Rapid identification of high-quality marine shale gas reservoirs based on the oversampling method and random forest algorithm

The identification of high-quality marine shale gas reservoirs has always been a key task in the exploration and development stage.However,due to the serious nonlinear relationship between the logging curve response and high-quality reservoirs,the rapid identification of high-quality reservoirs has always been a problem of low accuracy.This study proposes a combination of the oversampling method and random forest algorithm to improve the identification accuracy of high-quality reservoirs based on logging data.The oversampling method is used to balance the number of samples of different types and the random forest algorithm is used to establish a highprecision and high-quality reservoir identification model.From the perspective of the prediction effect,the reservoir identification method that combines the oversampling method and the random forest algorithm has increased the accuracy of reservoir identification from the 44%seen in other machine learning algorithms to 78%,and the effect is significant.This research can improve the identifiability of high-quality marine shale gas reservoirs,guide the drilling of horizontal wells,and provide tangible help for the precise formulation of marine shale gas development plans.

How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.

Multivariate form of Hermite sampling series

In this paper,we establish a new multivariate Hermite sampling series involving samples from the function itself and its mixed and non-mixed partial derivatives of arbitrary order.This multivariate form of Hermite sampling will be valid for some classes of multivariate entire functions,satisfying certain growth conditions.We will show that many known results included in Commun Korean Math Soc,2002,17:731-740,Turk J Math,2017,41:387-403 and Filomat,2020,34:3339-3347 are special cases of our results.Moreover,we estimate the truncation error of this sampling based on localized sampling without decay assumption.Illustrative examples are also presented.

Spatial Differentiation and Convergence Trend of High-quality Development Level of China’s Tourism Economy

This paper aims to interpret the connotation of high-quality development of tourism economy(HQTE)from the perspective of the new development concepts of innovation,coordination,green,openness and sharing,and then to evaluate the spatial differenti-ation of China’s HQTE based on provincial panel data from 2009 to 2018.Specifically,we employ the spatial convergence model to ex-plore the absolute and conditionalβconvergence trends of HQTE in the whole country and the eastern,central and western regions of China.Our empirical results reveal that:1)within the decade,from 2009 to 2018,regions of China with the highest HQTE index is its eastern region followed by the central region and then the western region,but the fastest growing one is the western region of China fol-lowed by the central region and then the eastern region.2)Whether or not the spatial effect is included,there are absolute and condition-alβconvergence in HQTE in the whole country and aforementioned three regions.3)The degree of government attention as well as the level of economic development and location accessibility are the positive driving factors for the convergence of HQTE in the whole country and the three regions.The degree of marketization and human capital have not passed the significance test either in the whole country or in the three regions.The above conclusions could deepen the understanding of the regional imbalance and spatial conver-gence characteristics of HQTE,clarify the primary development objects,and accomplish the goal of China’s HQTE.

Effects of high-quality neurosurgical nursing care on improving clinical nursing quality

BACKGROUND With continuous advancements in medical technology,neurosurgical nursing is constantly developing and improving to provide higher-quality nursing services.AIM To explore the effects of different types of high-quality nursing care on clinical nursing quality and patient satisfaction in neurosurgical nursing.METHODS Eighty patients who received neurosurgical treatment in the Affiliated Hospital of Southwest Medical University from June to December 2020 were selected as study participants and categorised into study and control groups.The study group comprised 40 patients who received 4 different types of high-quality nursing care,whereas the control group comprised 40 patients who received conventional nursing care.After a specific period,nursing satisfaction levels and adverse event and complication rates were compared between the two groups.RESULTS Satisfaction with high-quality care was higher than that with conventional care,and high-quality health services and regional services showed the highest satisfaction levels,with an average score of 12 on the Glasgow scale.The satisfaction levels of the study and control groups were 75%and 57%,respectively,with a statistically significant difference(t=7.314,P<0.05).During the nursing period,the adverse event and complication rates were the highest in patients with level III pathology grade and those who underwent neurosurgery(40.02%and 85.93%,respectively),and the difference was statistically significant.CONCLUSION In neurosurgical nursing,employing appropriate high-quality nursing methods can effectively reduce adverse event and complication rates in patients,thereby improving the quality of nursing care and increasing clinical nursing value.

Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

Genetic mechanisms of high-quality tight siliciclastic reservoirs:A case study from the Upper Triassic Xujiahe Formation in the Yuanba area,Sichuan Basin,China

This study analyzed the petrological characteristics,diagenesis,pore types,and physical properties of the tight coarse-grained siliciclastic sequences in the third member of the Upper Triassic Xujiahe Formation(also referred to as the Xu-3 Member)in the western Yuanba area in the northeastern Sichuan Basin,China,based on the results of 242.61-m-long core description,292 thin-section observations,scanning electron microscopy(SEM),and 292 physical property tests.The types and genetic mechanisms of high-quality tight coarse-grained siliciclastic reservoirs in this member was determined thereafter.The research objective is to guide the exploration and development of the tight coarse-grained siliciclastic sequences in the Xu-3 Member.The results of this study are as follows.Two types of high-quality reservoirs are developed in the coarse-grained siliciclastic sequences of the Xu-3 Member,namely the fractured fine-grained sandy conglomerate type and porous medium-grained calcarenaceous sandstone type.Hydrodynamic energy in the sedimentary environment is the key factor controlling the formation of high-quality reservoirs.These high-quality reservoirs are developed mainly in the transitional zone with moderately high hydrodynamic energy between delta-plain braided channels and delta-front subaqueous distributary channels.The dolomitic debris(gravel)content is the main factor affecting the reservoirs’physical properties.The micritic algal debris and sandy debris in the dolomitic debris(or gravels)tend to recrystallize during burial,forming intercrystalline pores within.In the medium-grained calcarenaceous sandstones,intercrystalline pores in the dolomitic debris are formed at the early diagenetic stage,and a pore system consisting of structural fractures connecting intergranular pores,intergranular dissolution pores,and kaolinite intergranular micropores is developed at the late stage of diagenesis.The formation of intercrystalline pores in dolomite gravels and gravel-edge fractures,a pore system connected by gravel-edge and tectonic fractures,is closely related to the dolomite gravels in the sandy fine-grained conglomerates.

High-quality Development Path of Graduate Education in Aquaculture Discipline:A Case Study of Guangdong Ocean University

Aquaculture is a discipline system that focuses on exploring the growth,development,reproduction,aquaculture,and resources of aquatic animals and plants,and their complex relationships.Under the tide of the"high-quality development"strategy,the aquaculture discipline is also facing new opportunities and challenges for transformation,upgrading,and deepening development.Therefore,exploring and practicing an effective path for the high-quality development of graduate education in aquaculture is not only the key to promoting the transformation of graduate education in aquaculture from scale expansion to quality improvement,but also has immeasurable value for implementing the strategy on developing a quality workforce and the strategy of scientific and technological powerhouse.The high-quality development of graduate education in aquaculture can be promoted from the following aspects:optimizing and improving the construction of the graduate education system,focusing on enhancing the quality of high-level talent cultivation,strengthening the overall strength of the graduate supervisor team,actively promoting the adjustment and upgrading of the disciplinary and professional structure,strengthening the construction of resource platforms and deepening the implementation of collaborative education mechanisms,and continuously expanding and deepening the new pattern of international exchange and cooperation.Through the comprehensive promotion of the above paths,the aim is to fully build a model for the improvement and governance of graduate education quality.

Exploring Paths for the High-Quality Development of Forest-Based Wel ness Industry:A Case Study of Sichuan Province

The forest-based wellness industry,as a rapidly growing sector that integrates various business forms with extensive coverage and an extended industrial chain,is undergoing rapid development due to the increasing aging population and people's suboptimal health conditions.As a forerunner in developing the forest-based wellness industry,Sichuan province is known for its early development,proactive efforts,diverse models,and significant impact in this industry.It has achieved certain milestones in terms of top-level design,pilot demonstration,standardized guidance,and public awareness campaigns to promote the development of this industry.Therefore,this paper utilizes Sichuan as a case study to systematically summarize and analyze the key practices made by the province in promoting the rapid development of the industry by investigating the development trajectory of the forest-based wellness industry.Additionally,it examines the development trends of this industry from the perspectives of supply,demand,and consumption.Finally,this paper proposes several measures to facilitate the high-quality development of the forest-based wellness industry.These measures encompass nurturing specialized talent in forest-based wellness,enhancing market players'capabilities in this domain,conducting extensive research on technologies that promote this industry,actively seeking support from relevant policies,and promoting integrated development across diverse sectors.

Enhancing Deep Learning Semantics:The Diffusion Sampling and Label-Driven Co-Attention Approach

The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms,yielding outstanding achievements across diverse domains.Nonetheless,self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures.In response,this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network(DSLD),which adopts a diffusion sampling method to capture more comprehensive semantic information of the data.Additionally,themodel leverages the joint correlation information of labels and data to introduce the computation of text representation,correcting semantic representationbiases in thedata,andincreasing the accuracyof semantic representation.Ultimately,the model computes the corresponding classification results by synthesizing these rich data semantic representations.Experiments on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

The Impact of Digital Finance on High-Quality Development of the Chengdu-Chongqing Economic Circle

This paper developed a comprehensive evaluation system that was able to quantify the levels of high-quality development across the cities within the Chengdu-Chongqing economic circle,and investigate the impact that digital finance had on the cities’high-quality development and the underlying mechanisms through which it achieved this.This comprehensive evaluation system was constructed using statistical data from these cities for the period 2014 to 2020 while also taking China’s high-quality development philosophy into account.The key findings revealed that:(a)Digital finance was able to significantly promote high-quality development in the Chengdu-Chongqing economic circle;(b)Digital finance had a significant positive effect in promoting innovative,coordinated,green,open,and shared development;(c)Digital finance was able to stimulate the high-quality development in the Chengdu-Chongqing economic circle by boosting entrepreneurial dynamism;(d)Digital finance had a significant impact on the high-quality development of the axis areas,while its impact was less discernible in non-axis areas.The insights from this research offer a deeper understanding of the factors that drive high-quality development,the role digital finance plays,and the mechanisms through which digital finance is able to propel high-quality development at the city cluster scale.

FPSblo:A Blockchain Network Transmission Model Utilizing Farthest Point Sampling

Peer-to-peer(P2P)overlay networks provide message transmission capabilities for blockchain systems.Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems.However,traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology.This mismatch results in redundant data transmission and inefficient routing,severely constraining the scalability of blockchain systems.To address these pressing issues,we propose FPSblo,an efficient transmission method for blockchain networks.Our inspiration for FPSblo stems from the Farthest Point Sampling(FPS)algorithm,a well-established technique widely utilized in point cloud image processing.In this work,we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritize message forwarding so that messages reach the network edge quickly and are evenly distributed.Moreover,we compare our model with the Kadcast transmission model,which is a classic improvement model for blockchain P2P transmission networks,the experimental findings show that the FPSblo model reduces 34.8%of transmission redundancy and reduces the overload rate by 37.6%.By conducting experimental analysis,the FPS-BT model enhances the transmission capabilities of the P2P network in blockchain.

Data-Empowered High-Quality Economic Development: Mechanisms and Pathways

To achieve high-quality economic development,it is imperative to prioritize the real economy and foster new factors for economic growth.Data,as a new factor of production,plays a pivotal role in facilitating the seamless integration between digital technology and the real economy.It possesses inherent attributes and techno-economic characteristics that enable the extraction of value across various processes,including production,transaction,consumption,and regulatory supervision.The integration with digital technology enhances the productivity and efficiency of the real economy by facilitating service sector digitalization,accelerating the growth of the new real economy,and supporting the virtual economy in its role of serving the real economy.At present,unleashing the value of data is hindered by inadequate fundamental systems for the data,a lack of activity in the transaction market,and the underutilization of the data as a factor of production by enterprises in the real economy.Therefore,it is advised that data be fully utilized to develop the real economy through the four-pronged approach of“enhancing support for the high-quality provision of the data,expediting the integration of the data into the real economy,promoting the high-quality development of the real economy,and enhancing public service and governance systems”.

A Path Planning Algorithm Based on Improved RRT Sampling Region

For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree(RRT)algorithm,a feedback-biased sampling RRT,called FS-RRT,is proposedbasedon RRT.Firstly,toimprove the samplingefficiency of RRT to shorten the search time,the search area of the randomtree is restricted to improve the sampling efficiency.Secondly,to obtain better information about obstacles to shorten the path length,a feedback-biased sampling strategy is used instead of the traditional random sampling,the collision of the expanding node with an obstacle generates feedback information so that the next expanding node avoids expanding within a specific angle range.Thirdly,this paper proposes using the inverse optimization strategy to remove redundancy points from the initial path,making the path shorter and more accurate.Finally,to satisfy the smooth operation of the robot in practice,auxiliary points are used to optimize the cubic Bezier curve to avoid path-crossing obstacles when using the Bezier curve optimization.The experimental results demonstrate that,compared to the traditional RRT algorithm,the proposed FS-RRT algorithm performs favorably against mainstream algorithms regarding running time,number of search iterations,and path length.Moreover,the improved algorithm also performs well in a narrow obstacle environment,and its effectiveness is further confirmed by experimental verification.

Influence of sampling on three-dimensional surface shape measurement

In order to accurately measure an object’s three-dimensional surface shape,the influence of sampling on it was studied.First,on the basis of deriving spectra expressions through the Fourier transform,the generation of CCD pixels was analyzed,and its expression was given.Then,based on the discrete expression of deformation fringes obtained after sampling,its Fourier spectrum expression was derived,resulting in an infinitely repeated"spectra island"in the frequency domain.Finally,on the basis of using a low-pass filter to remove high-order harmonic components and retaining only one fundamental frequency component,the inverse Fourier transform was used to reconstruct the signal strength.A method of reducing the sampling interval,i.e.,reducing the number of sampling points per fringe,was proposed to increase the ratio between the sampling frequency and the fundamental frequency of the grating.This was done to reconstruct the object’s surface shape more accurately under the condition of m>4.The basic principle was verified through simulation and experiment.In the simulation,the sampling intervals were 8 pixels,4 pixels,2 pixels,and 1 pixel,the maximum absolute error values obtained in the last three situations were 88.80%,38.38%,and 31.50%in the first situation,respectively,and the corresponding average absolute error values are 71.84%,43.27%,and 32.26%.It is demonstrated that the smaller the sampling interval,the better the recovery effect.Taking the same four sampling intervals in the experiment as in the simulation can also lead to the same conclusions.The simulated and experimental results show that reducing the sampling interval can improve the accuracy of object surface shape measurement and achieve better reconstruction results.

Nearest Neighbor Sampling of Point Sets Using Rays

We propose a new framework for the sampling,compression,and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces.Our approach involves constructing a tensor called the RaySense sketch,which captures nearest neighbors from the underlying geometry of points along a set of rays.We explore various operations that can be performed on the RaySense sketch,leading to different properties and potential applications.Statistical information about the data set can be extracted from the sketch,independent of the ray set.Line integrals on point sets can be efficiently computed using the sketch.We also present several examples illustrating applications of the proposed strategy in practical scenarios.

TCAS-PINN:Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.

Chord length sampling correction analysis for dispersion fuel in Monte Carlo simulation

Dispersion fuels,knowned for their excellent safety performance,are widely used in advanced reactors,such as hightemperature gas-cooled reactors.Compared with deterministic methods,the Monte Carlo method has more advantages in the geometric modeling of stochastic media.The explicit modeling method has high computational accuracy and high computational cost.The chord length sampling(CLS)method can improve computational efficiency by sampling the chord length during neutron transport using the matrix chord length?s probability density function.This study shows that the excluded-volume effect in realistic stochastic media can introduce certain deviations into the CLS.A chord length correction approach is proposed to obtain the chord length correction factor by developing the Particle code based on equivalent transmission probability.Through numerical analysis against reference solutions from explicit modeling in the RMC code,it was demonstrated that CLS with the proposed correction method provides good accuracy for addressing the excludedvolume effect in realistic infinite stochastic media.