 177–85. http://dx.doi.org/10.1049/ip-cta:19951767.

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Zuo, W. "A genetic approach to adaptive control system design." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 211, no. 1 (1997): 15–23. http://dx.doi.org/10.1243/0959651971539650.

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This paper presents an unconventional approach to design of an adaptive digital PID (proportional integral derivative) controller for multivariable plants, which includes two parts: a fast online recursive identifier to provide updated model parameters of the plant and a genetic tuner, which is based on artificial genetic algorithms, to tune on-line the parameter matrices of the controller. An example is presented to show the effectiveness of the approach.

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Feng, Ying Ying, Nan Mu Hui, Zong An Luo, and Dian Hua Zhang. "Optimization of the Hydraulic Control System Utilizing BP Neural Network Control Strategy Based on Genetic Algorithm." Applied Mechanics and Materials 397-400 (September 2013): 1245–52. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1245.

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For the characteristic of the MMS series Thermo-Mechanical Simulator hydraulic control system, using traditional PID control method can not achieve the desired control effect. Basing on genetic algorithm, BP neural network, which has the arbitrary non-linear approximation ability, self-learning ability and generalization ability, has been used into the hydraulic control system to achieve the online adjustment of the weighting coefficients and the adaptive adjustment of PID control parameters. The results of simulation and online tests show that the control effect of hydraulic system has been improved significantly, and the accurate control of hydraulic system hammer displacement has been realized.

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Abdelrahman, Bassant, Kenan Hazirbaba, Omar Mughieda, and Ghassan Abu Lebdeh. "Modelling time complexity of micro-genetic algorithms for online traffic control decisions." International Journal of Information and Decision Sciences 11, no. 2 (2019): 141. http://dx.doi.org/10.1504/ijids.2019.10022638.

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Lebdeh, Ghassan Abu, Kenan Hazirbaba, Omar Mughieda, and Bassant Abdelrahman. "Modelling time complexity of micro-genetic algorithms for online traffic control decisions." International Journal of Information and Decision Sciences 11, no. 2 (2019): 141. http://dx.doi.org/10.1504/ijids.2019.101141.

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Orszulik, Ryan, Jinjun Shan, and Michael Stachowsky. "Membrane structure active flatness control using genetic algorithm with online objective reweighting." Acta Astronautica 68, no. 11-12 (2011): 2012–24. http://dx.doi.org/10.1016/j.actaastro.2010.11.009.

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Liu, Xiao Xiong, Yan Wu, Peng Hui Li, and Heng Xu. "Dynamic Optimization Genetic Algorithms for Flight Control Laws Design." Applied Mechanics and Materials 278-280 (January 2013): 1581–84. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1581.

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The general flight control laws are designed by static designs and dynamic fits. To improve the adaptive capability, the method of control laws design was introduced by using dynamic optimization genetic algorithms. The control parameters were adjusted online in the flight envelope. The dynamic optimization model was built for aircraft longitudinal function. The fitness was set up by applying order track. And then the control parameters were regulated by dynamic optimization genetic algorithms. Finally an example of a longitudinal control augmented stability system of an aircraft is used with a simulation.

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Akram, Muhammad, Muhammad Farooq Sabar, Iqbal Bano, Muhammad Usman Ghani та Mariam Shahid. "SINGLE NUCLEOTIDE POLYMORPHISMS OF TRANSFORMING GROWTH FACTOR-Β1 GENE AS POTENTIAL ASTHMA SUSCEPTIBLE VARIANTS IN PUNJABI POPULATION OF PAKISTAN". Journal of Ayub Medical College Abbottabad 34, № 4(SUPPL 1) (2022): 944–48. http://dx.doi.org/10.55519/jamc-04-s4-10495.

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Background: Candidate gene approach based on case-control model is a valuable strategy to determined disease related genetic variants. Two single nucleotide polymorphisms rs1800469 and rs2241715 in TGF-β1gene have been reported to affect the asthmatic status in different populations. The main focus of this research was to find any relationship between these SNPs and asthma in Pakistani population. Methods Using case-control model, a total of 108 individuals including 52 asthma patients and 56 healthy controls were screened to find asthma susceptibility of variants rs1800469 and rs2241715. These SNPs were genotyped using SNaPshot minisequencing assay followed by capillary electrophoresis using ABI 3130xl genetic analyzer platform. The statistical analysis of genetic data was performed by using SPSS 21, SHEsis online platform and SNPStats online web software. Results: No association with asthma was seen in allelic model for both SNPs but genotypes analyzed under codominant, dominant, over dominant and recessive models of inheritance revealed that SNP rs2241715 is strongly associated with asthma under genotypic model. Conclusion: rs2241715 was found to be a genetic risk factor for asthma in Pakistani population.

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Sopov, Evgenii. "A selection hyper-heuristic with online learning for control of genetic algorithm ensemble." International Journal of Hybrid Intelligent Systems 13, no. 2 (2016): 125–35. http://dx.doi.org/10.3233/his-160230.

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Wang, Yuchao, Qiusu Wang, and Huixuan Fu. "Online High Performance Genetic Algorithm Based Sliding Mode Control for Controllable Pitch Propeller." Processes 8, no. 8 (2020): 953. http://dx.doi.org/10.3390/pr8080953.

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During the voyage of a ship, the performance of a controllable pitch propeller (CPP) is severely affected by the changing load demand and ever-present disturbance from ocean waves, which will also result in model uncertainty. In order to improve the performance of the CPP system, an online high-performance genetic algorithm (HPGA)-based sliding mode control (SMC) strategy is proposed. Firstly, the model of the CPP system is obtained according to the manufacturer’s instructions. Then, a chattering-free sliding mode controller (CF-SMC) is designed for the CPP system, after which the parameters in the CF-SMC are optimized with the HPGA method. Finally, the optimized CF-SMC is applied to an experimental setup of a prototype CPP system. In order to validate the effectiveness of the proposed method, it is compared with a proportional-integral-derivative (PID) controller, which is typically applied on real CPP-systems, with results indicating the superiority of the proposed method.

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Naeem, W., R. Sutton *, J. Chudley, F. R. Dalgleish, and S. Tetlow. "An online genetic algorithm based model predictive control autopilot design with experimental verification." International Journal of Control 78, no. 14 (2005): 1076–90. http://dx.doi.org/10.1080/00207170500228483.

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Siani, Merav, and Anat Yarden. "Introducing Evolution of the Human Lactase Gene using an Online Interactive Activity." American Biology Teacher 84, no. 1 (2022): 16–21. http://dx.doi.org/10.1525/abt.2022.84.1.16.

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Human evolution is a sensitive and controversial topic, which might explain why it is not included in science curricula or textbooks in many countries. We prepared an online student-centered human evolution activity dealing with lactose tolerance. In constructing the activity, we considered the following design principles: a medical issue connected to students’ lives, a noncontentious topic of human evolution, and a one-step genetic example that can be demonstrated by basic bioinformatics tools. The activity consists of four units dealing with the activity of the enzyme lactase in our small intestine, the differences in lactose tolerance in people from different origins, the genetic foundation of lactose tolerance, and an extension unit dealing with the control of lactase gene expression. The activity was experienced by a pilot group of approximately 100 students, preservice and in-service teachers who showed great interest in the genetics of a trait that has undergone evolutionary changes. We noted the need for a teacher as mediator while students perform the activity. We suggest using the activity in the context of evolution, genetics, or when teaching about systems of the human body, either all units in succession or as a modular activity.

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CAI, KAI-YUAN, TSONG YUEH CHEN, YONG-CHAO LI, YUEN TAK YU, and LEI ZHAO. "ON THE ONLINE PARAMETER ESTIMATION PROBLEM IN ADAPTIVE SOFTWARE TESTING." International Journal of Software Engineering and Knowledge Engineering 18, no. 03 (2008): 357–81. http://dx.doi.org/10.1142/s0218194008003696.

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Software cybernetics is an emerging area that explores the interplay between software and control. The controlled Markov chain (CMC) approach to software testing supports the idea of software cybernetics by treating software testing as a control problem, where the software under test serves as a controlled object modeled by a controlled Markov chain and the software testing strategy serves as the corresponding controller. The software under test and the corresponding software testing strategy form a closed-loop feedback control system. The theory of controlled Markov chains is used to design and optimize the testing strategy in accordance with the testing/reliability goal given explicitly and a priori. Adaptive software testing adjusts and improves software testing strategy online by using the testing data collected in the course of software testing. In doing so, the online parameter estimations play a key role. In this paper, we study the effects of genetic algorithm and the gradient method for doing online parameter estimation in adaptive software testing. We find that genetic algorithm is effective and does not require prior knowledge of the software parameters of concern. Although genetic algorithm is computationally intensive, it leads the adaptive software testing strategy to an optimal software testing strategy that is determined by optimizing a given testing goal, such as minimizing the total cost incurred for removing a given number of defects. On the other hand, the gradient method is computationally favorable, but requires appropriate initial values of the software parameters of concern. It may lead, or fail to lead, the adaptive software testing strategy to an optimal software testing strategy, depending on whether the given initial parameter values are appropriate or not. In general, the genetic algorithm should be used instead of the gradient method in adaptive software testing. Simulation results show that adaptive software testing does work and outperforms random testing.

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Qu, Shen, Tianyi He, and Guoming Zhu. "Model-Assisted Online Optimization of Gain-Scheduled PID Control Using NSGA-II Iterative Genetic Algorithm." Applied Sciences 13, no. 11 (2023): 6444. http://dx.doi.org/10.3390/app13116444.

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In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play a crucial role thanks to its simple structure and performance-oriented tuning process. To improve the control performance, advanced gain-scheduling methods are used to schedule the PID control gains based on the operating conditions and/or tracking error. However, determining the scheduled gain is a major challenge, as PID control gains need to be determined at each operating condition. In this paper, a model-assisted online optimization method is proposed based on the modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) to obtain the optimal gain-scheduled PID controller. Model-assisted offline optimization through computer-in-the-loop simulation provides the initial scheduled gains for an online algorithm, which then uses the iterative NSGA-II algorithm to automatically schedule and tune PID gains by online searching of the parameter space. As a summary, the proposed approach presents a PID controller optimized through both model-assisted learning based on prior model knowledge and model-free online learning. The proposed approach is demonstrated in the case of a nonlinear valve system able to obtain optimal PID control gains with a given scheduled gain structure. The performance improvement of the optimized gain-scheduled PID control is demonstrated by comparing it with fixed-gain controllers under multiple operating conditions.

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Chen, Yan-Kwang, Fei-Rung Chiu, Yueh-Chuen Huang, and Chien-Hua Yeh. "Product image allocation and inventory control of online stores." Kybernetes 43, no. 7 (2014): 968–83. http://dx.doi.org/10.1108/k-11-2013-0256.

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Purpose – This study assumes image size and location, product substitution, and product supply to be factors influencing the purchase decision. Under such assumption, shelf-space allocation and inventory theories are integrated, and operating profit and cost of the online store under the supply policy that prevent stockouts are analyzed to develop an optimal model for inventory control and product image allocation. The paper aims to discuss these issues. Design/methodology/approach – The developed model takes both order cost and holding cost into account when calculating the total cost. As the model presented is an integer non-linear programming problem, this study adopts genetic algorithm to solve the problem. Findings – Numerical examples are provided in this study to demonstrate the applicability of the model and to illustrate the search for parameters that possess greater influence over the operating profit of the store. Originality/value – This study provides a mixed integer non-linear programming model for the joint optimization of graphic design and inventory control problem. Online store owners may take the results of this study as a reference for decision-making purposes.

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Schneider, Hannah M., Stephanie P. Klein, Meredith T. Hanlon, et al. "Genetic control of root architectural plasticity in maize." Journal of Experimental Botany 71, no. 10 (2020): 3185–97. http://dx.doi.org/10.1093/jxb/eraa084.

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Abstract Root phenotypes regulate soil resource acquisition; however, their genetic control and phenotypic plasticity are poorly understood. We hypothesized that the responses of root architectural phenes to water deficit (stress plasticity) and different environments (environmental plasticity) are under genetic control and that these loci are distinct. Root architectural phenes were phenotyped in the field using a large maize association panel with and without water deficit stress for three seasons in Arizona and without water deficit stress for four seasons in South Africa. All root phenes were plastic and varied in their plastic response. We identified candidate genes associated with stress and environmental plasticity and candidate genes associated with phenes in well-watered conditions in South Africa and in well-watered and water-stress conditions in Arizona. Few candidate genes for plasticity overlapped with those for phenes expressed under each condition. Our results suggest that phenotypic plasticity is highly quantitative, and plasticity loci are distinct from loci that control phene expression in stress and non-stress, which poses a challenge for breeding programs. To make these loci more accessible to the wider research community, we developed a public online resource that will allow for further experimental validation towards understanding the genetic control underlying phenotypic plasticity.

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Liu, Zhi Feng, Ji Shi, Jia Liu, and Yang Li. "Strategy and Applied Research of Multi-Constrained Model of Automatic Test Paper Based on Genetic Algorithm." Applied Mechanics and Materials 37-38 (November 2010): 1223–30. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.1223.

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Test paper problem is a typical multi-constrained objective optimization problem. By using genetic algorithm, this paper analyzes the initial population generation, the chromosome coding and its genetic manipulation, control parameters. Solving that by natural-coded genetic algorithm, improves test paper success rate and convergence rate. This genetic algorithm is applied successfully on NHibernate architecture, and developed "automatic test paper" Online Examination system.

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Teng, T. K., J. S. Shieh, and C. S. Chen. "Genetic algorithms applied in online autotuning PID parameters of a liquid-level control system." Transactions of the Institute of Measurement and Control 25, no. 5 (2003): 433–50. http://dx.doi.org/10.1191/0142331203tm0098oa.

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GUH, RUEY-SHIANG. "OPTIMIZING FEEDFORWARD NEURAL NETWORKS FOR CONTROL CHART PATTERN RECOGNITION THROUGH GENETIC ALGORITHMS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 02 (2004): 75–99. http://dx.doi.org/10.1142/s0218001404003095.

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Pattern recognition is an important issue in statistical process control (SPC) because unnatural patterns exhibited by control charts can be associated with specific assignable causes adversely affecting the process. Artificial neural networks have been widely investigated as an effective approach to control chart pattern (CCP) recognition in recent years. However, an overwhelming majority of these applications has used trial-and-error experiments to determine the network architecture and training parameters, which are crucial to the performance of the network. In this paper, the genetic algorithm (GA) is used to evolve the configuration and the training parameter set of the neural network to solve the online CCP recognition problem. Numerical results are provided that indicate that the proposed GA can evolve neural network architecture while simultaneously determining training parameters to maximize efficiently the performance of the online CCP recognizers. Because the population size is a major parameter of GA processing speed, an investigation was also conducted to identify the effects of the population size on the performance of the proposed GA. This research further confirms the feasibility of using GA to evolve neural networks. Although a back-propagation-based CCP recognizer is the particular application presented here, the proposed GA methodology can be applied to neural networks in general.

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Niu, Xiang Jie. "The Optimization for PID Controller Parameters Based on Genetic Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 4102–5. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4102.

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as an important research field of automatic control problems, PID parameter optimization's control effect depends on the proportional, integral and derivative values. Using trial and error testing to manually realize optimization PID parameters, the traditional ways are often time-consuming and difficult to meet the requirements of real-time control. In order to solve the problems and improve system performance, the paper proposes a PID parameter optimization strategy based on genetic algorithm. The paper establishes the PID controller parameter model through genetic algorithm, uses the PID parameters as individuals in genetic algorithm during the control process, and takes the integral function of absolute error control time as the optimization object to dynamically adjust the three PID control parameters, thus realize online optimization for PID control parameters. Simulation results show that the introduction of genetic algorithms for PID control system improves the dynamic performance, enhance system stability and operation speed, and get better control effect.

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Zayts, Olga, and Zhengpeng Luo. "Commodification and marketisation of genetic testing through online direct-to-consumer platforms in Hong Kong." Discourse & Communication 11, no. 6 (2017): 630–47. http://dx.doi.org/10.1177/1750481317726926.

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In this article we examine commodification and marketisation of genetic testing by companies offering direct-to-consumer (DTC) genetic testing to the general public through online platforms in Hong Kong. Recently, offers of genetic testing have expanded from scientific and clinical genetic settings to general medicine (e.g. in hereditary cancer) and non-medical domains (e.g. aptitude tests). The wider availability of tests, however, has raised concerns about the currently available scientifically proven utility of these tests. Using theme-oriented discourse analysis, we analyse the specific discursive modalities through which the DTC companies in Hong Kong make inflated claims about the value of genetic tests to pursue their marketing agenda. We show that in this way the companies are selling ‘more’ than specific products to consumers: they are selling ‘hope’ and ‘increased autonomy’, that is, an opportunity to buy commodities online that promise consumers control of their health and wellbeing.

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Shan, Xianming, Huixin Liu, and Yefeng Liu. "Research on fault tolerant control system based on optimized neural network algorithm." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 9073–83. http://dx.doi.org/10.3233/jifs-189306.

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Due to the strict personnel control measures in COVID-19 epidemic, the control system cannot be maintained and managed manually. This puts forward higher requirements for the accuracy of its fault-tolerant performance. The control system plays an increasingly important role in the rapid development of industrial production. When the sensor in the system fails, the system will become unstable. Therefore, it is necessary to accurately and quickly diagnose the faults of the system sensors and maintain the system in time. This paper takes the control system as the object to carry out the fault diagnosis and fault-tolerant control research of its sensors. A network model of wavelet neural network is proposed, and an improved genetic algorithm is used to optimize the weights and thresholds of the neural network model to avoid the deficiencies of traditional neural network algorithms. For the depth sensor of a certain system, an online fault diagnosis scheme based on RBF (Radial Basis Function) neural network and genetic algorithm optimized neural network was designed. The disturbance fault, “stuck” fault, drift fault and oscillation fault of the depth sensor are simulated. Simulation experiments show that both online fault diagnosis schemes can accurately identify sensor faults and the genetic algorithm optimized neural network is superior to RBF neural network in both recognition accuracy and training time under the influence of COVID-19.

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Hagras, H. "A hierarchical fuzzy–genetic multi-agent architecture for intelligent buildings online learning, adaptation and control." Information Sciences 150, no. 1-2 (2003): 33–57. http://dx.doi.org/10.1016/s0020-0255(02)00368-7.

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Bai, Yu, Ping He, Yiding Zhao, et al. "Real-time online detection of trucks loading via genetic neural network." Automation in Construction 120 (December 2020): 103354. http://dx.doi.org/10.1016/j.autcon.2020.103354.

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Guo, Jiangtao, Lulu Liu, Tianjun Wang, Junqiang Jia, and Maosheng Wei. "Studies on Three-phase Imbalance Treatment Based on Beidou communication and Commutation Switch." Journal of Physics: Conference Series 2495, no. 1 (2023): 012013. http://dx.doi.org/10.1088/1742-6596/2495/1/012013.

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Abstract In this paper, genetic algorithm and commutation switch based three-phase imbalanced distribution network are studied. A genetic algorithm-based commutation switch three-phase imbalance management strategy was developed, and the three-phase current distribution station area was established. A mathematical model was developed for the multi-objective optimal commutation based on the minimum imbalance degree and the minimum switching times of the low-voltage load online automatic commutation device during the commutation process. Additionally, the real-time online governance control strategies were balanced. Finally, the effectiveness of the proposed method was verified using simulation studies.

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Bharadwaj, Y. N. S. R., and K. Raja Sekhar. "Highly effective Security Techniques in OSN Based on Genetic Programming Approach." International Journal of Advances in Applied Sciences 6, no. 3 (2017): 230. http://dx.doi.org/10.11591/ijaas.v6.i3.pp230-234.

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Assurance is one of the grinding centers that enhances when trades get mediated in Online Community Techniques (Online Social Networks). Diverse gatherings of utilization innovation specialists have limited the 'OSN security issue' as one of surveillance, institutional or open security. In taking care of these issues they have moreover overseen them just as they were person. We adapt that the elite security issues are caught and that evaluation on genuine feelings of serenity in Online Social Networks would advantage from a more exhaustive method. Nowadays, points of interest systems mean a critical piece of relationship; by losing security, these organizations will decrease a ton of pleasant areas to see as well. The inside motivation behind subtle elements security (Information Security) is risk control. There are a great deal of discovering works and exercises in security danger control (ISRM, for example, NIST 800-30 and ISO/IEC 27005. Regardless, only few works of appraisal focus on Information Security danger diminishment, while the signs depict normal determinations and suggestions. They don't give any use ideas concerning ISRM; truth be told diminishing the Information Security dangers in questionable conditions is cautious. Subsequently, this papers joined an acquired counts (GA) for Information Security danger loss of weaknesses. Finally, the parity of the associated system was broke down through a reflection.

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Li, G., M. Li, S. Azarm, S. Al Hashimi, T. Al Ameri, and N. Al Qasas. "Improving multi-objective genetic algorithms with adaptive design of experiments and online metamodeling." Structural and Multidisciplinary Optimization 37, no. 5 (2008): 447–61. http://dx.doi.org/10.1007/s00158-008-0251-6.

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Shickh, Salma, Daena Hirjikaka, Marc Clausen, et al. "Genetics Adviser: a protocol for a mixed-methods randomised controlled trial evaluating a digital platform for genetics service delivery." BMJ Open 12, no. 4 (2022): e060899. http://dx.doi.org/10.1136/bmjopen-2022-060899.

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IntroductionThe high demand for genetic tests and limited supply of genetics professionals has created a need for alternative service delivery models. Digital tools are increasingly being used to support multiple points in the genetic testing journey; however, none are transferable across multiple clinical specialties and settings nor do they encompass the entire trajectory of the journey. We aim to evaluate the effectiveness of the Genetics Adviser, an interactive, patient-facing, online digital health tool that delivers pre-test counselling, provides support during the waiting period for results, and returns results with post-test counselling, encompassing the entire patient genetic testing journey.Methods and analysisWe will compare the Genetics Adviser paired with a brief genetic counselling session to genetic counselling alone in a randomised controlled trial. One hundred and forty patients who previously received uninformative genetic test results for their personal and family history of cancer will be recruited from familial cancer clinics in Toronto and offered all clinically significant results from genomic sequencing. Participants randomised into the intervention arm will use the Genetics Adviser to learn about genomic sequencing, receive pre-test counselling, support during the waiting period and results, supplemented with brief counselling from a genetic counsellor. Participants in the control arm will receive standard pre-test and post-test counselling for genomic sequencing from a genetic counsellor. Our primary outcome is decisional conflict following pre-test counselling from the Genetics Adviser+genetic counsellor or counsellor alone. Secondary outcomes include: knowledge, satisfaction with decision-making, anxiety, quality of life, psychological impact of results, empowerment, acceptability and economic impact for patients and the health system. A subset of patients will be interviewed to assess user experience.Ethics and disseminationThis study has been approved by Clinical Trials Ontario Streamlined Research Ethics Review System (REB#20–035). Results will be shared through stakeholder workshops, national and international conferences and peer-reviewed journals.Trial registration numberNCT04725565.

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Yang, Lin, Chee-Meng Chew, Teresa Zielinska, and Aun-Neow Poo. "A uniform biped gait generator with offline optimization and online adjustable parameters." Robotica 25, no. 5 (2007): 549–65. http://dx.doi.org/10.1017/s026357470700344x.

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SUMMARYThis paper presents the Genetic Algorithm Optimized Fourier Series Formulation (GAOFSF) method for stable gait generation in bipedal locomotion. It uses a Truncated Fourier Series (TFS) formulation with its coefficients determined and optimized by Genetic Algorithm. The GAOFSF method can generate human-like stable gaits for walking on flat terrains as well as on slopes in a uniform way. Through the adjustment of only a single or two parameters, the step length and stride-frequency can easily be adjusted online, and slopes of different gradients are accommodated. Dynamic simulations show the robustness of the GAOFSF, with stable gaits achieved even if the step length and stride frequency are adjusted by significant amounts. With its ease of adjustments to accommodate different gait requirements, the approach lends itself readily for control of walking on a rough terrain and in the presence of external perturbations.

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Ali, Mohammad, Mohd Tariq, Deepak Upadhyay, et al. "Genetic Algorithm Based PI Control with 12-Band Hysteresis Current Control of an Asymmetrical 13-Level Inverter." Energies 14, no. 20 (2021): 6663. http://dx.doi.org/10.3390/en14206663.

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In this paper, a twelve-band hysteresis control is applied to a recent thirteen-level asymmetrical inverter topology by employing a robust proportional-integral (PI) controller whose parameters are decided online by genetic algorithm (GA). The asymmetrical inverter topology can generate thirteen levels of output voltage incorporating only ten switches and exhibits boosting capability. A 12-band hysteresis current control strategy is applied to ensure the satisfactory operation of the inverter. It is designed to provide a sinusoidal line current at the unity power factor. The tuning of the PI controller is achieved by a nature inspired GA. Comparative analysis of the results obtained after application of the GA and the conventional Ziegler–Nichols method is also performed. The efficacy of the proposed control on WE topology is substantiated in the MATLAB Simulink environment and was further validated through experimental/real-time implementation using DSC TMS320F28379D and Typhoon HIL real-time emulator (Typhoon-HIL-402).

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Mohammad, K. S., and A. S. Al-Dmour. "Vibration Control of Unknown Flexible Beam Structures." Journal of Vibration and Control 8, no. 1 (2002): 69–86. http://dx.doi.org/10.1177/1077546302008001524.

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Two approaches, namely, state space-principal component analysis (SS-PCA) and fuzzy logic-genetic algorithms (FL-GAs) controllers are introduced for constraining the transient motion of flexible structures. These two techniques require no prior knowledge about the physical modeling of such structures. The SS-PCA has the capability, among all the other available methods, of providing an estimation of the characteristics of the system under consideration from measured displacement outputs. Excellent control was achieved with both proposed techniques, though quite better results were obtained when applying FL-GAs. However, in the online processing, the FL-GA was found to need more computational time when compared with the SS-PCA.

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Hanchevici, Adrian Bogdan, and Ioan Dumitrache. "Intelligent PID Control for Linear SISO System with Random Communication Delay by Using Online Genetic Algorithms." IFAC Proceedings Volumes 45, no. 7 (2012): 140–45. http://dx.doi.org/10.3182/20120523-3-cz-3015.00029.

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Gautier, N., J. L. Aider, T. Duriez, B. R. Noack, M. Segond, and M. Abel. "Closed-loop separation control using machine learning." Journal of Fluid Mechanics 770 (April 10, 2015): 442–57. http://dx.doi.org/10.1017/jfm.2015.95.

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We present the first closed-loop separation control experiment using a novel, model-free strategy based on genetic programming, which we call ‘machine learning control’. The goal is to reduce the recirculation zone of backward-facing step flow at $\mathit{Re}_{h}=1350$ manipulated by a slotted jet and optically sensed by online particle image velocimetry. The feedback control law is optimized with respect to a cost functional based on the recirculation area and a penalization of the actuation. This optimization is performed employing genetic programming. After 12 generations comprised of 500 individuals, the algorithm converges to a feedback law which reduces the recirculation zone by 80 %. This machine learning control is benchmarked against the best periodic forcing which excites Kelvin–Helmholtz vortices. The machine learning control yields a new actuation mechanism resonating with the low-frequency flapping mode instability. This feedback control performs similarly to periodic forcing at the design condition but outperforms periodic forcing when the Reynolds number is varied by a factor two. The current study indicates that machine learning control can effectively explore and optimize new feedback actuation mechanisms in numerous experimental applications.

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Chan, Kit Yan, C. K. Kwong, and Gül E. Kremer. "Predicting customer satisfaction based on online reviews and hybrid ensemble genetic programming algorithms." Engineering Applications of Artificial Intelligence 95 (October 2020): 103902. http://dx.doi.org/10.1016/j.engappai.2020.103902.

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Forrest, Laura E., Rowan Forbes Shepherd, Erin Tutty, et al. "The Clinical and Psychosocial Outcomes for Women Who Received Unexpected Clinically Actionable Germline Information Identified through Research: An Exploratory Sequential Mixed-Methods Comparative Study." Journal of Personalized Medicine 12, no. 7 (2022): 1112. http://dx.doi.org/10.3390/jpm12071112.

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Background Research identifying and returning clinically actionable germline variants offer a new avenue of access to genetic information. The psychosocial and clinical outcomes for women who have received this ‘genome-first care’ delivering hereditary breast and ovarian cancer risk information outside of clinical genetics services are unknown. Methods: An exploratory sequential mixed-methods case-control study compared outcomes between women who did (cases; group 1) and did not (controls; group 2) receive clinically actionable genetic information from a research cohort in Victoria, Australia. Participants completed an online survey examining cancer risk perception and worry, and group 1 also completed distress and adaptation measures. Group 1 participants subsequently completed a semi structured interview. Results: Forty-five participants (group 1) and 96 (group 2) completed the online survey, and 31 group 1 participants were interviewed. There were no demographic differences between groups 1 and 2, although more of group 1 participants had children (p = 0.03). Group 1 reported significantly higher breast cancer risk perception (p < 0.001) compared to group 2, and higher cancer worry than group 2 (p < 0.001). Some group 1 participants described how receiving their genetic information heightened their cancer risk perception and exacerbated their cancer worry while waiting for risk-reducing surgery. Group 1 participants reported a MICRA mean score of 27.4 (SD 11.8, range 9–56; possible range 0–95), and an adaptation score of 2.9 (SD = 1.1). Conclusion: There were no adverse psychological outcomes amongst women who received clinically actionable germline information through a model of ‘genome-first’ care compared to those who did not. These findings support the return of clinically actionable research results to research participants.

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Yukselturk, Erman, Serhat Ozekes, and Yalın Kılıç Türel. "Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program." European Journal of Open, Distance and E-Learning 17, no. 1 (2014): 118–33. http://dx.doi.org/10.2478/eurodl-2014-0008.

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Abstract This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies Self-Efficacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, and Prior Knowledge Questionnaire). The collected data included 10 variables, which were gender, age, educational level, previous online experience, occupation, self efficacy, readiness, prior knowledge, locus of control, and the dropout status as the class label (dropout/not). In order to classify dropout students, four data mining approaches were applied based on k-Nearest Neighbour (k-NN), Decision Tree (DT), Naive Bayes (NB) and Neural Network (NN). These methods were trained and tested using 10-fold cross validation. The detection sensitivities of 3-NN, DT, NN and NB classifiers were 87%, 79.7%, 76.8% and 73.9% respectively. Also, using Genetic Algorithm (GA) based feature selection method, online technologies self-efficacy, online learning readiness, and previous online experience were found as the most important factors in predicting the dropouts.

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Jacobs, Benjamin M., Luisa Schalk, Angie Dunne, et al. "ADAMS project: a genetic Association study in individuals from Diverse Ancestral backgrounds with Multiple Sclerosis based in the UK." BMJ Open 13, no. 5 (2023): e071656. http://dx.doi.org/10.1136/bmjopen-2023-071656.

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PurposeGenetic studies of multiple sclerosis (MS) susceptibility and severity have focused on populations of European ancestry. Studying MS genetics in other ancestral groups is necessary to determine the generalisability of these findings. The genetic Association study in individuals from Diverse Ancestral backgrounds with Multiple Sclerosis (ADAMS) project aims to gather genetic and phenotypic data on a large cohort of ancestrally-diverse individuals with MS living in the UK.ParticipantsAdults with self-reported MS from diverse ancestral backgrounds. Recruitment is via clinical sites, online (https://app.mantal.co.uk/adams) or the UK MS Register. We are collecting demographic and phenotypic data using a baseline questionnaire and subsequent healthcare record linkage. We are collecting DNA from participants using saliva kits (Oragene-600) and genotyping using the Illumina Global Screening Array V.3.Findings to dateAs of 3 January 2023, we have recruited 682 participants (n=446 online, n=55 via sites, n=181 via the UK MS Register). Of this initial cohort, 71.2% of participants are female, with a median age of 44.9 years at recruitment. Over 60% of the cohort are non-white British, with 23.5% identifying as Asian or Asian British, 16.2% as Black, African, Caribbean or Black British and 20.9% identifying as having mixed or other backgrounds. The median age at first symptom is 28 years, and median age at diagnosis is 32 years. 76.8% have relapsing–remitting MS, and 13.5% have secondary progressive MS.Future plansRecruitment will continue over the next 10 years. Genotyping and genetic data quality control are ongoing. Within the next 3 years, we aim to perform initial genetic analyses of susceptibility and severity with a view to replicating the findings from European-ancestry studies. In the long term, genetic data will be combined with other datasets to further cross-ancestry genetic discoveries.

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Safitri, Nur Haliza, Yusuf Sabilu, and Hariati Lestari. "Analisis faktor perilaku sedentari, pemesanan makanan online dan faktor genetik terhadap kejadian obesitas di wilayah kerja BLUD UPTD Puskesmas Benu-Benua Kecamatan Kendari Barat tahun 2024." Indonesian Journal of Health Science 5, no. 3 (2025): 441–54. https://doi.org/10.54957/ijhs.v5i3.1502.

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Introduction: Based on the latest data in 2023, the prevalence of obesity in Indonesia in adults over 18 years old is 23.4%. The prevalence of obesity in Southeast Sulawesi Province in 2021 at the age of over 15 years from 2021 to 2023 increased by 57,259 cases. The prevalence of obesity in Kendari City from 2022 of 2,431 cases has increased in 2023 to 2,970 cases. Benu-Benua Health Center is one of the health centers in Kendari City with the highest obesity cases. The prevalence of obesity in 2022 was 68 cases, in 2023 it increased to 203 cases. Objective: this study aims to analyze the relationship between sedentary behavior, frequency of online meal orders, and genetic factors with the incidence of obesity in the work area of BLUD UPTD Benu-Benua Health Center, West Kendari District in 2024. Methods: This study is a quantitative study using a case control study design. This research was carried out in the BLUD Working Area of the Benu-Benua Health Center, West Kendari District in February 2025. Population: There are 834 people with 203 people who are obese. Sample: The number of samples in this study amounted to 80 samples consisting of 40 cases and 40 controls with the sampling technique being a random sampling technique. The statistical tests used are the chi square test and the odds ratio. Results: this study showed that the variable at risk for obesity was sedentary behavior (OR=2,914). And variables that were not at risk with the incidence of obesity were the frequency of online food ordering (OR=0.318) and genetic factors (OR=0.815). Conclusion: Based on the results of the study, it can be concluded that sedentary behavior is a risk factor for obesity while the frequency of online food ordering and genetic factors are not risk factors for obesity incidence in the BLUD UPTD Work Area of the Benu-Benua Health Center, West Kendari District in 2024. Pendahuluan: Berdasarkan data terbaru tahun 2023 prevalensi obesitas di Indonesia pada usia dewasa di atas 18 tahun terdapat 23,4 %. Prevalensi obesitas di Provinsi Sulawesi Tenggara Tahun 2021 pada usia di atas 15 tahun dari tahun 2021 hingga 2023 meningkat sebanyak 57.259 kasus. Prevalensi obesitas di Kota kendari dari tahun 2022 sebesar 2.431 kasus mengalami kenaikan pada tahun 2023 menjadi 2.970 kasus. Puskesmas Benu-Benua merupakan salah satu puskesmas di Kota Kendari dengan kasus obesitas tertinggi. Prevalensi obesitas tahun 2022 terdapat 68 kasus Tahun 2023 meningkat menjadi 203 kasus. Tujuan: studi yang dilaksanakan mempunyai tujuan dalam melaksanakan analisis hubungan perilaku sedentari, frekuensi pemesanan makan online, dan faktor genetik terhadap fenomena obesitas di wilayah kerja BLUD UPTD Puskesmas Benu-Benua Kecamatan Kendari Barat Tahun 2024. Metode: penelitian ini adalah penelitian kuantitatif dengan menggunakan desain studi case control. Penelititan ini dilaksanakan di Wilayah Kerja BLUD UPTD Puskesmas Benu-Benua Kecamatan Kendari Barat pada bulan Februari 2025. Populasi: Berjumlah 834 orang dengan 203 orang yang mengalami obesitas. Sampel: Jumlah sampel pada penelitian ini berjumlah 80 sampel tersusun atas 40 kasus dan 40 kontrol dengan teknik dalam mengambil sampel yaitu teknik random sampling. Uji statistik yang dipergunakan yaitu uji chi square serta Odds ratio. Hasil: studi ini menunjukan varibael yang beresiko terhadap fenomena obesitas yaitu perilaku sedentari (OR=2.914). Dan variabel yang tidak beresiko dengan kejadian obesitas adalah frekuensi pemesanan makanan online (OR=0.318) dan faktor genetik (OR=0.815). Kesimpulan: Berdasarkan hasil penelitian dapat disimpulkan Prilaku sedentari merupakan faktor risiko kejadian obesitas sedangkan frekuensi pemesaan makanan online dan faktor genetik bukan faktor risiko kejadian obesitas di Wilayah Kerja BLUD UPTD Puskesmas Benu-Benua Kecamatan Kendari Barat Tahun 2024.

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Chen, Jing, Chenghui Zhang, Ke Li, Yuedong Zhan, and Bo Sun. "Hybrid Adaptive Control for PEMFC Gas Pressure." Energies 13, no. 20 (2020): 5334. http://dx.doi.org/10.3390/en13205334.

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This paper addresses the issues of nonlinearity and coupling between anode pressure and cathode pressure in proton exchange membrane fuel cell (PEMFC) gas supply systems. A fuzzy adaptive PI decoupling control strategy with an improved advanced genetic algorithm (AGA) is proposed. This AGA s utilized to optimize the PI parameters offline, and the fuzzy adaptive algorithm s used to adjust the PI parameters dynamically online to achieve the approximate decoupling control of the PEMFC gas supply system. According to the proposed dynamic model, the PEMFC gas supply system with the fuzzy–AGA–PI decoupling control method was simulated for comparison. The simulation results demonstrate that the proposed control system can reduce the pressure difference more efficiently with the classical control method under different load changes.

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Song, Xiaogang, Yongjie Zhao, Chengwei Chen, Liang’an Zhang, and Xinjian Lu. "A PD Computed Torque Control Method with Online Self-gain Tuning for a 3UPS-PS Parallel Robot." Robotica 39, no. 9 (2021): 1575–87. http://dx.doi.org/10.1017/s0263574720001368.

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SUMMARYIn this paper, an online self-gain tuning method of a PD computed torque control (CTC) is used for a 3UPS-PS parallel robot. The CTC is applied to the 3UPS-PS parallel robot based on the robot dynamic model which is established via a virtual work principle. The control system of the robot comprises a nonlinear feed-forward loop and a PD control feedback loop. To implement real-time online self-gain tuning, an adjustment method based on the genetic algorithm (GA) is proposed. Compared with the traditional CTC, the simulation results indicate that the control algorithm proposed in this study can not only enhance the anti-interference ability of the system but also improve the trajectory tracking speed and the accuracy of the 3UPS-PS parallel robot.

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Li, Zeyu, Weidong Liu, Le Li, Zhi Liu, and Feihu Zhang. "Co-designed of network scheduling and sliding mode control for underwater shuttle based on adaptive genetic algorithm." Assembly Automation 38, no. 5 (2018): 635–44. http://dx.doi.org/10.1108/aa-11-2017-149.

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Purpose Underwater shuttle is widely used in scenarios of deep sea transportation and observation. As messages are transmitted via the limited network, high transmission time-delay often leads to information congestion, worse control performance and even system crash. Moreover, due to the nonlinear issues with respect to shuttle’s heading motion, the delayed transmission also brings extra challenges. Hence, this paper aims to propose a co-designed method, for the purpose of network scheduling and motion controlling. Design/methodology/approach First, the message transmission scheduling is modeled as an optimization problem via adaptive genetic algorithm. The initial transmission time and the genetic operators are jointly encoded and adjusted to balance the payload in network. Then, the heading dynamic model is compensated for the delayed transmission, in which the parameters are unknown. Therefore, the adaptive sliding mode controller is designed to online estimate the parameters, for enhancing control precision and anti-interference ability. Finally, the method is evaluated by simulation. Findings The messages in network are well scheduled and the time delay is thus reduced, which increases the quality of service in network. The unknown parameters are estimated online, and the quality of control is enhanced. The control performance of the shuttle control system is thus increased. Originality/value The paper is the first to apply co-design method of message scheduling and attitude controlling for the underwater unmanned vehicle, which enhaces the control performance of the network control system.

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WANG, Xiaoqiao. "Online Control Threshold Optimization for Complex Mechanical Products Assembly Process Based on Hybrid Genetic Particle Swarm Optimization." Journal of Mechanical Engineering 52, no. 1 (2016): 130. http://dx.doi.org/10.3901/jme.2016.01.130.

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Swisher, Elizabeth M., Nadine Rayes, Deborah Bowen, et al. "Results from MAGENTA: A national randomized four-arm noninferiority trial evaluating pre- and post-test genetic counseling during online testing for breast and ovarian cancer genetic risk." Journal of Clinical Oncology 38, no. 15_suppl (2020): 1506. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.1506.

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1506 Background: Hereditary breast and ovarian cancer (HBOC) is preventable when genetic risk is identified. We aimed to test whether pre and/or post-test genetic counseling is needed to optimally deliver online accessible genetic testing. Methods: MAGENTA (Making GENetic Testing Accessible) is a four-arm non-inferiority trial evaluating electronic genetic education and results delivery alone or combined with pre-test only, or post-test only telephone genetic counseling compared to mandatory pre- and post-test counseling (control arm) in women at risk of HBOC (NCT02993068). Regardless of assigned arm, all subjects with a pathogenic mutation received post-test telephone counseling. All subjects were enrolled electronically as part of either a family history cohort (FHC) or a cascade cohort (CC, known familial mutation). The primary outcome was cancer risk distress at 3 months and the trial was powered for the FHC. Secondary outcomes included completion of testing (i.e., received results), anxiety, depression, quality of life, and decisional regret, all measured by standardized scales. Results: Enrollment is complete and a total of 3,822 participants were randomized, 3,111 in FHC and 711 in CC. Participants were enrolled from all 50 states, but most were white/non-Hispanic (88%). Among participants that completed genetic testing, 173 (7.2%) had a mutation in a breast or ovarian cancer gene, with 114 (5.7%) of FHC and 59 (14.2%) of CC. In the primary intention-to-treat analysis of FHC, each of the three experimental arms was non-inferior to the control arm for distress at 3 months (p < 0.025/3 = 0.0083). In the CC, no and pre-test only counseling were also non-inferior (p < 0.025/3 = 0.0083). Distress was lowest in the arm with neither pre nor post-test counseling. Overall, 318 (18%) participants had very high distress at three month follow-up, and this rate was not significantly different across arms. Anxiety, depression and decisional regret did not have statistically significant differences across arms at follow-up. Test completion was highest in the no counseling arm (86.4%) and lowest in the control arm (60.6%). Conclusions: Electronic genetic education and results release without genetic counseling was non-inferior with regard to patient distress and was associated with higher test completion and lower distress. These results support use of a genetic testing paradigm providing individualized genetic counseling only for patients with positive test results. Clinical trial information: NCT02993068.

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Yordanova, S. T., M. N. Slavov, and D. R. Stoitseva-Delicheva. "Design and Genetic Algorithms Based Optimisation of Industrial Adaptive PID FLC System of Liquid Level." Mekhatronika, Avtomatizatsiya, Upravlenie 24, no. 4 (2023): 181–89. http://dx.doi.org/10.17587/mau.24.181-189.

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The level control of the precarbonised solution in a soda ash production plant requires intelligent approaches that can tackle process complexity, nonlinearity and industrial environment impact. Therefore, model-free fuzzy logic controllers (FLC) with empirical tuning are suggested which are implemented in a general purpose programmable logic controller (PLC) and operate in real time control. Online adaptation improves the FLC parameters tuning. The aim of the present research is to optimise the adaptation strategy and the parameters of an adaptive PLC PID FLC using genetic algorithms (GA) and simulations for reducing both the system error and the control variance. The PID FLC is based on a PD FLC and a parallel integrator of the system error. A Sugeno model is used for adaptation of the PID FLC tuning parameters. Depending on the level it defines empirically via its input membership functions three linearisation zones and performs soft blending of the local for each zone PD FLC gains and integrator time-constants. Two adaptation strategies are suggested for online auto-tuning of the integrator time-constant only, and together with the PD FLC gain. The local parameters, in turn, are GA optimised. Simulations show that the best system performance is achieved by auto-tuning both PID FLC parameters with optimised local values.

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Xiao, Jin Jian, Si Min Huang, and Yun Song Wang. "Study on Matching Optimization of EPS Fuzzy Control." Applied Mechanics and Materials 97-98 (September 2011): 730–34. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.730.

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In order to resolve matching optimization between EPS and mechanical steering system of vehicle, the difference and rate between operating torque and ideal torque of steering wheel is set as the control target. Fuzzy controller would be adopted to change parameter of PD controller. Through these, real-time assistant voltage is dynamically adjusted and ideal assistant torque is gained; further operating torque of steering wheel, vehicle yaw rate and slip angle as performance indicators, selecting retarding mechanism parameter and PD controller as optimal objects, utilizing genetic algorithm, electric power assistant system (EPS) would be dynamically multi objects optimized; finally utilizing EPS testing system controller is online simulated and verified, test proves that fuzzy PD controller via multi objects optimization could effectively increase vehicles steering portability and stability.

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Dassanayake, Palitha, Keigo Watanabe, and Kiyotaka Izumi. "Fuzzy Behavior-Based Control for a Task of Three-Link Manipulator with Obstacle Avoidance." Journal of Robotics and Mechatronics 11, no. 6 (1999): 502–9. http://dx.doi.org/10.20965/jrm.1999.p0502.

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Our objective is for a 3-link manipulator to reach a target while avoiding obstacles with online information using a fuzzy-behavior-based control approach. Control applied to mobile robots elsewhere is modified to suit to the manipulator. Fuzzy behavior elements are trained using a genetic algorithm. A component apart from the basic concept is introduced to overcome gravitation. Result shows the manipulator reaches the target with an acceptable solution for 3 simulations, so the proposed approach is suitable to multilink manipulator task control.

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Pantel, Jean Tori, Nurulhuda Hajjir, Magdalena Danyel, et al. "Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study." Journal of Medical Internet Research 22, no. 10 (2020): e19263. http://dx.doi.org/10.2196/19263.

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Background Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). Conclusions DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.

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Deng, Yaohua, Qiwen Lu, Jiayuan Chen, Sicheng Chen, Liming Wu, and Luxin Tang. "Study on the Extraction Method of Deformation Influence Factors of Flexible Material Processing Based on Information Entropy." Advances in Mechanical Engineering 6 (January 1, 2014): 547947. http://dx.doi.org/10.1155/2014/547947.

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Through analyzing the flexible material processing (FMP) deformation factors, it is pointed out that without a choice of deformation influence quantity would increase the compensation control predict model system input. In order to reduce the count of spatial dimensions of knowledge, we proposed the method by taking the use of FMP deformation compensation control knowledge extraction, which is based on decision table (DT) attribute reduction, deriving the algorithm that is based on information entropy attribute importance, to find the dependencies between attributes through attribute significance (AS) and to extract the intrinsic attributes which is the most close to deformation compensation control decision making. Finally, through an example presented in this paper to verify the efficiency of RS control knowledge extraction method. Compared with the Pawlak method and genetic extraction algorithm, the prediction accuracy of after reduction data is 0.55% less than Pawlak method and 3.64% higher than the genetic extraction algorithm; however, the time consumption of forecast calculation is 30.3% and 11.53% less than Pawlak method and genetic extraction algorithm, respectively. Knowledge extraction entropy methods presented in this paper have the advantages of fast calculating speed and high accuracy and are suitable for FMP deformation compensation of online control.

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Journal articles on the topic 'Online genetic control'

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