Glacial debris flow susceptibility mapping based on combined models in the Parlung Tsangpo Basin,China

Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".

Trends of Inequalities in the Use of Long-Term Reversible Contraceptives in Burkina Faso between 2010 and 2015

Background: Efforts have been made in Burkina Faso, a French-speaking country, since 2010 to improve healthcare access and provide affordable contraceptive methods to women. With the increasing prevalence of modern contraceptives in Burkina Faso, it is important to examine the socio-demographic factors that contribute to this new pattern of contraceptive use. This study aims to analyze the changes in socio-demographic factors associated with long-term contraceptive use and provide scientific evidence to guide policy development and action planning in family planning. Data and Methods: We utilized data from the 2010 Demographic and Health Survey, which included 17,087 women aged 15 - 49 years, and the 2015 Demographic and Health Module, which included 11,504 women in the same age group. For the analysis of contraceptive use, we focused on women who were in need of contraception (either met or unmet), of reproductive age, non-pregnant, and either married or sexually active but not married. We included users of modern reversible methods and excluded non-users, as well as users of traditional or permanent methods. Results: Our findings revealed a high prevalence of long-term contraceptive use across all categories;however, certain challenges were identified, such as lower levels of information about contraceptive methods among users and the persistence of inequalities. Family planning discussions and partner approval did not influence long-term contraceptive choice. Additionally, some providers selectively offered specific methods based on women’s life course characteristics, such as parity and marital status, despite evidence suggesting that young and nulliparous women can effectively use long-term methods. Conclusion: Given the high effectiveness of long-term contraceptive methods, it is crucial to address barriers that hinder their utilization among young and nulliparous women, as well as those who desire to delay pregnancy. Efforts should focus on improving knowledge and dispelling misconceptions surrounding long-term methods. Providers play a pivotal role in this process by adopting counseling strategies that enhance users’ understanding and facilitate informed decision-making regarding contraceptive options.

Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed,Gansu Province,China

Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.

Construction and Development of China’s Earthquake Information Release System

Focused on the current situation,monitoring system,technical management regulation,process,system composition,and information publication of the earthquake information release,we summarized the construction and development of China’s earthquake information release system and expected its future.In general,China’s earthquake information release systems is able to publish auto-results with MS≥3.0 from 1 to 3 minutes,M_S≥6.0 in global from 2 to 30 minutes,and formal results with MS≥3.0 in China from 8 to 30 minutes,MS≥6.0 in global from 20 to 60 minutes.These earthquake information is released by various channels such as short message,website,microblog,mobile application,etc.

Signal-to-noise ratio comparison of angular signal radiography and phase stepping method

Grating-based x-ray phase contrast imaging has the potential to be applied in future medical applications as it is compatible with both laboratory and synchrotron source. However, information retrieval methods are important because acquisition speed, scanning mode, image quality, and radiation dose depend on them. Phase-stepping （PS） is a widely used method to retrieve information, while angular signal radiography （ASR） is a newly established method. In this manuscript, signal-to-noise ratios （SNRs） of ASR are compared with that of PS. Numerical experiments are performed to validate theoretical results. SNRs comparison shows that for refraction and scattering images ASR has higher SNR than PS method, while for absorption image both methods have same SNR. Therefore, our conclusions would have guideline in future preclinical and clinical applications.

LINEAR REGRESSION OF INTERVAL-VALUED DATA BASED ON COMPLETE INFORMATION IN HYPERCUBES

Recent years have witnessed an increasing interest in interval-valued data analysis. As one of the core topics, linear regression attracts particular attention. It attempts to model the relationship between one or more explanatory variables and a response variable by fitting a linear equation to the interval-valued observations. Despite of the well-known methods such as CM, CRM and CCRM proposed in the literature, further study is still needed to build a regression model that can capture the complete information in interval-valued observations. To this end, in this paper, we propose the novel Complete Information Method （CIM） for linear regression modeling. By dividing hypercubes into informative grid data, CIM defines the inner product of interval-valued variables, and transforms the regression modeling into the computation of some inner products. Experiments on both the synthetic and real-world data sets demonstrate the merits of CIM in modeling interval-valued data, and avoiding the mathematical incoherence introduced by CM and CRM.

Mixed Aleatory-epistemic Uncertainty Modeling of Wind Power Forecast Errors in Operation Reliability Evaluation of Power Systems

As the share of wind power in power systems continues to increase, the limited predictability of wind power generation brings serious potential risks to power system reliability. Previous research works have generally described the uncertainty of wind power forecast errors(WPFEs) based on normal distribution or other standard distribution models, which only characterize the aleatory uncertainty. In fact, epistemic uncertainty in WPFE modeling due to limited data and knowledge should also be addressed. This paper proposes a multi-source information fusion method(MSIFM) to quantify WPFEs when considering both aleatory and epistemic uncertainties. An extended focal element(EFE) selection method based on the adequacy of historical data is developed to consider the characteristics of WPFEs. Two supplementary expert information sources are modeled to improve the accuracy in the case of insufficient historical data. An operation reliability evaluation technique is also developed considering the proposed WPFE model. Finally,a double-layer Monte Carlo simulation method is introduced to generate a time-series output of the wind power. The effectiveness and accuracy of the proposed MSIFM are demonstrated through simulation results.

Scheme optimum selection for dynamic control of reservoir limited water level

Dynamic control of reservoir limited water level is important to reservoir flood control operation.A reasonable limited water level can best utilize flood water resources in addition to flood control.This paper is a trial application of the fuzzy information entropy matter-element evaluation method(FIEMEM) as an optimal selection of dynamic control of limited water level.In this method,compound matter elements are established first,followed by establishment of an evaluation model and choice of the optimal scheme on the basis of fuzzy information entropy.In determining weights,a combined weighting method in game theory is adopted to combine experiential weights and mathematical weights so as to eliminate one-sidedness of the single weighting method.Finally,the feasibility of this optimization method is verified by citing dynamic control of Biliuhe reservoir limited water level as an example.

Unsupervised Nonlinear Adaptive Manifold Learning for Global and Local Information

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology structure.Our method aims at minimizing global pairwise data distance errors as well as local structural errors.In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear extractor.Also,we add a feature approximate error that can be used to learn a linear extractor.In addition,we use a method of adaptive neighbor selection to calculate local structural errors.This paper uses the kernel matrix method to optimize the original algorithm.Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.