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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 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 Zhejiang Electric Power is the property of Zhejiang Electric Power 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.)

Objective To construct a postpartum depression risk prediction model for multi - ethnic population in Yunnan Province of China, and identify predictive factors. Methods Women who were 42 days and within 1 year after childbirth were screened, and the Edinburgh Postnatal Depression Scale (EPDS M 9) was used for postpartum depression. 52 influencing factors from economics, social psychology, obstetrics, neonatology, spouse and family dynamics and other characteristics were included in the survey. A random forest algorithm was employed to construct a predictive model for postnatal depression risk in the multi 一 ethnic population of Yunnan Province. The model was evaluated on test sets with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (Area Under Curve, AUC) to assess its performance. Results A total of 459 women were analyzed, with a postpartum depression detection rate of 11. 55%. Among them, the detection rates for Han, Zhuang and other ethnic minorities were 7. 56%, 13. 94% and 13.92%, respectively. The top 14 variables in terms of importance scores were: anxiety, history of previous negative emotions, marital relationship, family support level, physical and mental exhaustion in caring for newborns, pregnancy risk classification, mother - infant rooming - in, feeding mode, education level, spouse's education level, frequency of nighttime newborn care, ethnicity, parity and age. The accuracy was 92.74%, specificity was 95. 50%, sensitivity was 69.23%, positive predictive value was 64. 29%, negative predictive value was 96. 36%, and the AUC value was 0. 925, using Han, Zhuang, and other ethnic minorities as validation sets respectively. The model also demonstrated good stability. Conclusion The random forest algorithm 一 based postpartum depression risk prediction model for the multi 一 ethnic population in Yunnan performed well, which can be utilized to predict risk factors for postpartum depression among women in minority ethnic areas, thereby facilitating targeted intervention measures. [ABSTRACT FROM AUTHOR]

Objective To analyze risk factors for occupational blood-borne exposures among healthcare workers, providing a basis for timely and targeted interventions to prevent occupational blood-borne exposure. Methods The study included 103 healthcare workers who reported occupational blood-borne exposures from January 2016 to June 2023 at a tertiary hospital in Hulunbuir as the exposure group and 625 healthcare workers from the same shifts who did not experience occupational exposures as the control group. Relevant information was collected for all subjects. Random Forest algorithms and multivariate logistic regression analysis were used to construct predictive models for occupational blood-borne exposure among healthcare workers. Results Among the 103 blood-borne exposure cases, the main exposure site was the hand in 89 cases (86.41%); the main mode of exposure was sharp force injury in 83 cases (80.58%); and the predominant exposure source was hepatitis B virus (HBV) in 65 cases (63.11%). Sixty-five cases (63.11%) were reported immediately after exposure, while 24 cases (23.30%) were reported within 12 hours. Logistic regression analysis revealed that compared to healthcare workers aged 30 years or more, likelihood of occupational blood-borne exposure increased to 4.142 times for those under 30 years (P < 0.05) ; compared with 5 or more years, the likelihood of occupational blood-borne exposure increased to 1.696 times for healthcare workers with less than 5 years of service (P < 0.05) the likelihood of occupational blood-borne exposure increased to 5.989 times for junior-title healthcare workers compared to intermediate and higher- level titles (P < 0.05); healthcare workers who attended occupational protection training only once per year were up to 1.864 times more likely to have occupational blood-borne exposure compared to those who participated multiple times annually (P < 0.05); the likelihood of occupational blood-borne exposure increased to 2.205 times for healthcare workers in the departmental categories of emergency, critical care, and operating room compared to those in other departments (P < 0.05). The Random Forest algorithm identified the top 6 influential factors in order of importance as professional title, age, years of work experience, annual training frequency of occupational protection, occupation type, and education level. The Random Forest predictive model demonstrated higher accuracy, precision, recall, and FI score (the reconciled mean of precision and recall) compared to the logistic regression model. The area under the receiver operating characteristic curve (AUC) for the Random Forest model was 0.829 (P < 0.001) which was also higher than that of the logistic regression model of 0.818 (P < 0.001). Conclusions The Random Forest model showed superior predictive performance for occupational blood-borne exposures, while the logistic regression model provided more intuitive analytical results. The combined use of both models can further enhance prediction accuracy. Enhanced training should be provided for high-risk groups, including younger healthcare workers, those with less work experience, and those with lower professional titles. Standardized measures should be implemented to prevent blood-borne exposure among healthcare workers. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to evaluate the spatiotemporal trends of soil erosion, to reveal the contributions of driving factors, to elucidate the impacts of ecological restoration policies, and to provide a scientific basis for the management of soil erosion on the Tibetan Plateau. [Methods] This study assessed the temporal and spatial characteristics of soil erosion by water on the Tibetan Plateau from 1990 to 2020, and identified the contribution of climatic, anthropogenic and topographic factors to soil erosion by Random Forest Regression. [Results] (1) The average soil erosion rate on the Tibetan Plateau was 305.78 t/km², and more than 80% of the area was slightly erosion during 1990-2020. Soil erosion decreased before 2010 and increased after 2010, showing a nonlinear variation. (2) Climate factors had a greater impact on soil erosion than anthropogenic factors in the Tibetan Plateau, and annual precipitation was the main factor on soil erosion. In addition, since 2010, the influence of anthropogenic factors on soil erosion gradually increased. [Conclusion] Climate factors are the primary driving force inducing soil erosion in the Tibetan Plateau. The ecological restoration policies implemented in the Tibetan Plateau have initially achieved results, but the impact of climate change on soil erosion is more significant. Additionally, Random Forest Regression is effective for exploring the driving factors on soil erosion. [ABSTRACT FROM AUTHOR]

To achieve rapid and accurate identification of valuable wood products, an analytical method was developed by combining electric soldering iron cauterization with soft ionization by chemical reaction in transfer-mass spectrometry (SICRIT-MS). SICRIT is a flow-through dielectric barrier discharge ionization technique pioneered by Zenobi et al. in 2016. The electric soldering iron cauterization-SICRIT-MS method requires no sample pretreatment, easy operation and a single analysis in less than 5 s, meeting the demands of rapid analysis. Operating parameters for the soldering iron and SICRIT ion source were optimized to achieve maximum total ion current intensity under soldering iron temperature of 450℃, ion source AC voltage amplitude of 2000 V, and sample transfer line temperature of 150 ℃. With the optimized parameters, the SICRIT-MS method was applied to analyze valuable wood samples, including 29 certified standard wood samples and 6 online-purchased real samples, resulting in a dataset of 210 sets of mass spectral fingerprint data. Based on the mass spectral fingerprint data acquired under positive ion mode, a predictive model was trained using the random forest algorithm. The random forest model underwent optimization for the number of decision trees, max feature algorithm, and feature selection criteria, was evaluated through out-of-bag and 10-fold cross-validation. The results showed the error rates of out-of-bag and 10-fold cross-validation are 4.76% and 4.74%, respectively. The established random forest model can accurately distinguish wood samples from the genera Dalbergia, Guibourtia, and Pterocarpus with a classification accuracy of larger than 95%. The importance of features in distinguishing the three wood genera was investigated through binary classification modeling, revealing features 269.1, 270.1, 255.1, 159.0, 182.1, 102.1 and 83.1 as crucial in classification. These features may correspond to characteristic compounds in different wood species or differences in the content of the same compound across species. The predictive model was successfully applied to rapid identification of genera in valuable wood products sold online. Three purchased Guibourtia samples are confirmed as authentic, while the other three are not identified as the claimed genera. This method provides a scientific basis and experimental reference for authenticity identification and quality evaluation. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & 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.)

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & 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.)

[Objective] The aim of this study is to explore the accuracy and applicability of different machine learning models for predicting the spatial distribution of surface soil organic carbon density (SOCD) with limited samples, which can provide a references for the study of watershed scale carbon pool in the Chinese Loess Plateau. [Methods] In this study, we compared the accuracy and stability of the predicted SOCD in topsoil (0-20 cm) by four machine learning models, namely Multiple Linear Stepwise Regression (SR), Random Forest (RF), Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM), based on the limited measured samples in a sub-watershed of Yanhe River in the Chinese Loess Plateau. [Results] (1) Under the condition of limited samples, all models successfully and appropriately predicte the spatial distribution of SOCD, among which the SVM model has the best model performance, and the average RMSE, R² and MAE of 50 predictions is 0.74. 0.43 and 0.64, respectively. (2) The average SOCD of different land use types are consistent between measured and predicted values but shows significant difference among land use types. SOCD decreases in the order: shrubland forestland > grassland > cropland. The total organic carbon of cultivated land in the study area is 2.39×106t (0-20 cm). (3) The evaluation of feature importance shows that terrain factors, NDVI max, near-infrared surface reflectance (B5) and Brightness index have significant contributions to the accuracy of predictions. [Conclusion] Under the condition of limited samples, the machine learning model combined with controlling features can be effectively applied to the prediction of the spatial distribution of topsoil SOCD at the watershed scale in the Chinese Loess Plateau. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to determine the distribution pattern of soil water in desert dunes, to clarify the relationship between soil water and various terrain and vegetation factors, so as to provide scientific and reasonable suggestions for the protection and reconstruction of vegetation in desert areas. [Methods] There were four different micro-landforms, including windward slope, leeward slope, hill top and bottom. The soil moisture was divided into surface (0-40 cm), middle (40-200 cm) and deep (200-300 cm) layers. The distribution characteristics of soil water on different micro-landforms and depths in Gurbantunggut Desert were studied on the small scale. Generalized linear model, generalized additive model and random forest were used to study the relationship between soil moisture and topographic- vegetation factors. [Results] (1) The soil moisture on the windward slope and the bottom of the hill was higher than that on the lee slope and the top of the hill at different depths. There were significant differences of surface soil moisture between the lee slope (including the top and bottom of the hill) and the windward slope. (2) The effect of topographic factors on soil moisture was greater than that of vegetation factors. The main factors affecting surface soil water were slope and slope direction. The main factors affecting middle and deep soil water was shrub cover. (3) The fitting effect of the random forest was significantly higher than that of the generalized linear model and generalized additive model. [Conclusion] As for soil moisture on different micro-landforms, topographic factors have a greater impact on mobile dunes in Gurbantunggut Desert than vegetation factors. Slope and slope direction have the important influence on surface soil moisture. Shrub cover has a major effect on middle and deep soil moisture. [ABSTRACT FROM AUTHOR]

Objective To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. Methods Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. Results SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. Conclusion The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical 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.)

Objective BackgroundMedical staff satisfaction is an important indicator for evaluating the reform of medical communities, which is crucial for the healthy development of medical communities. Objective To investigate the current situation of medical staff satisfaction from 20 primary medical institutions in the closely 一 knit county medical community in Xinyi City, Jiangsu Province, so as to provide reference for improving the construction of county medical communities. Methods A Stratified random sampling method was used to conduct a face - to - face questionnaire survey of medical staff within the closely - knit county medical community in Xinyi City, Jiangsu Province. A random forest model was used to rank the importance of independent variables and to filter the variables, and the filtered variables were included in a logistic regression for a multifactor analysis of satisfaction. Results The overall satisfaction score of medical staff in Xinyi city county medical community is 3. 86 ± 0. 76. The random forest model showed that the error rate of out - of 一 pocket estimation is the lowest when the number of variables is 7, and it is considered in turn whether the reform of medical insurance payment mode is helpful to control medical expenses, length of service, working hours per week, awareness of medical insurance payment mode reform, professional and technical post level, awareness of medical community, and whether the reform of medical insurance payment mode affects diagnosis and treatment behavior. The logistic regression showed that the reform of the health insurance payment mode was very helpful in controlling the unreasonable increase of medical expenses (OR = 8. 153,95% CI: 3. 404 一 19. 531, P <0. 001), where the higher the knowledge of the reform of the health insurance payment mode of the medical community (OR 碍血沁「強=1-986, 95% CI: 1. 190-3.314, P 二 0. 009; ORii in「o讪厂 2. 665, 95% CI: 1. 409 - 5. 038, P = 0. 003), and the longer the weekly working hours (OR40 一劲/叶应=0. 575, 95% CI: 0. 364 一 0. 910, P =0. 018; OR-8()h/week = 0. 286, 95% CI: 0. 167 一 0. 489, P <0. 001), the more satisfied the medical staff were. Conclusion The overall satisfaction of medical staff in the medical communities in Xinyi city County is not high, and their cognition and evaluation of the reform of medical insurance payment methods and working hours affect their satisfaction. It is necessary to comprehensively consider the actual needs of medical staff in the medical community, improve the level of medical community construction in the county, and improve the job satisfaction of medical staff. [ABSTRACT FROM AUTHOR]

Objective To investigate the influencing factors of lipoproteins and metabolites in Alzheimer's disease based on machine learning algorithm. Methods A total of 314 subjects diagnosed with cognitive normal (CN) and Alzheimer's disease (AD) in 2012 were selected from the ADNI database, and their lipoprotein and metabolite data were collected. Random forest, Lasso regression, and XG boost algorithm were used to sort and screen the important variables. Using the variables selected by the three methods and combining the gender, age, and marital status of the study population, a random forest model was constructed to predict the important factors affecting the incidence of AD. Results A total of 12 lipoprotein and metabolite variables were screened by three methods, and 15 variables were incorporated into the random forest model combined with the age, sex, and marital status of the study population. The accuracy of the model was 84.13%, the sensitivity was 93.75%, the specificity was 53.33%, the Kappa value was 0.5183, and the AUC (95%CI) was 0.735 (0.600-0.871). The most important variables selected in the random forest model based on Mean Decrease Accuracy and Mean Decrease Gini were phospholipid to total lipid ratio in large very low-density lipoprotein (L_VLDL_PL_PCT), age, triglycerides in cytokinome's and very low-density lipoprotein (XXL_VLDL_TG), and cholesterol to total lipid ratio in large very low-density lipoprotein (L_VLDL_C_PCT). The Mean Decrease Accuracy of the four variables were 6.68, 6.65, 6.10, and 5.49, respectively, and the Mean Decrease Gini values were 7.35, 11.71, 8.77, and 10.08, respectively. Conclusion L_VLDL_PL_PCT, AGE, XXL_VLDL_TG, and L_VLDL_C_PCT are closely related to the incidence of AD. This study can provide new insights for the diagnosis and prevention of AD. In-depth study of the relationship between lipoprotein levels and AD incidence can prevent and delay the occurrence of AD. [ABSTRACT FROM AUTHOR]

Objective To explore the possibility of using human skin and oral microorganisms to estimate the geographic origin of an individual through the sequencing analysis of bacterial 16S rRNA gene. Methods Microbial DNA was extracted from the palm and oral microorganisms of the Han population in Shanghai and Chifeng, Inner Mongolia, and the composition and diversity of the microbiota were analyzed by full-length 16S rRNA gene sequencing. Then, differential species were screened and a geographic location prediction model was constructed. Results The compositions of palm and oral microorganisms between Shanghai and Chifeng samples were both different. The abundance and uniformity of palm side skin microorganisms were higher in Chifeng samples than in Shanghai samples, while there was no significant difference in oral microorganisms. Permutational multivariate analysis of variance (PERMANOVA) confirmed that the β -diversity between the samples from the two places were statistically significant, and the coefficients of determination R2 for skin and oral samples were 0.129 and 0.102, respectively. Through principal co-ordinates analysis (PCoA), the samples from the two places could be preliminarily distinguished. The predictive model had the accuracies of 0.90 and 0.83 for the geographic origin using the skin and oral samples, respectively. Conclusion There are differences in the compositions of palm and oral microbiota between Han populations in Shanghai and Chifeng. The prediction model constructed by the random forest algorithm can trace the unknown individuals from the above two places. [ABSTRACT FROM AUTHOR]

Age estimation based on tissues or body fluids is an important task in forensic science. The changes of DNA methylation status with age have certain rules, which can be used to estimate the age of the individuals. Therefore, it is of great significance to discover specific DNA methylation sites and develop new age estimation models. At present, statistical models for age estimation have been developed based on the rule that DNA methylation status changes with age. The commonly used models include multiple linear regression model, multiple quantile regression model, support vector machine model, artificial neural network model, random forest model, etc. In addition, there are many factors that affect the level of DNA methylation, such as the tissue specificity of methylation. This paper reviews these modeling methods and influencing factors for age estimation based on DNA methylation, with a view to provide reference for the establishment of age estimation models. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency 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.)

Objective To analysis the influencing factors of perimenopausal syndrome in women, and providing a reference for perimenopausal syndrome intervention. Methods This was a cross-sectional Internet survey study. Using a snowball sampling method, 468 women aged 45 to 60 years were recruited from Shaanxi province. The random forest method was used to sort the importance and reduce the dimension of the independent variables. The Ordinal logistic regression model was used to analyze the action direction and relative risk (OR value) of independent variables. Results The results of random forest analysis showed that the OOB estimated error rate was the lowest when the number of variables was five, followed by the physical activity level, age, menopausal status, perceived health status and parity. Ordinal logistic regression analysis showed that physical activity level and perceived health status were protective factors for perimenopausal syndrome (OR < 1), menopausal status and parity were risk factors for perimenopausal syndrome (OR > 1). Conclusion The four influencing factors of perimenopausal syndrome were the physical activity level, perceived health status, menopausal status and parity. It is recommended to increase the education of healthy, and strengthen the protective factors and reduce the risk factors to improve perimenopausal women' s quality of life. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal 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.)

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal 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.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation 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 Coal Science & Technology (0253-2336) is the property of Coal Science & 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.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation 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 Mine Automation is the property of Industry & Mine Automation 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 Forensic Medicine / Fayixue Zazhi is the property of Journal of Forensic 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.)

Given the problem that the communication distance and quality of the vehicle-mounted ultrashort wave radio are affected by ground attachments and topography in the actual combat environment, a radio wave propagation prediction model of vehicle-mounted ultrashort wave radio is established based on ray tracing and machine learning. The integrated modeling of armored combat vehicle and vehicle antenna is established to obtain the antenna radiation pattern, and combined with electronic images, the radio wave propagation simulation model based on ray tracing technology is established. Based on the machine learning algorithm of the random forest and data results for the simulation model, the radio wave propagation prediction model based on the random forest was established. Compared with traditional classical radio wave propagation models such as the Egli and Okumura-Hata models, the radio wave propagation prediction model based on the random forest has higher accuracy. The root mean square error reaches 2.190 1 dB, and the coefficient of determination reaches 0.960 1. It can accurately predict radio wave propagation in the tactical communication environment. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower 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.)

The nonlinear relationship between the chlorophyll content of Hopea hainanensis, an endangered tree species, and image features was discussed, and the feasibility of estimating the chlorophyll content of H. hainanensis by image color and texture features was studied in this paper, providing a reference for image-based non-destructive monitoring. The canopy chlorophyll content (SPAD value) and RGB images were obtained using a 2-year-old H. hainanensis as the research object. The generalized additive model (GAM) was used to explore the nonlinear correlation between the image color and texture characteristics and the SPAD values. The SPAD values were then estimated using the multiple linear regression, GAM, random forest, and XGBoost models based on the features without multicollinearity filtered by the Lasso algorithm. Among the 22-image color and texture features, the feature with the strongest linear correlation was a correlation standard deviation, which was positively correlated, while the feature with the strongest nonlinear correlation was entropy mean, which was inverse J-shaped and negatively correlated. The Lasso algorithm effectively removed the severe collinearity among multiple image features, and the VIF values of the 15 image features retained after screening were all lower than 10. Based on the results of estimating SPAD values using multi-image color texture features, the test accuracy of the XGBoost model was highest, and the average testing R~2, MAE, and RMSE were 0.757, 2.682, and 3.242, respectively. Therefore, there are certain nonlinear correlations between SPAD values of H. hainanensis and image color and texture features. The image estimation method based on the GAM model with nonlinear interpretation ability can be used as the optimal method for estimating the chlorophyll content of H. hainanensis. [ABSTRACT FROM AUTHOR]

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications 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 Coal Geology & Exploration is the property of Xian Research Institute of China Coal 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.)

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.)

【Objective】 The spatial-temporal variation characteristics of soil pH and its driving factors were clarified to provide a basis for the improvement of soil acidification in southwest cigar area. 【Method】 Based on soil pH data of 26 sample points of the second general soil survey in Sichuan Province in 1984, 391 sample points of soil testing and fertilizer recommendation survey in Sichuan Province in 2009, and 164 sample points of cultivated land quality survey in Sichuan Province in 2019, Multi-statistics and Geostatistics were used to analyze the spatial-temporal variation characteristics of soil pH, and Random Forest model was used to explore the main driving factors of soil pH change.【Result】 The mean values of pH in Shifang City in 1984, 2009 and 2019 were 6.40, 6.24 and 5.97, respectively, and the average annual value decreased by 0.01 from 1984 to 2009 and by 0.02 from 2009 to 2019. From 1984 to 2019, except for Jiandi Town and Yinghua Town, where the average soil pH value showed an upward trend, the rest of the towns were on the decline, with the largest decrease of 30.62% in Hefeng Town and the smallest decrease of 0.30% in Shigu Town. The overall soil pH in 1984 was dominated by weakly acidic and neutral properties, with alkaline soils accounting for 2.59% of the total area planted with tobacco, and the spatial distribution was characterized by higher soil pH in three areas: Luoshui Town, Hefeng Town and Majing Town. In 2009, only weakly acidic and neutral soils existed in the study area, and soil pH was spatially high in the centre and low in the north and south. In 2019, the area of weakly acidic and neutral soils accounted for 95.25%, and the area of acidic soils (pH<5.5) accounted for 4.75%, which were mainly distributed in the towns of Majing, Mazu, Hefeng, Nanquan and Shigu. And soil pH in the south was spatially lower than that in the north. Soils planted with tobacco over the past 35 years have generally shown changes in acidification, with about 53.51% of the soil was acidified (ΔpH < -0.3), mainly distributed in the towns of Majing, Hefeng and Mazu, and about 11.63% of the soil was alkaline (ΔpH ＞ 0.3), mainly in the towns of Yinghua and Jiandi; The pH of 44.98% tobacco growing soils ranged from -1.0 to -0.3, 34.86% ranged from -0.3 to 0.3, and tobacco growing soils with ΔpH ≤ -1.0 accounted for 8.54%. The random forest results showed that mean annual precipitation and soil organic matter were the main factors causing soil acidification.【Conclusion】Over the past 35 years, the acidification of tobacco growing soils in Shifang City was aggravated. Some measures should be taken to adjust the situation in production, such as creating new special fertilizer and applying soil conditioner. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geomechanics is the property of Journal of Geomechanics 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 Harbin University of Science & Technology is the property of Journal of Harbin University of Science & 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.)

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation 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 Coal Science & Technology (0253-2336) is the property of Coal Science & 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.)

Aimed at the low accuracy of loudspeaker abnormal sound classification, an abnormal sound classification method based on the combination of adaptive variational mode decomposition(AVMD) and residual convolutional neural network(RCNN) is proposed. In AVMD, the grey wolf optimizer (GWO) algorithm was used to optimize the quadratic penalty factor(α) and modal decomposition number(K) of the variational mode decomposition (VMD), and then the VMD was used to extract the features. Finally, the random forest recursive feature elimination(RF-RFE) algorithm was used to extract the optimal feature data, and residual network (ResNet) was introduced by RCNN-3 to classify abnormal sound based on convolutional neural network(CNN). The experimental results show that the proposed method has better classification accuracy and stability, and its average classification accuracy can reach 99.3%. [ABSTRACT FROM AUTHOR]

Relying on the Hongshengsha-Yufengwei Section Tunnel Project of the second phase of Guangzhou Metro Line 7, this paper used the particle swarm optimization random forest method (PSO-RF) to establish a sealed cabin pressure prediction model in the dual-mode shield soil pressure boring mode. Through the correlation analysis of shield boring parameters, the tunneling parameters that had a great influence on the pressure of the sealing chamber were screened out, including screw speed, screw torque, cutterhead speed, propulsion speed, penetration degree, cutterhead torque, and total thrust, and the screened tunneling parameters were used as the input parameters of the prediction model, and the pressure of the sealing chamber was used as the output parameter of the model to predict the pressure of the sealing chamber. The results showed that the random forest PSO-RF prediction model improved by particle swarm algorithm could effectively predict the pressure of the dual-mode shield sealed cabin, and compared with the traditional neural network prediction model, the PSO-RF model had higher prediction accuracy, the average absolute error was within 10%, and the predicted value and the actual goodness-of-fit R2 were 0.901 4, which was significantly superior to the BP neural network in terms of prediction accuracy and generalization ability of the model. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design 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 Railway Standard Design is the property of Railway Standard Design 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 China Rural Water & Hydropower is the property of China Rural Water & Hydropower 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 Clean Coal Technology is the property of Clean Coal 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.)

Copyright of Railway Standard Design is the property of Railway Standard Design 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 Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation 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 China Rural Water & Hydropower is the property of China Rural Water & Hydropower 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 Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (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 Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency 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 Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology 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.)