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To dilute the financial difficulties in agricultural production and operation, the Chinese government has actively explored and developed rural supply chain finance (RSCF) service systems. The purpose of this study is to evaluate and analyze the performance of RSCF systems in China.

To evaluate the performance of RSCF systems in China, this study proposes a two-stage data envelopment analysis model. Compared with other models, the model proposed in this study considers not only the technical gap between RSCF systems but also the maximization of intermediate output to conform to the practice of RSCF.

Based on the empirical analysis, this study draws the following four conclusions. First, the overall efficiency of China's RSCF systems is low, and there remains great potential for improvement. Second, the technology gap ratio index score and meta-frontier efficiency of RSCF systems in Central China are the lowest in all regions, indicating that the technical level of RSCF systems in Central China is the lowest. Third, the relationship between rural residents' disposable income and the efficiency of RSCF systems is U-shaped, and the efficiency of RSCF systems in the high-income group is far greater than that of other income groups. Finally, the main reason for the lack of efficiency in RSCF seems to lie in management and technology.

This study divides all RSCF systems into four types according to management potential and technical potential, and recommend corresponding improvement suggestions for different kinds of RSCF systems.

The paper focuses on two topics, optimizing the proposed triangular tube for crashworthiness and solving a non‐linear programming problem by a “mapping” technique, which the condition of Lagrange Multiplier Theorem is violated within the feasible region. The purpose of studying optimized triangular tubes is to prepare them for redesigning vehicle bumpers. The dimension optimization of triangular tube is carried out for its thickness and lateral length, based on the accomplished shape optimization under an impact. The load uniformity is taken as the objective function, which is defined as the ratio of maximum peak force and means crushing force. Meanwhile the mean crushing force and absorbed energy are treated as constraints. Based on FEA analysis, the regression functions for load uniformity, mean crushing force, and absorbed energy are formulated by RSM. The result has shown that triangular tube possesses an optimization region, under which the better‐integrated property can be achieved to supply a more safety environment for vehicular occupants.

The purpose of this paper is to present a new methodology, used for dynamic reliability analysis of a gear transmission system (GTS) of wind turbine (WT), which could be used for assembly decision-making of the parts with errors to improve the GTS’s performance.

This paper involves the dynamic and dynamic reliability analysis of a GTS. The history curves of dynamic responses of the parts are obtained with the developed gear-bearing coupling dynamic model considering the random errors, failure dependency and random load. Then, the surrogate models of the mean and standard deviation of responses are presented by statistics, rain flow counting method and corrected-partial least squares regression response surface method. Further, a novel dynamic reliability model based on the maximum extreme theory, a theory of sequential statistics, equivalent principles and the inverse transform theory of random variable sampling, is developed to overcome the limitations of traditional methods.

The dynamic reliability of GTS considering the different impact factors are evaluated. The proposed reliability methodology not only overcomes the limitations associated with traditional approaches but also provides good guidance to assembly the parts in a GTS to its best performance.

Instead of constant errors, this paper considers the randomness of the impact factors to develop the dynamic reliability model. Further, instead of the limitation of the normal distribution of the random parameters in the traditional method, the proposed methodology can deal with the problems with non-normal distribution parameters, which is more suitable for the real engineering problems.

Man has been seeking an ideal existence for a very long time. In this existence, justice, love, and peace are no longer words, but actual experiences. How ever, with the American preemptive invasion and occupation of Afghanistan and Iraq and the subsequent prisoner abuse, such an existence seems to be farther and farther away from reality. The purpose of this work is to stop this dangerous trend by promoting justice, love, and peace through a change of the paradigm that is inconsistent with justice, love, and peace. The strong paradigm that created the strong nation like the U.S. and the strong man like George W. Bush have been the culprit, rather than the contributor, of the above three universal ideals. Thus, rather than justice, love, and peace, the strong paradigm resulted in in justice, hatred, and violence. In order to remove these three and related evils, what the world needs in the beginning of the third millenium is the weak paradigm. Through the acceptance of the latter paradigm, the golden mean or middle paradigm can be formulated, which is a synergy of the weak and the strong paradigm. In order to understand properly the meaning of these paradigms, however, some digression appears necessary.

This paper analyses the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces.

Resource scheduling is the study of how to effectively measure, evaluate, analyze, and dispatch resources in order to meet the demands of corresponding tasks. Aiming at the problem of resource scheduling in the private cloud environment, the purpose of this paper is to propose a resource scheduling approach from an efficiency priority point of view.

To measure the computational efficiencies for the resource nodes in a private cloud environment, the data envelopment analysis (DEA) approach is incorporated and a suitable DEA model is proposed. Then, based on the efficiency scores calculated by the proposed DEA model for the resource nodes, the 0-1 programming technique is introduced to build a simple resource scheduling model.

The proposed DEA model not only has the ability of ranking all the decision-making units into different positions but also can handle non-discretionary inputs and undesirable outputs when evaluating the resource nodes. Furthermore, the resource scheduling model can generate for the calculation tasks an optimal resource scheduling scheme that has the highest total computational efficiency.

The proposed method may also be used in studies of resource scheduling studies in the environments of public clouds and hybrid clouds.

The proposed approach can achieve the goal of resource scheduling in private cloud computing platforms by attaining the highest total computational efficiency, which is very significant in practice.

This paper uses an efficiency priority point of view to solve the problem of resource scheduling in private cloud environments.

Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one of the fastest growing markets in China. Data relating to healthcare search behavior may provide insights that could lead to better provision of healthcare services. However, discrepancies often arise between terminologies derived from professional medical domain knowledge and the more colloquial terms that users adopt when searching for information about ailments. This can make it difficult to match healthcare queries with doctors’ keywords in online medical searches. The paper aims to discuss these issues.

To help address this problem, the authors propose a transfer learning using latent factor graph (TLLFG), which can learn the descriptions of ailments used in internet searches and match them to the most appropriate formal medical keywords.

Experiments show that the TLLFG outperforms competing algorithms in incorporating both medical domain knowledge and patient-doctor Q&A data from online services into a unified latent layer capable of bridging the gap between lay enquiries and professionally expressed information sources, and make more accurate analysis of online users’ symptom descriptions. The authors conclude with a brief discussion of some of the ways in which the model may support online applications and connect offline medical services.

The authors used an online medical searching application to verify the proposed model. The model can bridge users’ long-tailed description with doctors’ formal medical keywords. Online experiments show that TLLFG can significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences.

The authors use transfer learning to map online users’ long-tail queries onto medical domain knowledge, significantly improving the relevance of queries and keywords in a search system reliant on sponsored links.

The purpose of this paper is to investigate the pose control of rigid spacecraft subject to dead-zone input, unknown external disturbance and parametric uncertainty in space maneuvering mission.

First, a 6-Degree of Freedom (DOF) dynamic model of rigid spacecraft with dead-zone input, unknown external disturbances and parametric uncertainty is derived. Second, a super-twisting-like fixed-time disturbance observer (FTDO) with strong robustness is developed to estimate the lumped disturbances in fixed time. Based on the proposed observer, a non-singular fixed-time terminal sliding-mode (NFTSM) controller with superior performance is proposed.

Different from the existing sliding-mode controllers, the proposed control scheme can directly avoid the singularity in the controller design and speed up the convergence rate with improved control accuracy. Moreover, no prior knowledge of lumped disturbances’ upper bound and its first derivatives is required. The fixed-time stability of the entire closed-loop system is rigorously proved in the Lyapunov framework. Finally, the effectiveness and superiority of the proposed control scheme are proved by comparison with existing approaches.

The proposed NFTSM controller can merely be applied to a specific type of spacecrafts, as the relevant system states should be measurable.

A NFTSM controller based on a super-twisting-like FTDO can efficiently deal with dead-zone input, unknown external disturbance and parametric uncertainty for spacecraft pose control.

This investigation uses NFTSM control and super-twisting-like FTDO to achieve spacecraft pose control subject to dead-zone input, unknown external disturbance and parametric uncertainty.

The purpose of this paper is to evaluate the detection performance of infrared photoelectric detection system and establish stable tracking platform.

This paper puts forward making use of the finite element analysis method to set up the infrared radiation characteristics calculation model of flying target in infrared photoelectric detection system; researches the target optical characteristics based on the target imaging detection theory; sets up the heat balance equation of target’s surface node and gives the calculation method of total radiation intensity of flying target; and deduces the target detection distance calculation function; studies the changed regulation of radiation energy that charge coupled device (CCD) gain comes from target surface infrared heat radiations under different sky background luminance and different target flight attitude.

Through calculation and experiment analysis, the results show that when the target’s surface area increases or the target flight velocity is higher, the radiation energy that CCD obtained is higher, which is advantageous to the target stable detection in infrared photoelectric detection system.

This paper uses the finite element analysis method to set up the infrared radiation characteristics calculation model of flying target and give the calculation and experiment results; those results can provide some data and improve the design method of infrared photoelectric detection system, and it is of value.