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The purpose of this paper is to put forward a multi-stage information aggregation method based on grey inspiriting control lines to evaluate the objects dynamically and comprehensively.

According to the evaluation value of the objects, the positive and negative incentive lines were set up and the predicted values were solved based on the grey GM(1, 1) model, so the value with expected information could be evaluated. In the evaluation, the part above the positive incentive line should be “rewarded” and that below the negative incentive line should be “punished” appropriately. Thereby the double incentive effects of “the current development situation and future development trend” to objects could be implemented on the basis of control.

This method can primarily describe the decision maker's expectancy of the development of evaluation objects and make the evaluation results have better practical application value.

Many comprehensive evaluations were always based on the past information. However, the future development trend of the evaluated object is also very important. This study can be used in the evaluation for future application and development.

The paper succeeds in providing not only a method of multi-phase information aggregation with expectancy information, but also a simple and convenient method solving nonlinear inspiring lines objectively.

What would have been the hypothetical effect of monetary policy shocks had a country never joined the euro area, in cases where we know that the country in question actually did join the euro area? It is one thing to investigate the impact of joining a monetary union, but quite another to examine two things at once: joining the union and experiencing actual monetary policy shocks. The authors propose a methodology that combines synthetic control ideas with the impulse response functions to uncover dynamic response paths for treated and untreated units, controlling for common unobserved factors. Focusing on the largest euro area countries, Germany, France, and Italy, the authors find that an unexpected rise in interest rates depresses inflation and significantly appreciates exchange rate, whereas gross domestic product (GDP) fluctuations are less successfully controlled when a country belongs to the monetary union than would have been the case under the independent monetary policy. Importantly, Italy turns out to be the overall beneficiary, since all three channels – price, GDP, and exchange rate – deliver the desired results. The authors also find that stabilizing an economy within a union requires somewhat smaller policy changes than attempting to stabilize it individually, and therefore provides more policy space.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

This paper aims to develop an assembly behaviour dynamic model of reheat stop valve assembly under run‐time situations and combined (assembly error, friction, fluid dynamics and thermal load behaviour) and to carry out assembly process evaluation and optimisation.

The fluid dynamic behaviour analysis is carried out for the dynamic torque characteristics of reheat stop valve and for the thermal load distribution of the valve shaft‐bush subassembly, which is used for evaluating the thermal deformation of valve shaft by using of finite elements method. The assembly behaviour dynamic model is developed by multibody dynamics theory, which is as the basis of developing virtual prototyping platform for analysing and evaluating the current assembly process.

It is revealed that the deformation (ε) of valve shaft due to the thermal load, and the assembly coaxial error (e) can change the motion clearance remarkably, which lead the dynamic properties and performance of reheat stop valve changed greatly. The current assembly behaviour variable are not optimum and the initial design clearance between valve shaft and bush 4# can be optimised by the developed virtual prototyping platform on the basis of ADAMS® API. The results of evaluation for the assembly behaviour reveal the well dynamic characteristics of reheat stop valve with the optimum assembly behaviour variable. This will be useful for improving the current assembly process of reheat stop valve.

The present assembly behaviour dynamic model based on virtual prototyping for optimum assembly process design uses only single objective optimisation (the most important clearance between valve shaft and bush 4#). For a complete optimum assembly process design has to be carried out with other three clearance variables (the clearance between valve shaft and bush 1#, bush 2# and bush 3#) together.

The present analysis provides some benchmarks for improving the current assembly process. In practice, the assembly coaxial tolerance of valve shaft‐bush subassembly and the initial design clearances must be limited strictly.

This paper provides a methodology for analysis and evaluation of reheat stop valve assembly behaviour with the consideration of combined environmental behaviours. Based on this methodology, it is possible to develop an assembly behaviour dynamic model, and further, to develop a virtual prototyping platform for analysing and evaluating the assembly process which will offer help to designers for improving the reheat stop valve assembly process.

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

We develop a framework for measuring and monitoring business cycles in real time. Following a long tradition in macroeconometrics, inference is based on a variety of indicators of economic activity, treated as imperfect measures of an underlying index of business cycle conditions. We extend existing approaches by permitting for heterogenous lead–lag patterns of the various indicators along the business cycles. The framework is well suited for high-frequency monitoring of current economic conditions in real time – nowcasting – since inference can be conducted in the presence of mixed frequency data and irregular patterns of data availability. Our assessment of the underlying index of business cycle conditions is accurate and more timely than popular alternatives, including the Chicago Fed National Activity Index (CFNAI). A formal real-time forecasting evaluation shows that the framework produces well-calibrated probability nowcasts that resemble the consensus assessment of the Survey of Professional Forecasters.

This paper aims to define an extended QoS model to accurately describe the quality of web service in the open distributed environment and propose a fuzzy evaluation approach for services selection based on the extended QoS model.

The extended QoS model classifies the quality criteria of web service as five composite quality criteria, and each composite quality criterion is composed by one or more sub‐quality criteria. Considering the multiple forms of representation for the quality criteria and different types of quality value could not be compared directly, a scaling fuzzy measure for quality criteria is introduced. Based on the scaling fuzzy measure of the quality criteria, a fuzzy synthetic evaluation system for services selection is proposed.

The quality of web service has multiple facets and multiple forms of representation. The fuzzy synthetic evaluation system can deal well with the fuzzy and implicit concepts about quality evaluations and provides higher expressive force and adaptability.

The quality of service in this paper is static preset.

The extended QoS model and the synthetic fuzzy evaluation system cover the shortage of the related researches and lay the foundations for QoS‐oriented service description and services selection.

This paper aims to propose a double-sided switched reluctance linxear generator (DSRLG) exclusively for wave power generation. The initial dimensions are given through design experience and principles. To ameliorate comprehensive performance of the DSRLG, the multi-objective optimization design is processed.

The multi-objective optimization design of the DSRLG is processed by adopting a modified entropy technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. First, sensitivity analyzes on geometric parameters of the DSRLG are conducted to determine several pivotal geometric parameters as optimization variables. Then, the multi-objective optimization is conducted on the basis of initial dimensions. After determination of synthetical evaluation value of each structure parameter, the best dimension scheme of the DSRLG is concluded.

After verification by finite element method simulation and dynamic simulation, the final dimension scheme proves to perform better than the initial scheme. Finally, experiments are conducted to verify the accuracy of both the stable finite element DSRLG model and dynamic simulation system model so that the conclusion of this paper proves to be reliable and compelling.

This paper proposes an improved structure of the DSRLG, which is superior for wave power generation. Meanwhile, a novel modified entropy TOPSIS algorithm is applied to the field of electrical machine multi-objective optimal design for the first time.

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.