Structural failure analysis with CMS-based ground motion selection using innovative cost function and weight factors

The selection and scaling of ground motion records is considered a primary and essential task in performing structural analysis and design.Conventional methods involve using ground motion models and a conditional spectrum to select ground motion records based on the target spectrum.This research demonstrates the influence of adopting different weighted factors for various period ranges during matching selected ground motions with the target hazard spectrum.The event data from the Next Generation Attenuation West 2(NGA-West 2)database is used as the basis for ground motion selection,and hazard de-aggregation is conducted to estimate the event parameters of interest,which are then used to construct the target intensity measure(IM).The target IMs are then used to select ground motion records with different weighted vector-valued objective functions.The weights are altered to account for the relative importance of IM in accordance with the structural analysis application of steel moment resisting frame(SMRF)buildings.Instead of an ordinary objective function for the matching spectrum,a novel model is introduced and compared with the conventional cost function.The results indicate that when applying the new cost function for ground motion selection,it places higher demands on structures compared to the conventional cost function.Moreover,submitting more weights to the first-mode period of structures increases engineering demand parameters.Findings demonstrate that weight factors allocated to different period ranges can successfully account for period elongation and higher mode effects.

An Adaptive Hybrid Optimization Strategy for Resource Allocation in Network Function Virtualization

With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation.

A Feature Selection Method Based on Hybrid Dung Beetle Optimization Algorithm and Slap Swarm Algorithm

Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.

Position-aware packet loss optimization on service function chain placement

The advent of Network Function Virtualization(NFV)and Service Function Chains(SFCs)unleashes the power of dynamic creation of network services using Virtual Network Functions(VNFs).This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term.However,the study shows with a set of test-bed experiments that packet loss at certain positions(i.e.,different VNFs)in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.To overcome this challenge,this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions.This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming(MILP)and proved that it is NP-hard.In this study,Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost.Extensive experiment results show that Palos can achieve up to 42.73%improvement on packet dropping cost and up to 33.03%reduction on average SFC latency when compared with two other state-of-the-art schemes.

Design of dual-functional protic porous ionic liquids for boosting selective extractive desulfurization

Porous ionic liquids have demonstrated excellent performance in the field of separation,attributed to their high specific surface area and efficient mass transfer.Herein,task-specific protic porous ionic liquids(PPILs)were prepared by employing a novel one-step coupling neutralization reaction strategy for extractive desulfurization.The single-extraction efficiency of PPILs reached 75.0%for dibenzothiophene.Moreover,adding aromatic hydrocarbon interferents resulted in a slight decrease in the extraction efficiency of PPILs(from 45.2%to 37.3%,37.9%,and 33.5%),indicating the excellent extraction selectivity of PPILs.The experimental measurements and density functional theory calculations reveal that the surface channels of porous structures can selectively capture dibenzothiophene by the stronger electrophilicity(Eint(HS surface channel/DBT)=-39.8 kcal mol^(-1)),and the multiple extraction sites of ion pairs can effectively enrich and transport dibenzothiophene from the oil phase into PPILs throughπ...π,C-H...πand hydrogen bonds interactions.Furthermore,this straightforward synthetic strategy can be employed in preparing porous liquids,offering new possibilities for synthesizing PPILs with tailored functionalities.

Progress in research on the exploration and evaluation of deep geothermal resources in the Fujian-Guangdong-Hainan region,China

Deep geothermal resources in the Fujian-Guangdong-Hainan region,China,offer significant potential for sustainable energy.The diverse igneous rock formations along the southeast coast present intricate geological challenges that impede exploration and evaluation efforts.In this study,we address critical concerns related to the Fujian-Guangdong-Hainan region's deep geothermal resources,encompassing heat source composition,formation conditions,strategic favorable areas,and exploration directions.Our methods involve the analysis of regional geothermal reservoirs and cap rocks.Major findings include:the primary heat sources in the Fujian-Guangdong-Hainan region consist of the radioactive heat generation from granites in the crust,heat conduction in the mantle,and,in specific areas like Yangjiang and Shantou,melts within the middle and lower crust;the deep,high-temperature geothermal resources in the region predominantly reside in basins'depressed areas.These areas are characterized by the confluence of triple heat sources:heat from the Earth's crust,mantle,and other supplementary sources;our analysis led to the identification of three strategic areas favorable for deep geothermal resources in the Fujian-Guangdong-Hainan region.These are the Beibu Gulf Basin's continental area,the Yuezhong Depression,and the Fuzhou-Zhangzhou area.

Service Function Chain Migration in LEO Satellite Networks

With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite networks.While due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service failure.In this paper,we consider migration and establish an online migration model,especially considering the dynamic topology.Given the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as possible.Due to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are rejected.Simulation results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity.

SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment

With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.

Revision of Water Function Division and Countermeasures for Water Resources Protection in Jiangjin District of Chongqing City in China

According to the current situation and development planning of water resources in Jiangjin District of Three Gorge Reservoir Area at the upper reaches of Yangtze River,by combining with social needs,through the survey on pollution source and analysis of water quality,based on the Report of Water Function Division of Jiangjin District(2005) ,the adjustment and revision have been conducted on water function divisions,and corresponding protection targets and countermeasures for water resources have been proposed,so that the water function division can comply with the development situation of Jiangjin District,providing a reliable reference for the protection and reasonable utilization of water resources,enhancing the unified and effective supervision of water resources,promoting the sustainable use of water resources in Jiangjin District,and ensuring the sustainable development of regional society and environment.

Model Selection in Estimation of Covariance Functions for Growth of Angora Goats

Covariance functions have been proposed as an alternative to model longitudinal data in animal breeding because of their various merits in comparison to the classical analytical methods.In practical estimation,different models and polynomial orders fitted can influence the estimates of covariance functions and thus genetic parameters.The objective of this study was to select model for estimation of covariance functions for body weights of Angora goats at 7 time points.Covariance functions were estimated by fitting 6 random regression models with birth year,birth month,sex,age of dam,birth type,and relative birth date as fixed effects.Random effects involved were direct and maternal additive genetic,and animal and maternal permanent environmental effects with different orders of fit.Selection of model and orders of fit were carried out by likelihood ratio test and 4 types of information criteria.The results showed that model with 6 orders of polynomial fit for direct additive genetic and animal permanent environmental effects and 4 and 5 orders for maternal genetic and permanent environmental effects,respectively,were preferable for estimation of covariance functions.Models with and without maternal effects influenced the estimates of covariance functions greatly.Maternal permanent environmental effect does not explain the variation of all permanent environments,well suggesting different sources of permanent environmental effects also has large influence on covariance function estimates.

Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark functions.Then,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques.Additionally,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection problem.The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)repository.The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.

Feature Selection Based on Adaptive Fuzzy Membership Functions

Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for classification.To overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural network.Furthermore,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.

The Core Mechanism of Natural Selection: How the Natural Cycle of Potassium Resources Affects the Biological Evolution and the Change of Human Society

Darwin’s theory of evolution believes that biological evolution is a process of natural selection. This theory has been supported by much evidence, but the internal biological mechanism is not clear. Here, I elaborate on the cycle of potassium resources on the earth and the biological utilization and efficiency, which may be the core mechanism of natural selection and affect the evolution of organisms and the development of human society.

Study on functions and rational allocation of Shule River Basin groundwater resources

Based on Investigation and Assessment on Rational Exploitation and Utilization of Groundwater Resources in Typical Areas of the Hexi Corridor, the thesis studies on groundwater and environmental problems arising from the large-scale agricultural development projects in Shule River Basin. The thesis analyzes problems in exploiting and utilizing water resources, defines the function zoning of groundwater resources in key areas and evaluates them. Finally, the thesis uses three-dimensional unsteady flow simulation and regional social and economic development plan to study on the allocation of groundwater in Shule River Basin. A proposal for rational allocation of Shule River Basin water resources has been put forward.

Evaluation and selection of the ship collaborative design resources based on AHP and genetic and simulated annealing algorithm

The characteristics of the design resources in the ship collaborative design is described and the hierarchical model for the evaluation of the design resources is established. The comprehensive evaluation of the co-designers for the collaborative design resources has been done from different aspects using Analytic Hierarchy Process （AHP） ,and according to the evaluation results,the candidates are determined. Meanwhile,based on the principle of minimum cost,and starting from the relations between the design tasks and the corresponding co-designers,the optimizing selection model of the collaborators is established and one novel genetic combined with simulated annealing algorithm is proposed to realize the optimization. It overcomes the defects of the genetic algorithm which may lead to the premature convergenee and local optimization if used individually. Through the application of this method in the ship collaborative design system,it proves the feasibility and provides a quantitative method for the optimizing selection of the design resources.

Intercalation Assembly and Chemical Product Engineering of Layered Intercalated Functional Materials Based on Efficient Utilization of Magnesium Resources in Salt Lakes

1 Introduction Magnesium salts are very important by-product of salt lake industry in West China.Nearly 200 million cubic meters of waste brine are released to the environment

A Note on the Selection Expectation and Support Function

In this paper, we prove the relationship between selection expectation and support function by a new method.

Functions, Selection and Collocation of Greening Plants in the Construction of Ecological Roads

From the perspective of ecological construction of roads, the reduction and purifying effects of greening plants on noise, raising dust and automobile exhaust, selection principles of arbors, shrubs, ground cover plants and herbaceous fl owers, and the methods of collocating arbors shrubs and grass in the construction of ecological roads were discussed in this study.

A Joint User Selection and Resources Optimization Scheduling Algorithm for CoMP

Coordinated Multi-point (CoMP) transmission technology is one of the key techniques in LTE-Advanced, Which can share the channel and data information in multiple cells, and optimize the whole system performance. In order to optimize the average sector throughput and improve the fairness of resource scheduling, a scheduling algorithm based on the resource is mainly investigated. In this algorithm, users in the network are classified firstly and then we combine the fixed resources division and flexible scheduling. System level simulation platform is set up to validate the algorithm and the results turn out that the average throughput is better compared with the traditional scheme.

A Comprehensive Price Prediction System Based on Inverse Multiquadrics Radial Basis Function for Portfolio Selection

Price prediction plays a crucial role in portfolio selection (PS). However, most price prediction strategies only make a single prediction and do not have efficient mechanisms to make a comprehensive price prediction. Here, we propose a comprehensive price prediction (CPP) system based on inverse multiquadrics (IMQ) radial basis function. First, the novel radial basis function (RBF) system based on IMQ function rather than traditional Gaussian (GA) function is proposed and centers on multiple price prediction strategies, aiming at improving the efficiency and robustness of price prediction. Under the novel RBF system, we then create a portfolio update strategy based on kernel and trace operator. To assess the system performance, extensive experiments are performed based on 4 data sets from different real-world financial markets. Interestingly, the experimental results reveal that the novel RBF system effectively realizes the integration of different strategies and CPP system outperforms other systems in investing performance and risk control, even considering a certain degree of transaction costs. Besides, CPP can calculate quickly, making it applicable for large-scale and time-limited financial market.