Development of multiple soft computing models for estimating organic and inorganic constituents in coal

The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the macerals.Unlike the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise.In this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the models.The MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.

Computing Power Network:A Survey

With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.

Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

Investigation of pore geometry influence on fluid flow in heterogeneous porous media:A pore-scale study

Brazilian pre-salt reservoirs are renowned for their intricate pore networks and vuggy nature,posing significant challenges in modeling and simulating fluid flow within these carbonate reservoirs.Despite possessing excellent petrophysical properties,such as high porosity and permeability,these reservoirs typically exhibit a notably low recovery factor,sometimes falling below 10%.Previous research has indicated that various enhanced oil recovery(EOR)methods,such as water alternating gas(WAG),can substantially augment the recovery factor in pre-salt reservoirs,resulting in improvements of up to 20%.Nevertheless,the fluid flow mechanism within Brazilian carbonate reservoirs,characterized by complex pore geometry,remains unclear.Our study examines the behavior of fluid flow in a similar heterogeneous porous material,utilizing a plug sample obtained from a vugular segment of a Brazilian stromatolite outcrop,known to share analogies with certain pre-salt reservoirs.We conducted single-phase and multi-phase core flooding experiments,complemented by medical-CT scanning,to generate flow streamlines and evaluate the efficiency of water flooding.Subsequently,micro-CT scanning of the core sample was performed,and two cross-sections from horizontal and vertical plates were constructed.These cross-sections were then employed as geometries in a numerical simulator,enabling us to investigate the impact of pore geometry on fluid flow.Analysis of the pore-scale modeling and experimental data unveiled that the presence of dead-end pores and vugs results in a significant portion of the fluid remaining stagnant within these regions.Consequently,the injected fluid exhibits channeling-like behavior,leading to rapid breakthrough and low areal swept efficiency.Additionally,the numerical simulation results demonstrated that,irrespective of the size of the dead-end regions,the pressure variation within the dead-end vugs and pores is negligible.Despite the stromatolite's favorable petrophysical properties,including relatively high porosity and permeability,as well as the presence of interconnected large vugs,the recovery factor during water flooding remained low due to early breakthrough.These findings align with field data obtained from pre-salt reservoirs,providing an explanation for the observed low recovery factor during water flooding in such reservoirs.

Predicting the Mechanical Behavior of a Bioinspired Nanocomposite through Machine Learning

The bioinspired nacre or bone structure represents a remarkable example of tough,strong,lightweight,and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials.The bioinspired structure consists of hard grains and soft material interfaces.While the material interface has a very low volume percentage,its property has the ability to determine the bulk material response.Machine learning technology nowadays is widely used in material science.A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite.This model was trained on a comprehensive dataset of material response and interface properties,allowing it to make accurate predictions.The results of this study demonstrate the efficiency and high accuracy of the machine learning model.The successful application of machine learning into the material property prediction process has the potential to greatly enhance both the efficiency and accuracy of the material design process.

Cancer models in preclinical research:A chronicle review of advancement in effective cancer research

Cancer is a major stress for public well-being and is the most dreadful disease.The models used in the discovery of cancer treatment are continuously changing and extending toward advanced preclinical studies.Cancer models are either naturally existing or artificially prepared experimental systems that show similar features with human tumors though the heterogeneous nature of the tumor is very familiar.The choice of the most fitting model to best reflect the given tumor system is one of the real difficulties for cancer examination.Therefore,vast studies have been conducted on the cancer models for developing a better understanding of cancer invasion,progression,and early detection.These models give an insight into cancer etiology,molecular basis,host tumor interaction,the role of microenvironment,and tumor heterogeneity in tumor metastasis.These models are also used to predict novel can-cer markers,targeted therapies,and are extremely helpful in drug development.In this review,the potential of cancer models to be used as a platform for drug screening and therapeutic discoveries are highlighted.Although none of the cancer models is regarded as ideal because each is associated with essential caveats that restraint its application yet by bridging the gap between preliminary cancer research and transla-tional medicine.However,they promise a brighter future for cancer treatment.

A Unified Framework of the Cloud Computing Service Model

After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system （HDFS）, Hbase, Hadoop, and virtualization. This study also compares Microsoft, Trend Micro, and the proposed unified cloud computing architecture to show that the proposed unified framework of the cloud computing service model is comprehensive and appropriate for the current complexities of businesses. The findings of this study can contribute to the knowledge for academics and practitioners to understand, assess, and analyze a cloud computing service application.

An improved memristor model for brain-inspired computing

Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.

Pulse cleaning flow models and numerical computation of candle ceramic filters

Analytical and numerical computed models are developed for reverse pulse cleaning system of candle ceramic filters. A standard turbulent model is demonstrated suitably to the designing computation of reverse pulse cleaning system from the experimental and one dimensional computational result. The computed results can be used to guide the designing of reverse pulse cleaning system, which is optimum Venturi geometry. From the computed results, the general conclusions and the designing methods are obtained.

Parallel scheduling strategy of web-based spatial computing tasks in multi-core environment

In order to improve the concurrent access performance of the web-based spatial computing system in cluster,a parallel scheduling strategy based on the multi-core environment is proposed,which includes two levels of parallel processing mechanisms.One is that it can evenly allocate tasks to each server node in the cluster and the other is that it can implement the load balancing inside a server node.Based on the strategy,a new web-based spatial computing model is designed in this paper,in which,a task response ratio calculation method,a request queue buffer mechanism and a thread scheduling strategy are focused on.Experimental results show that the new model can fully use the multi-core computing advantage of each server node in the concurrent access environment and improve the average hits per second,average I/O Hits,CPU utilization and throughput.Using speed-up ratio to analyze the traditional model and the new one,the result shows that the new model has the best performance.The performance of the multi-core server nodes in the cluster is optimized;the resource utilization and the parallel processing capabilities are enhanced.The more CPU cores you have,the higher parallel processing capabilities will be obtained.

AN IMPROVED MODEL FOR COMPUTING SOLUTION DYNAMICS OF NATURAL PRODUCTS WITH ^(13)C NUCLEAR MAGNETIC RELAXATION

The fully anisotropic molecular overall tumbling model with methyl conformation jumps internal rotation among three equivalent sites is proposed,the overall tumbling rotation rates and the methyl internal rotation rates of ponicidin are computed with this model from ~C relaxation parameters.

A Multi-Level Authorization Based Tenant Separation Mechanism in Cloud Computing Environment

Separation issue is one of the most important problems about cloud computing security. Tenants should be separated from each other based on cloud infrastructure and different users from one tenant should be separated from each other with the constraint of security policies. Learning from the notion of trusted cloud computing and trustworthiness in cloud, in this paper, a multi-level authorization separation model is formally described, and a series of rules are proposed to summarize the separation property of this model. The correctness of the rules is proved. Furthermore, based on this model, a tenant separation mechanism is deployed in a real world mixed-critical information system. Performance benchmarks have shown the availability and efficiency of this mechanism.

Theoretical Analysis of Effect of LUT Size on Area and Delay of FPGA

Based on architecture analysis of island-style F PGA,area and delay models of LUT FPGA are proposed.The models are used to analyze the effect of LUT size on FPGA area and performance.Results show optimal LUT size obtained by computation models is the same as that from experiments:a LUT size of 4 produces the best area results,and a LUT size of 5 provides the better performance.

Agricultural Policy Simulation Based on Computable General Equilibrium Model

[Objective] This study aimed to examine the simulated effect of Computable General Equilibrium （CGE）-based agricultural policy simulation system. [Method] The policy simulation platform based on CGE model was constructed by integrating policy simulation, CGE model and Decision Supporting System （DSS）. The scenario analysis method was used to analyze the agricultural subsides policy simulation through empirical analysis. [Result] Farmers were the main beneficiaries of increasing agricultural production subsidies, which increased farmers＇ income and improved the export of agriculture products. The prototype system could solve the problems in actual policy simulation. [Conclusion] The results lay the foundation for the quantitative study on agricultural subsidy policy in China.

The effects of different sea surface temperature distributions over the western Pacific on the summer monsoon properties

A modified and improved primitive equation numerical model with p-sigma incorporated vertical coordinates is used to simulate the effects of different sea surface temperature distributions over the western Pacific on the summer monsoon properties. The different sea surface temperature (SST) distributions are automatically generated in the time integrations by using two different SST models, one of which is called the deep ocean model (DOM) and the other the shallow ocean model (SOM). The SST generated by the DOM has the distribution pattern of the initial SST which is similar to the pattern in the cold water years over the western Pacific, while the SST generated by the SOM has the pattern similar to that in the warm water years. The differences between the experimental results by using DOM and SOM are analyzed in detail. The analyses indicate that the most basic and important characteristics of the summer monsoon climate can be simulated successfully in both experiments, that means the climatic properties in the monsoonal climate regions are mainly determined by the seasonal heating, the contrast between the land and the sea, the topography, and the physical properties of the underlying surfaces. However, the differences between the two experiments tell us that the climatic properties in the summer monsoon regions in the cold water year and the warm water year do differ from each other in details. In the warm water year, the thermal contrast between the land and the sea becomes weaker. Over the warm water area, the upward motions are induced and the dynamical conditions favorable for the convective activities are formed, the Somali low-level cross equatorial current is somewhat weakened, while the cross equatorial currents, east of 90°E, are strongly strengthened, the precipitation amount in the tropical regions largely increases, and the precipitation over the coastal regions increases, too. However the precipitation over the southeast China and its coastal area decreases. The precipitation amount mainly depends on the strength of the convective activity.

Gastrointestinal tract modelling in health and disease

The gastrointestinal (GI) tract is the system of organs within multi-cellular animals that takes in food, digests it to extract energy and nutrients, and expels the remaining waste. The various patterns of GI tract function are generated by the integrated behaviour of multiple tissues and cell types. A thorough study of the GI tract requires understanding of the interactions between cells, tissues and gastrointestinal organs in health and disease. This depends on knowledge, not only of numerous cellular ionic current mechanisms and signal transduction pathways, but also of large scale GI tissue structures and the special distribution of the nervous network. A unique way of coping with this explosion in complexity is mathematical and computational modelling; providing a computational framework for the multilevel modelling and simulation of the human gastrointestinal anatomy and physiology. The aim of this review is to describe the current status of biomechanical modelling work of the GI tract in humans and animals, which can be further used to integrate the physiological, anatomical and medical knowledge of the GI system. Such modelling will aid research and ensure that medical professionals benefit, through the provision of relevant and precise information about the patient's condition and GI remodelling in animal disease models. It will also improve the accuracy and efficiency of medical procedures, which could result in reduced cost for diagnosis and treatment.

Closed circle DNA algorithm of change positive-weighted Hamilton circuit problem

Chain length of closed circle DNA is equal. The same closed circle DNA＇s position corresponds to different recognition sequence, and the same recognition sequence corresponds to different foreign DNA segment, so closed circle DNA computing model is generalized. For change positive-weighted Hamilton circuit problem, closed circle DNA algorithm is put forward. First, three groups of DNA encoding are encoded for all arcs, and deck groups are designed for all vertices. All possible solutions are composed. Then, the feasible solutions are filtered out by using group detect experiment, and the optimization solutions are obtained by using group insert experiment and electrophoresis experiment. Finally, all optimization solutions are found by using detect experiment. Complexity of algorithm is concluded and validity of DNA algorithm is explained by an example. Three dominances of the closed circle DNA algorithm are analyzed, and characteristics and dominances of group delete experiment are discussed.

A Review of the Application of Additive Manufacturing in Prosthetic and Orthotic Clinics from a Biomechanical Perspective

Prostheses and orthoses are common assistive devices to meet the biomechanical needs of people with physical disabilities.The traditional fabrication approach for prostheses or orthoses is a materialwasting,time-consuming,and labor-intensive process.Additive manufacturing(AM)technology has advantages that can solve these problems.Many trials have been conducted in fabricating prostheses and orthoses.However,there is still a gap between the hype and the expected realities of AM in prosthetic and orthotic clinics.One of the key challenges is the lack of a systematic framework of integrated technologies with the AM procedure;another challenge is the need to design a prosthetic or orthotic product that can meet the requirements of both comfort and function.This study reviews the current state of application of AM technologies in prosthesis and orthosis fabrication,and discusses optimal design using computational methods and biomechanical evaluations of product performance.A systematic framework of the AM procedure is proposed,which covers the scanning of affected body parts through to the final designed adaptable product.A cycle of optimal design and biomechanical evaluation of products using finite-element analysis is included in the framework.A mature framework of the AM procedure and sufficient evidence that the resulting products show satisfactory biomechanical performance will promote the application of AM in prosthetic and orthotic clinics.

Analysis on Collapse Strength of Casing Wear

Carrying capacity of the casing will reduce after the casing is worn, which seriously affects the subsequent well drilling, well completion, oil extraction and well repair. A lot of researches on calculation of casing wear collapse strength have been done, but few of them focus on collapsing failure mechanism, and influencing factors and law of collapse strength. So, significant difference between estimated value and actual value of collapse strength comes into being. By theoretical analysis, numerical simulation and actual test, the collapsing failure mechanism of casing wear as well as the influencing factors and laws of collapse strength are investigated, and the investigation results show that collapse of crescent casing wear belongs to 'three hinged' instability. The severely-worn position on the casing is yielded into the plastic zone first then deformed greatly, which causes the plastic instability of the whole structure. The casing wear collapse strength presents changes of exponent, power function and linear trend with the residual casing wall thickness, wear radius and axial load, respectively. When the flexibility is less than 10°/30 m, the borehole bending has less impact on casing collapse strength. Thus, the computation model for the casing wear collapsing strength is established by introducing wear radius coefficient and casing equivalent yield strength, at the same time, the model is tested. The test results show that the relative error for the computation model is less than 5%. The research results provide a basis for design of the casing string strength and evaluation of down-hole safety.

Evolution of gas kick and overflow in wellbore and formation pressure inversion method under the condition of failure in well shut-in during a blowout

With ongoing development of oil exploration and techniques,there is a significant need for improved well control strategies and formation pressure prediction methods.In this paper,a gas-liquid transient drift flow model was established according to the gas-liquid two-phase flow characteristics during the gas kick.A Roe scheme was used for numerical calculation based on the finite volume method.The changes of bottom-hole pressure,casing pressure,the development law of cross-sectional gas holdup,and gas velocity,along with the vertical well depth,were analyzed through simulation examples.The time-series characteristics of mud pit gain were obtained by adjusting the formation parameter.The complex nonlinear mapping relationship between the formation parameters and the mud pit gain was established.The long short-term memory network(LSTM)of deep learning was used to obtain a formation pressure inversion when the blowout is out of control and the well cannot be shut-in.Experimental data from a well were used to verify the gas-liquid two-phase transient drift flow model based on the finite volume method,demonstrating that this method is reliable,with greatly improved prediction accuracy.This approach provides theoretical support for the early monitoring of gas kick during drilling,and for well-killing design and construction after uncontrolled blowout.