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This paper aims to propose a numerical method to calculate the convective heat transfer coefficient of spiral bevel gears under the condition of splash lubrication and to reveal the lubrication and temperature characteristics between the gears and the oil-air two-phase flow.

Based on computational fluid dynamics, the multiple reference frames (MRF) method was used to simulate the rotational characteristics of gears and the motions of their surrounding fluid. The lubrication and temperature characteristics of gears were studied by combining the MRF method with the volume of the fluid multiphase flow model.

The convective heat transfer coefficient can be improved by increasing the rotational speed and the oil immersion depth. Moreover, the temperature of the tooth surface having a large convective heat transfer coefficient is also found to be low. A large convection heat transfer coefficient could lead to a good cooling effect.

This method can be used to obtain the convective heat transfer coefficient values at different meshing positions, different radii and different tooth surface positions. It also can provide research methods for improving the cooling effect of gears under the condition of splash lubrication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2020-0233/

Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.

The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.

The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.

The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.

To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

Bridge plans are a complicated grey system. It depends on various natural and social factors. The purpose of this paper is to provide a scientific method for optimization of bridge construction plan.

Grey relational analysis (GRA) is completely new analysis method has been proposed in the grey system theory. Grey relational order can be used to describe the relation between the related factors based on data series rather than linear relation and typical distribution. First, this paper describes the basic steps and formulae of GRA. Then provides an example to show how to select best bridge construction plan with the method. Specially discusses significant influence of weight selection on decision making for a bridge plan.

The optimization of bridge construction plan will be selected more reasonable and more objective with the method GRA.

This paper will be further studied on how to quantify indicators more objectively and how to decide weight factor more reasonably.

It has significant practical value to apply GRA to optimization of bridge plans and other engineering projects.

A scientific method‐GRA has been applied to the selection of bridge plans for seeking the best comprehensive result.

The purpose of this paper is to bridge the gap between human emotions and wearable technologies for interactive fashion innovation. To consider the reasons why smart clothing should satisfy the internet of things (IoT) technical functions and human emotional expression simultaneously, to investigate the manner in which artistic design perspectives and engineering methods combined effectively, to explore the R&D elements of future smart clothing based on the IoT technology.

This study combines artistic design perspectives with information-sensing engineering methods as well as kansei evaluation method. Micro-sensors and light-emitting diodes (LEDs) embedded in couples clothing prototype. The first experiment step in the design and production of prototype clothing, and do the initial emotional evaluation. The second experiment is the comparative evaluation of the prototype and other typical smart clothing.

The interactive clothing prototype was proven to correlate well with human emotional expressive patterns. The evaluation I indicated the prototype can stimulate the emotional response of the participants to achieve a higher score in the activate sensor state. Evaluation II revealed that in the process of interactive clothing design, the technical functionality should synchronize with the requirements of human emotional expression.

This study builds the research and development theoretical model of interactive clothing that can be integrated into daily smart clothing life design, and analyze the methods and means of blending IoT smart information-sensing technology with emotional design. By means of this experimental demonstration of human-centered interactive clothing design, the authors provide smart clothing 3.0 evolutionary roadmap and propose a new concept of internet of clothes (IoC) for further research reference.

The purpose of this paper is to propose a new group decision‐making approach, which can only use simple mathematical calculations to perform a group decision‐making task.

The large column and large row (LCLR) method is designed and applied.

The paper finds four propositions to support LCLR methods to be a simple and effectual means for group decision making.

The aggregated matrix constructed in LCLR methods should be generated from non‐contradictory circles.

Effective group decision‐making results can be obtained by easily used methods, not necessarily by using complex mathematics technology to conduct the task.

The new approach based on LCLR methods proposed in this paper may be one of the most easily used and effective means for group decision making.

The chemical behaviour within the occluded cell of simulated cast iron artefact in 3.5 percent NaCl solution has been investigated by means of a simulated occluded cell. It was observed that the pH value and the amount of Cl⁻ migration in the occluded cell were related to the quantity of passing electric current. Electrochemical techniques were capable of providing information on the behaviour of the cast iron in a simulated occluded cell at various time intervals. The results of potentiodynamic polarisation and impedance measurements indicated that corrosion potentials became more negative and the cast iron was corroded more seriously. SEM micrographs clearly revealed the morphologies of specimens after simulated occluded cell galvanostatic tests for different time intervals at 1 mA/cm² anodic current density. An auto‐catalysing process was responsible for the enrichment of chloride ions in occluded cell which was confirmed by Energy Dispersive Spectroscopy (EDS).

The paper attempts to establish a two‐dimension frame to analyze dynamic supplier risks of large‐scale and complex equipment and study the probability distribution of the occurring risks.

Starting from the dynamic and correlated supplier risks of large‐scale and complex equipment, a two‐dimension frame to analyze these risks is established. A maximum entropy model is also built to research the probability distribution of the risks; then, K‐T conditions are proved to solve the model. A real case is also studied in the last part of the paper.

The results are convincing: in order to analyze the supplier risk dynamically of large‐scale and complex equipment development project, a two‐dimension analysis frame is established and a maximum entropy model is worked out. The case study shows that they are valuable.

The two‐dimension frame gives a new viewpoint for risk management, but also the maximum entropy model supplies a valuable method for risk management.

The paper succeeds in creating a dynamic analysis frame to study risks and building a new method to research the disciplines of the dynamic risks.