P-SV wave elastic impedance and fluid identification factor in weakly anisotropic media

The P-SV wave reflection coefficients in VTI and HTI media were obtained by approximation of the Jilek（2002a and b）equation in orthotropic anisotropic media.An approximate equation for P-SV wave elastic impedance can be derived from the combination of the new coefficients with S-wave elastic impedance（Duffaut et al.,2000）.On this basis, the fluid identification factor in weakly anisotropic media was constructed and used to identify the Castagna and Smith（1994）lithologic combination and achieved good results. Finally,we specifically analyzed the anisotropic parameter impacts P-SV wave elastic impedance and fluid factor trends.

Assessment of early factors for identification or prediction severe acute pancreatitis in pregnancy

BACKGROUND Acute pancreatitis in pregnancy(APIP)is a rare and serious condition,and severe APIP(SAPIP)can lead to pancreatic necrosis,abscess,multiple organ dysfunction,and other adverse maternal and infant outcomes.Therefore,early identification or prediction of SAPIP is important.AIM To assess factors for early identification or prediction of SAPIP.METHODS The clinical data of patients with APIP were retrospectively analyzed.Patients were classified with mild acute pancreatitis or severe acute pancreatitis,and the clinical characteristics and laboratory biochemical indexes were compared between the two groups.Logical regression and receiver operating characteristic curve analyses were performed to assess the efficacy of the factors for identification or prediction of SAPIP.RESULTS A total of 45 APIP patients were enrolled.Compared with the mild acute pancreatitis group,the severe acute pancreatitis group had significantly increased(P<0.01)heart rate(HR),hemoglobin,neutrophil ratio(NEUT%),and neutrophil–lymphocyte ratio(NLR),while lymphocytes were significantly decreased(P<0.01).Logical regression analysis showed that HR,NEUT%,NLR,and lymphocyte count differed significantly(P<0.01)between the groups.These may be factors for early identification or prediction of SAPIP.The area under the curve of HR,NEUT%,NLR,and lymphocyte count in the receiver operating characteristic curve analysis was 0.748,0.732,0.821,and 0.774,respectively.The combined analysis showed that the area under the curve,sensitivity,and specificity were 0.869,90.5%,and 70.8%,respectively.CONCLUSION HR,NEUT%,NLR,and lymphocyte count can be used for early identification or prediction of SAPIP,and the combination of the four factors is expected to improve identification or prediction of SAPIP.

Identifying influencing factors and characterizing key issues in urban sustainable development capacity through machine learning

In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.

Impact factors identification of spatial heterogeneity of herbaceous plant diversity on five southern islands of Miaodao Archipelago in North China

Herbaceous plants are widely distributed on islands and where they exhibit spatial heterogeneity.Accurately identifying the impact factors that drive spatial heterogeneity can reveal typical island biodiversity patterns.Five southern islands in the Miaodao Archipelago,North China were studied herein.The spatial distribution of herbaceous plant diversity on these islands was analyzed,and the impact factors and their degree of impact on spatial heterogeneity were identified using CCA ordination and ANOVA.The results reveal 114 herbaceous plant species,belonging to 94 genera from 34 families in the50 plots sampled.The total species numbers on different islands were significantly positively correlated with island area,and the average a diversity was correlated with human activities,while the(3 diversity among islands was more affected by island area than mutual distances.Spatial heterogeneity within islands indicated that the diversities were generally high in areas with higher altitude,slope,total nitrogen,total carbon,and canopy density,and lower moisture content,pH,total phosphorus,total potassium,and aspect.Among the environmental factors,pH,canopy density,total K,total P,moisture content,altitude,and slope had significant gross effects,but only canopy density exhibited a significant net effect.Terrain affected diversity by restricting plantation,plantation in turn influenced soil properties and the two together affected diversity.Therefore,plantation was ultimately the fundamental driving factor for spatial heterogeneity in herbaceous plant diversity on the five islands.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Fluid sensitivity study of elastic parameters in lowmedium porosity and permeability reservoir rocks

In this article,based on the acoustic measurements of core samples obtained from the low to medium porosity and permeability reservoirs in the WXS Depression,the densities and P and S wave velocities of these core samples were obtained.Then based on these data,a series of elastic parameters were computed.From the basic theory and previous pore fluid research results,we derived a new fluid identification factor（F）.Using the relative variations,Ag/w and Ao/w,of the elastic parameters between gas and water saturated samples and between oil and water saturated samples,λρ,σHSFIF,Kρ,λρ-2μρ,and F as quantitative indicators,we evaluate the sensitivity of the different fluid identification factors to identify reservoir fluids and validate the effects by crossplots.These confirm that the new fluid identification factor（F） is more sensitive for distinguishing oil and water than the traditional method and is more favorable for fliud identification in low to medium porosity and permeability reservoirs.

Genetic types and geochemicai characteristics of natural gases in the Jiyang Depression,China

Natural gases were widely distributed in the Jiyang Depression with complicated component composition, and it is difficult to identify their genesis. Based on investigation of gas composition, carbon isotope ratios, light hydrocarbon properties, as well as geological analysis, natural gases in the Jiyang Depression are classified into two types, one is organic gas and the other is abiogenic gas. Abiogenic gas is mainly magmatogenic or mantlederived CO2. Organic gases are further divided into coaltype gas, oil-type gas, and biogas according to their kero- gen types and formation mechanisms. The oil-type gases are divided into mature oil-type gas （oil-associated gas） and highly mature oil-type gas. The highly mature oil-type gases can be subdivided into oil-cracking gas and kerogen thermal degradation gas. Identification factors for each kind of hydrocarbon gas were summarized. Based on genesis analysis results, the genetic types of gases buried in different depths were discussed. Results showed that shallow gases （〈1,500 m） are mainly mature oil-type gases, biogas, or secondary gases. Secondary gases are rich in methane because of chromatographic separation during migration and secondary biodegradation. Secondary biodegradation leads to richness of heavy carbon isotope ratios in methane and propane. Genesis of middle depth gases （1,500-3,500 m） is dominated by mature oil-type gases.Deep gases （3,500-5,500 m） are mainly kerogen thermal degradation gas, oil-cracking gas, and coal-type gas.

Effect of Exposure to Trace Elements in the Soil on the Prevalence of Neural Tube Defects in a High-Risk Area of China

Objective Our objective is to build a model that explains the association between the exposure to trace elements in the soil and the risk of neural tube defects. Methods We built a function with different parameters to describe the effects of trace elements on neural tube defects. The association between neural tube defects and trace element levels was transformed into an optimization problem using the maximum likelihood method. Results Tin, lead, nickel, iron, copper, and aluminum had typical layered effects （dosage effects） on the prevalence of neural tube defects. Arsenic, selenium, zinc, strontium, and vanadium had no effect, and molybdenum had one threshold value that affected the prevalence of birth defects. Conclusion As an exploratory research work, our model can be used to determine the direction of the effect of the trace element content of cultivated soil on the risk of neural tube defects, which shows the clues by the dosage effect of their toxicological characteristics. Based on our findings, future biogeochemical research should focus on the direct effects of trace elements on human health.

Suitability Evaluation of Rural Settlements Based on Accessibility of Production and Living： A Case Study of Tingzu Town in Hubei Prov- ince of China

Rural settlements are the main carriers of agriculture, rural areas and farmers; thus, optimizing the production and living space of rural settlements is highly significant to rural development. Taking the effective allocation of resources as the starting point, a suitability evaluation system of rural settlements, based on accessibility of production and living, was proposed in this study to provide scientific basis for the optimization of production and living space. The accessibility of production and living was measured by an im- proved two-step floating catchment area method, which considered proximity and availability based on the inclination of rural residents. The suitability evaluation system consisted of traditional suitability evaluation and newly proposed limiting factor identification based on the loss score proportion of suitability. Tingzu Town of Hubei Province, China, was chosen as the case study area. Based on the re- suits of the suitability evaluation system, corresponding suggestions on rural land consolidation, industry division, as well as the layout of health care and education facilities were proposed to optimize the production and living space of rural settlements in Tingzu Town. It is found that the suitability evaluation based on accessibility of production and living is more scientific and accurate than the traditional ones which significantly overestimate production and living convenience. Moreover, the limiting factor identification can help us put forward suggestions according to local conditions and bring about the highly targeted optimization of production and living space of rural settlements.

An efficient equivariant adaptive separation via independence algorithm for acoustical source separation and identification

To balance the convergence rate and steadystate error of blind source separation(BSS) algorithms, an efficient equivariant adaptive separation via independence(Efficient EASI) algorithm is proposed based on separating indicator, which was derived from the convergence condition of EASI, and can be used to evaluate the separation degree of separated signals. Furthermore, a nonlinear monotone increasing function between suitable step sizes and separating indicator is constructed to adaptively adjust step sizes, and forgetting factor is employed to weaken effects of data at the initial stage. Numerical case studies and experimental studies on a test bed with shell structures are provided to validate the efficiency improvement of the proposed method. This study can benefit for vibration & acoustic monitoring and control, and machinery condition monitoring and fault diagnosis.

A Review on the Driving Mechanisms of Ecosystem Services Change

Ecosystem services have rapidly changed at the global and regional scales in recent years. Exploring the driving mechanisms of ecosystem services change and projecting future change are of increasing importance to inform policy and decision-making options for ecosystem conservation and sustainable use. Although some research has analyzed the influences of land use or climate changes on ecosystem services, a systematic review on the mechanisms of ecosystem services change has not been carried out so far. This work elaborated on the mechanisms of ecosystem services change based on a literature review, and reached four main conclusions.(1)Climate change and land use jointly determine the ecosystem services change through complex and interacting pathways.(2) Whereas the present research progresses mainly focus on the identification of a single influencing factor, they fail in the determination of multiple influencing factors.(3) Although multi-scenario simulations based on remote sensing and climate models are the main means used to predict future changes in ecosystem services,clarifying the interactive mechanisms of multiple factors is the precondition for future projection of ecosystem services change;(4) Therefore, future research should strengthen the analysis and simulation of the effects of human activities on ecosystem services, especially the development of technology to detect the dynamic responses of ecosystem services to major ecological projects, which is crucial to the selection of restoration measures and the regional arrangement of ecosystem conservation projects.