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Lithium is an important mineral resource and a critical element in the production of lithium batteries, which are currently in high demand. Oilfield brine has significant value as a raw material for lithium extraction. However, it is often considered a byproduct of oil and gas production and is either abandoned or reinjected underground. Exploration and development of oilfield brines can enhance the economic benefits of oilfields and avoid wasting resources. Current methods for predicting brine distribution rely on geological genetic analysis, which results in low accuracy and reliability. To address this issue, we propose a workflow for lithium brine prediction that uses seismic and logging data. We introduced waveform clustering control and used the mapping relationship between seismic waveforms and well-logging curves to predict high-quality reservoirs based on the electrical and physical properties of lithium brine reservoirs. In this workflow, the seismic waveforms were first clustered using singular value decomposition. The sample sets of well-logging properties were established for the target location. The target properties were divided into high- and low-frequency components and predicted separately. The predicted results of the high-quality reservoirs in the study area were verified using elemental content test results to demonstrate the effectiveness of the method. Our study indicates that well-logging property prediction constrained by waveform clustering can predict lithium brines in a carbonate reservoir. [ABSTRACT FROM AUTHOR]

Background: Pancreatic cancer is a highly lethal disease. The preoperative distinction between pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) remains a clinical challenge., Objective: The goal of this study is to provide clinicians with supportive advice and avoid overtreatment by constructing a convolutional neural network (CNN) classifier to automatically identify pancreatic cancer using computed tomography (CT) images., Methods: We construct a CNN model using a dataset of 6,173 CT images obtained from 107 pathologically confirmed pancreatic cancer patients at Shanghai Changhai Hospital from January 2017 to February 2022. We divide CT slices into three categories namely, SCN, MCN, and no tumor, to train the DenseNet201-based CNN model with multi-head spatial attention mechanism (MSAM-DenseNet201). The attention module enhances the network's attention to local features and effectively improves the network performance. The trained model is applied to process all CT image slices and finally realize the two categories classification of MCN and SCN patients through a joint voting strategy., Results: Using a 10-fold cross validation method, this new MSAM-DenseNet201 model achieves a classification accuracy of 92.52%, a precision of 92.16%, a sensitivity of 92.16%, and a specificity of 92.86%, respectively., Conclusions: This study demonstrates the feasibility of using a deep learning network or classification model to help diagnose MCN and SCN cases. This, the new method has great potential for developing new computer-aided diagnosis systems and applying in future clinical practice.

Kongyu Gold Mining Area is located in the northern margin of the Sichuan-Yunnan tectonic belt. This area has gone through two large tectonic cycles of Mesoproterozoic, Neoproterozoic and Phanerozoic. The strata are accompanied by a large number of tensile cracks, shear cracks and microfissures. The voidage, permeability and transitional effect of the rock are increased, and the transformation between surface water and groundwater is enhanced. On the other hand, tectonic movement controls the distributions and allocations of elements in the earth's crust, resulting in directional geochemical changes in crustal materials and rules. Driven by the tectonic force, the elements are reallocated. The ion content and ion compressibility of those elements increase and decrease inversely. The manifestations of rocks and minerals under directional pressure can be characterized by the flow and puncture of plastic minerals and the fragmentation of hard and brittle minerals. In the process of leaching, the pollutant elements precipitate from the deposits of different granular ores, and most are absorbed by the soil column; thus, the mass fraction of polluting elements in the soil column obviously increases. The variability is the largest when the leachate passes through the fine ore. The rate of change of the element mass fraction from high to low is Ni, Zn, Cu, Cd, Pb, As and Cr. A small part migrated from the soil column to the collection fluid, and the leaching effect is related to the rate of change of soil mass fraction, the ion radius of elements and the compressibility.