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Objective To summarize and evaluate the characteristics of current recurrent spontaneous abortion (RSA) animal models at home and abroad, and to provide reference and guidance for the standardized preparation of RSA models.Methods"Recurrent spontaneous abortion" and "animal model" were used as co-keywords in CNKI, Wanfang, VIP, PubMed and Web of Science databases to search the RSA animal experimental literature, covering the period up to January 20, 2024, and a total of 1 411 articles were collected. The analysis focused on construction methods and essential elements of RSA animal models, the modeling process and result evaluation, as well as the application of these models in pharmacological and pharmacodynamic research. An Excel table was established for systematic analysis and discussion. Results A total of 138 experimental studies were obtained after screening. In constructing RSA animal models, immunological models were the most widely used in Western medicine (96.92%), with the Clark model being the main one (92.31%). In traditional Chinese medicine (TCM) models, 70.00% were kidney deficiency-luteal inhibition-syndrome combination models, 20.00% were kidney deficiency and blood stasis models, and 10.00% were deficiency-heat syndrome models. Most animals were selected at 6-8 weeks (33.86%) and 8 weeks (32.28%) of age. The majority of animals were paired for mating at 18:00 on the day of cage pairing. In 81.03% of literatures, vaginal plugs were checked once the following morning, with 8:00 being the most common time (17.02%). The most commonly used drug administration cycle was 14 days of continuous gavage after pregnancy. Among the tested drugs, Western drugs were mainly protein-based (29.17%), while TCM drugs were mainly TCM decoction (81.11%). The most frequently used methods for detecting indicators included visual observation of embryos (22.54%), western blot (15.96%), PCR (13.58%), ELISA (12.91%), HE staining (10.80%) and immunohistochemistry (9.39%). Conclusion The etiology of RSA is complex, and corresponding animal models should be established based on different etiologies. Clark model is commonly used in the construction of Western medicine model, while the kidney deficiency-luteal inhibition-syndrome combination model is predominant in TCM. RSA animal model is widely used in related research, but systematic evaluation needs to be strengthened.

Wind farm wakes have a significant impact on momentum and turbulence fluxes within the atmospheric boundary layer， thereby influencing the local climate and environment.Mesoscale numerical models incorporating wind farm parameterizations are powerful tools for studying the climate and environmental impacts of wind farms.In this study， the wind speed and turbulence kinetic energy profiles of the Fitch wind farm parameterization scheme in the WRF mesoscale model are evaluated in the turbine and wake regions using high-resolution Large Eddy Simulations （LES） as “true values” and a method based on the relation derived from classical momentum theory is proposed to correct the grid-inflow wind speed.The method takes into account the blocking effect caused by the grid equivalent thrust and the corrected wind speed is closer to the free-stream wind speed.Results show that the difference between the grid-inflow wind speed from the original Fitch parameterization scheme and the LES is significant and sensitive to the model horizontal resolution.The Fitch-new parameterization with corrected grid-inflow wind speed reduces the relative error in absolute value between grid-inflow and free-stream wind speed to less than 1% across different horizontal resolutions （1000 m， 500 m， and 250 m）.The spatially averaged thrust and output power are consistent with LES results.The Fitch-new parameterization improves the simulated wind speed deficit in the wake zone of wind turbine， especially in the grid with turbine under high resolution.Although the simulated increase in turbulent kinetic energy and its vertical distribution in the wake zone is improved compared to the original Fitch scheme， there are certain issues that require further investigation.

Objective To summarize and evaluate the characteristics of current recurrent spontaneous abortion (RSA) animal models at home and abroad, and to provide reference and guidance for the standardized preparation of RSA models. Methods "Recurrent spontaneous abortion" and "animal model" were used as co-keywords in CNKI, Wanfang, VIP, PubMed and Web of Science databases to search the RSA animal experimental literature, covering the period up to January 20, 2024, and a total of 1 411 articles were collected. The analysis focused on construction methods and essential elements of RSA animal models, the modeling process and result evaluation, as well as the application of these models in pharmacological and pharmacodynamic research. An Excel table was established for systematic analysis and discussion. Results A total of 138 experimental studies were obtained after screening. In constructing RSA animal models, immunological models were the most widely used in Western medicine (96.92%), with the Clark model being the main one (92.31%). In traditional Chinese medicine (TCM) models, 70.00% were kidney deficiency-luteal inhibition-syndrome combination models, 20.00% were kidney deficiency and blood stasis models, and 10.00% were deficiency-heat syndrome models. Most animals were selected at 6-8 weeks (33.86%) and 8 weeks (32.28%) of age. The majority of animals were paired for mating at 18:00 on the day of cage pairing. In 81.03% of literatures, vaginal plugs were checked once the following morning, with 8: 00 being the most common time (17.02%). The most commonly used drug administration cycle was 14 days of continuous gavage after pregnancy. Among the tested drugs, Western drugs were mainly proteinbased (29.17%), while TCM drugs were mainly TCM decoction (81.11%). The most frequently used methods for detecting indicators included visual observation of embryos (22.54%), western blot (15.96%), PCR (13.58%), ELISA (12.91%), HE staining (10.80%) and immunohistochemistry (9.39%). Conclusion The etiology of RSA is complex, and corresponding animal models should be established based on different etiologies. Clark model is commonly used in the construction of Western medicine model, while the kidney deficiencyluteal inhibition-syndrome combination model is predominant in TCM. RSA animal model is widely used in related research, but systematic evaluation needs to be strengthened. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology 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 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.)

Objective To explore the establishment of a subclinical hypothyroidism（SCH） rat model during pregnancy using total thyroidectomy and L-T4 subcutaneous injection method and evaluate the stability of the model.Methods A total of 26 female SD rats weighing 180-200 g were randomly divided into the control group（n=10） and the model group（n=16）.The control group was treated with total thyroidectomy, while the model group was treated with total thyroidectomy and postoperative subcutaneous injection of L-T4 1.0 μg/（100 g·d）.The levels of TSH,TT4 and body weight were observed at three time points: one month after operation, after total thyroidectomy+L-T410 d after operation and 13 d after pregnancy.Results One month after operation, the serum TSH level of the rats in the model group was significantly higher than that of the control group, and the difference was statistically significant（P＜0.01）.After total thyroidectomy +L-T410 d, the serum TSH level of the rates in the model group was higher than that in the control group, and the difference was statistically significant（P＜0.01）.At the 13th day of pregnancy, the serum TSH level of the rates in the model group was significantly higher than that in the control group（P＜0.01）.The serum TT4 level in the model group was lower than that in the control group one month after operation, and the difference was statistically significant（P＜0.01）.There was no statistically significant difference in serum TT4 levels of the rats in two groups after total thyroidectomy+L-T410 d（P＞0.05）.At the 13th day of pregnancy, there was no statistically significant difference in TT4 levels between the two groups（P＞0.05）.There was no significant difference in preoperative weight between the two groups（P＞0.05）.The weight of the model group was significantly lower than that of the control group after non-pregnancy surgery and drug administration, and the difference was statistically significant（P＜0.01）.At 13th day of pregnancy, the weight of model group was significantly lower than that of control group, and the difference was statistically significant（P＜0.01）.There was no significant difference in the average number of offspring between the two groups（P＞0.05）.Conclusion The method of subcutaneous injection of L-T4 can successfully establish a SCH rat model during pregnancy, with good stability.  [ABSTRACT FROM AUTHOR]

The scenario data of Coupled Model Intercomparison Project Phase 6 (CMIP6) are processed by Delta method in statistical downscaling method. Based on the centralized root mean square error (RMSE), correlation coefficient (R), standard deviation (STD) and interannual variability skill score (Ts), comprehensively evaluate the simulation ability of scenario data from 10 CMIP6 models on the extreme temperature index in Shandong, and select models with better simulation results. The results indicate that:In the simulation of extreme temperature indices TXx and TNx that characterize intensity, after downscaling, more than 50% of the models simulate that the median absolute deviation of extreme temperature index in Shandong is closer to the observed value than before downscaling, in the simulation of extreme temperature indices TR, SU, FD and ID that characterize frequency, more than 60% of the models absolute deviation medians are closer to the observations than before downscaling.Using the ranking method, the simulation ability of the 10 models was compared. The CMCC-ESM2 score was 178 points, NorESM2-MM score was 191 points, and TaiESM1 score was 191 points. Therefore, these three models are preferred models. The error percentage of extreme temperature indices simulated by the ensemble mean of optimized models is lower than that of all models.After optimization, the absolute value of error percentage of TXx, TNx, TR and ID decreased from 4.2%, 2.2%, 28.67% and 14.3% to 3.3%, 1.8%, 23.58% and 9.7%, the error percentage of SU was within ± 1% before and after optimization. [ABSTRACT FROM AUTHOR]

During the process of food storage and circulation, there will be different degrees of quality deterioration. With the improvement of people's attention to food quality and safety, it is of great significance to carry out quality prediction research in the process of food storage and transportation for quality control. This paper reviews the research progress of machine learning in food storage quality prediction, including conventional quality prediction methods and limitations, and then focuses on the rapid development and wide application of integrated learning and artificial neural network algorithms, and prediction performance evaluation methods in recent years. Finally, it summarizes and looks forward to the future development trend of machine learning in the food field, and provides relevant references for the development of food science cross research. [ABSTRACT FROM AUTHOR]

Objective To analysis the modeling characteristics of diminished ovarian reserve (DOR) animal models, and provide the reference for the standardization of DOR animal models. Methods The research articles on DOR animal models were retrieved. Microsoft Excel 2010 software was used to summarize the experimental animal species, modeling methods, modeling cycles, high-frequency detection indexes and types of positive drugs documented in the literature, and the data results were evaluated and analyzed by NoteExpress software. Results A total of 93 research articles on DOR that met the criteria were enrolled. And it was found that, SD rat was the most frequently used animal type (68 times, 73.12%), followed by C57BL/6 mouse (13 times, 13.98%), while the tripterygium wilfordii treatment was the most frequently used modeling method (38 times, 40.86%), followed by the cyclophosphamide treatment (28 times, 30.11%) for DOR animal models. The high frequency detection indicators were vaginal exfoliation cytology detection (93 times, 23.97%) and HE staining to observe histopathological changes (72 times, 18.56%). Among these 64 research articles containing positive control drugs, the most frequently used western drug was estrogen (50 times,62.50%) and Chinese Traditional medicine was Kuntai capsules (2 times, 2.50%). Conclusions SD rats are mostly used to induce modeling in animal experiments on DOR through tripterygium wilfordii and cyclophosphamide, which can effectively improve the experimental efficiency. It is recommended to integrate the results of ovarian histopathology and serum biochemical indexes for model assessment.

“Carbon Peak” and “carbon neutrality” are a social revolution affecting all mankind. How to accurately predict and reduce carbon emissions is the key to the success of this revolution, and also gives the research on this topic a more difficult mission. In order to accurately predict the carbon emissions in the energy sector, based on the energy security considerations such as power balance, power amount balance and peak balancing, a hybrid calculation model is adopted to optimize the installed structure of electric power and energy consumption structure in the process of carbon emission prediction, so as to obtain the optimal minimum carbon emission value based on the installed cost of electric power and the cost of energy consumption structure according to the optimized energy consumption structure. The whole forecasting process is mainly divided into three parts. Firstly, the influencing factors of carbon emissions in the energy sector are analyzed according to the LMDI decomposition method. GDP has a positive driving effect on carbon emissions, while energy consumption intensity and industrial structure have a negative driving effect on carbon emissions. Secondly, the structure of the carbon system is preliminarily optimized and the initial carbon emission value is predicted based on the preliminary optimization results. Finally, based on the rationality and accuracy of the process, a model evaluation method is proposed. The key index verification method is adopted to perform feedback verification on the binding indicators such as the proportion of electric energy in terminal energy consumption, the proportion of non-fossil energy consumption and the cumulative decline rate of carbon intensity in the prediction process. After the key index verification and multiple feedback adjustment, the carbon emission value meeting the constraints of the multi-objective function and each constraint index is obtained. The feasibility and reliability of the optimization and prediction process are verified by using the actual data of a province in central China. The results show that only in the peak reaching scenario, the province can meet the target of carbon peak and pass the verification of three key indicators.

Objective To analysis the modeling characteristics of diminished ovarian reserve (DOR) animal models, and provide the reference for the standardization of DOR animal models. Methods The research articles on DOR animal models were retrieved. Microsoft Excel 2010 software was used to summarize the experimental animal species, modeling methods, modeling cycles, high-frequency detection indexes and types of positive drugs documented in the literature, and the data results were evaluated and analyzed by NoteExpress software. Results A total of 93 research articles on DOR that met the criteria were enrolled. And it was found that, SD rat was the most frequently used animal type (68 times, 73.12%), followed by C57BL/6 mouse (13 times, 13.98%), while the tripterygium wilfordii treatment was the most frequently used modeling method (38 times, 40.86%), followed by the cyclophosphamide treatment (28 times, 30.11%) for DOR animal models. The high frequency detection indicators were vaginal exfoliation cytology detection (93 times, 23.97%) and HE staining to observe histopathological changes (72 times, 18.56%). Among these 64 research articles containing positive control drugs, the most frequently used western drug was estrogen (50 times,62.50%) and Chinese Traditional medicine was Kuntai capsules (2 times, 2.50%). Conclusions SD rats are mostly used to induce modeling in animal experiments on DOR through tripterygium wilfordii and cyclophosphamide, which can effectively improve the experimental efficiency. It is recommended to integrate the results of ovarian histopathology and serum biochemical indexes for model assessment. [ABSTRACT FROM AUTHOR]

To investigate the soil hydraulic characteristics across a wide range of suctions (0-10 MPa) of Benggang soil, the soil water holding capacities of different soil layers in Benggang was determined using the pressure film meter and the filter paper methods. Traditional and modified Van Genuchten-Mualem (VG) models were used to fit the soil moisture characteristics curves. The results showed that the water-holding curves determined by the pressure film meter and filter paper methods intersected and tended to overlap. This observation suggested combining the two measurement methods to obtain soil moisture characteristic curves over a wide suction range. Further, within the wide suction range, the water-holding capacity of the sandy soil layer was higher than that of the red and detritus soil layers in the low suction stage (h<0.1 MPa). However, in the high suction stage (h>0.1 MPa), the red soil layer had the highest water-holding capacity. Both the traditional and modified VG models were able to fit the soil moisture characteristics of each soil layer of the Benggang wall with a root mean square error of less than 0.021 5. The Nash efficiency and correlation coefficients were greater than 0.926 6 and 0.899 1, respectively. Noticeably, the modified VG model was more suitable for describing the soil moisture characteristic curves of the Benggang wall across a wide suction range. The residual values of the soil moisture characteristic curves for the red, sandy, and detritus layers were 378 kPa (corresponding to a water content of 18.82%), 173 kPa (corresponding to a water content of 18.95%), and 92 kPa (corresponding to a water content of 18.41%), respectively. Finally, the form of the segmental function (with the residual value as the inflection point) was more suitable for describing the unsaturated hydraulic conductivity over a wide suction range of Benggang soils than the conventional VG model. [ABSTRACT FROM AUTHOR]

This study compares the CMIP6 （Coupled Model Intercomparison Project Phase 6） historical experiment results of BCC-CSM2-MR （Beijing Climate Center-Climate System Model-Medium Reslution） with GLDAS （Global Land Data Assimilation System） dataset and site-observed data to systematically evaluate the performance of BCC-CSM2-MR in simulating the land surface variables， such as surface soil temperature， upper soil moisture and surface energy balance components in China.And the causes of the model biases are also deeply discussed.The spatial correlation coefficient， temporal correlation coefficient， Taylor score and root mean square error between the observed data and GLDAS data and the model data are calculated to quantitatively analyze the simulation ability of BCC-CSM2-MR to land surface variables.The results show that the model can well simulate the spatial distribution and variability of land surface variables， but the model biases in quantity are still obvious.The spatial correlation coefficient and Taylor score between the simulated land surface soil temperature， upward net long wave radiation flux and surface upward latent heat flux by BCC-CSM2-MR and GLDAS are above 0.8 in each season， and it shows that the simulation performance of above variables is relatively great.Compared with GLDAS data， the model overestimates the summer surface soil temperature over southeastern China， but it tends to underestimate the surface soil temperature over most China in all seasons with much larger underestimation over the Qinghai-Xizang plateau in winter and spring.From the model error analysis， it can be concluded that the precipitation underestimated by the model leads to the overestimated surface downward shortwave radiation over southeastern China in summer， which leads to the overestimated surface soil temperature there.The surface albedo overestimated by the model results in the underestimated surface net downward shortwave radiation over the Qinghai-Xizang plateau， which further leads to the underestimated surface soil temperature， obviously in winter and spring.In addition， the model seriously underestimates （overestimates） the upper soil moisture over southeastern China in all seasons （Qinghai-Xizang plateau in winter and spring）， and the simulation effect of the characteristics of time evolution of deep soil time is better than that of upper soil， this is mainly resulted from the biases in the modeled precipitation.Meanwhile， the overestimation of upper soil moisture and 10m wind speed leads to the overestimated surface upward latent heat flux over Qinghai-Xizang Plateau in the winter and spring.After the model evaluation， it can be found that the simulation performance of BCC-CSM2-MR to land surface variables still needs to be improved and the causes of the model biases are complicated.

As an important part of surface hydrological cycle and energy distribution， lake evaporation is of great practical significance.The lake photovoltaic power plant is a new form of power generation， which changes the energy distribution in the process of converting solar energy into electric energy， thus changing the evaporation of the lake.Based on the observation data inside and outside the fish-lighting complementary photovoltaic power plant from October 2020 to January 2021 in Yingfang Village， Yangzhong City， Jiangsu Province， this paper analyzed the characteristics of the changes in evaporation， and evaluated the main evaporation models.The results show that： the evaporation inside the station was less than that outside the station which affected by the shielding effect of the photovoltaic power plant， and the evaporation was reduced by 44.9%.The evaporation in autumn was obviously larger than that in winter， and the evaporation in daytime was much larger than that at night.Three evaporation models were evaluated in this paper， among which the mass transfer model had the best simulation effect.Correlation analysis showed that the daily evaporation of lake photovoltaic power plants was significantly correlated with water vapor pressure difference， temperature difference between lake and air， and net radiation flux.The hourly evaporation could be approximated as a linear regression value of net radiation， 10m wind speed， relative humidity and temperature difference between lake and air.The linear model was expressed as： y=0.08+0.0003Rn+0.0754u10-0.0035RH+0.0165Ts.

Evapotranspiration is an important link to water and heat exchange between soil and atmosphere， and its changes have an important impact on water resources management and ecological environment.In the context of global warming， the permafrost on the Qinghai-Xizang Plateau is continuously degraded， and the near-surface hydrothermal conditions in the permafrost regions have changed， which further affect the evapotranspiration process.In this paper， four semi-empirical models based on the complementary principle of evapotranspiration are selected to evaluate the ability of different models to simulate the actual evapotranspiration on a daily scale before and after calibrating parameters， and also analyze the sensitivity of the model parameters， which using the meteorological and eddy covariance measurements from January 1， 2010 to December 31， 2011 at the Tanggula comprehensive observation site located in the hinterland of the permafrost region of the Qinghai-Xizang Plateau.The results show that the simulated actual evapotranspiration is very sensitive to the value of parameter α in the models， unreasonable parameter values led to poor simulation ability.Specifically， the use of default parameter values will lead to marked higher simulated values of daily evapotranspiration， the S2017 model has the highest simulation accuracy with RMSE value of 0.44 mm·d-1.After parameters calibration， all models can simulate variation characteristics of daily actual evapotranspiration， RMSE values are between 0.3 and 0.4 mm·d-1， the model with the highest simulation accuracy is the C2016 model， followed by H2018 model， B2015 model and S2017 model， in comparison， evapotranspiration simulated by H2018 model exhibits more stable results， which indicates lower parameter dependency.In addition， accuracy of simulation exists significant difference during various period within the year， and the simulation results of the thawed period are closer to the measured values than those of the frozen period and the daily freeze-thaw cycle period， mean relative deviation values are 6.0%， 82.3%， 43.4%， respectively.The purpose of this paper is to explore the applicability of different complementary evapotranspiration models to the simulation of actual surface evapotranspiration in the permafrost region of the Qinghai-Tibet Plateau， and to some extent， the results can provide reference basis for the estimation of long time series evapotranspiration in this region.

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High Resolution Model Intercomparison Project (HighResMIP) from the Coupled Model Intercomparison Project 6 (CMIP6), for the first time, provides global high-resolution (25—50 km) multi-model ensemble simulations. Based on simulations of eight CMIP6 HighResMIP models, the performance of the state of the art global high-resolution models in simulating hourly precipitation and extreme precipitation in summer over the Tibetan Plateau is evaluated. The results show that CMIP6 high-resolution models overestimate (underestimate) the precipitation amount and frequency (intensity). The positive bias of precipitation amount is mostly contributed by the overestimated frequency, especially the frequency of weak precipitation (< 2 mm·h-1). The simulation deviations are closely related to terrain elevation, and the areas with large bias are mainly located in the steep terrain on the south and east slopes of the plateau. The models fail to accurately reproduce the relationship between precipitation amount and elevation, with overestimation (underestimation) of the amount in high(low)-altitude areas. The models underestimate the rate of precipitation intensity decreasing with increasing elevation. In terms of diurnal variation, the models can simulate the late-afternoon and midnight peaks of precipitation over the Tibetan Plateau, but obviously underestimates the diurnal amplitude of precipitation. In terms of hourly extreme precipitation, the models underestimate the 95th percentile threshold of the area-average precipitation intensity in the plateau, which was only 57% of the observed value.

Based on statistical theory, the Bayesian inversion method adopts the posterior probability distribution to evaluate the model parameters under the constraints of prior information and observation data. Compared to deterministic inversion theory, Bayesian inference is beneficial to quantitative evaluation of inversion uncertainty by the model parameter marginal probability distribution, maximum a posterior estimation (MAP), mean model estimation and correlation coefficient, which accurately reflects the constraint ability of observation data and prior information on model parameters. We systematically summarized the application of the Bayesian inference in geophysical inversion and proposed the basic flowchart to realize Bayesian model evaluation. Firstly, the Bayesian theory is simply introduced. The prior information probability distribution, the likelihood function formula, and the construction of the posterior probability equation are explained in detail. Secondly, the implementation process of Bayesian inversion is discussed in detail. As for model parameter updates, the concepts of fixed and trans-dimensional inversion with the hyperparameter optimization are discussed. In terms of inversion methods, the Markov Chain Monte Carlo (MCMC) sampling methods of fixed and trans-dimensional inversion are highlighted. In consideration of model parameter evaluation, the calculation of Bayesian statistical parameters under different conditions is introduced. Then the specific measures to improve the sampling efficiency of Bayesian inversion are discussed. Finally, the application of Bayesian inference in geophysical inversion is summarized.

Convolutional neural network models have different advantages and disadvantages, it is becoming more and more difficult to select an appropriate convolutional neural network model in an actual fish identification project. The identification of underwater fish is a challenge task due to varies in illumination, low contrast, high noise, low resolution and sample imbalance between each type of image from the real underwater environment. In addition, deploying models to mobile devices directly will reduce the accuracy of the model sharply. In order to solve the above problems, Fish Recognition Ground-Truth dataset was used to training model in this study, which is provided by Fish4Knowledge project from University of Edinburgh. It contains 27,370 images with 23 fish species, and has been labeled manually by marine biologists. AlexNet, GoogLeNet, ResNet and DenseNet models were selected initially according to the characteristics of real-time underwater fish identification task, then a comparative experiment was designed to explore the best network model. Random image flipping, rotation and color dithering were used to enhance data based on ground-truth fish dataset in response to the limited number of underwater fish images. Considering that there was a serious imbalance in the number of samples in each category, the label smoothing technology was used to alleviate model overfitting. The Ranger optimizer and Cosine learning rate attenuation strategy were used to further improve the training effect of the models. The accuracy and recall rate information of each model were recorded and counted. The results showed that, the accuracy and recall rate of the fish recognition model based on DenseNet reached 99.21% and 96.77% in train set and validation set respectively, its F1 value reached 0.9742, which was the best model obtained in the experiment. Finally, a remote fish identification system was designed based on Python language, in this system the model was deployed to linux server and the Android APP was responsible for uploading fish images via http to request server to identify the fishes and displaying the identification information returned by server, such as fish species, profiles, habits, distribution, etc. A set of recognition tests were performed on real Android phone and the results showed that in the same local area net the APP could show fish information rapidly and exactly within 1 s.

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Copyright of Plateau Meteorology is the property of Plateau Meteorology 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 Plateau Meteorology is the property of Plateau Meteorology 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 Plateau Meteorology is the property of Plateau Meteorology 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.)

Convolutional neural network models have different advantages and disadvantages, it is becoming more and more difficult to select an appropriate convolutional neural network model in an actual fish identification project. The identification of underwater fish is a challenge task due to varies in illumination, low contrast, high noise, low resolution and sample imbalance between each type of image from the real underwater environment. In addition, deploying models to mobile devices directly will reduce the accuracy of the model sharply. In order to solve the above problems, Fish Recognition Ground-Truth dataset was used to training model in this study, which is provided by Fish4Knowledge project from University of Edinburgh. It contains 27, 370 images with 23 fish species, and has been labeled manually by marine biologists. AlexNet, GoogLeNet, ResNet and DenseNet models were selected initially according to the characteristics of real-time underwater fish identification task, then a comparative experiment was designed to explore the best network model. Random image flipping, rotation and color dithering were used to enhance data based on ground-truth fish dataset in response to the limited number of underwater fish images. Considering that there was a serious imbalance in the number of samples in each category, the label smoothing technology was used to alleviate model overfitting. The Ranger optimizer and Cosine learning rate attenuation strategy were used to further improve the training effect of the models. The accuracy and recall rate information of each model were recorded and counted. The results showed that, the accuracy and recall rate of the fish recognition model based on DenseNet reached 99.21% and 96.77% in train set and validation set respectively, its F1 value reached 0.9742, which was the best model obtained in the experiment. Finally, a remote fish identification system was designed based on Python language, in this system the model was deployed to linux server and the Android APP was responsible for uploading fish images via http to request server to identify the fishes and displaying the identification information returned by server, such as fish species, profiles, habits, distribution, etc. A set of recognition tests were performed on real Android phone and the results showed that in the same local area net the APP could show fish information rapidly and exactly within 1 s. [ABSTRACT FROM AUTHOR]

To tackle the problems of users not having intuitive cognition of the dynamic privacy changes contained in their sensing data in mobile edge crowdsensing (MECS) and lack of personalized privacy risk warning values in the data uploading stage, a dynamic privacy measurement (DPM) model was proposed.A structured representation of data obtained by a user participating in a sensing task was introduced and was transformed it into a numerical matrix.Then privacy attribute preference and timeliness were presented to quantify the dynamic privacy changes of data.With this, personalized privacy thresholds of users based on the numerical matrix were reasonably calculated.Finally, differential privacy processing was performed on the numerical matrix, and a model evaluation system was designed for the proposed model.The simulation results show that the DPM model was effective and practical.According to the given example, a data utility of approximately 0.7 can be achieved, and the degree of privacy protection can be significantly improved as the noise level increases, adapting to the MECS of IoT.

To tackle the problems of users not having intuitive cognition of the dynamic privacy changes contained in their sensing data in mobile edge crowdsensing (MECS) and lack of personalized privacy risk warning values in the data uploading stage, a dynamic privacy measurement (DPM) model was proposed. A structured representation of data obtained by a user participating in a sensing task was introduced and was transformed it into a numerical matrix. Then privacy attribute preference and timeliness were presented to quantify the dynamic privacy changes of data. With this, personalized privacy thresholds of users based on the numerical matrix were reasonably calculated. Finally, differential privacy processing was performed on the numerical matrix, and a model evaluation system was designed for the proposed model. The simulation results show that the DPM model was effective and practical. According to the given example, a data utility of approximately 0.7 can be achieved, and the degree of privacy protection can be significantly improved as the noise level increases, adapting to the MECS of IoT.

[Introduction] To address the issue of decisions under nuclear emergency and nuclear accident consequence assessment of common concern at home and abroad, in this paper. [Method] Tracer experiment data set KINCAID and model validation and evaluation method MVK both used widely were reviewed. Model ARTM was evaluated using MVK. [Result] Statistical results show that: the simulation results of ARTM model are basically reliable. [Conclusion] The forecasts in 3 km indicate a tendency of underestimate, while the forecasts out of 5 km indicate a tendency of overestimate.

Copyright of Refractories / Naihuo Cailiao is the property of Naihuo Cailia (Refractories) 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.)

To ensure the quality and efficiency of civil aircraft gust load measurement flight tests, avoid risks, ensure safety, and overcome the difficulty in finding turbulence, this paper starts with the pilot reports of aircraft turbulence and uses the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecast to give a comprehensive forecast index for turbulence potential under the background of upper-level jets. Research methods include the use of the weighted integration algorithm based on ten predictable aircraft turbulence indexes. The weight of a single index in the comprehensive index is jointly determined by five evaluation indicators, namely the forecast accuracy of turbulence (PODY), the forecast accuracy of null turbulence (PODN), the overall forecast accuracy (PODA), the true skill score (TSS) and the area occupied by the moderate or greater turbulence (fMOG). The results show that the Dutton index has the best overall forecast performance among these 10 single indexes as its forecast score φ is 1.043, followed by Brown index and L-P index. The comprehensive index developed from Dutton index, Brown index, L-P index, TI1 index and wind-related index integrates the advantages of each single index with its individual index all being relatively superior. Its PODA reaches 90%, the TSS reaches 0.80, the fMOG reaches 9.2%, the forecast score φ reaches 1.225, and the overall effect is the best. As the number of single index increases, the diagnostic effect of the comprehensive index increases first and then decreases. When the number of single index increases to 5, the effect becomes optimal. As the number of aircraft turbulence samples involved in modeling increases, the performance of the comprehensive index gradually improves. This comprehensive index can facilitate the search for potential turbulence areas in civil aircraft gust load measurement flight tests. [ABSTRACT FROM AUTHOR]

Copyright of Refractories / Naihuo Cailiao is the property of Naihuo Cailia (Refractories) 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.)

To accurately calculate the compressive strength of fibre reinforced polymer ( FRP) confined rubber concrete, the existing compressive strength models of FRP confined rubber concrete of related literature were analyzed. The 122 test data collected from literature were used to evaluate the accuracy of the existing model. The evaluation results show that rubber particle volume replacement rate of concrete coarse and fine aggregates and the FRP type have significant effect on the compressive strength of FRP confined rubber concrete. Based on the evaluation results, a new compressive strength model with considering rubber particle replacement rate coefficient and FRP type was established. The predicted value of the new compressive strength model is relatively close to the test value, and the error evaluation index value is 0. 09. [ABSTRACT FROM AUTHOR]

设施 蔬菜 水分 氮素 WHCNS_Veg 模型评&#2 Excessive water and nitrogen (N) input and shallow root systems have led to serious N loss in greenhouse vegetable production system (GVPS), thereby threatening the soil and water-body environments. Identifying the fates of water and N is crucial to develop best management strategies in intensive GVPS. Soil-crop models are important as a water and N management tool to trade off crop yield and environmental cost. The objectives of this study were to develop a scientific water and N management tool for intensive GVPS in China, and evaluate the model performance in the simulating water dynamic, N fate and vegetable growth under different water and N management practices in China. In this study, based on the EU-Rotate_N model, a vegetable growth module was developed and fully incorporated into a soil water heat carbon nitrogen simulator (WHCNS). The coupled model, i.e., WHCNS_Veg, combined the soil module of the WHCNS model with the vegetable growth module of the EU-Rotate_N model. The key processes included soil movement, soil water evaporation, crop transpiration, soil N transport and transformation (net N mineralization, nitrification, ammonia volatilization, and denitrification) and vegetable growth. Two field experiments conducted in Shouguang city, Shandong province, and Tianjin city, Hebei province were used to test the coupled WHCNS_Veg model. Cucumber and tomato were planted in solar greenhouses at Shouguang and Tianjing experiments, respectively. Similar irrigation and fertilization management practices were both setup in the two experiments: 1) Furrow irrigation with conventional N fertilizer (farmer’s practice), FP; 2) Drip irrigation with optimal N fertilizer, OPT; 3) FP plus crop residues, FPR; and 4) OPT plus crop residues, DOR. Field experiment data including soil water (soil water content and matrix potential), soil N (nitrate concentration and nitrate leaching), plant N uptake, and marketable fresh yield under different water and N management practices were collected in the experiments. Results showed that the WHCNS_Veg model performed better in crop growth simulation than soil water and N dynamic simulation, with normalized root mean square error (nRMSE) not higher than 12.1%, index of agreement (IA) not less than 0.934 and Nash-Sutcliffe efficiency (NSE) not less than 0.829 for simulation of cucumber and tomato yield and plant N uptake. Soil water content had the highest simulation accuracy. The nRMSE, IA and NSE values for soil water content simulation were 6.2%-9.1%, 0.851-0.960 and 0.477-0.846, respectively. For soil nitrate concentration and nitrate leaching, the nRMSE values were 22.2%-40.1% and 4.6-26.0%, respectively, and the NSE values were −0.810-0.636 and 0.442-0.956, respectively. It indicated that the model simulation results are within the acceptable range. However, WHCNS_Veg model showed relative low simulation accuracy in soil matrix potential under drip irrigation condition, with nRMSE, IA and NSE values ranged from 22.9%-30.1%, 0.223-0.846, and -6.344-0.113, respectively. It indicated that the model has to be improved to simulate soil matrix potential under drip condition. In general, the coupled WHCNS_Veg model gives satisfactory results for soil water and N dynamics simulations as well as vegetable growth simulations. It has great potential to simulate and analyze water and N fates, and vegetable growth for the intensive greenhouse vegetable production in China. 0215; [ABSTRACT FROM AUTHOR]

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.)

Objective To induce the psychological stress-asthmatic rat model by ovalbumin (OVA) mixture sensitization with restraint, which simulated the pathogenic factors of bronchial asthma with liver - qi stagnation pattern, as well as to evaluate the model using indicators of the macro characterization, behaviors, pulmonary function, peripheral blood inflammatory cells, bronchial tissue pathomorphology. Methods A total 32 male rats were randomly divided into four groups-control group, stress group, asthmatic group, and stress-asthmatic group. Rats of stress group and stress-asthmatic group were immobilized in restrained box once daily at different time, ten minutes more than the day before with the first duration of 2 hours. Rats of stress-asthmatic group and asthmatic group were sensitized by intraperitoneal injection 1 mL mixture of OVA with aluminium hydroxide on the first day, and after 13 days, they were stimulated by inhalation of 1 % OVA for 20 min for consecutive 14 days. The rats of control group were sensitized and stimulated by saline, without immobilizing . 28 days later, the macro characterization, behaviors, peripheral blood eosinophils, pulmonary function, bronchial tissue pathomorphology were observed. Results Stress asthmatic rats showed low mood, loss of interest, dull hair, loose stools, reducing food intake , slowly weight gain, and scratched noses and abdominal skin, coughed, sneezed and wheezed in the throat when stimulated. Compared with control group, the points of vertical movement decreased significantly (P <0. 05 or P < 0. 01). Preference for sucrose, the times of active moves in tail suspension test (TST) of stress-asthmatic rats obviously decreased (P <0. 01) , and the duration of immobility increased significantly (P < 0. 01). Pulmonary function indicators, including peak expiratory flow (PEF), forced expiratory flow (FEF 25% -75%), forced expiratory volume in 0. 3 s [FEV(0. 3]/forced expiration volume capacity ( FVC) significantly declined (P <0. 05, P <0. 01, P <0. 01 ), while mean inspiratory airway resistance (Ri) and mean expiratory airway resistance (Re) increased remarkably (P < 0. 05, P <0. 01). Peripheral blood eosinophil cells increased (P <0. 01), and infiltration of inflammatory cell in lung tissue was obvious. Compared with the stress group or asthmatic group, the signs and indicators of stress-asthmatic group were all much worse. Contusion The psychological stress-asthmatic rat model, induced by VOA mixture with restraint, was proved to be successful. It is in accordance with clinical characteristics of bronchial asthma with liver-qi stagnation pattern, which can be used to study asthma caused by emotion . [ABSTRACT FROM AUTHOR]

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.)

Spatial relation research has important applications in conflict detection, cartographic generalization, queries and analysis. Refinement and integrated description of spatial relationships to solve practical problems have become an active direction spatial relationship research. This paper analyzes the shortcomings of current integrated methods to express spatial relationships between lines and regions. We analyze and classify simple topological relations between lines and regions and propose the meta-relation concept. Then, the position, orientation and connecting mode of meta-relations are discussed and an integrated representation model of line-region is detailed. Evaluation criteria for the integrated representation model of spatial relations are brought forward and the proposed model is analyzed using these evaluation criteria. [ABSTRACT FROM AUTHOR]

Objective: To summarize the research progress on rodent models of cervical spinal cord injury (SCI)., Methods: The relevant domestic and foreign literature in recent years was reviewed, the methods of establishing the rodent models of cervical SCI and the evaluation methods of behavior, imaging, neuroelectrophysiology, and histology were summarized., Results: Cervical SCI involves primary and secondary injuries. Primary cervical SCI can be simulated with contusion, contusion compression, fracture dislocation, spinal cord traction, and spinal cord transection; scondary cervical SCI can be simulated with photochemical model and excitotoxicity model. Certain evaluation methods such as behavior, imaging, neuroelectrophysiology, and histology are used to evaluation during model building and research., Conclusion: Different rodent models of cervical SCI have different advantages and application directions, and it is critical importance for the study of cervical SCI to establish effective animal models.

In the process of structure strength analysis, the modeling efficiency would be rising if the static finite element model whose stiffness is very accurate could be transferred into dynamical model. In this paper, a method of transformation of static structure models to dynamic models based on modal tests and optimization is given. Firstly, the stiffness matrix of static model is updated to satisfy the demand of dynamics. Secondly, the node masses are preliminarily distributed in light of the mass, center of mass of parts of the structure, inertia moment and element volume. Then the amount of correction of mass is optimized using the modal parameters obtained in the modal test. In this way an finite element model of structural dynamics can be built. An example of the complicated structure comprising wings and part of fuselage is given in this paper. It is proved that the method is feasible for modeling and analysis. [ABSTRACT FROM AUTHOR]

To explore the method of establishing a cell model of traditional Chinese medicine (TCM) syndromes, HepG2 cells were induced by human serum of liver-depression and spleen-deficiency syndrome(LDSDS) to establish a cell model of LDSDS in this research. The concentration of cells, the content of human serum in culture medium and the growth characteristics of model-cell (cell growth curve, the survival rate and apparent morphology were investigated by MTT assay and microscopy. Evaluation of syndrome cell model: metabolomics was used to analyze the human serum of normal individuals and patients with LDSDS, and cell models induced by these serums, respectively. We obtained the difference metabolites from serums and cell models of LDSDS, respectively; then compared the biomarkers from two metabolomics and their metabolic pathways, to verify that the reliability and applicability of the model. Metabolomics data were collected by UPLC-Q-TOF-MS, and then all data were analyzed by multivariate statistical (PCA，OPLS-DA). The results showed that, model cells have the characteristics of normal growth, slow proliferation and stable morphological structure inducted by 10% serum of LDSD in 24-72 h. There were the same 19 difference metabolites which from the human serum of normal individuals and patients with LDSDS, and cell models induced by these serums; including 9 metabolic pathways that play an important role in maintaining normal physiological activities of the human body, such as lipids, amino acids, nucleotides, and energy metabolism etc. It was shown that the established syndrome cell model can reflect the biological basis of LDSDS to some extent. This research provides a reference method for the establishment of TCM syndrome cell model., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Chinese Pharmaceutical Association.)

The relationship between the distribution of halobios and environmental variables has been a focus of present research in marine ecology. Species distribution models (SDMs) have been widely employed to predict the distribution patterns and potential habitat suitability assessments of marine species and provide an efficient approach for marine biodiversity conservation, invasive species prevention, and fishery management. SDMs mainly include habitat suitability index models, mechanism models, and statistical models. In this paper, the theoretical basis of SDMs was firstly concluded and summarized. Next, the exploitation and case-studies of SDMs, especially the statistical models, for predicting potential distribution of marine species were reviewed. Then, comparisons of various methods for variable selection and model validation were made. Conclusions could be drawn that Akaike information criterion showed excellent performance when it came to variable selection, while Kappa coefficient and Area Under receiver operating character Curve (AUC) were widely used in relation to model validation. Finally, problems and prospects of SDMs were discussed. With the development of research on physiological characteristics, using mechanism models to predict potential habitats of halobios would become a trend.

The high-risk invasive apple snail Pomacea canaliculata has greatly threatened the agriculture, ecosystem integrity and public health. In order to provide scientific evidence for effective prevention and control of P. canaliculata, a most suitable ecological niche model was selected to predict the potential suitable distribution areas of P. canaliculata in China. Based on 377 reported occurrence points and 19 bioclimatic variables, four ecological niche models, MaxEnt, GARP, BIOCLIM, and DOMAIN, the potential geographic suitable distribution areas were predicted for the invasive snail. Then, the results of different models were analyzed and compared with two statistical criteria, the area under the Receiver Operating Characteristic curve (AUC) and Kappa value. The results showed that all of the four ecological niche models could simulate the snail's distributions very well. More specifically, the MaxEnt model outperformed the others in all aspects of predicting the snail's potential distribution (AUC=0.955±0.004, Kappa=0.845±0.017), followed by GARP and DOMAIN. Although BIOCLIM offered the lowest prediction accuracy, its AUC was 0.898±0.017 and its Kappa value was 0.771±0.025. Based on the MaxEnt model, the prediction results showed that the potential suitable distribution areas of P. canaliculata were mainly located in the south of 30° N in China, but there was some regions spreading over the north of 30° N. The potential areas accounted for 13.2% of the national land in area. Notably, Guangdong, Guangxi, Hunan, Chongqing, Zhejiang and the coastal areas of Fujian were potentially high-risk areas. In conclusion, this study would be an important reference for the prevention and control of the invasive apple snail P. canaliculata and it also would be an example of predicting the potential distribution of aquatic alien species on large scale.