Carbon dioxide partial pressure and its diffusion flux in karst surface aquatic ecosystems:a review

Carbon dioxide(CO_(2))emissions from aquatic ecosystems are an important component of the karst carbon cycle process and also a key indicator for assessing the effect of karst carbon sinks.This paper reviewed the CO_(2)partial pressure(pCO_(2))and its diffusion flux(FCO_(2))in karst surface aquatic ecosystems,mainly rivers,lakes,and reservoirs,and their influencing factors summarized the methods for monitoring CO_(2)emissions in karst aquatic ecosystems and discussed their adaptation conditions in karst areas.The pCO_(2)and FCO_(2)decreased in the order of rivers>reservoirs>lakes,and the values in karst lakes were eventually significantly lower than those in global lakes.The pCO_(2)and FCO_(2)of karst aquatic ecosystems had patterns of variation with diurnal,seasonal,water depth and hydrological cycles,and spatial and temporal hetero-geneity.The sources of CO_(2)in karst waters are influenced by both internal and external sources,and the key spatial and temporal factors affecting the CO_(2)emissions from karst rivers,lakes,and reservoirs were determined in terms of physicochemical indicators,biological factors,and bio-genic elements;additionally,the process of human activity interference on CO_(2)emissions was discussed.Finally,a conceptual model illustrating the impacts of urban devel-opment,agriculture,mining,and dam construction on the CO_(2)emissions at the karst surface aquatic ecosystem is presented.Meanwhile,based on the disadvantages existing in current research,we proposed several important research fields related to CO_(2)emissions from karst surface aquatic ecosystems.

Chemical weathering and CO_2 consumption of a high-erosion-rate karstic river: a case study of the Sanchahe River, southwest China

The Sanchahe River in southwest China is a tributary of the Wujiang River and experiences high erosion rates. Geochemical analysis was conducted on Sanchahe River basin samples collected in the wet and dry seasons of 2014 in order to better understand local chemical weathering processes, anthropogenic influences, and associated CO2 consumption. The samples' total dissolved solid concentrations were found to be significantly higher than that of the global river average. Ca2+was the dominant cation in the samples and accounted for 64 % and73 % of the total cations in the dry and wet seasons,respectively. HCO3-and SO42-were the dominant anions,accounting for 92 % of the total anions. Stoichiometry analyses of the river waters suggested that the water chemistry is controlled by carbonate dissolution by both carbonic and sulfuric acid. The chemical weathering rates of carbonate and silicate evaporites in the Sanchahe River basin were estimated to be approximately 109.2 and 11.0 t/(km2a), respectively, much higher than both the global mean values and the Wujiang River, a typical karstic river.The CO2 consumption by carbonate and silicate weathering are estimated to be 597.4 9 103 and 325.5 9 103mol/(km2a), which are much higher than corresponding values in the Wujiang River, indicating a high erosion rate in the Sanchahe River basin.

Computer-aided diagnosis of retinopathy based on vision transformer

Age-related Macular Degeneration(AMD)and Diabetic Macular Edema(DME)are two com-mon retinal diseases for elder people that may ultimately cause irreversible blindness.Timely and accurate diagnosis is essential for the treatment of these diseases.In recent years,computer-aided diagnosis(CAD)has been deeply investigated and effectively used for rapid and early diagnosis.In this paper,we proposed a method of CAD using vision transformer to analyze optical co-herence tomography(OCT)images and to automatically discriminate AMD,DME,and normal eyes.A classification accuracy of 99.69%was achieved.After the model pruning,the recognition time reached 0.010 s and the classification accuracy did not drop.Compared with the Con-volutional Neural Network(CNN)image classification models(VGG16,Resnet50,Densenet121,and EfficientNet),vision transformer after pruning exhibited better recognition ability.Results show that vision transformer is an improved alternative to diagnose retinal diseases more accurately.

Chemical characteristics of rainwater in karst rural areas,Guizhou Province, Southwest China

The chemical composition of rainwater has been studied in a karst rural area from September 2012 to August 2013 in Guizhou Province,Southwest China.The results indicated that the VWM value of p H was 5.4,varied from 4.6 to 6.9.Ca^(2+)and NH_4^+were the major cations,and SO_4^(2-)was the dominant anion.Neutralization factors show that the acid was mainly neutralized by Ca^(2+),NH_4^+and Mg^(2+).Investigations of correlation coefficients and enrichment factors revealed that Ca^(2+)and Mg^(2+)were mainly crust origins,and NH_4^+was from agriculture and livestock manure.SO_4^(2-)and NO_3^-were mainly from anthropogenic sources.

Android Malware Detection Using Local Binary Pattern and Principal Component Analysis

Nowadays,analysis methods based on big data have been widely used in malicious software detection.Since Android has become the dominator of smartphone operating system market,the number of Android malicious applications are increasing rapidly as well,which attracts attention of malware attackers and researchers alike.Due to the endless evolution of the malware,it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information.In this paper,we propose a novel Android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis,which can visualize malware and detect malware accurately.Also,our method analyzes malware binary directly without any decompiler,sandbox or virtual machines,which avoid time and resource consumption caused by decompiler or monitor in this process.Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.

Sensitive system calls based packed malware variants detection using principal component initialized MultiLayers neural networks

Malware detection has become mission sensitive as its threats spread from computer systems to Internet of things systems.Modern malware variants are generally equipped with sophisticated packers,which allow them bypass modern machine learning based detection systems.To detect packed malware variants,unpacking techniques and dynamic malware analysis are the two choices.However,unpacking techniques cannot always be useful since there exist some packers such as private packers which are hard to unpack.Although dynamic malware analysis can obtain the running behaviours of executables,the unpacking behaviours of packers add noisy information to the real behaviours of executables,which has a bad affect on accuracy.To overcome these challenges,in this paper,we propose a new method which first extracts a series of system calls which is sensitive to malicious behaviours,then use principal component analysis to extract features of these sensitive system calls,and finally adopt multi-layers neural networks to classify the features of malware variants and legitimate ones.Theoretical analysis and real-life experimental results show that our packed malware variants detection technique is comparable with the the state-of-art methods in terms of accuracy.Our approach can achieve more than 95.6\%of detection accuracy and 0.048 s of classification time cost.