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In order to improve the multi-parameter prediction accuracy of dissolved oxygen, an ISSA-LSTM (Improved Sparrow Search Algorithm-Long and Short-Term Memory Neural Networks) dissolved oxygen prediction model is developed based on the ISSA and LSTM. The model is applied to the prediction of dissolved oxygen in eel breeding ponds at Shanghai academy of agricultural sciences. The sparrow search algorithm is optimized using chaos mapping, lensing imaging backward learning, adaptive adjustment and Cauchy variation. The data are pre-processed by wavelet transform, the input parameters for model training are determined using principal component analysis. The training results show that the correlation coefficient, root mean square error, mean square error and mean absolute error are 0.911, 1.392 mg·L-1, 1.938 mg·L-1 and 0.992 mg·L-1, which are all better than those in the control model. The choice of model input parameters also have an impact on the model prediction results, with the best model predictions using both moderately and strongly correlated parameters with dissolved oxygen as input parameters. The training results provide a new perspective for the development of the dissolved oxygen multi-parameter prediction model. [ABSTRACT FROM AUTHOR]

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Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Polymer Materials Science & Engineering is the property of Sichuan University, Polymer Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Dissolved oxygen content is an important factor affecting eel culture. To improve the prediction accuracy of dissolved oxygen concentration in eel ponds, this paper proposes a model for predicting dissolved oxygen concentration in eel ponds based on the sparrow search algorithm (SSA) and long short-term memory neural network ( LSTM), after optimizing the hyperparameters of LSTM model using SSA algorithm, the dissolved oxygen in circulating water eel culture ponds was predicted. The results showed that the prediction accuracy of SSA-LSTM model was 96. 77%, which was 2. 09%, 3. 34% and 0. 55% higher than the control models LSTM, GRU and PSO-LSTM, respectively. The other indicators of this model, EMSE, EMAE and ERMSE, were 0. 67, 0. 53, and 0. 81, respectively, which also showed significant decreases compared with the control model. The results indicate that the SSA-LSTM model has good accuracy and robustness in predicting dissolved oxygen concentration in eel ponds, which can provide a basis for accurate regulation of water quality parameters in eel culture. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of much extended nodes and long path-finding time in traditional global path planning algorithms, a global path planning algorithm based on JPS + (Jump Point Search Plus) algorithm was proposed to improve the robot's intelligence and efficiency in complex environment. First, a density-based obstacle corner judgment rule was introduced to realize the number of main jump points and reduce the number of scalable nodes in the process of searching the path. At the same time, the decision rule of the target jump point was modified in the path solving process, and the goal of reducing the calculation amount and shortening the calculation time was finally realized. To verify the effectiveness of the proposed improved JPS + algorithm, A ∗ and JPS + algorithms were compared with the improved JPS + algorithm in different types of maps. Simulation results show that compared with A ∗ algorithm, the improved JPS + algorithm has obvious improvements in path length, pathfinding time and number of extension nodes. Compared with the traditional JPS + algorithm, the path generated is the same, the search time in the map with 33.25% obstacles is reduced by 7.58%, and the number of nodes is reduced by 9. 38%. Therefore, the improved JPS + algorithm can meet the requirements of fast global path planning for mobile robots. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Changzhou University (Natural Science Edition) / Changzhou Daxue Xuebao (Ziran Kexue Ban) is the property of Editorial Department of Journal of Changzhou University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

目的 通过分析抗菌药物使用效果及发生的不良反应，总结其合理使用方法。 方法 选取2015年1~12月该院收治的需要使用抗菌药物的患者1 000例进行用药分析，通过观察用药疗效及出现的用药不良反应，制订调查表调查患者对抗菌药物使用的基本情况的了解。 结果 通过观察用药效果发现，44例患者耐药性严重，用药不良反应发生率为30.60%；通过统计患者的抗菌药物使用调查表发现，患者对抗菌药物了解较少，其中了解最多的抗菌药物是青霉素类。 结论 抗菌药物效果好，使用广泛，但是在使用时要注意观察用药人群，注意剂量合理，规范用药，减少不良反应，降低患者耐药性。 [ABSTRACT FROM AUTHOR]