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With the improvement of people' s living standards people have higher requirements for the comfort of household apparel. Breathability is one of the key factors affecting the comfort of household apparel and is the most concerned by household apparel consumers. At present research on the comfort of household apparel is still in a blank period both domestically and internationally. There is a lack of research on the breathability of various household apparel fabrics with different fabric compositions and textures and there is relatively little research on predicting the comfort of household apparel. Based on this this article selects 58 common household apparel fabrics with different fabric compositions and textures on the market and constructs a genetic algorithm improved BP neural network model to predict the breathability performance of household apparel. Firstly to study the relationship between various influencing factors and air permeability of household apparel fabrics the grey relational analysis GRA method was used to analyze the degree of influence of each influencing factor on the air permeability of household apparel fabrics. The factors with higher correlation were selected as input parameters for the model in this study namely density yarn diameter thickness and weight. Secondly due to the shortcomings of BP neural network such as proneness to local minima slow learning rate and long training time this study used genetic algorithm GA to optimize the structural parameters of BP neural network and constructed a genetic algorithm optimized BP GRA-GA-BP neural network prediction model based on grey correlation analysis. Genetic algorithm can optimize the structural parameters of the model find the best parameter combination and solve complex and high-dimensional problems without being affected by local optimal solutions. 58 household apparel fabrics with different fabric compositions and textures were selected of which 42 were model training samples and 16 were test samples to validate the established model. The parameters of each factor including fabric density yarn diameter thickness weight and air permeability were tested as input parameters for the GRA-GA-BP neural network. The results show that the measured and predicted values of air permeability had a small error with a relative error of between 0. 80% and 28. 53% and an average relative error of 8. 39% a comparison chart between the measured and predicted values of air permeability was drawn and it was found that the two curves are basically consistent indicating high prediction accuracy of the model. Finally OriginPro software was used to analyze the correlation between the measured and predicted values of air permeability and the goodness of fit R² was 0. 976 very close to 1 indicating that the model' s prediction effect is good. The prediction model has a small prediction error good prediction effect high prediction accuracy good fitting effect between the measured and predicted values of air permeability and a strong correlation between the measured and predicted values. This article enriches the research on predicting the comfort of household apparel. The model can accurately predict the breathability of household apparel fabrics to a certain extent saving manpower and costs required for experiments. It has important reference significance for household apparel designers to design based on household apparel comfort performance. At the same time it provides a reference route for predicting the comfort of household apparel. Researchers can start from the perspective of household apparel comfort combine subjective and objective experiments and construct corresponding household apparel comfort evaluation and prediction models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Food Research & Development is the property of Food Research & Development 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 Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) 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 Science & Technology of Food Industry is the property of Science & Technology of Food Industry 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 Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality 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 Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage 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 Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan 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.)

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Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research 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 Journal of Dalian Polytechnic University is the property of Journal of Dalian Polytechnic University 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 Electronic Communication & Computer Science is the property of Omniscient Pte. Ltd. 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 Feed Industry is the property of Liaoning Provincial Institute of Agricultural Mechanization 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.)

bjectives: Pose estimation of light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) relies on scan matching algorithm with high accuracy and reliability. Methods: Based on the iterative closest point (ICP) algorithm in LiDAR odometry and mapping (LOAM), we propose a re⁃ silient real-time LiDAR SLAM algorithm where the normal distributions transform (NDT) is flexibly se⁃ lected according to a so-called environmental feature value (EFV) for identifying unstructured scenes. Re⁃ sults: Experimental results show that the EFV can effectively distinguish unstructured scenes, and the de⁃ bugging method of EFV threshold is given. The analysis of localization and mapping experiments show that compared with the classic LiDAR SLAM algorithms such as LOAM, the proposed algorithm has a great improvement in accuracy and reliability, of which the outdoor accuracy can be obtained from meter level to decimeter level. Moreover, the method can build a map and obtain a global consistent map when facing handheld data. Conclusions: Therefore, the proposed method has good environmental adaptability, thus enriching and developing the SLAM method for complex environments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Urban Architecture & Development is the property of Omniscient Pte. Ltd. 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 Engineering Management & Technology Discussion is the property of Omniscient Pte. Ltd. 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.)

3D datum transformation is widely used in geodesy, photogrammetry, point cloud registration and many other fields, and there have been many studies of 3D datum transformation problems for large angles and arbitrary scales. When the observations contain gross errors, the estimated transformation parameters are adversely affected and even severely distorted. In order to deal with large-angle 3D datum transformation problems that contain gross errors, this paper first abstracts the large-angle 3D datum transformation problem as constrained least squares (CLS) problem, and derives the least-squares solutions for the parameters under orthogonal constraints. Then, a distinctive sensitivity analysis approach is introduced into this CLS problem. The local sensitivity of the weighted sum of squared residuals to the perturbations of observations in the CLS problem is discussed, and then, the local test statistics are constructed based on these sensitivity indicators, deducing a data snooping algorithm for CLS problem. Finally, simulations and experiments with real data are carried out to verify the effectiveness of the algorithm. The computational results of the simulated and real experiment show that the proposed data-snooping algorithm using the sensitivity-based test statistics can effectually decrease the negative impact of the outliers and derive reliable parameters, which effectively solves the problem of processing gross errors in large-angle 3D datum transformation. [ABSTRACT FROM AUTHOR]

Copyright of Electroplating & Finishing is the property of Electroplating & Finishing 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 Chinese Journal of Wildlife / Yesheng Dongwu Xuebao is the property of Chinese Journal of Wildlife 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.)

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Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University 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 Journal of Jinan University (Social Science Edition) is the property of Jinan 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.)

The purpose of this experiment was to study the effect of oryzanol on determination of acid value in forage rice bran oil. Four forage rice bran oil from market or extracted by forage rice bran were selected and content of ooryzanol in oil were determination by HPLC method. Three method, thymol phthalein indicator, Alkali Blue 6B indicator and potentiometric titration, were used to determine acid value in forage rice bran oil. The treatment group were selected edible rice bran oil and forage soybean oil. Then, different quantitative ooryzanol were added to the forage rice bran oil and blank Solvent. The acid value of these oid were determined by thymol phthalein indicato and potentiometric titration individually. The results showed that: ① Alkali Blue 6B indicator was un applicable on determination of acid value in forage rice bran oil. ② The apparent acid value by thymol phthalein indicator titration was the content of oryzanol increased linearly with the content of oryzanol increasing. ③ The content of oryzanol had no significant influence on the acid value by potentiometric titration. ④ Oryzanol would caused a second jump around pH 12 during potential titration. ⑤ The volume of the titrated liquid was significantly linear with the content of oryzanol in both methods. ⑥ Using the solvent with the same content of oryzanol as a blank control, the acid valence determined by thymol phthalein indicator method was no different from the potential titration method. ⑦ The precision of potential titration was higher than that of thymol phthalein indicator. In sum‐ mary, potential titration should be preferred for determination of acid value in forage rice bran oil, and thymol phthalein indicator requires a solvent containing the same content of oryzanol as a blank control. [ABSTRACT FROM AUTHOR]

[Objective] The aim of this study is to explore the spatial structure pattern and spatial-temporal change rule of land use ecological risk in the Pearl River Delta (PRD) region, and then to provide scientific support for the sustainable utilization of land resources and ecological security. [Methods] A land-use ecological risk assessment model was constructed with landscape metrics, and land-use ecological risk indexes at multiple scales in the Pearl River Delta in 2000, 2010 and 2020 were measured by this model. Then, the semi-variogram, spatial autocorrelation and GIS spatial analysis methods were used to determine appropriate research scales and explore the spatiotemporal characteristics of land-use ecological risk at these scales. [Results] (1) The 3 km quadratic scale was the best research scale, and the small watershed scale was also appropriate. At both scales, the spatial variability of land-use ecological risk from 2000 to 2010 was mainly influenced by structural factors, i.e., there was a strong spatial autocorrelation. (2) The land-use ecological risk in the study area generally had strong positive spatial agglomeration characteristics, but the degree of agglomeration trended to weaken. The local spatial agglomeration characteristics of most units were relatively stable. The distribution of high-high agglomeration and low-low agglomeration areas were obvious, and the distribution of low-high agglomeration and high-low agglomeration areas were sporadic and did not change much. (3) The degree of land-use ecological risk had gradually decreased. Low-risk level and lower-risk level were dominant risks and the distribution areas were increasing, while the areas above medium-risk level were decreasing. [Conclusion] The overall land use ecological risk in the PRD region showed positive spatial clustering characteristics, and the level of land use ecological risk gradually decreased, and the results of this study can provide decision-making reference for the optimal use of regional land space and ecological risk control. [ABSTRACT FROM AUTHOR]

Objective：To establish an effective method for predicting intracranial pressure (ICP) by measuring optic sheath diameter (ONSD) using ultrasonography. Methods：52 patients with severe neurological conditions were continuously monitored for postoperative invasive ICP. The mean diameter of the optic nerve sheath in vertical and horizontal directions was measured by transorbital ultrasound. The diagnostic efficacy of ONSD in intracranial hypertension was analyzed by receiver operating characteristic (ROC) curve，and the sensitivity and specificity of the method were evaluated. Results：The mean binocular ONSD (5.27±0.47) mm was well correlated with invasive ICP (11.77±6.18) mmHg in all patients (r=0.79，P < 0.01) . ROC curve showed that the ONSD cutoff for detecting intracranial hypertension (ICP≥20 mmHg) was 5.625 mm，with a sensitivity of 74.3% and a specificity of 90.4% . Meanwhile，the ONSD cutoff corresponding to ICP 5，10，15 and 25 mmHg were 5.175，5.325，5.375 and 5.825 mm. Conclusions： Bedside ultrasound measurements of ONSD through orbit can determine changes of ICP，especially in the case of invasive ICP or limited imaging examinatior in patients with severe neurological conditions . The ICP can be evaluated dynamically，quickly and non⁃ invasively，which has significant clinical application value and is worthy of promotion [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Preventive Veterinary Medicine / Zhongguo Yufang Shouyi Xuebao is the property of Chinese Journal of Preventive Veterinary Medicine 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 Chinese Journal of Preventive Veterinary Medicine / Zhongguo Yufang Shouyi Xuebao is the property of Chinese Journal of Preventive Veterinary Medicine 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 Mechanical & Electronic Control Engineering is the property of Omniscient Pte. Ltd. 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 Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology 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 Food Research & Development is the property of Food Research & Development 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 Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal 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 Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica 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.)

To solve the problem of high false positive rate of existing driver call behavior recognition, an improved Faster RCNN was proposed based on driver behavior detection method for detecting the illegal hand-held call of driver. An optimization strategy for the region proposal network (RPN) and the loss function was introduced, and the robustness of the network in detecting targets with different sizes was enhanced by applying multi-scale training, increasing the number of anchor points and introducing the residual expansion network on the original Faster RCNN. The simulation experiments of the proposed method were conducted with the images of driver behavior collected on an in-vehicle platform. The results show that compared with original Faster RCNN, RPN and Faster RCNN can realize efficient target detection by alternatively optimizing the shared feature extraction network part with 3. 8% improvement in detection precision and better adaptation to the environment. [ABSTRACT FROM AUTHOR]