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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.

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.

This paper describes the use of grey system theory in mathematical programming problems. In particular, the linear programming problem, which is one of the most widely used mathematical programming problems, with grey interval and grey forecasting are developed. The adaptability of both these linear programming problems is rather satisfactory.

Denoising of the vibration signal is crucial to identify a structure's damage. Based on noise frequency character, the “real” vibration signal can be gotten. The purpose of this paper is to propose a novel method for denoising a signal based on the wavelet transform.

The vibration signal with noise which can be collected by wireless network is decomposed by wavelet transform. In order to select optimal level of wavelet decomposition, based on noise's frequency, power spectral density is used. A soft thresholding method based on minimum mean‐variance is used for vibration signal de‐noising with Gaussian noise.

A novel method has been described in his paper. Based on the relationship between vibration signal's character and noise frequency, the way to get rid of noise is combined wavelet transform with power spectral density.

In order to select optimal level of wavelet decomposition, based on noise's frequency, power spectral density is used. A soft thresholding method based on minimum mean‐variance is used for vibration signal denoising with Gaussian noise.

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).