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An ever‐growing body of electronic information resources is gradually making traditional research methodologies obsolete and transforming the ways in which students learn how research is carried out in their chosen fields of study. A clear example can be found in the case of Asian studies. Many areas of Asian studies traditionally have depended upon complex, printed bibliographic tools that could only be used by researchers who possessed both a mastery of Asian languages and an in‐depth understanding of the historical timelines and literary traditions of Asia. Students have acquired these research skills and methodologies through semester‐long courses and, in some cases, advanced specialized degrees. Until recently, responsibility for the teaching of such techniques and methodologies to students belonged almost exclusively to the teaching faculty of the discipline. Over the past few years, however, the increased availability of electronic research resources in Asian studies is transforming what students need to know about conducting research. This situation provides an unprecedented opportunity for campus librarians to participate in reforming the curricula for a new generation of Asian studies scholars.

The purpose of this research is to propose a flexible sensor with a weft-knitted float stitch structure and to explore knitting techniques that allow conductive yarns to be skin-tight and less exposed, reducing production processes and increasing productivity. Study its electrical conductivity in different yarn materials, knit processes and deformation ranges. The analysis is compared to provide some basis for the design of the electrodes.

The method includes five operations: (1) Analysis of the morphological appearance, tensile variation, fiber material properties and electrical conductivity of high-elastic and filament silver-plated conductive yarns. (2) Based on the knitting process of the floating yarn structure, three-dimensional modeling of the flexible sensor was carried out to explore the influence of knitting process changes on appearance characteristics. (3) The fabric samples are knitted by different silver-plated conductive yarns with different structures. Processing of experimental samples to finished size by advance shrinkage. (4) Measure the resistance of the experimental sample after the machine has been lowered and after pre-shrinking. Use the stretching machine to simulate a wearing experiment and measure the change in resistance of the sample in the 0–15% stretching range. (5) Analyze the influence factors on the conductive performance of the flexible sensor to determine whether it is suitable for textile flexible sensors.

For the float knitted flexible sensors, the floating wire projection is influenced by the elasticity of the fabric and the length of the floating wire. Compared to the plain knitted flexible sensors, it has less resistance variation and better electrical properties, making it suitable for making electrodes for textile structures. In addition, the knitting method is integrated with the intelligent monitoring clothing, which saves the process for the integration of the flexible sensor, realizes positioning and fixed-point knitting.

The sensor technology of the designed weft-knitted float structure is varied and can be freely combined and designed in a wide range. Within the good electrical conductivity, the flexible sensor can realize integrated knitting, positioning monitoring, integrating into the appearance of clothing. It can also focus on the wearing experience of wearable products so that the appearance of the monitoring clothing is close to the clothes we wear in our daily life.

In this paper, an integrated positioning knitting flexible sensor based on the weft knitting float structure is studied. The improved knitting process allows the sensing contact surface to be close to the skin and reduces the integration process. The relationship between the exposure of the silver-plated yarn on the clothing surface and the electrical conductivity is analyzed. Within a certain conductive performance, reduces the exposed area of the conductive yarn on the clothing surface and proposes a design reference for the flexible sensor appearance.

The asymmetry of the velocity profile caused by geometric deformation, complex turbulent motion and other factors must be considered to effectively use the flowmeter on any section. This study aims to better capture the flow field information and establish a model to predict the profile velocity, we take the classical double elbow as the research object and propose to divide the flow field into three categories with certain common characteristics.

The deep learning method is used to establish the model of multipath linear velocity fitting profile average velocity. A total of 480 groups of data are taken for training and validation, with ten integer velocity flow fields from 1 m/s to 10 m/s. Finally, accuracy research with relative error as standard is carried out.

The numerical experiment yielded the following promising results: the maximum relative error is approximately 1%, and in the majority of cases, the relative error is significantly lower than 1%. These results demonstrate that it surpasses the classical optimization algorithm Equal Tab (5%) and the traditional artificial neural network (3%) in the same scenario. In contrast with the previous research on a fixed profile, we focus on all the velocity profiles of a certain length for the first time, which can expand the application scope of a multipath ultrasonic flowmeter and promote the research on flow measurement in any section.

This work proposes to divide the flow field of double elbow into three categories with certain common characteristics to better capture the flow field information and establish a model to predict the profile velocity.

The existing literature has been mainly focused on local problems but without an overall framework for studying the top-level planning of intelligent construction from a systematic perspective. The purpose of this paper is to fill this gap.

This research adopts a deductive research approach.

This research proposes a reference architecture and related business scenario framework for intelligent construction based on the existing theory and industrial practice.

The main contribution of this research is to provide a useful reference to the Chinese government and industry for formulating digital transformation strategies, as well as suggests meaningful future research directions in the construction industry.

The size prediction of garment is an important part in the process of the garment design and production, and it is also one of the most important features in warp-knitted computer-aided design system. The purpose of this paper is to realize the auto-generation of the garment templates using JavaScript and WebGL technologies, based on the prediction of the size of warp-knitted seamless sportswear.

The warp-knitted jacquard technology is used to produce the warp-knitted seamless sportswear, which is divided into suits and tights. In order to achieve the purpose of this study, the dimensions of four kinds of jacquard patterns knitted under different knitting conditions are measured and the crosswise and longitudinal size shrinkage percentages are also calculated. Then, the relationship between the yarn count and the drawing density as well as the size shrinkage percentage is studied and a size prediction model for warp-knitted jacquard fabric is established. Next, according to the results of the size calculation, the point positions of the garment boundary in the mathematical coordinate system is determined. The color formula is built by the two-dimensional mathematical matrix. Finally, combined with the coordinate position and color information, the template can be drawn automatically.

Based on the size prediction model of warp-knitted garment, the template generation of warp-knitted full-form sportswear on WebGL-enabled web browser is realized, which is proven to be an effective computer-aided design method for warp-knitted garments.

Because of limited researches, only two groups of yarns and four kinds of jacquard patterns were studied. A vaster database should be built and smooth curve, accurate coordinate needs to be optimized in the further research.

The size prediction model for warp-knitted jacquard garment and garment template auto-generation of warp-knitted computer-aided design system will simplify the fabric technical design process, shorten design time and improve the efficiency of new product development.

The size prediction model for warp-knitted jacquard garment and garment template auto-generation of warp-knitted computer-aided design system will provide the industries a guidance for new sample development and it also can shorten the development time and lower cost.

This author analyzes the relationship between the size characteristics and knitting technology of warp-knitted jacquard patterns, proposes a model of size prediction and realizes the auto-drawing of the garment template in the warp-knitted CAD system, which provides a reference for the new product design and development of warp-knitted seamless sportswear.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

Nano graphitic-carbon nitride (g-C₃N₄) is an emerging lubrication technology with excellent performance and significant potential for future applications. This study aims to investigate the effect of nano g-C₃N₄ as a lubricant additive on the wear performance of bearing steel disk.

Various mass fractions of g-C₃N₄ were introduced into the base oil. Combining tribological testing, rheological testing and surface analysis methods, the anti-wear properties and lubrication mechanisms were analyzed.

Transmission electron microscopy images revealed that the size of the nanoparticles of g-C₃N₄ ranges from 10 to 100 nm. Phase analysis of the g-C₃N₄ sample was conducted using X-ray diffraction. Further, 1.0% mass fraction of g-C₃N₄ in the base oil provides excellent anti-wear and friction-reducing performance. Compared to the base oil alone, it reduces the average friction coefficient by 63.8% and decreases the wear rate by 43.1%, significantly reducing the depth and width of the wear scar. Energy-dispersive X-ray spectroscopy and scanning electron microscope analysis revealed that the oil sample containing nano g-C₃N₄ can form a lubricating film on the sliding surface of bearing steel after wear, which enhances the lubricating properties of the base oil.

The synergistic effect of the base oil and nanoparticles reduces friction and wear and is expected to extend the service life of bearing steel. These findings suggest that incorporating nano g-C₃N₄ as a lubricant additive offers significant potential for improving the performance of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0456/

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.