The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated...The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.展开更多
With the integration of ultrafast reflectivity and polarimetry probes,we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni(111)single crystals.The carrier relaxation time within...With the integration of ultrafast reflectivity and polarimetry probes,we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni(111)single crystals.The carrier relaxation time within the linear excitation range reveals that electron-phonon coupling and dissipation of photon energy into the bulk of the crystal take tens of picoseconds.On the other hand,the observed spin dynamics indicate a longer time of about 120 ps.To further understand how the lattice degree of freedom is coupled with these dynamics may require the integration of an ultrafast diffraction probe.展开更多
Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of tra...Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.展开更多
Background:Recognizing sport-related concussion(SRC)is challenging and relies heavily on subjective symptom reports.An objective,biological marker could improve recognition and understanding of SRC.There is emerging e...Background:Recognizing sport-related concussion(SRC)is challenging and relies heavily on subjective symptom reports.An objective,biological marker could improve recognition and understanding of SRC.There is emerging evidence that salivary micro-ribonucleic acids(miRNAs)may serve as biomarkers of concussion;however,it remains unclear whether concussion-related miRNAs are impacted by exercise.We sought to determine whether40 miRNAs previously implicated in concussion pathophysiology were affected by participation in a variety of contact and non-contact sports.Our goal was to refine a miRNA-based tool capable of identifying athletes with SRC without the confounding effects of exercise.Methods:This case-control study harmonized data from concussed and non-concussed athletes recruited across 10 sites.Levels of salivary miRNAs within 455 samples from 314 individuals were measured with RNA sequencing.Within-subjects testing was used to identify and exclude miRNAs that changed with either(a)a single episode of exercise(166 samples from 83 individuals)or(b)season-long participation in contact sports(212 samples from 106 individuals).The miRNAs that were not impacted by exercise were interrogated for SRC diagnostic utility using logistic regression(172 samples from 75 concussed and 97 non-concussed individuals).Results:Two miRNAs(miR-532-5p and miR-182-5p)decreased(adjusted p<0.05)after a single episode of exercise,and 1 miRNA(miR-4510)increased only after contact sports participation.Twenty-three miRNAs changed at the end of a contact sports season.Two of these miRNAs(miR-26b-3p and miR-29c-3p)were associated(R>0.50;adjusted p<0.05)with the number of head impacts sustained in a single football practice.Among the 15 miRNAs not confounded by exercise or season-long contact sports participation,11 demonstrated a significant difference(adjusted p<0.05)between concussed and non-concussed participants,and 6 displayed moderate ability(area under curve>0.70)to identify concussion.A single ratio(miR-27a-5p/miR-30a-3p)displayed the highest accuracy(AUC=0.810,sensitivity=82.4%,specificity=73.3%)for differentiating concussed and non-concussed participants.Accuracy did not differ between participants with SRC and non-SRC(z=0.5,p=0.60).Conclusion:Salivary miRNA levels may accurately identify SRC when not confounded by exercise.Refinement of this approach in a large cohort of athletes could eventually lead to a non-invasive,sideline adjunct for SRC assessment.展开更多
Gestational diabetes mellitus(GDM)is the most prevalent metabolic disorder during pregnancy.Women diagnosed with GDM have a substantially greater risk of developing type 2 diabetes within 5-10 years after delivery,and...Gestational diabetes mellitus(GDM)is the most prevalent metabolic disorder during pregnancy.Women diagnosed with GDM have a substantially greater risk of developing type 2 diabetes within 5-10 years after delivery,and the risk is increased by excess body weight.Uncontrolled hyperglycemia during pregnancy is potentially harmful to both mother and fetus,resulting in a greater need for Caesarian-section deliveries,delivery of larger infants with more excess body fat,a greater risk of infant death and stillbirth,and an elevated risk of infant hypoglycemia immediately after birth.Fortunately,engaging in physical activity prior to and during pregnancy may lower the risk of developing GDM.Pregnant women should also be advised how to safely increase their physical activity during pregnancy and the postpartum period.An initial approach to becoming more physically active can simply be to encourage women to incorporate more unstructured physical activity into daily living,both before and during pregnancy.Giving women an appropriate exercise prescription can encourage them to participate in physical activity safely and effectively throughout pregnancy to prevent and/or manage GDM.Engaging in 30 min of moderate intensity physical activity on most,if not all,days of the week has been adopted as a recommendation for all pregnant women.展开更多
The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced ...The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion.To achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV systems.Existing research have made great efforts in investigating different aspects of applying AI in AV development.However,few studies have offered the research community a thorough examination of current practices in implementing AI in AVs.Thus,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue.Specifically,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision making.It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation.Based on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected AVs.This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.展开更多
Background:Health-related fitness knowledge(HRFK)has been an essential concept for many health and physical education programs.There has been limited understanding and longitudinal investigation on HRFK growth.This lo...Background:Health-related fitness knowledge(HRFK)has been an essential concept for many health and physical education programs.There has been limited understanding and longitudinal investigation on HRFK growth.This longitudinal study examined HRFK growth and its individual-and school-level correlates in middle school years under 1 curriculum condition:Five for Life.Methods:Participants were 12,044 students from 47 middle schools.Data were collected at both individual/participant and school/institution levels.Individual-level variables included gender,grade,and HRFK test scores.School-level variables included percentage of students receiving free and reduced meals(FARM),student-to-faculty ratio for physical education,and school academic performance(SAP).We used hierarchical linear modeling to examine HRFK 3-year growth in relation to individual-and school-level correlates.Results:The average HRFK score at 6th grade for females was 42.81%±1.32%.The predicted HRFK growth was 17.06%±1.02%per year,holding other factors constant.A 1-standard deviation increase in FARM correlated with a 14.68%-point decrease in predicted test score(p=0.02).A 1-standard deviation increase in SAP was associated with an 11.90%-point increase in HRFK score.Males had a significantly lower growth rate than females during the middle school years(0.78%/year,p=0.02).Conclusion:The result showed that both individual-and school-level variables such as gender,FARM,and SAP influenced HRFK growth.Educators should heed gender differences in growth curves and recognize the correlates of school-level variables.展开更多
Liver cancer is one of the most malignant cancers. It is reported that 600 000 patients died from liver cancer every year. [1,2] Hepatocellular carcinoma (HCC) is a particular problem because symptoms are not evident ...Liver cancer is one of the most malignant cancers. It is reported that 600 000 patients died from liver cancer every year. [1,2] Hepatocellular carcinoma (HCC) is a particular problem because symptoms are not evident until the disease has progressed and hepatitis B, which is prominent specific regions of Asia, is a common precursor of the disease. There are many liver cancer展开更多
In this paper, an interactive image enhancement (HE) technique based on fuzzy relaxation is presented, which allows the user to select different intensity levels for enhancement and intermit the enhancement process ...In this paper, an interactive image enhancement (HE) technique based on fuzzy relaxation is presented, which allows the user to select different intensity levels for enhancement and intermit the enhancement process according to his/her preference in applications. First, based on an analysis of the convergence of a fuzzy relaxation algorithm for image contrast enhancement, an improved version of this algorithm, which is called FuzzIIE Method 1, is suggested by deriving a relationship between the convergence regions and the parameters in the transformations defined in the algorithm. Then a method called FuzzIIE Method 2 is introduced by using a different fuzzy relaxation function, in which there is no need to re-select the parameter values for interactive image enhancement. Experimental results are presented demonstrating the enhancement capabilities of the proposed methods under different conditions.展开更多
Soil organic matter (SOM) plays an important role in maintaining vegetation cover and thus mitigating land erosion of fragile terrestrial ecosystems such as in the Northern Ordos Plateau of China (NOPC). However, ...Soil organic matter (SOM) plays an important role in maintaining vegetation cover and thus mitigating land erosion of fragile terrestrial ecosystems such as in the Northern Ordos Plateau of China (NOPC). However, little information is available on whether and how SOM varies spatially as an intrinsic characteristic of landform in NOPC. The objective of this study was to examine the spatial associations of SOM with landform and vegetation cover. The study was conducted in a 23,000-km2 area within NOPC because this area has landforms of mobile dunes (MD), flat dunes (FD), grassy sandy land (GSL), flat sandy bedrocks (FSB), and swamps and salt lakes (SW), which are typical landforms in semiarid ecosystems. SOM was determined using a standard laboratory analysis method for 5 cm topsoil samples collected at 72 locations across the study area. In addition, the 250 m Multitem- poral Moderate Resolution Imaging Spectroradiometer (MODIS) imageries taken in the period from August 2006 to August 2010 were used to extract Normalized Difference Vegetation Index (NDVI) which in turn was used as the surrogate of vegetation cover. Classic and geostatistical methods were used to compare SOM concentration across different landforms. The results indicated that an area with a greater value for NDVI (i.e. better vegetation cover) tended to have a higher SOM concentration regardless of the landform types. However, the association between SOM and NDVI varied from one landform to another. The SW and GSL had a highest SOM concentration, while MD had a lowest concentration. For the study area as a whole and the FD, GSL, and MD, SOM was found to be the sole function of NDVI, whereas, for the FSB, SOM was influenced by several intrinsic variables, namely ground surface altitude, slope, and aspect, as well as NDVI. SOM for the SW landform was found to be a function of NDVI. Furthermore, SOM and NDVI exhibited a consistent spatial pattern of increasing from north to south and from west to east. The highest SOM concentration of 3.5% occurred along an east-westward belt, which is adjacent to water pathways, in the mid part of the study area.展开更多
In the Tuoshi oilfield, located in the Cenozoic Jianghan Basin of southeastern China, there have been found hydrocarbon reservoirs hosted in lacustrine sandstones of the Eogene Xingouzui Formation. The main diagenetic...In the Tuoshi oilfield, located in the Cenozoic Jianghan Basin of southeastern China, there have been found hydrocarbon reservoirs hosted in lacustrine sandstones of the Eogene Xingouzui Formation. The main diagenetic features identified in these sandstones include the dissolution of detrital K\|feldspar and albite grains, the precipitation of quartz as overgrowths and/or cements, and the precipitation and/or transformation of clay minerals. These diagenetic features were interpreted to have occurred in early, intermediate and late stages, based on the burial depth. The kinetics of fluid\|mineral reactions and the concentrations of aqueous species at each stage of diagenesis were simulated numerically for these lacustrine sandstones, using a quasi\|stationary state approximation that incorporates simultaneous chemical reactions in a time\|space continuum. During the early diagenetic stage, pore fluid was weakly acidic, which resulted in dissolution of K\|feldspar and albite and, therefore, led to the release of K\++, Na\++, Al\+ 3+ and SiO\- 2(aq) into the diagenetic fluid. The increased K\++, Na\++, Al\+ 3+ and SiO\- 2(aq) concentrations in the diagenetic fluid caused the precipitation of quartz, kaolinite and illite. At the beginning of the intermediate diagenetic stage the concentration of H\++ was built up due to the decomposition of organic matter, which was responsible for further dissolution of K\|feldspar and albite and precipitation of quartz, kaolinite, and illite. During the late diagenetic stage, the pore fluid was weakly alkaline, K\|feldspar became stable and was precipitated with quartz and clay minerals. When the burial depth was greater than 3000 m, the pore fluids became supersaturated with respect to albite, but undersaturated with respect to quartz, resulting in the precipitation of albite and the dissolution of quartz. The diagenetic reactions forecasted in the numerical modeling closely matched the diagenetic features identified by petrographic examination, and therefore, can help us to gain a better understanding of the diagenetic processes and associated porosity evolution in sandstone reservoirs .展开更多
文摘The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.
基金Project supported by the National Key R&D Program of China (Grant Nos. 2022YFA1604402 and 2022YFA1604403)the National Natural Science Foundation of China (NSFC) (Grant No. 11721404)+3 种基金the Shanghai Rising-Star Program (Grant No. 21QA1406100)the Technology Innovation Action Plan of the Science and Technology Commission of Shanghai Municipality (Grant No. 20JC1416000)support by the Air Force Office of Scientific Research (AFOSR) (Grant No. FA9550-20-10139)the Texas A&M Engineering Experimental Station (TEES)
文摘With the integration of ultrafast reflectivity and polarimetry probes,we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni(111)single crystals.The carrier relaxation time within the linear excitation range reveals that electron-phonon coupling and dissipation of photon energy into the bulk of the crystal take tens of picoseconds.On the other hand,the observed spin dynamics indicate a longer time of about 120 ps.To further understand how the lattice degree of freedom is coupled with these dynamics may require the integration of an ultrafast diffraction probe.
文摘Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.
基金supported by a sponsored research agreement between Quadrant Biosciences and the Penn State College of Medicine to ACLsupported by the National Center for Advancing Translational Sciences(Grant KL2 TR002015,Grant UL1 TR002014)。
文摘Background:Recognizing sport-related concussion(SRC)is challenging and relies heavily on subjective symptom reports.An objective,biological marker could improve recognition and understanding of SRC.There is emerging evidence that salivary micro-ribonucleic acids(miRNAs)may serve as biomarkers of concussion;however,it remains unclear whether concussion-related miRNAs are impacted by exercise.We sought to determine whether40 miRNAs previously implicated in concussion pathophysiology were affected by participation in a variety of contact and non-contact sports.Our goal was to refine a miRNA-based tool capable of identifying athletes with SRC without the confounding effects of exercise.Methods:This case-control study harmonized data from concussed and non-concussed athletes recruited across 10 sites.Levels of salivary miRNAs within 455 samples from 314 individuals were measured with RNA sequencing.Within-subjects testing was used to identify and exclude miRNAs that changed with either(a)a single episode of exercise(166 samples from 83 individuals)or(b)season-long participation in contact sports(212 samples from 106 individuals).The miRNAs that were not impacted by exercise were interrogated for SRC diagnostic utility using logistic regression(172 samples from 75 concussed and 97 non-concussed individuals).Results:Two miRNAs(miR-532-5p and miR-182-5p)decreased(adjusted p<0.05)after a single episode of exercise,and 1 miRNA(miR-4510)increased only after contact sports participation.Twenty-three miRNAs changed at the end of a contact sports season.Two of these miRNAs(miR-26b-3p and miR-29c-3p)were associated(R>0.50;adjusted p<0.05)with the number of head impacts sustained in a single football practice.Among the 15 miRNAs not confounded by exercise or season-long contact sports participation,11 demonstrated a significant difference(adjusted p<0.05)between concussed and non-concussed participants,and 6 displayed moderate ability(area under curve>0.70)to identify concussion.A single ratio(miR-27a-5p/miR-30a-3p)displayed the highest accuracy(AUC=0.810,sensitivity=82.4%,specificity=73.3%)for differentiating concussed and non-concussed participants.Accuracy did not differ between participants with SRC and non-SRC(z=0.5,p=0.60).Conclusion:Salivary miRNA levels may accurately identify SRC when not confounded by exercise.Refinement of this approach in a large cohort of athletes could eventually lead to a non-invasive,sideline adjunct for SRC assessment.
文摘Gestational diabetes mellitus(GDM)is the most prevalent metabolic disorder during pregnancy.Women diagnosed with GDM have a substantially greater risk of developing type 2 diabetes within 5-10 years after delivery,and the risk is increased by excess body weight.Uncontrolled hyperglycemia during pregnancy is potentially harmful to both mother and fetus,resulting in a greater need for Caesarian-section deliveries,delivery of larger infants with more excess body fat,a greater risk of infant death and stillbirth,and an elevated risk of infant hypoglycemia immediately after birth.Fortunately,engaging in physical activity prior to and during pregnancy may lower the risk of developing GDM.Pregnant women should also be advised how to safely increase their physical activity during pregnancy and the postpartum period.An initial approach to becoming more physically active can simply be to encourage women to incorporate more unstructured physical activity into daily living,both before and during pregnancy.Giving women an appropriate exercise prescription can encourage them to participate in physical activity safely and effectively throughout pregnancy to prevent and/or manage GDM.Engaging in 30 min of moderate intensity physical activity on most,if not all,days of the week has been adopted as a recommendation for all pregnant women.
基金supported by the FundamentalResearch Funds for the Central Universities(2662019QD002)
文摘The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion.To achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV systems.Existing research have made great efforts in investigating different aspects of applying AI in AV development.However,few studies have offered the research community a thorough examination of current practices in implementing AI in AVs.Thus,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue.Specifically,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision making.It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation.Based on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected AVs.This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.
基金supported by the Focused on Community Health Foundation(#16-353)。
文摘Background:Health-related fitness knowledge(HRFK)has been an essential concept for many health and physical education programs.There has been limited understanding and longitudinal investigation on HRFK growth.This longitudinal study examined HRFK growth and its individual-and school-level correlates in middle school years under 1 curriculum condition:Five for Life.Methods:Participants were 12,044 students from 47 middle schools.Data were collected at both individual/participant and school/institution levels.Individual-level variables included gender,grade,and HRFK test scores.School-level variables included percentage of students receiving free and reduced meals(FARM),student-to-faculty ratio for physical education,and school academic performance(SAP).We used hierarchical linear modeling to examine HRFK 3-year growth in relation to individual-and school-level correlates.Results:The average HRFK score at 6th grade for females was 42.81%±1.32%.The predicted HRFK growth was 17.06%±1.02%per year,holding other factors constant.A 1-standard deviation increase in FARM correlated with a 14.68%-point decrease in predicted test score(p=0.02).A 1-standard deviation increase in SAP was associated with an 11.90%-point increase in HRFK score.Males had a significantly lower growth rate than females during the middle school years(0.78%/year,p=0.02).Conclusion:The result showed that both individual-and school-level variables such as gender,FARM,and SAP influenced HRFK growth.Educators should heed gender differences in growth curves and recognize the correlates of school-level variables.
基金supported by grants from the National Natural Science Foundation of China(3070078)a National S&T Major Project(2012ZX10002017)+1 种基金the National Basic Research Program of China(973 Program)(2009CB522403)Zhejiang Medical Research Funding(2008B079)
文摘Liver cancer is one of the most malignant cancers. It is reported that 600 000 patients died from liver cancer every year. [1,2] Hepatocellular carcinoma (HCC) is a particular problem because symptoms are not evident until the disease has progressed and hepatitis B, which is prominent specific regions of Asia, is a common precursor of the disease. There are many liver cancer
文摘In this paper, an interactive image enhancement (HE) technique based on fuzzy relaxation is presented, which allows the user to select different intensity levels for enhancement and intermit the enhancement process according to his/her preference in applications. First, based on an analysis of the convergence of a fuzzy relaxation algorithm for image contrast enhancement, an improved version of this algorithm, which is called FuzzIIE Method 1, is suggested by deriving a relationship between the convergence regions and the parameters in the transformations defined in the algorithm. Then a method called FuzzIIE Method 2 is introduced by using a different fuzzy relaxation function, in which there is no need to re-select the parameter values for interactive image enhancement. Experimental results are presented demonstrating the enhancement capabilities of the proposed methods under different conditions.
基金supported by the National Natural Science Foundation of China(51139002 and 51069005)the Inner Mongolia Agricultural University Innovation Team Building Program (NDTD 2010-6)+1 种基金the Inner Mongolia Scientific and Technology Bureau (20090516)the Chinese Ministry of Science and Technology (2010DFA71460)
文摘Soil organic matter (SOM) plays an important role in maintaining vegetation cover and thus mitigating land erosion of fragile terrestrial ecosystems such as in the Northern Ordos Plateau of China (NOPC). However, little information is available on whether and how SOM varies spatially as an intrinsic characteristic of landform in NOPC. The objective of this study was to examine the spatial associations of SOM with landform and vegetation cover. The study was conducted in a 23,000-km2 area within NOPC because this area has landforms of mobile dunes (MD), flat dunes (FD), grassy sandy land (GSL), flat sandy bedrocks (FSB), and swamps and salt lakes (SW), which are typical landforms in semiarid ecosystems. SOM was determined using a standard laboratory analysis method for 5 cm topsoil samples collected at 72 locations across the study area. In addition, the 250 m Multitem- poral Moderate Resolution Imaging Spectroradiometer (MODIS) imageries taken in the period from August 2006 to August 2010 were used to extract Normalized Difference Vegetation Index (NDVI) which in turn was used as the surrogate of vegetation cover. Classic and geostatistical methods were used to compare SOM concentration across different landforms. The results indicated that an area with a greater value for NDVI (i.e. better vegetation cover) tended to have a higher SOM concentration regardless of the landform types. However, the association between SOM and NDVI varied from one landform to another. The SW and GSL had a highest SOM concentration, while MD had a lowest concentration. For the study area as a whole and the FD, GSL, and MD, SOM was found to be the sole function of NDVI, whereas, for the FSB, SOM was influenced by several intrinsic variables, namely ground surface altitude, slope, and aspect, as well as NDVI. SOM for the SW landform was found to be a function of NDVI. Furthermore, SOM and NDVI exhibited a consistent spatial pattern of increasing from north to south and from west to east. The highest SOM concentration of 3.5% occurred along an east-westward belt, which is adjacent to water pathways, in the mid part of the study area.
基金This project was funded by the National Natural science Foundation of China (GrantNo .4 91 330 80 ) and by the Trans centuryTraining Program me Foudation for the Talentssponsored by the State Education Commission of China
文摘In the Tuoshi oilfield, located in the Cenozoic Jianghan Basin of southeastern China, there have been found hydrocarbon reservoirs hosted in lacustrine sandstones of the Eogene Xingouzui Formation. The main diagenetic features identified in these sandstones include the dissolution of detrital K\|feldspar and albite grains, the precipitation of quartz as overgrowths and/or cements, and the precipitation and/or transformation of clay minerals. These diagenetic features were interpreted to have occurred in early, intermediate and late stages, based on the burial depth. The kinetics of fluid\|mineral reactions and the concentrations of aqueous species at each stage of diagenesis were simulated numerically for these lacustrine sandstones, using a quasi\|stationary state approximation that incorporates simultaneous chemical reactions in a time\|space continuum. During the early diagenetic stage, pore fluid was weakly acidic, which resulted in dissolution of K\|feldspar and albite and, therefore, led to the release of K\++, Na\++, Al\+ 3+ and SiO\- 2(aq) into the diagenetic fluid. The increased K\++, Na\++, Al\+ 3+ and SiO\- 2(aq) concentrations in the diagenetic fluid caused the precipitation of quartz, kaolinite and illite. At the beginning of the intermediate diagenetic stage the concentration of H\++ was built up due to the decomposition of organic matter, which was responsible for further dissolution of K\|feldspar and albite and precipitation of quartz, kaolinite, and illite. During the late diagenetic stage, the pore fluid was weakly alkaline, K\|feldspar became stable and was precipitated with quartz and clay minerals. When the burial depth was greater than 3000 m, the pore fluids became supersaturated with respect to albite, but undersaturated with respect to quartz, resulting in the precipitation of albite and the dissolution of quartz. The diagenetic reactions forecasted in the numerical modeling closely matched the diagenetic features identified by petrographic examination, and therefore, can help us to gain a better understanding of the diagenetic processes and associated porosity evolution in sandstone reservoirs .