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A serious global public health emergency (GPHE) like the COVID-19 aggravates the inequilibrium of medical care and other critical resources between wealthy and poor nations, which, coupled with the collision of cultures, indicates the vital need for developing humanitarian knowledge transcending cultures. Given the scarcity of literature addressing such unprecedent issues, this paper thus proposes new, unconventional viewpoints and future themes at the intersection of knowledge management (KM) and humanitarian inquiry.

This paper is conceptual in nature. The data of the World Bank and the Office for the Coordination of Humanitarian Affairs are analysed to introduce some emerging real impact topics regarding cross-cultural conflicts and humanitarian knowledge in the post-COVID business world. The theoretical foundation was built upon a critical literature review.

This paper synthesizes the perspectives of culture, KM and the humanistic philosophy to distil the core component of cultural intelligence and comparatively and thereby illuminating why cross-cultural metacognition acts as a priori for achieving cosmopolitan humanitarian knowledge.

This paper provides profound implications to academics by highlighting the importance to formulating new, inter-disciplinary themes or unorthodox, phenomenon-driven assumptions beyond the traditional KM domain. This paper also offers practitioners and policymakers valuable insights into coping with the growing disparity between high- and low-income countries by showing warning signs of a looming humanitarian crisis associated with a GPHE context.

This paper does not aim to claim the birth of a new domain but call for more research on developing a normative theory of humanitarian knowledge as transcendence of cultures. It implies uncharted territories of great interest and potential for the real impact KM community.

This paper aims to improve the diversity and richness of haptic perception by recognizing multi-modal haptic images.

First, the multi-modal haptic data collected by BioTac sensors from different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based on these features. The haptic images with different dimensions and modalities are provided for testing the recognition performance of this model.

The results imply that multi-modal data fusion has a better performance than single-modal data on tactile understanding, and the haptic images with larger dimension are conducive to more accurate haptic measurement.

The proposed method has important potential application in unknown environment perception, dexterous grasping manipulation and other intelligent robotics domains.

This paper proposes a new deep learning model for extracting multiple haptic features and recognizing objects from multi-modal haptic images.

The purpose of this paper is to design a biomimetic surface structure for use in a glass transport device to enhance the suspension lift of a glass transport unit.

This paper presents a surface structure of a suspended glass transport device based on the principle of bionics. First, a mapping model is constructed based on the wing structure. Second, the optimal structural parameters are given according to genetic algorithm optimization. Finally, the experimental comparison of the test bench verified the feasibility of the theory.

Through experimental comparison, the biomimetic suspension glass transport device saves 20% of air pressure compared with the ordinary suspended glass transport device, which verifies the effectiveness of the theoretical method.

This paper proposes a suspended glass transport device based on the principle of bionics, which saves the air pressure required for work. It is expected to be used in suspension glass transport devices.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2019-0389/

The purpose of this paper is to design a kind of air bearing which is based on bionics. Compare with ordinary air bearing, the air pressure consumption is reduced and energy is saved.

This paper puts forward a proposition that a bionic bearing structure is designed based on the bionics principle. First, the authors analyze the microstructure of the wings of long-eared owls and the structural mapping model is established. Second, the theoretical formula is derived through the model, and the structural parameters are optimized by sequence quadratic program (SQP). Lastly, the experimental model is made by 3D printing technology, and the experimental data are analyzed to verify the feasibility of the theory.

By comparing the experimental data, it can be seen that the air pressure of the original air bearing is reduced by 27 per cent, and the validity of the theory and design method is verified.

In this paper, a design method of air bearing based on bionic principle is presented, which can save the air pressure required for working of air bearing, and the structure of air bearing is expected to be applied in engineering.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

The purpose of this paper is to improve the comfort and shaping function of seamless shapewear on the material and structure and develop new seamless shapewear products. The shaping figure effect will be verified as well.

The performance of the knitted fabrics made of Polytrimethylene terephthalate (PTT) filament was analyzed by orthogonal experiment and fuzzy mathematical methods analysis, in order to get the optimal conditions for the best performance. The new products were designed and made based on the results of the material research with the consideration of the aesthetic requirements. The shaping effect of seamless shapewears on local and global figure was tested by the methods of the combination of subjective and objective evaluation.

The sample which renders the optimal performance for shapewear is the one with PTT filament as face yarn, nylon core-spun yarn as ground yarn and 3+1 simulate rib knit structure. The material of face yarn, mixed proportion and structure can influence the shape retention, appearance and comfort of PTT fabric in various degrees. Three shapewears which were developed according to the results of material research have different shaping effect. And women with different figures put different satisfaction degrees on each shapewear’s shaping effect.

This paper provides scientific basis and reference for enterprise to design good tight seamless shapewear as well as for consumer to buy suitable products.

In the view of the problems of present shapewears, this paper completed the development of the shapewears and verified the shaping effect of them on women with different figures. The shapewears can be put into production directly.

This paper aims to present a broad review of near-a titanium alloys for high-temperature applications.

Following a brief introduction of titanium (Ti) alloys, this paper considers the near-α group of Ti alloys, which are the most popular high-temperature Ti alloys developed for a high-temperature application, particularly in compressor disc and blades in aero-engines. The paper is relied on literature within the past decade to discuss phase stability and microstructural effect of alloying elements, plastic deformation and reinforcements used in the development of these alloys.

The near-a Ti alloys show high potential for high-temperature applications, and many researchers have explored the incorporation of TiC, TiB SiC, Y₂O₃, La₂O₃ and Al₂O₃ reinforcements for improved mechanical properties. Rolling, extrusion, forging and some severe plastic deformation (SPD) techniques, as well as heat treatment methods, have also been explored extensively. There is, however, a paucity of information on SiC, Y₂O₃ and carbon nanotube reinforcements and their combinations for improved mechanical properties. Information on some SPD techniques such as cyclic extrusion compression, multiaxial compression/forging and repeated corrugation and straightening for this class of alloys is also limited.

This paper provides a topical, technical insight into developments in near-a Ti alloys using literature from within the past decade. It also outlines the future developments of this class of Ti alloys.

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.

A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.

Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.

First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment.

A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included.

Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition.

The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.