Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Open banking and inclusive finance in the European Union:perspectives from the Dutch stakeholder ecosystem

In the European Union(EU),the revised Payment Services Directive(PSD2)aims to provide more convenient and customized financial products through open banking(OB)platforms.However,little attention has been paid to the role of OB in improving the financial well-being of the growing number of the EU’s underserved groups,which currently constitute approximately a quarter of its population.This study examines how the PSD2 and OB impact inclusive finance in the EU based on the perspectives of the Netherlands’ecosystem,one of the leaders in the EU’s financial technology(FinTech)landscape.A fundamental distinction can be drawn between the OB users and the ecosystem’s players.Regarding the impact of financial services on the users’inclusivity,while the PSD2 strengthens the infrastructure necessary for financial inclusion,many challenges remain,mainly because it was not designed for this purpose.This study identifies several areas of improvement that include adjustments to the know your customer and anti-money laundering processes for underserved customers,innovative ways to communicate the PSD2’s potential,and the regulation of technology providers’activities to build trust.Meanwhile,from the ecosystem’s position,there is a need to strengthen and improve microfinance regulation according to the opportunities provided by the PSD2 to support microfinance institutions(MFIs)in scaling up and reaching underserved clients across borders with innovative services.OB improvements can also be achieved by organizations formed by MFIs and FinTechs in collaboration with banks.Such hybrid institutions will combine the best features of each of them:knowledge of the needs of local underserved clients from MFIs,technological innovations from FinTechs,and large and trusted customer bases,infrastructures,and access to institutional investments and governments from banks.Finally,an EU inclusive OB sector depends on the centrality of trusted regulators as coordination bodies.The PSD2 requires adjustments for underserved populations’specific needs.OB improvements can be achieved by organizations formed by MFIs and Fin-Techs in collaboration with banks.Regulated technical service providers(TSPs)are crucial to building trust and customer adoption in OB.The European Banking Authority(EBA)may function as coordination body to design inclusive rules by engaging with OB stakeholders.As inclusive finance moves into open-finance and data eras,an increasing regulatory complexity and scope will require networks of innovative and trusted regulators.

Spatiotemporal characteristics of seed rain and soil seed bank of artificial Caragana korshinskii Kom. forest in the Tengger Desert, China

Vegetation restoration and reconstruction are effective approaches to desertification control and achieving social and economic sustainability in desert areas.However,the self-succession ability of native plants during the later periods of vegetation restoration remains unclear.Therefore,this study was conducted to bridge the knowledge gap by investigating the regeneration dynamics of artificial forest under natural conditions.The information of seed rain and soil seed bank was collected and quantified from an artificial Caragana korshinskii Kom.forest in the Tengger Desert,China.The germination tests were conducted in a laboratory setting.The analysis of species quantity and diversity in seed rain and soil seed bank was conducted to assess the impact of different durations of sand fixation(60,40,and 20 a)on the progress of vegetation restoration and ecological conditions in artificial C.korshinskii forest.The results showed that the top three dominant plant species in seed rain were Echinops gmelinii Turcz.,Eragrostis minor Host.,and Agropyron mongolicum Keng.,and the top three dominant plant species in soil seed bank were E.minor,Chloris virgata Sw.,and E.gmelinii.As restoration period increased,the density of seed rain and soil seed bank increased first and then decreased.While for species richness,as restoration period increased,it gradually increased in seed rain but decreased in soil seed bank.There was a positive correlation between seed rain density and soil seed bank density among all the three restoration periods.The species similarity between seed rain or soil seed bank and aboveground vegetation decreased with the extension of restoration period.The shape of the seeds,specifically those with external appendages such as spines and crown hair,clearly had an effect on their dispersal,then resulting in lower seed density in soil seed bank.In addition,precipitation was a crucial factor in promoting rapid germination,also resulting in lower seed density in soil seed bank.Our findings provide valuable insights for guiding future interventions during the later periods of artificial C.korshinskii forest,such as sowing and restoration efforts using unmanned aerial vehicles.

Automated Clearing System on Deposit Money Banks’Performance:Experience from the Nigerian Banks

This study majorly examined the effect of automated cheque clearing system on banks.Specifically,this study examined the effect of automated cheque clearing system on total deposits of deposit money banks in Nigeria.Data were extracted from the CBN Statistical Bulletin(2020).Using the ordinary least square method of analysis via E-views 10,the findings of the study revealed that deposit money banks’total deposits increased significantly owing to the decline in the value of cheques cleared,since the adoption of the automated cheque clearing system in Nigeria.Guided by the findings of this study,the researcher recommended that the regulatory authorities and banks should continue to liaise with each other on how to improve the efficiency of automated services so as to improve customer satisfaction and patronage.

Research on Early Warning of Banking Crises from the Perspective of Credit Structures

The relationship between credit expansion and banking crises is complex and cannot be fully explained by total credit alone.A systematic analysis of the relationship between the amount and structure of total credit and banking crises is important for an objective prediction of the influence of potential financial risks.This paper,drawing on data from 15 selected countries,delves into the power of credit indicators in the early warning of banking crises from the perspectives of industrial structure,sector structure,and term structure of credit.Various machine learning methods were used,including Logistic Regression,Random Forest,Decision Tree,Support Vector Machine(SVM),Bagging,and Boosting models.The empirical findings indicate that credit expansion plays a crucial role in triggering banking crises.However,total credit is better suited for the early warning of short-term banking crises,whereas credit structure is more useful for the early warning of long-term banking crises.Moreover,in an early warning system,identifying key early warning indicators is more meaningful than merely increasing the number of indicators.Machine learning can somewhat enhance the early warning power,but it may not always be robust.Therefore,more attention should be paid to potential systemic banking crises resulting from an imbalance in credit structure while regulating the total credit threshold.

A novel DPSS filter optimization scheme to reduce the intrinsic interference of FBMC-QAM systems

In order to reduce the intrinsic interference of the filter bank multicarrier-quadrature amplitude modulation(FBMC-QAM)system,a novel filter optimization scheme based on discrete prolate spheroidal sequences(DPSS)is proposed.Firstly,a prototype filter function based on DPSS is designed,since the eigenvalue can be used as an indicator of the energy concentration of DPSS,so a threshold is set,and the sequence with the most concentrated energy is selected under the threshold,that is,the sequence with the eigenvalue higher than the threshold,and the prototype filter function is rewritten as a weighted sum function of multiple eigenvectors.Under the energy constraints of the filter,the relationship between the eigenvectors and the intrinsic interference function is established,and the function problem is transformed into an optimization problem for the weighted coefficients.Through the interior point method,the most suitable weight is found to obtain the minimum intrinsic interference result.Theoretical analysis and simulation results show that compared with the prototype filters such as Type1 and CaseC,the DPSS filter applying the proposed optimization algorithm can effectively suppress the intrinsic interference of the system and obtain a better bit error rate(BER)performance.

The Impact of the Monetary Policy During the COVID-19 in the Brazilian Banking Sector

The COVID-19 pandemic caused an outbreak on the global productive chains,transforming companies and society in general.Governments adopted anticyclical policies such as basic interest rates reduction as response.Brazil basic interest rate is denominated Selic.The application of these policies requires the protagonist of bank’s financial intermediation.This study aims to verify two events-under the perspective of the efficient markets’theory.The first is the communication of the first death by COVID-19-and the subsequent,Selic rate reduction to 2%p.a.-and its effects on bank’s shares prices.The hypotheses presented are:H1-The announcement of the first death by COVID-19 negatively impacts the banks’shares returns and H2-The announcement of the Selic interest rate-during the COVID-19-positively impacts the return of the banks’shares.We adopt event study methodology in a final sample of nine and 15 banks to Events 1 and 2 respectively.The results confirm H1 as well as the semi-strong informational efficiency market.H2 is not confirmed.Results confirm the non-effectiveness of the anticyclical policy of basic interest rate reduction.This contributes to the discussion about the impacts of COVID-19 and the efficacy of anticyclical policies.

ISO Certification and Technical Efficiency in the Banking Sector:An Evidence from WAEMU’s Countries Using the DEA Method

In the view of the international organization of standardization,the introduction of ISO standards is typical of quality management systems oriented towards strengthening the efficiency of enterprises.Business banks are not excluded from this requirement.The current study aims to evaluate the effect of ISO certification on the technical performance of WAEMU banks.Using BCEAO data(2020),the DEA model and Tobit estimates show that ISO certification has no effect on the efficiency scores of the banks in the sample.It is therefore appropriate for these banks to work on improving their business climate.

Research on the Impact of the Digital Transformation of Commercial Banks

The development of the digital economy has presented both opportunities and challenges for the transformation of commercial banks.This article aims to conduct a literature review on the impact of digital transformation on commercial banks and to explore the multi-dimensional effects it brings.Through a review and analysis of relevant domestic and international literature,this paper first defines the connotation of digital transformation in commercial banks.It then conducts a literature analysis and research on the strategic governance,business performance,and risk management aspects of the digital transformation of commercial banks from multiple perspectives.Based on the analysis of the existing literature,the paper explores directions for future in-depth research and offers corresponding policy suggestions,providing a theoretical and practical foundation for the future digital development of commercial banks.

Research on the Impact of Digital Transformation on the Business Performance of Commercial Banks

Nowadays,with the development of the digital economy,commercial banks are actively conducting digital transformation.Studying the impact of the digital transformation of commercial banks on their operating performance can help commercial banks form a stronger core competitiveness and promote high-quality financial development.Based on the above background,this article first describes the status and development of digital transformation and development of commercial banks,and secondly analyzes whether Chinese commercial banks’digital transformation is conducive to improving their operating performance.Thirdly,by selecting the data of the listed commercial banks in the ten years of 2012–2022,this article obtains the empirical testing of the digital transformation on different property rights.Finally,the higher the level of digitalization,the higher the digital level,the more significantly promotes the performance of commercial banks.Finally,based on the above analysis,this article puts forward feasibility opinions on commercial banks and related regulators.

Study on the Countermeasures to Improve the Public Outdoor Leisure and Fitness Function on the Two Rivers and Four Banks in the Main Urban Area of Chongqing

The main urban area of Chongqing is surrounded by two rivers and set against each other.With its unique waterfront landscape,it has the resource conditions to become a leisure tourism destination.Intending to enhance people’s happiness,improve city quality,and promote Chongqing’s main urban area to become a tourist destination,this paper finds out the existing problems in the construction of public outdoor leisure and fitness facilities on the two rivers and four banks of Chongqing’s main urban area through investigation and analysis based on relevant experiences at home and abroad,takes the value chain theory as the guidance,and to find solutions to the problem.On this basis,combined with the law of economic operation,this paper puts forward the guiding ideology,principles,development goals,functional orientation,and development path for the improvement of public outdoor leisure and fitness on two rivers and four banks in the main city of Chongqing,and accordingly puts forward the policy system and guarantee measures for its improvement.

基于Auto bank的某水库大坝渗流安全评价

以富水水库为例,依托达西定律和渗流理论基础,并结合Auto bank有限元软件分析了水库在正常高水位、设计洪水位、校核洪水位下坝体及坝基的渗流状况,计算结果表明水库日渗漏量远小于允许日渗漏量,水库渗漏量满足规范要求。大坝各工况最大渗透坡降均小于规范允许值,渗透变形满足规范要求。左坝肩存绕渗现象,建议加强监测。

Adaptive bands filter bank optimized by genetic algorithm for robust speech recognition system

Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results.

Application of uniform DFT filter bank in radar jamming system

The principle of uniform DFT filter bank is presented. Exploiting poly-phase structure, radar jamming system samples the intercepted wideband radar signals through analysis filter bank by different channels and linearly modulates the intercepted radar signal according to the theory of signal and system, then synthesizes the jamming signal through the synthesis filter hank. The method merely requires lower sample frequency, reduces the computational complexity and the data quantity to be processed. The un-ideal filter＇s influence to the result of signals processing is analyzed by simulating the match filter in radar jamming system.

Studies on the Mutant Systems of the Bombyx mori Gene Bank

Through over ten years of study, more than 1 000 genetic materials including mutant genes, chromosomal variation strains and special genetic materials of Bombyx mori, Linnaeus, collected, introduced or created since 1940s especially late 1980s, have been sorted out and put in order. After identifications and genetic analyses of their morphological, physiological and biochemical characters, the silkworm gene bank was constructed and the preservation system was perfected, and more than 600 silkworm strains were kept in this gene bank. The preserved silkworm mutant genes have covered more than 90% of existent ones across the world, in which, more than 100 are rare and precious mutant genes, and over 60 mutant genes were found and studied for the first time. Through hybrid analyses, linkage tests and three-point gene location tests, a perfect linkage retrieval labeling system of silkworm was established, which included 230 marker genes covering all the 28 linkage groups of Bombyx mori. The gene location system (composite system of recessive genes) of different linkage groups was set up. The intergenic complementation of mutant egg color and third type of maternal heredity egg color have been found, and indicated that the epistatic effect of mutant gene of white egg is universal. Twenty eight independent near isogenic lines marked with morphological mutation gene have been created and a series of novel breeding materials possessing great potential for application such as high feeding efficiency, special sex markers, natural colored silk, resistance to disease, wider feeding range and adjustable parthenogenesis, etc. , have been developed. The sustainable maintenance and management technique system of silkworm gene resources were well established.

Bank erosion in an Andean páramo river system: Implications for hydro-development and carbon dynamics in the neotropical Andes

The páramo of the Northern Andes provide critically important ecosystem services to the Northern Andean region in the form of water provisioning and carbon sequestration, both of which are a result of the páramo?s organic-rich soils. Little is known, however, about the hydro-geomorphic characteristics of the rivers that drain these ecosystems. With impending plans for widespread hydro-development and increasing implementation of carbon-sequestering compensation for ecosystem services programs in the region it is imperative that we develop a thorough understanding of the hydrogeomorphic role that rivers play in this unique ecosystem. The objective of this study was to quantify bank erosion along an Amazonian headwater stream draining a small, relatively undisturbed páramo catchment to gain a better understanding of the natural erosion regime and the resulting sediment contributions from this unique ecosystem. This study implemented a combination of field, laboratory, and Geographic Information Systems techniques to quantify bank erosion rates and determine a bank erosion sediment yield from the Ningar River, a small páramo catchment(22.7 km^2) located in the eastern Andean cordillera of Ecuador. Results show that bank erosion rates range from 3.0 to ≥ 390.0 mm/yr, are highly episodic, and yield at least 487 tons of sediment annually to the Ningar River. These results imply that 1) páramo ecosystems substantially contribute to the sediment load of the Amazon River basin; 2) bank erosion is a potentially significant flux component of basin-scale carbon cycles in páramo ecosystems; and 3) hydrologic alteration campaigns(dam building) will likely critically alter these contributions and concomitantly disconnect a critical source of sediment and nutrients to downstream ecosystems.

Intelligent Decision Support System for Bank Loans Risk Classification

Intelligent Decision Support System (IISS) for Bank Loans Risk Classification (BLRC), based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BLRC, the key financial and non-financial factors are analyzed. Meanwhile, ES and Model Base (MB) which contain ANN are designed . The general framework,interaction and integration of the system are given. In addition, how the system realizes BLRC is elucidated in detail.

Testing the governance-performance relationship for the Tunisian banks: a GMM in system analysis

Rationale:This study aims to contribute to settling the lack of consensus regarding the determinants of bank performance,not only by considering bank governance,but also by including factors such as CEO compensation and risk management committee.Previous literature has included bank governance and considered only large banks in their surveys.The exclusion of other factors such as small-and medium-size banks may render the findings of these studies limited in applicability.Objective:The objective of this paper is to examine the impact of internal governance on bank performance.Methodology:To achieve this goal,we used annual data of a sample of ten Tunisian commercial banks listed in the Tunisian Stock Exchange observed during the period 1998–2015.We use the Generalized Method of Moments(GMM)to estimate the parameters of our econometric model.Results:Our study finds that the correlation between the size of the board of directors,the state’s inclusion,and the presence of independent directors is positive and significant.On the other hand,we have found that CEO compensation,as well as foreign and institutional investors negatively affect the performance of the banks.Conclusions and implications:Tunisian banks are invited to broaden their size through appropriate restructuring,adopt new remuneration policies and define the optimal number of directors representing the state within the board of directors.Our results suggest managerial implications that can be of great value to ensuring the success of Tunisian banks.The latter should favor a higher presence of independent directors to reduce the bank control ineffectiveness caused by having a significant number of foreign and institutional investors in the board of directors.

Designing of Commercial Bank Loans Risk Early Warning System Based on BP Neural Networks

According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loaning corporation. Except the structure description of the system structure the demonstration of attemptive designing is also elaborated.