Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations.We evaluated the performance of the CNN model in terms of its vertical and spatial distribution,as well as seasonal variation of OSSS estimation.Results demonstrate that the CNN model accurately estimates the most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS.However,the estimation accuracy of the CNN model varies with depth,with the most challenging depth being approximately 70 m,corresponding to the halocline layer.Validations of the CNN model’s accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes.The results show that the CNN model effectively captures the seasonal variability of salinity,demonstrating its high performance in salinity estimation using sea surface data.Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers,while sea surface height anomaly plays a more significant role in deeper layers.These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques.

Quality Evaluation of Threshing and Redrying Process Based on Analytic Hierarchy Process

Improving the stability of the homogenization process to achieve the homogeneity of tobacco products is one of important targets for the redrying industry.According to the specification for threshing and redrying process,a total of 14 indicators in three categories that affect the quality of the threshing and redrying process were selected.Using analytic hierarchy process,combined with expert experiences,a judgment matrix was constructed to conduct consistency test.The weights of indices in production were obtained.This will help in evaluating the actual production quality,finding the weak links of process and adjusting the parameters of the corresponding links in a targeted manner,thereby improving the quality of production process.

Does burning fat make tumor immune hot? Discovery of cD47 overexpression by radiation induced fatty acid oxidation

Although extensively studied,it is unknown what is the major cellular energy driving tumor metastasis after anti-cancer radiotherapy.Metabolic reprogramming is one of the fundamental hallmarks in carcinogenesis and tumor progression featured with the increased glycolysis in solid tumors.However,accumulating evidence indicates that in addition to the rudimentary glycolytic pathway,tumor cells are capable of reactivating mitochondrial OxPHOS under genotoxic stress condition to meet the increasing cellular fuel demand for repairing and surviving anti-cancer radiation.Such dynamic metabolic rewiring may play a key role in cancer therapy resistance and metastasis.Interestingly,data from our group and others have demonstrated that cancer cells can re-activate mitochondrial oxidative respiration to boost an annexing energy to meet the increasing cellular fuel demand for tumor cells surviving genotoxic anti-cancer therapy with metastatic potential.