Articles de revues : « GA. Information industry »

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Rao, Xiaoyang, et Xuesong Yan. « Particle Swarm Optimization Algorithm Based on Information Sharing in Industry 4.0 ». Wireless Communications and Mobile Computing 2022 (10 mars 2022) : 1–11. http://dx.doi.org/10.1155/2022/4328185.

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Intelligent manufacturing is an important part of Industry 4.0; artificial intelligence technology is a necessary means to realize intelligent manufacturing. This requires the exploration of pattern recognition, computer vision, intelligent optimization, and other related technologies. Particle swarm optimization (PSO) algorithm is an optimization algorithm inspired by the foraging behavior of birds. PSO was an intelligent technology and an efficient optimization algorithm verified by a lot of research and experiments. In this paper, the traditional PSO algorithm is compared with genetic algorithms (GA) to illustrate the performance of the traditional PSO algorithm. By analyzing the advantages and disadvantages of the traditional PSO algorithm, the traditional PSO algorithm is improved through introducing into the sharing information mechanism and the competition strategy, called information sharing based PSO (IPSO). The novel algorithm IPSO was the rapid convergence speed similar to the traditional PSO and enhanced the global search capability. Our experimental results show that IPSO has better performance than the traditional PSO and the GA algorithm on benchmark functions, especially for difficult functions.

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Kim, Seonhyeon, Ephraim Kwashie Thompson et Changki Kim. « Insurance Transactions through Blockchain Applications : Current Status and Implications for the Domestic Insurance Industry ». Crisis and Emergency Management : Theory and Praxis 17, n^o 11 (30 novembre 2021) : 137–56. http://dx.doi.org/10.14251/crisisonomy.2021.17.11.137.

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This paper discusses the necessity for introducing blockchain technology into insurance sales channels. We focus on two factors that have hindered the sustainable development of the domestic insurance industry. The first problem is that insurance companies’ profitability is on the decline and the second issue relates to information asymmetry problems besetting insurance sales channels. The channels are heavily concentrated on general agencies (GA) but the GA channel has been characterized by problems such as incomplete sales and high termination rates. Due to the advantages of blockchain technology such as the transparency of information and reduction in transaction and security costs, insurance sales channels such as GA can help reduce insurance companies’ business costs and alleviate information asymmetry by adopting blockchain technology. Thus, in this paper, we discuss ways to utilize applications using blockchain platforms in insurance sales channels. This paper also introduces the web-based blockchain application of International Business Machines (IBM) and suggests that various stakeholders of insurance can interact through blockchain applications.

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Jendrzejczyk-Handzlik, D. « Phase equilibria in the ternary Ag-Au-Ga system : Isothermal sections at 250°C and 450°C ». Journal of Mining and Metallurgy, Section B : Metallurgy 53, n^o 3 (2017) : 215–22. http://dx.doi.org/10.2298/jmmb170531043j.

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The ternary Ag-Au-Ga system seems to be interesting in jeweller?s craft as a joint. Moreover, the ternary systems based on gold and silver have found applications in the dental industry. A literature overview of the Ag-Au-Ga system shows that the information about phase equilibria of this system does not exist. In the present work, phase equilibria in the Ag-Au-Ga ternary system have been studied by using scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) analysis and X-ray diffraction analysis (XRD). Twenty two annealed alloys in the 10-70 at.% Ga region have been investigated. Obtained experimental results were compared with the predicted isothermal sections at two temperatures (250?C and 450?C). No ternary compounds are found.

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Liu, Chao, Yixin Fan et Xiangyu Zhu. « Fintech Index Prediction Based on RF-GA-DNN Algorithm ». Wireless Communications and Mobile Computing 2021 (7 juin 2021) : 1–9. http://dx.doi.org/10.1155/2021/3950981.

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The Fintech index has been more active in the stock market with the Fintech industry expanding. The prediction of the Fintech index is significant as it is capable of instructing investors to avoid risks and provide guidance for financial regulators. Traditional prediction methods adopt the deep neural network (DNN) or the combination of genetic algorithm (GA) and DNN mostly. However, heavy computational load is required by these algorithms. In this paper, we propose an integrated artificial intelligence-based algorithm, consisting of the random frog algorithm (RF), GA, and DNN, to predict the Fintech index. The proposed RF-GA-DNN prediction algorithm filters the key input variables and optimizes the hyperparameters of DNN. We compare the proposed RF-GA-DNN with the traditional GA-DNN in terms of convergence time and prediction accuracy. Results show that the convergence time of GA-DNN is up to 20 hours and its prediction accuracy is 97.4%. In comparison, the convergence time of our RF-GA-DNN is only about 1.5 hours and the prediction accuracy reaches 97.0%. These results demonstrate that the proposed RF-GA-DNN prediction algorithm significantly reduces the convergence time with the promise of competitive prediction accuracy. Thus, the proposed algorithm deserves to be widely recommended for predicting the Fintech index.

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Kandiri, Amirreza, Farid Sartipi et Mahdi Kioumarsi. « Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms ». Applied Sciences 11, n^o 2 (6 janvier 2021) : 485. http://dx.doi.org/10.3390/app11020485.

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Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.

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Wang, Di, Lin Xie, Simon Yang et Fengchun Tian. « Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors ». Sensors 18, n^o 10 (25 septembre 2018) : 3222. http://dx.doi.org/10.3390/s18103222.

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Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data.

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Praveen, S. Phani, Hesam Ghasempoor, Negar Shahabi et Fatemeh Izanloo. « A Hybrid Gravitational Emulation Local Search-Based Algorithm for Task Scheduling in Cloud Computing ». Mathematical Problems in Engineering 2023 (4 février 2023) : 1–9. http://dx.doi.org/10.1155/2023/6516482.

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The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.

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Chen, Chen, Thomas Phang et Lee Kong Tiong. « Planning semi-automated precast production using GA ». International Journal of Industrialized Construction 1, n^o 1 (27 juillet 2020) : 48–63. http://dx.doi.org/10.29173/ijic215.

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Although fully automated production systems have been developed and used in some industry leaders, most of the precast factories have yet to be developed to that stage. Semi-automated production lines are still popularly used. As production productivity can be maximally improved within the physical constraints by applying a sound production plan, this paper tends to propose a production planning method for the semi-automated precast production line using genetic algorithm (GA). The production planning problem is formulated into a flexible job shop scheduling problem (FJSSP) model and solved using an integrated approach. Thanks to the development of new technologies such as building information modeling (BIM) platform and radio frequency identification (RFID), implementation of a just-in-time (JIT) schedule in the semi-automated precast production line becomes practicable on the grounds of risk mitigation and enhanced demand forecast capability. In this regard, the optimization objectives are minimum makespan, station idle time, and earliness and tardiness penalty. An example was applied to validate the integrated GA approach. The experimental results show that the developed GA approach is a useful and effective method for solving the problem that it can return high-quality solutions. This paper thus contributes to the body of knowledge new precast production planning method for practical usage.

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Kourousis, Kyriakos I. « A HOLISTIC APPROACH TO GENERAL AVIATION AIRCRAFT STRUCTURAL FAILURE PREVENTION IN AUSTRALIA ». Aviation 17, n^o 3 (7 octobre 2013) : 98–103. http://dx.doi.org/10.3846/16487788.2013.840055.

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Ageing aircraft are becoming a major issue in the general aviation (GA) industry, both in terms of safety and maintenance and support cost. Ensuring a sound structure is considered one of the primary challenges in this area, it is, therefore, attracting the attention of the regulating authorities. The Civil Aviation Safety Agency (CASA) has taken a mixture of actions to tackle the various issues related to the diverse Australian GA ageing aircraft fleet. Further efforts focus on increasing the awareness of the different parties engaged in aircraft operations, maintenance and design, as well as quantification of the associated risk. In this frame a holistic approach is proposed to cover the various aspects, emphasizing the use of cost-effective structural health monitoring (SHM) systems and web-based education and information dissemination on ageing aircraft issues.

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Yang, Zeyin. « Application and Development of Digital Enhancement of Traditional Sculpture Art ». Scientific Programming 2022 (3 février 2022) : 1–8. http://dx.doi.org/10.1155/2022/9095577.

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Sculpture art, as an important carrier of spiritual civilization, also portrays a prosperous scene as an industry with urban and cultural development. Three-dimensional technology offers a new platform for sculpture creation, allowing for the digitization of sculpture works via electronic information technology, and the display of sculpture works in front of people via displays, facilitating the exchange and dissemination of information and promoting the growth and progress of the entire sculpture creation industry. We plan to use digital enhancement technology to conduct small-scale creation experiments on traditional sculpture works, discuss the method of GA (Genetic Algorithm) in image restoration processing, investigate the method of image segmentation processing based on the genetic algorithm, and propose the method of image segmentation processing based on the fuzzy membership surface genetic algorithm, in order to verify and solve the creation difficulties of traditional sculpture works.

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Li, Yuming, Shuting Fu, Qiyang Zhang, Hongyu Liu et Yajun Wang. « Recent Progress of Ga-Based Catalysts for Catalytic Conversion of Light Alkanes ». Catalysts 12, n^o 11 (5 novembre 2022) : 1371. http://dx.doi.org/10.3390/catal12111371.

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The efficient and clean conversion of light alkanes is a research hotspot in the petrochemical industry, and the development of effective and eco-friendly non-noble metal-based catalysts is a key factor in this field. Among them, gallium is a metal component with good catalytic performance, which has been extensively used for light alkanes conversion. Herein, we critically summarize recent developments in the preparation of gallium-based catalysts and their applications in the catalytic conversion of light alkanes. First, we briefly describe the different routes of light alkane conversion. Following that, the remarkable preparation methods for gallium-based catalysts are discussed, with their state-of-the-art application in light alkane conversion. It should be noticed that the directional preparation of specific Ga species, strengthening metal-support interactions to anchor Ga species, and the application of new kinds of methods for Ga-based catalysts preparation are at the leading edge. Finally, the review provides some current limitations and future perspectives for the development of gallium-based catalysts. Recently, different kinds of Ga species were reported to be active in alkane conversion, and how to separate them with advanced in situ and ex situ characterizations is still a problem that needs to be solved. We believe that this review can provide base information for the preparation and application of Ga-based catalysts in the current stage. With these summarizations, this review can inspire new research directions of gallium-based catalysts in the catalysis conversion of light alkanes with ameliorated performances.

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He, Wei, Yichao Shi et Dewei Kong. « Construction of a 5D duration and cost optimisation model based on genetic algorithm and BIM ». Journal of Engineering, Design and Technology 17, n^o 5 (10 août 2019) : 929–42. http://dx.doi.org/10.1108/jedt-12-2018-0214.

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Purpose The construction industry is characterized by a long construction period, high cost and many uncontrollable factors. The owners and contractors are increasingly focusing on the efficiency of their construction and costs in pursuit of greater economic benefits. However, current methods used in the construction period and cost optimization analysis with multiple constraints the have their own limitations. Therefore, this study aims to propose a combination of genetic algorithm (GA) and building information modeling (BIM) to construct a five-dimensional construction duration-cost optimization model with the advantages of optimization and simulation for optimization. Design/methodology/approach This design first analyzed the characteristics of changing construction period and cost and then improved the genetic mechanism and the data processing method in the GA according to the aforementioned characteristics. Then, BIM technology was combined with GA to testify the feasibility of the model in the practical engineering project. Findings The result proved that this new method was reasonable and effective in dealing with the complicated problem of period and cost. GA accelerated the optimization process and yielded a reliable Pareto solution. BIM technology simulated the construction process before construction to increase the feasibility of the construction scheme. Originality/value This method not only can rapidly provide the best construction period/cost decision to the architect according to the previous working period/cost or contract data that can meet the demands of the architect but also visualize the construction and give a dynamic schedule of the project.

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Kamalakannan, T., et P. Mayilvaghnan. « Optimal customer relationship management in telecalling industry by using data mining and business intelligence ». International Journal of Engineering & ; Technology 7, n^o 1.1 (21 décembre 2017) : 12. http://dx.doi.org/10.14419/ijet.v7i1.1.8907.

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Decision making system in telecommunication industries plays a more important role where it is required to find customer churn. Customer churn prediction requires finding out and analyzing the information about the business data intelligence techniques which can be done efficiently by adapting the business intelligence techniques. Business intelligence provides tools to predict and analyze the historical, current and predictive views of business operations. However, this would be more complex task with high volume of data which are gathered from million of telephone users for the time being. It can be handled effectively by introducing the data mining techniques which select the most useful information from the gathered data set from which decision making can be done efficiently. In this research method, telecommunication industry is considered in which customer churn prediction application is focused. The main goal of this research method is to introduce the data mining technique which can select the most useful information from the telecommunication industry dataset. This is done by introducing the Hybrid Genetic Algorithm with Particle Swarm Optimization (HGAPSO) method which can select the most useful information. In this research, the hybrid HGAPSO combines the advantages of PSO and GA optimally. From the selected information, decision making about the customer churn prediction can be done accurately. Finally decision making is done by predicting the customer behaviour using Support Vector Machine classification approach. The performance metrics are considered such as precision, recall, f-measure, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), time complexity and ROC. Experimental results demonstrated that HGAPSO provides highly scalable which is used for prediction examination in the business intelligence.

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Lee, Sanghyeop, Junyeob Kim, Hyeon Kang, Do-Young Kang et Jangsik Park. « Genetic Algorithm Based Deep Learning Neural Network Structure and Hyperparameter Optimization ». Applied Sciences 11, n^o 2 (14 janvier 2021) : 744. http://dx.doi.org/10.3390/app11020744.

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Alzheimer’s disease is one of the major challenges of population ageing, and diagnosis and prediction of the disease through various biomarkers is the key. While the application of deep learning as imaging technologies has recently expanded across the medical industry, empirical design of these technologies is very difficult. The main reason for this problem is that the performance of the Convolutional Neural Networks (CNN) differ greatly depending on the statistical distribution of the input dataset. Different hyperparameters also greatly affect the convergence of the CNN models. With this amount of information, selecting appropriate parameters for the network structure has became a large research area. Genetic Algorithm (GA), is a very popular technique to automatically select a high-performance network architecture. In this paper, we show the possibility of optimising the network architecture using GA, where its search space includes both network structure configuration and hyperparameters. To verify the performance of our Algorithm, we used an amyloid brain image dataset that is used for Alzheimer’s disease diagnosis. As a result, our algorithm outperforms Genetic CNN by 11.73% on a given classification task.

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Kasim Hawari, Mohd Zarifitri, et Nur Ilyana Anwar Apandi. « Industry 4.0 with intelligent manufacturing 5G mobile robot based on genetic algorithm ». Indonesian Journal of Electrical Engineering and Computer Science 23, n^o 3 (1 septembre 2021) : 1376. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1376-1384.

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A manufacturing fifth-generation (5G) mobile robot is a new development of industry 4.0 application, deploying an unmanned system. This study aims to implement a robot system for industrial applications in real-time with remote sensors to enable humans. Moreover, there is still some obstacle to cope with the better optimization solution for manufacturing 5G robot. This paper proposed a latency network algorithm for the manufacturing 5G mobile robot. An improved genetic algorithm (GA) by restructuring the genes is applied to plan a mobile robot path. The process of the robot path in a complex workspace is proposed, considering the node's collision-free constraint in the moving phase of a robot. The proposed scheme improves the robot path and delivery efficiency of the robot on average at 68% by moving on the industrial environment's shortest path and time average of the mobile robot reach its destination.

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Kim, Sangyong, et Jae Heon Shim. « Combining case-based reasoning with genetic algorithm optimization for preliminary cost estimation in construction industry ». Canadian Journal of Civil Engineering 41, n^o 1 (janvier 2014) : 65–73. http://dx.doi.org/10.1139/cjce-2013-0223.

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In this paper, we propose a hybrid case-based reasoning (CBR) system for predicting the construction cost of high-rise buildings at the preliminary design stage. First, the extracted cost factors (CFs) of a high-rise building were shown to significantly improve the cost estimation system’s performance. For developing a CBR system, a hybrid approach that combines CBR with genetic algorithms (GAs) for cost estimation was adopted. Genetic algorithms were used for optimized weight generation and applied to real project cases. Additionally, this paper proposes the identification of an alternative similarity score measurement formula. The proposed formula evaluates the contrast between the alternative case matching approach and the classical formula in a scenario involving the use of cost factors describing a case. The results indicate that the proposed GA-based CBR system can consistently reduce errors and potentially be useful to owners and contractors in the early financial planning stage. Accordingly, it is expected that the developed CBR system would provide decision-makers with accurate cost information to assess and compare multiple alternatives for obtaining the optimal solution and controlling the cost.

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Stevie, F. A., S. W. Downey, S. Brown, T. Shofner, M. Decker, T. Dingle et L. Christman. « Microscale Elemental Imaging of Semiconductor Materials Using Focused Ion Beam Sims ». Microscopy and Microanalysis 4, S2 (juillet 1998) : 650–51. http://dx.doi.org/10.1017/s1431927600023370.

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The semiconductor industry demands elemental information from ever smaller regions. Two types of information in demand are two dimensional dopant profiles for the MOS transisitor and identification of particles as small as 30 nm diameter. The work of Levi-Setti and others resulted in liquid metal ion source (LMIS) instruments that provided secondary ion mass spectrometry (SIMS) images using Ga+ beams with 20 nm lateral resolution. It is now possible to purchase focused ion beam (FIB) systems with 5 nm beam capability and SIMS detection.The application of LMIS SIMS to meet semiconductor demands has been pursued in our laboratory with a FEI-800 FIB. SIMS imaging of semiconductor patterns after etch has shown the ability to identify boron and carbon contamination. Figure 1 shows boron in a comb structure after a BC13 etch. The boron can be shown to be removed by a cleaning step.

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An, Yonghong. « Mathematical Model and Genetic Algorithm in Computer Programming Optimization and Network Topology Structure ». Wireless Communications and Mobile Computing 2022 (12 août 2022) : 1–11. http://dx.doi.org/10.1155/2022/1917172.

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Recent emphasis has been focused on the industrial Internet of Things (Ind-IoT) in the context of the Fourth Industrial Revolution (Industry 4.0). IoT devices are used in the Ind-IoT to increase manufacturing productivity. The problem, however, is that these instruments will create enormous volumes of data records that must be handled efficiently. Cloud computing (CC) is frequently cited as a viable option for providing effective support for Ind-IoT applications. However, the high-latency and unstable connectivity problem between the cloud and Ind-IoT endpoints continues to plague Ind-IoT operations. Fog computing (FC), which extends computation and storage to the edge of the network, is a possible answer to these problems. Cloud-fog integrated Internet of Things (CFI-Ind-IoT) is discussed in this study as an approach to integrating FC with cloud-based industrial Internet of Things (Ind-IoT). A constrained multiparent crossover genetic algorithm (CMPC-GA) for optimization of the load adjusting challenge in the distributed cloud-fog network is proposed in order to attain ultralow response latency in the CFI-Ind-IoT system. Furthermore, we develop a duty reallocation and retransmission method in order to lower the average delivery latency of the CFI-Ind-IoT architecture due to the unreliable scenario. Effectiveness measurements show that the CMPC-GA technique can deliver ultralow latency functionality in a typical Ind-IoT.

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Eltoukhy, Abdelrahman E. E., Felix T. S. Chan, S. H. Chung, Ben Niu et X. P. Wang. « Heuristic approaches for operational aircraft maintenance routing problem with maximum flying hours and man-power availability considerations ». Industrial Management & ; Data Systems 117, n^o 10 (4 décembre 2017) : 2142–70. http://dx.doi.org/10.1108/imds-11-2016-0475.

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Purpose The purpose of this paper is twofold. First, to propose an operational model for aircraft maintenance routing problem (AMRP) rather than tactical models that are commonly used in the literature. Second, to develop a fast and responsive solution method in order to cope with the frequent changes experienced in the airline industry. Design/methodology/approach Two important operational considerations were considered, simultaneously. First one is the maximum flying hours, and second one is the man-power availability. On the other hand, ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA) approaches were proposed to solve the model, and the upper bound was calculated to be the criteria to assess the performance of each meta-heuristic. After attempting to solve the model by these meta-heuristics, the authors noticed further improvement chances in terms of solution quality and computational time. Therefore, a new solution algorithm was proposed, and its performance was validated based on 12 real data from the EgyptAir carrier. Also, the model and experiments were extended to test the effect of the operational considerations on the profit. Findings The computational results showed that the proposed solution algorithm outperforms other meta-heuristics in finding a better solution in much less time, whereas the operational considerations improve the profitability of the existing model. Research limitations/implications The authors focused on some operational considerations rather than tactical considerations that are commonly used in the literature. One advantage of this is that it improves the profitability of the existing models. On the other hand, identifying future research opportunities should help academic researchers to develop new models and improve the performance of the existing models. Practical implications The experiment results showed that the proposed model and solution methods are scalable and can thus be adopted by the airline industry at large. Originality/value In the literature, AMRP models were cast with approximated assumption regarding the maintenance issue, while neglecting the man-power availability consideration. However, in this paper, the authors attempted to relax that maintenance assumption, and consider the man-power availability constraints. Since the result showed that these considerations improve the profitability by 5.63 percent in the largest case. The proposed operational considerations are hence significant. Also, the authors utilized ACO, SA, and GA to solve the model for the first time, and developed a new solution algorithm. The value and significance of the new algorithm appeared as follow. First, the solution quality was improved since the average improvement ratio over ACO, SA, and GA goes up to 8.30, 4.45, and 4.00 percent, respectively. Second, the computational time was significantly improved since it does not go beyond 3 seconds in all the 12 real cases, which is considered much lesser compared to ACO, SA, and GA.

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Wang, Fei-Peng. « Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network ». Complexity 2018 (2 décembre 2018) : 1–16. http://dx.doi.org/10.1155/2018/7616537.

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The arrival of the era of big data has provided a new direction of development for internet financial credit collection. First of all, the article introduced the situation of internet finance and traditional credit industry. Based on that, the mathematical model was used to demonstrate the necessity of developing big data financial credit information. Then, the Internet financial credit data are preprocessed, the variables suitable for modeling are selected, and the dynamic credit tracking model of BP neural network based on adaptive genetic algorithm is constructed. It is found that both LM training algorithm and Bayesian algorithm can converge the error to 10e-6 quickly in the model training, and the overall training effect is ideal. Finally, the rule extraction algorithm is used to simulate the test samples. The accuracy rate of each sample method is over 90%, and some accuracy rate is even more than 90%, which indicates that the model is applicable to the credit data of big data in internet finance.

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Chou, Wei-I., Tun-Wen Pai, Shi-Hwei Liu, Bor-Kai Hsiung et Margaret D. T. Chang. « The family 21 carbohydrate-binding module of glucoamylase from Rhizopus oryzae consists of two sites playing distinct roles in ligand binding ». Biochemical Journal 396, n^o 3 (29 mai 2006) : 469–77. http://dx.doi.org/10.1042/bj20051982.

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The starch-hydrolysing enzyme GA (glucoamylase) from Rhizopus oryzae is a commonly used glycoside hydrolase in industry. It consists of a C-terminal catalytic domain and an N-terminal starch-binding domain, which belong to the CBM21 (carbohydrate-binding module, family 21). In the present study, a molecular model of CBM21 from R. oryzae GA (RoGACBM21) was constructed according to PSSC (progressive secondary structure correlation), modified structure-based sequence alignment, and site-directed mutagenesis was used to identify and characterize potential ligand-binding sites. Our model suggests that RoGACBM21 contains two ligand-binding sites, with Tyr32 and Tyr67 grouped into site I, and Trp47, Tyr83 and Tyr93 grouped into site II. The involvement of these aromatic residues has been validated using chemical modification, UV difference spectroscopy studies, and both qualitative and quantitative binding assays on a series of RoGACBM21 mutants. Our results further reveal that binding sites I and II play distinct roles in ligand binding, the former not only is involved in binding insoluble starch, but also facilitates the binding of RoGACBM21 to long-chain soluble polysaccharides, whereas the latter serves as the major binding site mediating the binding of both soluble polysaccharide and insoluble ligands. In the present study we have for the first time demonstrated that the key ligand-binding residues of RoGACBM21 can be identified and characterized by a combination of novel bioinformatics methodologies in the absence of resolved three-dimensional structural information.

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Hoke, Eva, Kamil Peterek, Katerina Vichova et Pavel Taraba. « Effect of crises on human resources management in small and medium enterprises : Evidence from manufacturing industry in the Czech Republic ». Problems and Perspectives in Management 20, n^o 2 (11 avril 2022) : 10–21. http://dx.doi.org/10.21511/ppm.20(2).2022.02.

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The current turbulent times have never been so demanding to adapt to ever-changing conditions. Small and medium enterprises (SMEs) form the cornerstone of the economy. Moreover, they are the driving force of economic processes in all countries. Therefore, this paper aims to empirically map and identify the causes of the crisis and statistically verify how these crises affect the personnel measures taken and human resource management (HRM) in SMEs. The chi-square test and Cramer’s coefficient were used to verify the statistical dependences of research questions and hypotheses. A statistically significant impact of external economic influences on enterprises’ activities was empirically confirmed (59%). It was proved that nowadays, the most important external factor influencing the activities of enterprises in the market is the COVID-19 pandemic. The study also focused on the personnel measures taken during the crisis. It was confirmed that small businesses apply alternative personnel measures, namely reducing variable wage components before radical redundancies. AcknowledgmentThis study was supported by the project DKRVO Tomas Bata University in Zlín – Risk management in logistics – RVO/FLKŘ/2021/03, Faculty of Logistics and Crisis Management, and project GAAA – Project risk management in the conditions of SMEs in the Czech Republic – GA/16/2019.

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Nakandala, Dilupa, Henry Lau et Jingjing Zhang. « Cost-optimization modelling for fresh food quality and transportation ». Industrial Management & ; Data Systems 116, n^o 3 (11 avril 2016) : 564–83. http://dx.doi.org/10.1108/imds-04-2015-0151.

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Purpose – All along the length of the supply chain, fresh food firms face the challenge of managing both product quality, due to the perishable nature of the products, and product cost. The purpose of this paper to develop a method to assist logistics managers upstream in the fresh food supply chain in making cost optimized decisions regarding transportation, with the objective of minimizing the total cost while maintaining the quality of food products above acceptable levels. Design/methodology/approach – Considering the case of multiple fresh food products collected from multiple farms being transported to a warehouse or a retailer, this study develops a total cost model that includes various costs incurred during transportation. The practical application of the model is illustrated by using several computational intelligence approaches including genetic algorithms (GA), fuzzy genetic algorithms (FGA) as well as an improved simulated annealing (SA) procedure applied with a repair mechanism for efficiency benchmarking. The authors demonstrate the practical viability of these approaches by using a simulation study based on pertinent data and evaluate the simulation outcomes. Findings – The application of the proposed total cost model was demonstrated using three approaches of GA, FGA and SA with a repair mechanism. All three approaches are adoptable; however, based on the performance evaluation, it was evident that the FGA is more likely to produce a better performance than the other two approaches of GA and SA. Practical implications – This study provides a pragmatic approach for supporting logistics and supply chain practitioners in fresh food industry in making important decisions on the arrangements and procedures related to the transportation of multiple fresh food products to a warehouse from multiple farms in a cost-effective way without compromising product quality. Originality/value – This study extends the literature on cold supply chain management by investigating cost and quality optimization in a multi-product scenario from farms to a retailer and, minimizing cost by managing the quality above expected quality levels at delivery. The scalability of the proposed generic function enables the application to alternative situations in practice such as different storage environments and transportation conditions, etc.

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Reyes-Barquet, Luis Miguel, José Octavio Rico-Contreras, Catherine Azzaro-Pantel, Constantino Gerardo Moras-Sánchez, Magno Angel González-Huerta, Daniel Villanueva-Vásquez et Alberto Alfonso Aguilar-Lasserre. « Multi-Objective Optimal Design of a Hydrogen Supply Chain Powered with Agro-Industrial Wastes from the Sugarcane Industry : A Mexican Case Study ». Mathematics 10, n^o 3 (29 janvier 2022) : 437. http://dx.doi.org/10.3390/math10030437.

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This paper presents an optimization modeling approach to support strategic planning for designing hydrogen supply chain (HSC) networks. The energy source for hydrogen production is proposed to be electricity generated at Mexican sugar factories. This study considers the utilization of existing infrastructure in strategic areas of the country, which brings several advantages in terms of possible solutions. This study aims to evaluate the economic and environmental implications of using biomass wastes for energy generation, and its integration to the national energy grid, where the problem is addressed as a mixed-integer linear program (MILP), adopting maximization of annual profit, and minimization of greenhouse gas emissions as optimization criteria. Input data is provided by sugar companies and the national transport and energy information platform, and were represented by probability distributions to consider variability in key parameters. Independent solutions show similarities in terms of resource utilization, while also significant differences regarding economic and environmental indicators. Multi-objective optimization was performed by a genetic algorithm (GA). The optimal HSC network configuration is selected using a multi-criteria decision technique, i.e., TOPSIS. An uncertainty analysis is performed, and main economic indicators are estimated by investment assessment. Main results show the trade-off interactions between the HSC elements and optimization criteria. The average internal rate of return (IRR) is estimated to be 21.5% and average payback period is 5.02 years.

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Chen, Lili, Lingyun Sun, Chien-Ming Chen, Mu-En Wu et Jimmy Ming-Tai Wu. « Stock Trading System Based on Machine Learning and Kelly Criterion in Internet of Things ». Wireless Communications and Mobile Computing 2021 (3 décembre 2021) : 1–9. http://dx.doi.org/10.1155/2021/7632052.

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The evolution of the Internet of Things (IoT) has promoted the prevalence of the financial industry as a variety of stock prediction models have been able to accurately predict various IoT-based financial services. In practice, it is crucial to obtain relatively accurate stock trading signals. Considering various factors, finding profitable stock trading signals is very attractive to investors, but it is also not easy. In the past, researchers have been devoted to the study of trading signals. A genetic algorithm (GA) is often used to find the optimal solution. In this study, a long short-term (LSTM) memory neural network is used to study stock price fluctuations, and then, genetic algorithms are used to obtain appropriate trading signals. A genetic algorithm is a search algorithm that solves optimization. In this paper, the optimal threshold is found to determine the trading signal. In addition to trading signals, a suitable trading strategy is also crucial. In addition, this research uses the Kelly criterion for fund management; that is, the Kelly criterion is used to calculate the optimal investment score. Effective capital management can not only help investors increase their returns but also help investors reduce their losses.

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Dutta, Pijush, et Asok Kumar. « Modelling of Liquid Flow control system Using Optimized Genetic Algorithm ». Statistics, Optimization & ; Information Computing 8, n^o 2 (20 février 2020) : 565–82. http://dx.doi.org/10.19139/soic-2310-5070-618.

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Estimation of a highly accurate model for liquid flow process industry and control of the liquid flow rate from experimental data is an important task for engineers due to its non linear characteristics. Efficient optimization techniques are essential to accomplish this task.In most of the process control industry flowrate depends upon a multiple number of parameters like sensor output,pipe diameter, liquid conductivity ,liquid viscosity & liquid density etc.In traditional optimization technique its very time consuming for manually control the parameters to obtain the optimial flowrate from the process.Hence the alternative approach , computational optimization process is utilized by using the different computational intelligence technique.In this paper three different selection of Genetic Algorithm is proposed & tested against the present liquid flow process.The proposed algorithm is developed based on the mimic genetic evolution of species that allow the consecutive generations in population to adopt their environment.Equations for Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) are being used as non-linear models and these models are optimized using the proposed different selection of Genetic optimization techniques. It can be observed that the among these three different selection of Genetic Algorithm ,Rank selected GA is better than the other two selection (Tournament & Roulette wheel) in terms of the accuracy of final solutions, success rate, convergence speed, and stability.

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Zhu, Min, Haoan Zhao, Qian Wang, Fanhua Wu et Wei Cao. « A Novel Chinese Honey from Amorpha fruticosa L. : Nutritional Composition and Antioxidant Capacity In Vitro ». Molecules 25, n^o 21 (9 novembre 2020) : 5211. http://dx.doi.org/10.3390/molecules25215211.

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False indigo (Amorpha fruticosa L., A. fruticosa) is the preferred tree indigenous for windbreak and sand control in Northwest China, while information on nutritional and bioactive characteristics of its honey is rare. Herein, 12 honey of Amorpha fruticosa L. (AFH) were sampled in Northwest China and the nutritional composition was determined. Sixteen mineral element and ten dominant polyphenols content were identified and quantified by ICP-MS (Inductively coupled plasma mass spectrometry) and HPLC-QTOF-MS (High performance liquid chromatography-Quadrupole time-of-flight mass spectrometry), respectively. Moreover, AFH demonstrated high levels of DPPH (1,1-Diphenyl-2-picrylhydrazyl) radical scavenging activity (IC50 100.41 ± 15.35 mg/mL), ferric reducing antioxidant power (2.04 ± 0.29 µmol FeSO4·7H2O/g), and ferrous ion-chelating activity (82.56 ± 16.01 mg Na2EDTA/kg), which were significantly associated with total phenolic contents (270.07 ± 27.15 mg GA/kg) and ascorbic acid contents (213.69 ± 27.87 mg/kg). The cell model verified that AFH exhibited dose-dependent preventive effects on pBR322 plasmid DNA and mouse lymphocyte DNA damage in response to oxidative stress. Taken together, our findings provide evidence for the future application of AFH as a potential antioxidant dietary in food industry.

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Wade, Gary L. « Georgia Center for Horticulture : A Statewide Network for Serving Urban Clientele and Environmental Horticulture Professionals ». HortScience 32, n^o 4 (juillet 1997) : 591E—591. http://dx.doi.org/10.21273/hortsci.32.4.591e.

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A proposed Center for Horticulture within the College of Agricultural and Environmental Sciences of The University of Georgia will target both Environmental Horticulture professionals and homeowners. To be headquartered at the Georgia Experiment Station in Griffin, Ga., with satellite units in Atlanta, Athens, Tifton, and Savannah, the Center will utilize advanced communications technology in developing and delivering educational outreach programs for clientele. Distance learning via fiber optics telecommunications will be used to provide educational short courses and seminars to clientele across the state. Distance imaging will be used for plant problem solving and plant identification. Newsletters, pest alerts, program announcements and other information will be sent electronically to clients via fax, e-mail, or the World Wide Web. Marketing of Georgia-grown crops will be a major thrust of the Center. A second component of the Center will be a public outreach unit, staffed by trained Master Gardeners, professional coordinator, and computer technician housed at the various satellite units. Citizens throughout the state will be able to phone one of the satellite units to get their gardening questions answered. Information will be sent directly to clients via fax, e-mail, or from the local county Extension agent when prompted via the computer to send the client an informational bulletin. A central server and database of information to support the Center will be maintained at the Georgia Experiment Station. The Center will utilize an interdisciplinary approach, involving teaching, research, and Extension personnel in responding to industry and consumer needs.

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Purba, Elida. « DETERMINATION OF REACTION KINETICS USING ONLINE X-RAY DIFFRACTION ». Indonesian Journal of Chemistry 8, n^o 3 (17 juin 2010) : 337–41. http://dx.doi.org/10.22146/ijc.21588.

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X-ray diffraction (XRD) is a powerful technique for the study of polymorphism and polymorphic phase transformations. Monitoring of phase transformation directly has been very limited to-date. The XRD system used in this study was used to determine the rate of transformation of pure glutamic acid a form to b form in a solution mediated phase. On every run starting from the pure a form, the transformation process was monitored continuously at fixed temperature, and separate experiments were performed as a function of temperature. The operating temperature was varied from 36 to 57 °C with 10% w/w solid concentration. Data were taken every 200 seconds until the transformation was completed. This paper is concerned with a study of the transformation of the alpha (a) form of L-glutamic acid (L-GA) to the beta (b) form in order to determine the kinetic reaction. The rate constant (k), activation energy (Ea) and pre-exponential factor (A) were obtained. Sensitivity tests were also carried out to examine minimum detection limit when both a and b present in the mixture. In addition, effect of particle size on XRD patterns was also determined. The results show that XRD gives useful information to observe polymorphism for pharmaceutical industry. Keywords: XRD, polymorphism, glutamic acid, reaction kinetics

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Wu, Feng-Shang, et Chia-Chang Tsai. « A Framework of the Value Co-Creation Cycle in Platform Businesses : An Exploratory Case Study ». Sustainability 14, n^o 9 (6 mai 2022) : 5612. http://dx.doi.org/10.3390/su14095612.

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Platform businesses, linking producers and consumers, have emerged as a very important industry. Meanwhile, value co-creation has become one of the critical issues concerning the operation of platform enterprises and the focus of researchers in this area. Platform businesses usually need to strengthen the interactions between all participants to maximize the commercial value. However, the majority of the literature has concentrated on the “platform business–consumer” interaction only, i.e., both “platform business–producer” and “platform producer–consumer” interactions have been almost completely neglected. Consequently, this study aims to fill the research gap by investigating “all-around interactions” and the relationships between the interaction with the value co-creation performance. A holistic framework of the value co-creation cycle is developed and validated. One of the largest platform businesses in Taiwan was examined, and Google Analytics (GA) code was embedded into its information system for data generation. The results confirmed the proposed framework and hypotheses. The study concludes that platform businesses need to gain insight into producers and consumers through data tracking and analysis as well as to provide innovative services that elevate satisfaction, user loyalty, and usage frequency, with a final goal of establishing a cycle of value co-creation.

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Azizi, Aydin. « Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing ». Complexity 2017 (2017) : 1–18. http://dx.doi.org/10.1155/2017/8728209.

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Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID) system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP) has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI) techniques. This effort has led the author to propose a novel AI algorithm, which has been named “hybrid AI optimization technique,” to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE) and Ring Probabilistic Logic Neural Networks (RPLNN). The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS), and results have been compared with Genetic Algorithm (GA) that demonstrates the feasibility of the proposed architecture successfully.

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Warrier, Preeti, et Pritesh Shah. « Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm ». Applied System Innovation 4, n^o 3 (4 août 2021) : 50. http://dx.doi.org/10.3390/asi4030050.

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The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.

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Baiocco, Daniele, et Zhibing Zhang. « Microplastic-Free Microcapsules to Encapsulate Health-Promoting Limonene Oil ». Molecules 27, n^o 21 (25 octobre 2022) : 7215. http://dx.doi.org/10.3390/molecules27217215.

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Fast-moving consumer goods (FMCG) industry has long included many appealing essential oils in products to meet consumers’ needs. Among all, the demand for limonene (LM) has recently surged due to its broad-spectrum health benefits, with applications in cosmetic, detergent, and food products. However, LM is extremely volatile, hence has often been encapsulated for a longer shelf-life. To date, mostly non-biodegradable synthetic polymers have been exploited to fabricate the microcapsule shells, and the resulting microcapsules contribute to the accumulation of microplastic in the environment. So far, information on LM-entrapping microcapsules with a natural microplastic-free shell and their mechanism of formation is limited, and there is lack of an in-depth characterisation of their mechanical and adhesive properties, which are crucial for understanding their potential performance at end-use applications. The present research aims towards developing safe microcapsules with a core of LM fabricated via complex coacervation (CC) using gum Arabic (GA) and fungally sourced chitosan (fCh) as shell precursors. The encapsulation efficiency (EE) for LM was quantified by gas chromatography (GC) separation method. The morphology of microcapsules was investigated via bright-field optical microscopy and scanning electron microscopy, and their mechanical properties were characterised using a micromanipulation technique. Moreover, the adhesive properties of the resulting microcapsules were studied via a bespoke microfluidic device fitted with a polyethylene-terephthalate (PET) substrate and operating at increasingly hydrodynamic shear stress (HSS). Spherical core-shell microcapsules (EE ~45%) with a mean size of 38 ± 2 μm and a relatively smooth surface were obtained. Their mean rupture force and nominal rupture stress were 0.9 ± 0.1 mN and 2.1 ± 0.2 MPa, respectively, which are comparable to those of other microcapsules with synthetic shells, e.g., urea- and melamine-formaldehyde. It was also found that the fCh-GA complexed shell provided promising adhesive properties onto PET films, leading to a microcapsule retention of ~85% and ~60% at low (≤ 50 mPa) and high shear stress (0.9 Pa), respectively. Interestingly, these values are similar to the adhesion data available in literature for microplastic-based microcapsules, such as melamine-formaldehyde (50%–90%). Overall, these findings suggest that microplastics-free microcapsules with a core of oil have been successfully fabricated, and can offer a potential for more sustainable, consumer- and environmentally friendly applications in FMCGs.

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Cho, Kyung Won. « A Study on new Insurance Distribution Channel’s Right to Receive the Duty of Disclosure and Legal Issues : Focusing on AI (Artificial Intelligence) Insurance Solicitors and Insurance Companies Specializing in Insurance Product Sales ». Korean Insurance Law Association 16, n^o 2 (30 juin 2022) : 3–46. http://dx.doi.org/10.36248/kdps.2022.16.2.003.

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The insurance industry has undergone many changes due to the era of the 4th industrial revolution, which interconnects our digital and real worlds. Advances in big data have cleared the path for insurers to acquire the material information of the insured. New methods of collecting information about individual customers cause a need to reevaluate responsibilities and rights of both parties. In the current industry, the manufacturing and sales of insurance products is separate; the insurance company is in charge of the manufacturing of insurance products, and sales companies were established exclusively for the sale of insurance products. New types of sales channels such as Artificial Intelligence (AI) insurance solicitors have emerged. In April of 2021, Hanwha Life Financial Services, an insurance product sales company made by Hanwha Life Insurance company, launched, and immediately took the first place in the GA agency rankings. In general, large insurance agencies have larger capital and number of solicitors than small and medium-sized insurance companies. Therefore, it is necessary to emphasize responsibility at scale. According to Article 45 of the Financial Consumer Protection Act, it is not reasonable for an insurance company that entrusts sales to large general agencies, which are larger than small and medium-sized insurance companies, to assume primary responsibility for consumer damage caused by insurance sales. Through the amendment of the Act, major insurance agencies, which are classified as having 500 or more affiliated insurance solicitors, insurance companies specializing in insurance product sales such as Hanwha life financial services, and subsidiary-type insurance agencies are required to assume primary responsibility prior to the insurance company entrusting insurance sales. It is needed the size of the institution should make it possible to impose responsibilities that are reasonable. With the advent of the 4th industrial revolution, AI insurance solicitors are being introduced in addition to traditional insurance solicitors. It is necessary to revise the Insurance Business Act to allow to sale insurance products by solicits. It is necessary to grant the right to receive the duty of disclosure to exclusive insurance solicitors directly or indirectly under the influence of insurance companies, such as sales training from insurance companies and ethics training to prevent incomplete sales. It is necessary to protect insurance consumers who are trusted with the insurance company’s agent by granting the right to receive the duty of disclosure to the insurance solicitor in charge of explaining insurance products to the subject working as an insurance company’s exclusive solicitor.

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Ibrahim, Alasmer, Fatih Anayi, Michael Packianather et Osama Ahmad Alomari. « New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection ». Energies 15, n^o 4 (17 février 2022) : 1488. http://dx.doi.org/10.3390/en15041488.

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Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology for induction-motor fault detection. Some of the motor parameters such as the stator currents and vibration signals provide a great deal of information about the motor’s conditions. Therefore, these signals of the motor were selected to test the proposed model. The induction motor was assessed in a laboratory under healthy, mechanical, and electrical faults with different loadings. In this study a new hybrid model was developed using the collected signals, an optimal features selection mechanism is proposed, and machine learning classifiers were trained for fault classification. The procedure is to extract some statistical features from the raw signal using Matching Pursuit (MP) and Discrete Wavelet Transform (DWT). Then, the Invasive Weed Optimization algorithm (IWO)-based optimal subset was selected to reduce the data dimension and increase the average accuracy of the model. The optimal subset of features was fed into three classification algorithms: k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), which were trained using k-fold cross-validation to distinguish between the induction motor faults. A similar strategy was performed by applying the Genetic Algorithm (GA) to compare with the performance of the proposed method. The suggested fault detection model’s performance was evaluated by calculating the Receiver Operation Characteristic (ROC) curve, Specificity, Accuracy, Precision, Recall, and F1 score. The experimental results have proved the superiority of IWO for selecting the discriminant features, which has achieved more than 99.7% accuracy. The proposed hybrid model has successfully proved its robustness for diagnosing the faults under different load conditions.

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Barozzi, Marco, Sabrina Copelli, Martina Silvia Scotton et Vincenzo Torretta. « Application of an Enhanced Version of Recursive Operability Analysis for Combustible Dusts Risk Assessment ». International Journal of Environmental Research and Public Health 17, n^o 9 (28 avril 2020) : 3078. http://dx.doi.org/10.3390/ijerph17093078.

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Organic dust explosions were and are still today a critical issue in the food, pharmaceutical, and fine chemical industry. Materials such as flour, corn starch, sugar and APIs represent a cause of severe accidents. In this framework, we investigated a modified version of Recursive Operability Analysis−Incidental Sequence Diagrams (ROA–ISD), called ROA Plus−ISD, specifically tailored to describe industrial processes involving organic combustible dusts. Compared to more classical techniques such as Hazard and Operability (HazOp), ROA−ISD allows for a direct generation of fault trees, providing a useful tool to connect Qualitative with Quantitative Risk Analysis (QRA). ROA Plus−ISD is very similar to ROA−Cause Consequence Diagrams (CCD), which has already proven to be an effective tool to perform both risk assessment on existing plants and reconstructing already occurred accidents, given its logical structure and width of the application fields. In this work, we modified specific parts of the standard ROA−CCD method: (1) the Failure Mode and Operability Analysis (FMEA) database has been structured in order to retrieve the well-known explosion pentagon (for dusts) and all the instruments, devices, apparatuses and controllers typical of industries which process organic dusts; (2) a new comprehensive list of process variables has been compiled. In this way, it is possible to tailor the information required for the generation of the fault trees concerning top events involving mainly dust explosions and fires. This method has been implemented in order to reconstruct the dynamics of the February 2008 Imperial Sugar refinery plant accident (Port Wentworth, GA, USA). Results demonstrated the applicability of the enhanced method by highlighting the criticalities of the process already showed by a previously detailed reconstruction performed by the Chemical Safety Board.

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Baeza, J. Antonio, et Donald C. Behringer. « Integrative taxonomy of the ornamental ‘peppermint’ shrimp public market and population genetics ofLysmata boggessi, the most heavily traded species worldwide ». PeerJ 5 (18 septembre 2017) : e3786. http://dx.doi.org/10.7717/peerj.3786.

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The ornamental trade is a worldwide industry worth >15 billion USD with a problem of rampant product misidentification. Minimizing misidentification is critical in the face of overexploitation of species in the trade. We surveyed the peppermint shrimp ornamental marketplace in the southeastern USA, the most intense market for peppermint shrimps worldwide, to characterize the composition of species in the trade, reveal the extent of misidentification, and describe the population genetics of the true target species. Shrimps were bought from aquarium shops in FL, GA, SC, and NC. We demonstrated, contrary to popular belief (information from dealers), that the most heavily traded species in the market wasLysmata boggessi, an endemic species to the eastern Gulf of Mexico, and notLysmata wurdemanni. Importantly, only when color pattern or genetic markers in conjunction with morphological traits were employed, was it was possible to unequivocally identifyL. boggessias the only species in the trade. The intensity of the market for peppermint shrimps in the USA has led toL. boggessibeing the most traded species worldwide. Misidentification in the shrimp aquarium trade is accidental and involuntary, and is explained by remarkable similarity among congeneric species. Using sequences of the 16S-mt-DNA marker, we found no indication of population genetic structure in the endemicL. boggessiacross 550 km of linear coast. Therefore, this species can be considered genetically homogeneous and a single fished stock. Still, we argue in favor of additional studies using more powerful markers (e.g., SNPs) capable of revealing genetic structure at a finer spatial-scale. Our results will help advance management and conservation policies in this lucrative yet understudied fishery. Future studies of other ornamental fisheries will benefit from using an integrative taxonomic approach, as we demonstrate here.

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Warner, David. « Shale gas in Australia : a great opportunity comes with significant challenges ». APPEA Journal 53, n^o 2 (2013) : 476. http://dx.doi.org/10.1071/aj12087.

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Australia could have shale gas resources several times bigger than the existing conventional gas resource base, which is estimated at about 5,300 BCM (190 TCF) by Geoscience Australia (GA). The Australian Government has no estimate of potential shale gas resources. The US Department of Energy (EIA) in 2011 estimated Australian shale gas resources to be 400 TCF. The quantity of this estimate is supported by an Australian study—which estimates resources of 600 TCF—conducted by Advanced Well Technologies (AWT) in conjunction with DSWPET. While there are significant technical differences between the shale gas plays in the US and Australia, it is too early to tell if the technical differences are barriers. There are also significant differences in the commercial landscape. The lack of capacity in Australia has lead to much higher costs for drilling and fracture stimulation than in the US. The size of the domestic gas market is much greater in the US and its existing infrastructure allows for production to come onstream quickly. In Australia this infrastructure is not present in most areas and the domestic market cannot support another large gas development. Perhaps the greatest challenge to this great opportunity is politics. There is a public, hence political,perception that all gas sources have the same gasland problems. These perceptions can be changed. First, the petroleum industry and governments need to understand the potential size of the gas resource and the possible strategic opportunity for Australia. Also these parties need to recognise that the shale gas resources are often located away from areas of high social and environmental impact. Once these factors are understood by these parties, factual information about the environmental impact of shale gas plays in comparison with coal seam methane and other alternative gas supplies can be factored into gas resource planning.

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Zhou, Yixin, et Zhen Guo. « Research on Intelligent Solution of Service Industry Supply Chain Network Optimization Based on Genetic Algorithm ». Journal of Healthcare Engineering 2021 (19 août 2021) : 1–6. http://dx.doi.org/10.1155/2021/9429872.

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With the advent of the era of big data (BD), people’’s living standards and lifestyle have been greatly changed, and people’s requirements for the service level of the service industry are becoming higher and higher. The personalized needs of customers and private customization have become the hot issues of current research. The service industry is the core enterprise of the service industry. Optimizing the service industry supply network and reasonably allocating the tasks are the focus of the research at home and abroad. Under the background of BD, this paper takes the optimization of service industry supply network as the research object and studies the task allocation optimization of service industry supply network based on the analysis of customers’ personalized demand and user behavior. This paper optimizes the supply chain network of service industry based on genetic algorithm (GA), designs genetic operator, effectively avoids the premature of the algorithm, and improves the operation efficiency of the algorithm. The experimental results show that when m = 8 and n = 40, the average running time of the improved GA is 54.1 s. The network optimization running time of the algorithm used in this paper is very fast, and the stability is also higher.

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Florea, Adrian, Anca Sipos et Melisa-Cristina Stoisor. « Applying AI Tools for Modeling, Predicting and Managing the White Wine Fermentation Process ». Fermentation 8, n^o 4 (22 mars 2022) : 137. http://dx.doi.org/10.3390/fermentation8040137.

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This paper reveals two of the challenges faced by Romania and proposes a sustainable and simple solution for its wine industry. First, substantial areas with vineyards that may produce qualitative wine, and second, the very low digitalization rate of industrial sectors. More precisely, this work proposes a solution for digitalizing the fermentation process of white wine, allowing it to be adapted for other control techniques (i.e., knowledge-based systems, intelligent control). Our method consists of implementing a pre-trained multi-layer perceptron neural network, using genetic algorithms capable of predicting the concentration of alcohol and the amount of substrate at a certain point in time that starts from the initial configuration of the fermentation process. The purpose of predicting these process features is to obtain information about status variables so that the process can be automatically driven. The main advantage of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. After comprehensive simulations using experimental data obtained from previous fermentation processes, we concluded that a configuration that is close to the optimal one, for which the prediction accuracy is high, is a neural network (NN) having an input layer with neurons for temperature, time, initial substrate concentration, and the biomass concentration, a hidden layer with 10 neurons, and an output layer with 2 neurons representing the alcohol and substrate concentration, respectively. The best results were obtained with a pre-trained NN, using a genetic algorithm (GA) with a population of 50 individuals for 20 generations, a crossover probability of 0.9, and a probability of mutation of 0.5 that uniformly decreases depending on the generations, based on a beta coefficient of 0.3 and an elitist selection method. In the case of a data set with a larger number of variables, which also contains data regarding pH and CO2, the prediction accuracy is even higher, leading to the conclusion that a larger data set positively influences the performance of the neural network. Furthermore, methods based on artificial intelligence applications like neural networks, along with various heuristic optimization methods such as genetic algorithms, are essential if hardware sensors cannot be used, or if direct measurements cannot be made.

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Azmi, Nora, Irawadi Jamaran, Yandra Arkeman et Djumali Mangunwidjaja. « PENJADWALAN PESANAN MENGGUNAKAN ALGORITMA GENETIKA UNTUK TIPE PRODUKSI HYBRID AND FLEXIBLE FLOWSHOP PADA INDUSTRI KEMASAN KARTON ». JURNAL TEKNIK INDUSTRI 2, n^o 2 (20 juillet 2012) : 176–88. http://dx.doi.org/10.25105/jti.v2i2.7028.

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This research was intended to produce an order (job) scheduling model at Carton Packaging Industries (CPI) that is useful for giving information about the time delivery to customers. The proposed model is quite complicated because of the characteristic of Make to Order (MTO) varies production process greatly between each order. The job’s schedule for CPI is prepared for production process that consists of 5 stages where in each stage uses different type of machinery. Not all jobs can be processed by all machines at a given production stage. Every job flow through 5 stage in the same order, but not all stages have to visited by all jobs. Stages may be skipped for a particular job. This condition makes CPI is classified as Hybrid and flexible flowshop for machine eligibility (HFFME). HFFME is complicated and is difficult to calculate by using conventional heuristic model. This research used genetic algorithm for solving the complex problem of HFFME and the resulting model called the Genetic Algorithm for hybrid and flexible flowshop with machine eligibility (GA-HFFME). This model is developed to minimized makespan, the objective of scheduling. The experiment was conducted towards 11 orders and it was found that the GA-HFFME is effective to solve that problem.

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Hirota, Toshio Fukudand Kaoru. « Message from Editors-in-Chief ». Journal of Advanced Computational Intelligence and Intelligent Informatics 1, n^o 1 (20 octobre 1997) : 0. http://dx.doi.org/10.20965/jaciii.1997.p0000.

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We are very pleased and honored to have an opportunity to publish a new journal the "International Journal of Advanced Computational Intelligence" (JACI). The JACI is a new, bimonthly journal covering the field of computer science. This journal focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and evolutionary computations, in order to assist in fostering the application of intelligent systems to industry. This new field is called computational intelligence or soft computing. It has already been studied by many researchers, but no single, integrated journal exists anywhere in the world. This new journal gives readers the state of art of the theory and application of Advanced Computational Intelligence. The Topics include, but are not limited to: Fuzzy Logic, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Network Systems, Multimedia, the Human Interface, Biologically-Inspired Evolutionary Systems, Artificial Life, Chaos, Fractal, Wavelet Analysis, Scientific Applications and Industrial Applications. The journal, JACI, is supported by many researchers and scientific organizations, e.g., the International Fuzzy Systems Association (IFSA), the Japan Society of Fuzzy Theory and Systems (SOFT), the Brazilian Society of Automatics (SBA) and The Society of Instrument and Control Engineers (SICE), and we are currently negotiating with the John von Neumann Computer Society (in Hungary). Our policy is to have world-wide communication with many societies and researchers in this field. We would appreciate it if those organizations and people who have an interest in co-sponsorship or have proposals for special issues in this journal, as well as paper submissions, could contact us. Finally our special thanks go to the editorial office of Fuji Technology Press Ltd., especially to its president, Mr. K. Hayashi, and to the editor, Mr. Y. Inoue, for their efforts in publishing this new journal. Lotti A. Zadeh The publication of the International Journal of Advanced Computational Intelligence (JACI) is an important milestone in the advancement of our understanding of how intelligent systems can be conceived, designed, built, and deployed. When one first hears of computational intelligence, a question that naturally arises is: What is the difference, if any, between computational intelligence (CI) and artificial intelligence (AI)? As one who has witnessed the births of both AI and CI, I should like to suggest an answer. As a branch of science and technology, artificial intelligence was born about four decades ago. From the outset, AI was based on classical logic and symbol manipulation. Numerical computations were not welcomed and probabilistic techniques were proscribed. Mainstream AI continued to evolve in this spirit, with symbol manipulation still occupying the center of the stage, but not to the degree that it did in the past. Today, probabilistic techniques and neurocomputing are not unwelcome, but the focus is on distributed intelligence, agents, man-machine interfaces, and networking. With the passage of time, it became increasing clear that symbol manipulation is quite limited in its ability to serve as a foundation for the design of intelligent systems, especially in the realms of robotics, computer vision, motion planning, speech recognition, handwriting recognition, fault diagnosis, planning, and related fields. The inability of mainstream AI to live up to expectations in these application areas has led in the mid-eighties to feelings of disenchantment and widespread questioning of the effectiveness of AI's armamentarium. It was at this point that the name computational intelligence was employed by Professor Nick Cercone of Simon Fraser University in British Columbia to start a new journal named Computational Intelligence -a journal that was, and still is, intended to provide a broader conceptual framework for the conception and design of intelligent systems than was provided by mainstream AI. Another important development took place. The concept of soft computing (SC) was introduced in 1990-91 to describe an association of computing methodologies centering on fuzzy logic (FL), neurocomputing (NC), genetic (or evolutionary) computing (GC), and probabilistic computing (PC). In essence, soft computing differs from traditional hard computing in that it is tolerant of imprecision, uncertainty and partial truth. The basic guiding principle of SC is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality. More recently, the concept of computational intelligence had reemerged with a meaning that is substantially different from that which it had in the past. More specifically, in its new sense, CI, like AI, is concerned with the conception, design, and deployment of intelligent systems. However, unlike mainstream AI, CI methodology is based not on predicate logic and symbol manipulation but on the methodologies of soft computing and, more particularly, on fuzzy logic, neurocomputing, genetic(evolutionary) computing, and probabilistic computing. In this sense, computational intelligence and soft computing are closely linked but not identical. In basic ways, the importance of computational intelligence derives in large measure from the effectiveness of the techniques of fuzzy logic, neurocomputing, genetic (evolutionary) computing, and probabilistic computing in the conception and design of information/intelligent systems, as defined in the statements of the aims and scope of the new journal of Advanced Computational Intelligence. There is one important aspect of both computational intelligence and soft computing that should be stressed. The methodologies which lie at the center of CI and SC, namely, FL, NC, genetic (evolutionary) computing, and PC are for the most part complementary and synergistic, rather than competitive. Thus, in many applications, the effectiveness of FL, NC, GC, and PC can be enhanced by employing them in combination, rather than in isolation. Intelligent systems in which FL, NC, GC, and PC are used in combination are frequently referred to as hybrid intelligent systems. Such systems are likely to become the norm in the not distant future. The ubiquity of hybrid intelligent systems is likely to have a profound impact on the ways in which information/intelligent systems are conceived, designed, built, and interacted with. At this juncture, the most visible hybrid intelligent systems are so-called neurofuzzy systems, which are for the most part fuzzy-rule-based systems in which neural network techniques are employed for system identification, rule induction, and tuning. The concept of neurofuzzy systems was originated by Japanese scientists and engineers in the late eighties, and in recent years has found a wide variety of applications, especially in the realms of industrial control, consumer products, and financial engineering. Today, we are beginning to see a widening of the range of applications of computational intelligence centered on the use of neurofuzzy, fuzzy-genetic, neurogenetic, neurochaotic and neuro-fuzzy-genetic systems. The editors-in-chief of Advanced Computational Intelligence, Professors Fukuda and Hirota, have played and are continuing to play majors roles both nationally and internationally in the development of fuzzy logic, soft computing, and computational intelligence. They deserve our thanks and congratulations for conceiving the International Journal of Advanced Computational Intelligence and making it a reality. International in both spirit and practice, JACI is certain to make a major contribution in the years ahead to the advancement of the science and technology of man-made information/intelligence systems -- systems that are at the center of the information revolution, which is having a profound impact on the ways in which we live, communicate, and interact with the real world. Lotfi A. Zadeh Berkeley, CA, July 24, 1997

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Mufarrih, AM, Moh Nasir Hariyanto et Nanang Qosim. « Analisa Kekasaran Permukaan Titanium Grade 2 pada Proses Frais ». Jurnal Pendidikan Teknik Mesin Undiksha 8, n^o 2 (1 août 2020) : 53. http://dx.doi.org/10.23887/jptm.v8i2.27766.

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Titanium Grade 2 termasuk jenis bahan yang sering dipergunakan di industri, utamanya pada bahan untuk implan biomedis. Titanium Grade 2 mempunyai sifat perbandingan kekakuan terhadap berat yang baik, tahan terhadap korosi dan memiliki sifat biokompatibel yang baik di dalam tubuh. Namun memiliki konduktifitas panas yang rendah, sehingga perlu memilih perameter pemesinan yang tepat untuk menghasilkan nilai kekasaran permukaan yang baik. Penelitian ini bertujuan untuk mengetahui karakteristik Titanium Grade 2 yaitu kekasaran permukaan hasil pemesinan frais. Desain penelitian menggunakan metode Taguchi L9, dengan 2 faktor dan 3 level. Parameter pemesinan yang digunakan ialah putaran spindel 500; 700; 900 rpm dan kecepatan pemakanan 25; 50; 75 mm/menit. Variabel respon yang diteliti ialah kekasaran permukaan. Proses frais dilakukan menggunakan Mesin CNC Dahlih. Kekasaran permukaan diukur menggunakan Mitutoyo surface roughess tester. Analisis data menggunakan analisis ANOVA. Hasil penelitian menunjukan bahwa ada pengaruh variasi parameter pemesinan terhadap respon kekasaran permukaan. Variabel putaran spindel mempunyai p-value sebesar 0,039 dan variabel gerak makan memiliki p-value sebesar 0,025. Hal ini menunjukkan bahwa kedua variabel bebas tersebut memiliki pengaruh yang signifikan terhadap respon kekasaran permukaan. Kekasaran permukaan terendah dapat dicapai dengan pengaturan putaran spindel sebesar 700 rpm dan kecepatan pemakanan sebesar 25 mm/menit. Kata kunci: titanium grade 2, kekasaran permukaan, frais, anova Daftar RujukanBagno, A., & Di Bello, C. (2004). Surface treatments and roughness properties of Ti-based biomaterials. Journal of Materials Science: Materials in Medicine. https://doi.org/10.1023/B:JMSM.0000042679.28493.7fBruce, 2011. (2013). Analisis Kekasaran Permukaan Dan Getaran Pada Pemesinan Bubut Menggunakan Pahat Putar Modular (Modular Rotary Tools) Untuk Material Titanium 6Al-4V Eli. Journal of Chemical Information and Modeling. https://doi.org/10.1017/CBO9781107415324.004Davim, J. P. (2011). Machining of hard materials. Machining of Hard Materials. https://doi.org/10.1007/978-1-84996-450-0Ganguli, S., & Kapoor, S. G. (2016). Improving the performance of milling of titanium alloys using the atomization-based cutting fluid application system. Journal of Manufacturing Processes. https://doi.org/10.1016/j.jmapro.2016.05.011Karkalos, N. E., Galanis, N. I., & Markopoulos, A. P. (2016). Surface roughness prediction for the milling of Ti-6Al-4V ELI alloy with the use of statistical and soft computing techniques. Measurement: Journal of the International Measurement Confederation. https://doi.org/10.1016/j.measurement.2016.04.039Kiswanto, G., Mandala, A., Azmi, M., & Ko, T. J. (2020). The effects of cutting parameters to the surface roughness in high speed cutting of micro-milling titanium alloy ti-6al-4v. Key Engineering Materials, 846 KEM, 133–138. https://doi.org/10.4028/www.scientific.net/KEM.846.133Mufarrih, A., Istiqlaliyah, H., & Ilha, M. M. (2019). Optimization of Roundness, MRR and Surface Roughness on Turning Process using Taguchi-GRA. In Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1179/1/012099Nithyanandam, J., Das, S. L., & Palanikumar, K. (2015). Inluence of Cutting Parameters in Machining of Titanium Alloy. Indian Journal of Science and Technology, 8(8), 556–562. https://doi.org/10.17485/ijst/2015/v8i/71291Oshida, Y. (2012). Bioscience and Bioengineering of Titanium Materials: Second Edition. Bioscience and Bioengineering of Titanium Materials: Second Edition. https://doi.org/10.1016/C2011-0-07805-5Setyowidodo, I., Sutanto, S., Mufarrih, A., & Sholehah, I. M. (2020). Exhaust temperature and peltier element optimization of thermoelectric generator output. In IOP Conference Series: Materials Science and Engineering. https://doi.org/10.1088/1757-899X/850/1/012007Shucai, Y., Chunsheng, H., & Minli, Z. (2019). A prediction model for titanium alloy surface roughness when milling with micro-textured ball-end cutters at different workpiece inclination angles. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-018-2852-6Soepangkat, B. O. P., Pramujati, B., Effendi, M. K., Norcahyo, R., & Mufarrih, A. M. (2019). Multi-objective Optimization in Drilling Kevlar Fiber Reinforced Polymer Using Grey Fuzzy Analysis and Backpropagation Neural Network–Genetic Algorithm (BPNN–GA) Approaches. International Journal of Precision Engineering and Manufacturing. https://doi.org/10.1007/s12541-019-00017-zTapiero, H., Townsend, D. M., & Tew, K. D. (2003). Trace elements in human physiology and pathology. Copper. Biomedicine and Pharmacotherapy. https://doi.org/10.1016/S0753-3322(03)00012-XThepsonthi, T., & Özel, T. (2012). Multi-objective process optimization for micro-end milling of Ti-6Al-4V titanium alloy. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-012-3980-zWennerberg, A., & Albrektsson, T. (2009). Effects of titanium surface topography on bone integration: A systematic review. Clinical Oral Implants Research. https://doi.org/10.1111/j.1600-0501.2009.01775.x

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Wu, Zhijiang, et Guofeng Ma. « Automatic generation of BIM-based construction schedule : combining an ontology constraint rule and a genetic algorithm ». Engineering, Construction and Architectural Management, 16 août 2022. http://dx.doi.org/10.1108/ecam-12-2021-1105.

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PurposeThe purpose of this study is to automatically generate a construction schedule by extracting data from the BIM (Building Information Modeling) model and combining an ontology constraint rule and a genetic algorithm (GA).Design/methodology/approachThis study developed a feasible multi-phase framework to generate the construction schedule automatically through extracting information from the BIM, utilizing the ontology constraint rule to demonstrate the relationships between all the components and finally using the GA to generate the construction schedule.FindingsTo present the functionality of the framework, a prototype case is adopted to show the whole procedure, and the results show that the scheme designed in this study can quickly generate the schedule and ensure that it can satisfy the requirements of logical constraints and time parameter constraints.Practical implicationsA proper utilization of conceptual framework can contribute to the automatic generation of construction schedules and significantly reduce manual errors in the Architectural, Engineering, and Construction (AEC) industry. Moreover, a scheme of BIM-based ontology and GA for construction schedule generation may reduce additional manual work and improve schedule management performance.Social implicationsThe hybrid approach combines the ontology constraint rule and GA proposed in this study, and it is an effective attempt to generate the construction schedule, which provides a direct indicator for the schedule control of the project.Originality/valueIn this study, the data application process of the BIM model is divided into four modules: extraction, processing, optimization, and output. The key technologies including secondary development, ontology theory, and GA are introduced to develop a multi-phase framework for the automatic generation of the construction schedule and to realize the schedule prediction under logical constraints and duration interference.

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Bryant, Katja, Alexis Thomas, Kelli Brennan, Susan Zimmermann, Denise Goings, Deborah Camp, Shannon Doppelheuer et al. « Abstract TMP75 : The Georgia Stroke Professional Alliance (GA-SPA) - a Six Year Review, of a Collaborate, State-wide Stroke Professional Network, Reaching Across State-lines and the Nation to Improve Stroke Care, Professional Education, and Community Outreach ». Stroke 47, suppl_1 (février 2016). http://dx.doi.org/10.1161/str.47.suppl_1.tmp75.

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Background: In 2009, the Georgia Stroke Professional Alliance (GA-SPA) was established. GA-SPA success has been marked by unsurpassed transparency and collaboration among the members and their partners. GA-SPA continues to make professional stroke education and community outreach as one of their greatest priorities. A focus and commitment to improvements of stroke care is an underpinning for sustained success. Purpose: To expand the state-wide collaboration of health care professionals committed to improving the quality of stroke care and stroke education throughout the region. Methods: GA-SPA members meet regularly at varying locations throughout the state and participate in exchanges of professional knowledge, provide clinical expertise regarding process improvement and best practices. GA-SPA collaborates closely with key stakeholders like the AHA/ASA, the Georgia Coverdell Acute Stroke Registry, the Department of Public Heath, the State Office of EMS, and key industry supporters to provide up to date stroke education and community outreach efforts. Results: GA-SPA serves as a subject matter resource on the delivery of stroke care and prevention for healthcare professionals across the continuum of care. Web based communication, a Facebook page and a membership list serve enhances the experience of professional collaboration and information exchange. Accomplishments: Proclamation by the Governor for a Stroke Awareness Day, FAST regional stroke awareness campaign, and annual community events with the Atlanta Braves, partnering with schools for stroke education, SCRN and CNRN review courses, promoting ASLS, ISC presentations. Mentoring amongst GA-SPA members has contributed to the successful certification and re-certification for many of the 35 certified Primary Stroke Centers and 4 Comprehensive Stroke Centers in Georgia, and 4 Remote Stroke Treatment Centers. GA-SPA consults with other states interested in these common goals, and assist with the creation of similar alliances. Conclusion: An alliance of state wide health professionals has been an effective method to promote professional stroke education and community stroke outreach. Other states should consider creating similar professional alliances to advance stroke care.

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Богоявленский, А., A. Bogoyavlenskiy, А. Боков et A. Bokov. « Certification software special SI air transport ». World of measurement, 25 novembre 2012, 14–22. http://dx.doi.org/10.35400/1813-8667-2012-11-14-22.

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In order to implement the provisions of the Federal law No. 102-FZ and the regulations of the state system of ensuring the unity of measurements on the basis of the metrological service in the Federal state unitary Enterprise GosNII GA test laboratory software SI and information and measurement systems (IIS). In terms of software testing, the scope of authority of the laboratory includes algorithms and software (standalone and embedded) MI and IIS used in air transport and in the aviation industry. The article describes the main tasks solved by the laboratory in the process of certification of the SYSTEM software.

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Kang, He-Yau, Amy H. I. Lee et Yu-Fan Yeh. « An optimization approach for traveling purchaser problem with environmental impact of transportation cost ». Kybernetes ahead-of-print, ahead-of-print (28 septembre 2020). http://dx.doi.org/10.1108/k-04-2020-0237.

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Purpose The traveling purchaser problem (TPP) has gained attention in academics to deal with different variants in real business world. This study aims to study a green TPP with quantity discounts and soft time windows (TPPQS), in which a firm needs to purchase products from a set of available markets and deliver the products to a set of customers. Design/methodology/approach Vehicles are available to visit the markets, which offer products at different prices and with different quantity discount schemes. Soft time windows are present for the markets and the customers, and earliness cost and tardiness may incur if a vehicle cannot arrive a market or a customer within the designated time interval. The environmental impact of transportation activities is considered. The objective of this research is to minimize the total cost, including vehicle-assigning cost, vehicle-traveling cost, purchasing cost, emission cost, earliness cost and tardiness cost, while meeting the total demand of the customers and satisfying all the constraints. A mixed integer programming (MIP) model and a genetic algorithm (GA) approach are proposed to solve the TPPQS. Findings The results show that both the MIP and the GA can obtain optimal solutions for small-scale cases, and the GA can generate near-optimal solutions for large-scale cases within a short computational time. Practical implications The proposed models can help firms increase the performance of customer satisfaction and provide valuable supply chain management references in the service industry. Originality/value The proposed models for TPPQS are novel and can facilitate firms to design their green traveling purchasing plans more effectively in today’s environmental conscious and competitive market.

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Bodaghi, Asadollah, Hamid Reza Ansari et Mahsa Gholami. « Optimized support vector regression for drilling rate of penetration estimation ». Open Geosciences 7, n^o 1 (14 décembre 2015). http://dx.doi.org/10.1515/geo-2015-0054.

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Abstract In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called ‘optimized support vector regression’ is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.

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Hirunyawanakul, Anusara, Nuntawut Kaoungku, Nittaya Kerdprasop et Kittisak Kerdprasop. « Feature Selection to Improve Performance of Yield Prediction in Hard Disk Drive Manufacturing ». International Journal of Electrical and Electronic Engineering & ; Telecommunications, 2020, 420–28. http://dx.doi.org/10.18178/ijeetc.9.6.420-428.

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Hard Disk Drive (HDD) manufacturing is one real-world application area that machine learning has been extensively adopted for problem solving. However, most problem solving activities in HDD industry tackle on failure root-cause analysis task. Machine learning is rarely applied in a task of yield prediction. This research presents the application of machine learning and statistical techniques to select appropriate features to be used in yield prediction for the HDD manufacturing process. The seven well-known algorithms are used in the feature selection step. These algorithms are decision tree (C5 and CART), Support Vector Machine (SVM), stepwise regression, Genetic Algorithm (GA), chi-square and information gain. The two prominent learning algorithms, Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), are used in the yield prediction modeling step. Yield prediction performance has been assessed based on the two evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Yield prediction with MLR shows higher accuracy than yield estimation traditionally performed by human engineers. Resulting to conclusion that the proposed novel learning steps can help HDD process engineers to predict yield with the better performance, especially on applying GA as feature selection tool, the MAE is reduced from 0.014 (yield estimated by human engineer) to 0.0059 (yield predicted by MLR). That means error reduction is about 60%.

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« Optimization Algorithm in Supply Chain Management ». International Journal of Innovative Technology and Exploring Engineering 8, n^o 12 (10 octobre 2019) : 5072–79. http://dx.doi.org/10.35940/ijitee.l2724.1081219.

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Optimization in the field of Operations Research has applications in various industries, be it medicine, business, analytics or education. Likewise Supply Chain Management (SCM) is required in every industry and with the need comes various challenges to get the optimized and best quality solution. There are stochastic, analytical models working on attaining optimization in various sub events involved in SCM. Supply Chain is a network at global level used for delivering of products and services from unprocessed materials to consumers through well-structured and planned flow of information, physical distribution and money. The process of managing this supply chain is Supply Chain Management. A major work on the previous research done using various mathematical models, be it mixed integer linear, nonlinear programming or evolutionary have been depicted in this paper. The aim is to get the best result and comparative approach is focused. This article provides a detailed study on various techniques, algorithms and mathematical models in optimization of SCM and in particular it focuses on Genetic Algorithm (GA) in SCM.

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