The Denglou Cape, southwest of the Leizhou Peninsula, is the most typical tropical coast in the continent of China.The coastal geomorphic development basics of the geology and Quaternary environment change are discus...The Denglou Cape, southwest of the Leizhou Peninsula, is the most typical tropical coast in the continent of China.The coastal geomorphic development basics of the geology and Quaternary environment change are discussed. Aerial photograph interpretation with fieldwork is applied to draw the outlines of geomorphic types. Based on the investigative data, the exogenic forces and marine organism conditions concerning tropical coast development in the area are expounded, and coastal dynamo-deposition geomorphic bodies are analysed, mainly with sea cliff-abrasion platform,barrier-lagoon system, modern beach, coral reef and mangrove tidal flat, and the general process of coastal evolution at this area, as well as coastline changes since middle Holocene transgression.展开更多
Objectives:Food quality assessment is critical for indicating the shelf-life and ensuring food safety or value.Due to high environmental sensitivity,the post-harvest quality of fresh fruit will undergo complex changes...Objectives:Food quality assessment is critical for indicating the shelf-life and ensuring food safety or value.Due to high environmental sensitivity,the post-harvest quality of fresh fruit will undergo complex changes in the supply chain,with various dynamic quality-related features.It is diffcult to effciently and accurately extract comprehensive quality feature of post-harvest fruits from high-dimensional monitoring data with heterogeneous characteristics(numerical and categorical).Therefore,we proposed a dynamic comprehensive quality assessment method based on self-adaptive analytic hierarchy process(SAHP)integrated with the CatBoost model.Materials and Methods:By adaptive weight optimization,the SAHP was utilized to analyze the multi-source quality information and obtain the quantized fusion value,as an output sample of CatBoost machine learning.Then,using heterogeneous monitoring data as input,the CatBoost model was directly trained through unbiased boosting with categorical features for dynamic assessment of overall quality status.Results:Three quality index monitoring data sets for‘Jufeng’grape in different transportation chains(normal temperature,cold insulation,and cold chain)were individually constructed as the research samples.Furthermore,compared to other machine learning methods,the SAHP-CatBoost had more accurate results in comprehensive quality feature extraction.In actual transportation chains,the mean absolute error,mean absolute percentage error,and root mean squared error of dynamic comprehensive assessment were limited to 0.0044,1.012%,and 0.0078,respectively.Conclusions:The proposed method is effcient in handling heterogeneous monitoring data and extracting comprehensive quality information of post-harvest grape as a robust shelf-life indicator.It can reasonably guide post-harvest quality management to reduce food loss and improve economic benefts.展开更多
文摘The Denglou Cape, southwest of the Leizhou Peninsula, is the most typical tropical coast in the continent of China.The coastal geomorphic development basics of the geology and Quaternary environment change are discussed. Aerial photograph interpretation with fieldwork is applied to draw the outlines of geomorphic types. Based on the investigative data, the exogenic forces and marine organism conditions concerning tropical coast development in the area are expounded, and coastal dynamo-deposition geomorphic bodies are analysed, mainly with sea cliff-abrasion platform,barrier-lagoon system, modern beach, coral reef and mangrove tidal flat, and the general process of coastal evolution at this area, as well as coastline changes since middle Holocene transgression.
基金funded by the National Natural Science Foundation of China(No.31971808)the Central Publicinterest Scientifc Institution Basal Research Fund(No.CAASZDRW202107),China.
文摘Objectives:Food quality assessment is critical for indicating the shelf-life and ensuring food safety or value.Due to high environmental sensitivity,the post-harvest quality of fresh fruit will undergo complex changes in the supply chain,with various dynamic quality-related features.It is diffcult to effciently and accurately extract comprehensive quality feature of post-harvest fruits from high-dimensional monitoring data with heterogeneous characteristics(numerical and categorical).Therefore,we proposed a dynamic comprehensive quality assessment method based on self-adaptive analytic hierarchy process(SAHP)integrated with the CatBoost model.Materials and Methods:By adaptive weight optimization,the SAHP was utilized to analyze the multi-source quality information and obtain the quantized fusion value,as an output sample of CatBoost machine learning.Then,using heterogeneous monitoring data as input,the CatBoost model was directly trained through unbiased boosting with categorical features for dynamic assessment of overall quality status.Results:Three quality index monitoring data sets for‘Jufeng’grape in different transportation chains(normal temperature,cold insulation,and cold chain)were individually constructed as the research samples.Furthermore,compared to other machine learning methods,the SAHP-CatBoost had more accurate results in comprehensive quality feature extraction.In actual transportation chains,the mean absolute error,mean absolute percentage error,and root mean squared error of dynamic comprehensive assessment were limited to 0.0044,1.012%,and 0.0078,respectively.Conclusions:The proposed method is effcient in handling heterogeneous monitoring data and extracting comprehensive quality information of post-harvest grape as a robust shelf-life indicator.It can reasonably guide post-harvest quality management to reduce food loss and improve economic benefts.
