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The purpose of this paper is to show how, with a view to the shortcomings of traditional optimization methods, a multi‐objective optimization concerning the structure sizes of micro‐channel heat sink is performed by adaptive genetic algorithm. The optimized micro‐channel heat sink is simulated by computational fluid dynamics (CFD) method, and the total thermal resistance is calculated to compare with that of thermal resistance network model.

Taking the thermal resistance and the pressure drop as goal functions, a multi‐objective optimization model was proposed for the micro‐channel cooling heat sink based on the thermal resistance network model. The coupled solution of the flow and heat transfer is considered in the optimization process, and the aim of the procedure is to find the geometry most favorable to simultaneously maximize heat transfer while obtaining a minimum pressure drop. The optimized micro‐channel heat sink was numerically simulated by CFD software.

The results of optimization show that the base convection thermal resistance contributes to maximum the total thermal resistance, and base conduction thermal resistance contributes to least. The width of optimized micro‐channel and fin are 197 and 50 μm, respectively, and the corresponding total thermal resistance of the whole micro‐channel heat sink is 0.838 K/W, which agrees well with the analysis result of thermal resistance network model.

The convection heat transfer coefficient is calculated approximately here for convenience, and that may induce some errors.

The maximum difference in temperature of the optimized micro‐channel cooling heat sink is 84.706 K, which may satisfy the requirement for removal of high heat flux in new‐generation chips. The numerical simulation results are also presented, and the results of numerical simulation show that the optimized micro‐channel heat sink can enhance thermal transfer performance.

This study uses big data analysis aimed at discovering city bus passenger ridership patterns. Hence, marketing managers can get sufficient insights to formulate effective business plans and make timely decisions about company operations.

This study uses a mixed-method analysis to analyze the results. First uses the RFM (recency, frequency, and monetary) model combined with a big data technique (K-means) to analyze bus passenger boarding behavior. In order to improve the validity and quality of the research, this study also conducted interviews with senior managers of the bus company from which the data was obtained.

The study identifies six distinct groups of passengers with different boarding behaviors, ranging from “general passengers” to “most valuable passengers”. General passengers constituted the largest group. As such, they should be the main target for municipal governments when promoting bus ridership as part of energy conservation and carbon-reduction activities. This group of passengers should be encouraged to take public transport vehicles more, instead of relying on personal vehicles. The fourth group identified included elderly passengers with hospitals as their destinations. Bus companies can cooperate with municipal government to provide morning “medical bus” services for the elderly. Interviews with bus company managers confirmed that the analytical results of this study correspond with the observations, experiences, and actual business operating plans of bus companies.

Only few studies have analyzed passengers' boarding behavior applying a mixed-method analysis.

With development of the modern electronic and mechanical devices, cooling requirement has become a serious challenge. Innovative heat transfer enhancement methods are generally accompanied by undesirable increase of pressure drop and consequently a pumping power penalty. The current study aims to present a novel and easy method to manufacture a mini heat sink using porous fins and magnetite nanofluid (Fe₃O₄/water) as the coolant for simultaneous heat transfer enhancement and pressure drop reduction.

A three-dimensional numerical study is carried out to evaluate the thermal and hydrodynamic performance of the mini heat sink at different volume fractions, porosities and Reynolds numbers, using finite volume method. The solver specifications for discretization of the domain involve the SIMPLE, second-order upwind and second order for pressure, momentum and energy, respectively.

Results show that porous fins have a favorable effect on both heat transfer and pressure drop compared to solid fins. Creation of a virtual velocity slip on the channel-fin interfaces similar to the micro scale conditions and the flow permeation into the porous fins are the main mechanisms of pressure drop reduction. On the other hand, the heat transfer enhancement is attributed to the increase of the solid-fluid contact area and the improvement of the flow mixing because of the flow permeation into the porous fins. An optimal porosity for maximum convective heat transfer enhancement is obtained as a function of Reynolds number. However, taking both pressure drop and heat transfer effects into account, the overall heat sink performance is shown to be improved at high of Reynolds numbers, volume fractions and fin porosities.

Thermal radiation and gravity effects are ignored, and thermal equilibrium is assumed between solid and fluid phases.

A maximum of 32 per cent increase of convective heat transfer is achieved along with a maximum of 33 per cent reduction in the pressure drop using porous fins and ferrofluid in heat sink.

Unlike traditional industries, the e-cigarette is an epoch-making innovative product originating in China and occupying an absolute competitive advantage in the international market. The traditional A-U model describes the laws and characteristics of technological innovation in developed countries. In contrast, the inverse A-U model depicts the process of “secondary innovation” in late-developing countries through digestion and absorption. This paper aims to find out that if the e-cigarette, as a “first innovation” industry in a late-developing country, conform to the A-U model or conform to the “inverse A-U model”.

This paper takes the patent data of e-cigarettes from 2004 to 2021 as the research object, and uses Python’s Jieba segment words to divide product innovation and process innovation, and then uses statistical analysis methods to conduct empirical analyses on these data.

Thus, an improved A-U model suitable for the e-cigarette industry is proposed. In this model, product innovation in the e-cigarette industry appeared earlier than process innovation, but the synchronous development of product and process innovation is not lagging. The improved A-U model in the e-cigarette industry is not only different from the traditional A-U model but also does not conform to the inverse A-U model.

It is conducive to expanding and clarifying the theoretical contribution and applicable boundaries of the A-U model and has sparked thinking and exploration of the A-U model in e-cigarettes and emerging industries.

On this basis, suggestions on the development path and countermeasures of the e-cigarette industry are put forward.

Based on the e-cigarette industry, this paper takes patents as the research object and provides the method of dividing product innovation and process innovation, and proposes an A-U model suitable for the e-cigarette industry on this basis. By comparing the traditional A-U model with the inverse A-U model in latecomer countries, the background and causes of e-cigarette A-U model heterogeneity are analyzed from different stages and overall morphology. Based on this, the heterogeneity characteristics of e-cigarette innovation are summarized and sorted out.

The purpose of this paper is to develop a model that determines whether how much effort of preventive maintenance action is worthwhile for the consumer over the post-sale product life cycle of a repairable complex product where the product is under warranty and subject to stochastic multimode failure process, that is, damaging failure and light failure with different probabilities.

The expected life cycle cost is designed for a warranted product from the consumer perspective. The product failure is quantified with failure rate function, which is the number of failures incurred over the product life cycle. The authors consider the failure rate function reduction method in their model where the scale parameter of a failure rate function is maximized by applying the optimal preventive maintenance level. The scale parameter of any failure distribution refers to the meantime to failure (MTTF). The first-order condition is applied with respect to the maintenance level in order to achieve the convexity of the nonlinear function of the expected life cycle cost function.

The authors have found analytically the close form of the preventive maintenance level, which can be used to find the optimal reduced form of the failure rate function of the product and the minimum product expected life cycle cost under the given condition of multimode stochastic failure process. The authors have suggested different maintenance policies to consumers in order to implement the proposed preventive maintenance model under different conditions. A numerical example further illustrated the analytical model by considering the Weibull distribution.

The consumer may use this study in the accurate modeling of the life cycle cost of a product that is under warranty and fails with a multimode failure process. Also, the suggested preventive maintenance approach of this study helps the consumer in making appropriate maintenance decisions such as to minimize the expected life cycle cost of a product.

This study proposes an accurate estimation of a life cycle cost for a product that is under the support of warranty and fails with multimode. Furthermore, for such a kind of product, which is under warranty and fails with multimode, this study suggests a new preventive maintenance approach that assures the minimum expected life cycle cost.