Role of Wind-Sand Disturbance in the Formation and Development of Tamarix taklamakanensis Community

Formation, development and degeneration of Tamarix taklamakanensis M.T. Liu community may be affected by wind-sand disturbance. On the basis of field survey and experiments that took eight years from 1994, it was found that the plant community was formed in wet and low-lying land, developed in sand burying land, and died out in the land with strong wind erosion and severe sand burying. In the initial stage of the community formation, the low-lying land that was formed by wind erosion provided a suitable habitat for seed germination and plant rooting, but salt stress from ground water reduced seedling survival rate. In the developing stage of the community, sand burying stimulated vigorous growth of the shrubs. In the degenerative stage of the community, the wind erosion and sand burying resulted in dying out of the shrubs. It is therefore concluded that wind-sand disturbance has a positive role in the formation and development of the community, and is in a close association with the process of the community degeneration. Taking the habitat of T taklamakanensis into account, it may be appropriate to note that, under the conditions of frequent disturbance and severe stress, desert pioneer species are adaptable and able to survive. This might be a supplement to the CSR strategy.

Linguistic approaches to multiple attribute decision making in uncertain linguistic setting

We studied multiple attribute decision-making problems with uncertain linguistic information, in which the preference values took the form of uncertain linguistic variables. We introduced some operational laws of uncertain linguistic variables and a formula for the comparison between two uncertain linguistic variables. We proposed two new aggregation operators called extended uncertain linguistic aggregation (EULA) operator and interval linguistic aggregation (ILA) operator, and then develop an EULA operator-based linguistic approach and an ILA operator-based linguistic approach, respectively, to multiple attribute decision making in uncertain linguistic setting. The approaches were straightforward and do not produce any loss of information. Finally, an illustrative example was given to verify the developed approaches and to demonstrate their practicality and effectiveness.

DECISION-MAKING OF SLICING SCHEME IN FUSED DEPOSITION MODELING PROCESS BASED ON ANALYTICAL HIERARCHICAL PROCESS

Based on analyzing the influences of a slicing scheme on stair-stepping effect, supporting structure, efficiency and deformation, etc. , analytical hierarchical process （AHP） combining with fuzzy synthetic evaluation is introduced to make decision in slicing schemes for a processing part. The application in determining the slicing scheme for a computer mouse during prototyping shows that the method increases the rationality during decision- making and improves quality and efficiency for the prototyping part.

Co-evolutionary cloud-based attribute ensemble multi-agent reduction algorithm

In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorithm is proposed.First, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation.Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance.Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.

Multi-objective integrated optimization based on evolutionary strategy with a dynamic weighting schedule

The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.

Strategies on improving the electrocatalytic hydrogen evolution performances of metal phosphides

Among the sustainable energy sources,hydrogen is the one most promising for alleviating the pollution issues related to the usage of conventional fuels,as it can be produced in an efficient and eco-friendly way via electrocatalytic water splitting.The hydrogen evolution reaction(HER,a half-reaction of water splitting)plays a pivotal role in decreasing the price and increasing the catalytic efficiency of hydrogen production and is efficiently promoted by metal phosphides in different electrolytes.Herein,we summarize the recent advances in the development of metal phosphides as HER electrocatalysts,focus on their synthesis(post-treatment,in situ generation,and electrodeposition methods)and the enhancement of their electrocatalytic activity(via elemental doping,interface and vacancy engineering,construction of specific supports and nanostructures,and the design of bior polymetallic phosphides),and highlight the crucial issues and challenges of future development.

Recent advances in fixation of CO_(2) into organic carbamates through multicomponent reaction strategies

Carbon dioxide(CO_(2)) is the main greenhouse gas and also an ideal C1 feedstock in organic synthesis because it is abundant,nontoxic,nonflammable,and renewable.The synthesis of organic carbamates using CO_(2) as a phosgene alternative has attracted extensive attention because of the importance of carbamates in organic synthesis and in the pharmaceutical and agrochemical industries.In recent decades,many multicomponent reaction strategies have been designed for constructing different types of organic carbamate molecules.Most of these methods rely on the in situ generation of carbamate anions from CO_(2) and amines,followed by reactions with other coupling partners.Synthetic strategies for acyclic carbamates include nucleophile‐electrophile coupling,nucleo‐phile‐nucleophile oxidative coupling,difunctionalization of unsaturated hydrocarbons,and C–H bond functionalization.Strategies for the synthesizing cyclic carbamates include carboxylative cyclization of in situ‐generated unsaturated amines and difunctionalization of unsaturated amines with CO_(2) and other electrophilic reagents.This review summarizes the recent advances in the synthesis of organic carbamates from CO_(2) using different multicomponent reaction strategies.Future perspectives and challenges in the incorporation of CO_(2) into carbamates are also presented.

Integration strategies of hydrogen network in a refinery based on operational optimization of hydrotreating units

Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries.It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries.An integration strategy of hydrogen network and an operational optimization model of hydrotreating(HDT)units are proposed based on the characteristics of reaction kinetics of HDT units.By solving the proposed model,the operating conditions of HDT units are optimized,and the parameters of hydrogen sinks are determined by coupling hydrodesulfurization(HDS),hydrodenitrification(HDN)and aromatic hydrogenation(HDA)kinetics.An example case of a refinery with annual processing capacity of eight million tons is adopted to demonstrate the feasibility of the proposed optimization strategies and the model.Results show that HDS,HDN and HDA reactions are the major source of hydrogen consumption in the refinery.The total hydrogen consumption can be reduced by 18.9%by applying conventional hydrogen network optimization model.When the hydrogen network is optimized after the operational optimization of HDT units is performed,the hydrogen consumption is reduced by28.2%.When the benefit of the fuel gas recovery is further considered,the total annual cost of hydrogen network can be reduced by 3.21×10~7CNY·a^(-1),decreased by 11.9%.Therefore,the operational optimization of the HDT units in refineries should be imposed to determine the parameters of hydrogen sinks base on the characteristics of reaction kinetics of the hydrogenation processes before the optimization of the hydrogen network is performed through the source-sink matching methods.

A Novel Close-loop Strategy for Integrating Process Operations of Fluidized Catalytic Cracking Unit with Production Planning Optimization

Production planning models generated by common modeling systems do not involve constraints for process operations, and a solution optimized by these models is called a quasi-optimal plan. The quasi-optimal plan cannot be executed in practice some time for no corresponding operating conditions. In order to determine a practi- cally feasible optimal plan and corresponding operating conditions of fluidized catalytic cracking unit （FCCU）, a novel close-loop integrated strategy, including determination of a quasi-optimal plan, search of operating conditions of FCCU and revision of the production planning model, was proposed in this article. In the strategy, a generalized genetic algorithm （GA） coupled with a sequential process simulator of FCCU was applied to search operating conditions implementing the quasi-optimal plan of FCCU and output the optimal individual in the GA search as a final genetic individual. When no corresponding operating conditions were found, the final genetic individual based correction （FGIC） method was presented to revise the production planning model, and then a new quasi-optimal production plan was determined. The above steps were repeated until a practically feasible optimal plan and corresponding operating conditions of FCCU were obtained. The close-loop integrated strategy was validated by two cases, and it was indicated that the strategy was efficient in determining a practically executed optimal plan and corresponding operating conditions of FCCU.

Rotation forest based on multimodal genetic algorithm

In machine learning,randomness is a crucial factor in the success of ensemble learning,and it can be injected into tree-based ensembles by rotating the feature space.However,it is a common practice to rotate the feature space randomly.Thus,a large number of trees are required to ensure the performance of the ensemble model.This random rotation method is theoretically feasible,but it requires massive computing resources,potentially restricting its applications.A multimodal genetic algorithm based rotation forest(MGARF)algorithm is proposed in this paper to solve this problem.It is a tree-based ensemble learning algorithm for classification,taking advantage of the characteristic of trees to inject randomness by feature rotation.However,this algorithm attempts to select a subset of more diverse and accurate base learners using the multimodal optimization method.The classification accuracy of the proposed MGARF algorithm was evaluated by comparing it with the original random forest and random rotation ensemble methods on 23 UCI classification datasets.Experimental results show that the MGARF method outperforms the other methods,and the number of base learners in MGARF models is much fewer.

Application of new VBHF optimization strategy to improve formability of automobile panels with aluminum alloy sheet

A VBHF(Variable Blank Holder Force) optimization strategy was employed to determine the optimal time-variable and spatial-variable BHF trajectories,aiming at improving the formability of automobile panels with aluminum alloy sheet.The strategy was implemented based on adaptive simulation to calculate the critical wrinkling BHF for each segmented binder of the Numisheet' 05 deck lid in a single round of simulation.The thickness comparison of the stamped part under optimal VBHF and constant BHF shows that the variance of the four sections is decreased by 70%,44%,64% and 61%,respectively,which indicates significant improvement in thickness distribution and variation control.The investigation through strain path comparison reveals the fundamental reason of formability improvement.The study proves the applicability of the new VBHF optimization strategy to complex parts with aluminum alloy sheet.

Rescheduling Problems with Bicriteria

In the rescheduling on a single machine,a set of original jobs has already been scheduled to minimize some cost objective,when a new set of jobs arrives and creates a disruption.The decision maker needs to insert the new jobs into the existing schedule without excessively disrupting it.In this paper,we consider hierarchical optimization between the scheduling cost of all the jobs and the degree of this disruption.For every problem,we provide either a polynomial time algorithm or an intractable result.

Multifunctional graphene‐based composite photocatalysts oriented by multifaced roles of graphene in photocatalysis

Graphene(GR),a single‐layer carbon sheet with a hexagonal packed lattice structure,has displayed attractive potential and demonstrably become the research focus in artificial photocatalysis due to its enchanting properties in enhancing light absorption,electron transfer dynamics,and surface reactions.Currently,numerous efforts have shown that the properties of GR,which are closely correlated to the photocatalytic performance of GR‐based composites are significantly affected by the synthesis methods.Herein,we first introduce the optimization strategies of GR‐based hybrids and then elaborate the synthesis of GR‐based composite photocatalysts oriented by manifold roles of GR in photoredox catalysis,containing photoelectron mediator and acceptor,improving adsorption capacity,regulating light absorption range and intensity,as well as macromolecular photosensitizer.Beyond that,a brief outlook on the challenges in this burgeoning research field and potential evolution strategies for enhancing the photoactivity of GR‐based hybrids is presented and we anticipate that this review could provide some enlightenments for the rational construction and application of multifunctional GR‐based composite photocatalysts.

Inventory control model based on external and interior reverse logistics

To improve the inventory control strategy for enterprise and optimize inventory control parameters of existing external and interior reverse logistics, a multi-resource inventory control model is proposed to better simulate the logistics fact, which is aimed at periodic inventory checking and pull mode of inventory control strategy, based on the return product arrival time obeying Poisson distribution, the return product employing lotsize process and the nonzero lead time of manufaeturinge/remanufacturing. The rational cost function with multiple constraints is employed to describe the inventory model. The genetic algorithm is employed to solve the inventory cost function to obtain the optimal solution of inventory checking periods, safe inventory point, product lot-sizes and process lot-sizes of return product. An example is presented to prove the feasibility and validity of the proposed method. Moreover, the influence of manufacturing/remanufacturing lead time and reuse rate of return production on the inventory control strategy of enterprise is analyzed.

Hepatitis elimination by 2030: Progress and challenges

Globally, over 300 million people are living with viral he-patitis with approximately 1.3 million deaths per year. In 2016, World Health Assembly adopted the Global Health Sector Strategy on viral hepatitis to eliminate hepatitis by 2030. Different World Health Organization member countries are working on hepatitis control strategies to achieve hepatitis elimination. So far, only 12 countries are on track to achieve hepatitis elimination targets. The aim of the study was to give an update about the progress and challenges to achieving hepatitis elimination by 2030. According to the latest data, 87% of infants had received the three doses of hepatitis B virus （HBV） vaccination in the frst year of their life and 46% of infants had received a timely birth dose of HBV vaccination.There is a strong need to improve blood and injection safety. Rates of hepatitis B and C diagnosis are very low and only 11% of hepatitis B and C cases are diagnosed. There is a dire need to speed up hepatitis diagnosis and find the missing millions of people living with viral hepatitis. Up to 2016, only 3 million hepatitis C cases have been treated. Pricing of hepatitis C virus drugs is also reduced in many countries. The major hurdle to ach-ieve hepatitis elimination is lack of finances to support hepatitis programs. None of the major global donors are committed to invest in the fght against hepatitis. It will be very diffcult for the low and middle-income countries to fund their hepatitis control program. Hepatitis elimination needs strong fnancial and political commitment, support from civil societies, and support from pharmaceutical and medical companies around the globe.

Key strategies for predictive exploration in mature environment: model innovation, exploration technology optimization and information integration

Prediction has become more and more difficult in mineral exploration, especially in the mature exploration environment such as Tongling copper district. For enhancing predictive discovery of hidden ore deposits in such mature environment, the key strategies which should be adopted include the innovation of the exploration models, application of the advanced exploration techniques and integration of multiple sets of information. The innovation of the exploration models should incorporate the new metallogenic concepts that are based on the geodynamic anatomization. The advanced techniques applied in the mature exploration environment should aim at the speciality and complexity of the geological setting and working environments. The information synthesis is to integrate multiple sets of data for giving a more credible and visual prospectivity map by using the geographic imformation system(GIS) and several mathematical methods, such as weight of evidence and fuzzy logic, which can extract useful information from every set of data as much as possible. Guided by these strategies, a predictive exploration in Fenghuangshan ore field of Tongling copper district was implemented, and a hidden ore deposit was discovered.

Decision Cost Feature Weighting and Its Application in Intrusion Detection

This paper introduces the cost-sensitive feature weighting strategy and its application in intrusion detection. Cost factors and cost matrix are proposed to demonstrate the misclassification cost for IDS. How to get the whole minimal risk, is mainly discussed in this paper in detail. From experiments, it shows that although decision cost based weight learning exists somewhat attack misclassification, it can achieve relatively low misclassification costs on the basis of keeping relatively high rate of recognition precision. Key words decision cost - feature weighting - intrusion detection CLC number TP 393. 08 Foundation item: Supported by the National Natural Science Foundation Key Research Plan of China (90104030) and “20 Century Education Development Plan”Biography: QIAN Quan(1972-), male, Ph. D. research direction: computer network, network security and artificial intelligence

Optimal pricing decision model based on activity-based costing

In order to find out the applicability of the optimal pricing decision model based on conventional cost behavior model after activity based costing has given strong shock to the conventional cost behavior model and its assumptions, detailed analyses have been made using the activity based cost behavior and cost volume profit analysis model, and it is concluded from these analyses that the theory behind the construction of optimal pricing decision model is still tenable under activity based costing, but the conventional optimal pricing decision model must be modified as appropriate to the activity based costing based cost behavior model and cost volume profit analysis model, and an optimal pricing decision model is really a product pricing decision model constructed by following the economic principle of maximizing profit.

Maintenance decision-making method for manufacturing system based on cost and arithmetic reduction of intensity model

A cost-based selective maintenance decision-making method was presented.The purpose of this method was to find an optimal choice of maintenance actions to be performed on a selected group of machines for manufacturing systems.The arithmetic reduction of intensity model was introduced to describe the influence on machine failure intensity by different maintenance actions (preventive maintenance,minimal repair and overhaul).In the meantime,a resolution algorithm combining the greedy heuristic rules with genetic algorithm was provided.Finally,a case study of the maintenance decision-making problem of automobile workshop was given.Furthermore,the case study demonstrates the practicability of this method.

Constructed Model of Cost/Benefit Analysis Strategy for Stem Corn Borer Sesamia cretica

An integrated pest management model of Cost/Benefit analysis strategy was constructed for stem corn borer Sesamia cretica using complementary control measures of different resistant genotypes of corn with the chemical insecticide diazinon 60%. Based on Cost/Benefit analysis result, the resistant genotype （SAKHA 9433） provides maximum economic value of production at the model point where no spray of insecticide is applied. The applications of one or two sprays do not justify the use of chemical insecticide but rather result in economic loss since the reduction in borer damage value, due to diazinon use, is lower than the cost of control （spray）. However, the applications of one spray for the moderate resistant genotype （IPA 2052） and two sprays for the sensitive genotype （CML 323） during corn growing season would be of value to cover the cost of control （spray） but do not achieve similar economic value of revenue comparing with the resistant genotype.