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The purpose of this study is to design a closed hydrostatic guideway has the ability to resist large-side load, pitch moments and yaw moments, has good stiffness and damping characteristics, and provides certain beneficial guidance for the design of large-span closed hydrostatic guideway on the basis of providing a large vertical load bearing capacity.

The Reynolds’ equation and flow continuity equation are solved simultaneously by the finite difference method, and the perturbation method and the finite disturbance method is used for calculating the dynamic characteristics. The static and dynamic characteristics, including recess pressure, flow of lubricating oil, carrying capacity, pitch moment, yaw moment, dynamic stiffness and damping, are comprehensively analyzed.

The designed closed hydrostatic guideway has the ability to resist large lateral load, pitch moment and yaw moment and has good stiffness and damping characteristics, on the basis of being able to provide large vertical carrying capacity, which can meet the application requirements of heavy two-plate injection molding machine (TPIMM).

This paper researches static and dynamic characteristics of a large-span six-slider closed hydrostatic guideway used in heavy TPIMM, emphatically considering pitch moment and yaw moment. Some useful guidance is given for the design of large-span closed hydrostatic guideway.

For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.

Posting and sharing about food on social media has surged in popularity amongst younger generations such as Millennials and Generation Z. This study aims to analyse and compare food-tourism sharing behaviour on social media across generations. First, this study specifically investigates the factors influencing the intention to share food experiences on social media; second, it examines the impact of sharing intention on actual behaviour and loyalty; and third, it determines whether Millennials and Generation Z differ in these relationships.

A survey was carried out of Millennial and Generation Z travellers who shared food experiences on social media. Structural equation modelling (SEM) and multi-group analysis were performed to examine the cause-and-effect relationship in both generations.

The findings reveal differences in motivation, satisfaction, sharing intention, sharing behaviour and loyalty between generations (Millennials and Generation Z).

This study contributes to the literature on the antecedents of food-sharing behaviour in online communities by indicating factors that influence the sharing of culinary experiences and brand or destination loyalty across generations. Suggestions for future research include exploring online food-sharing behaviour through cross-cultural comparisons in various regions.

As Millennials and Generation Z will expand their market share in the coming years, the findings of this study can help improve marketing strategies for culinary tourism and generate more intense food experiences for both generations.

The outcome of the research provides new insights to develop a conceptual model of food-sharing behaviour and tourism on social media by drawing comparisons across generations.

Industry 4.0 has an inimitable potential to create competitive advantages for the apparel industry by enhancing productivity, production, profitability, efficiency and effectiveness. This study, thus, aims to assess the digitalisation level of the Tanzanian apparel industry through the Industry 4.0 perspectives.

A mixed-methods-based approach was deployed. This study deployed semi-structured interviews, document review and observation methods for the qualitative approach. For the quantitative approach, closed-ended questionnaires were used to ascertain the digitalisation levels and maturity level of the textiles and apparel (T&A) factories and small and medium-sized textile enterprises in Tanzania. The sample size was 110, with participants engaged through the purposive sampling technique.

Industry 4.0 frameworks evolved into practices mainly since 2011 in several service and manufacturing industries globally. For Tanzania, the findings indicate that the overall maturity level of the T&A industries is 2.5 out of 5.0, demonstrating a medium level of adoption. Thus, the apparel industries are not operating under the industry 4.0 framework; they are operating within the third industrial revolution – Industry 3.0 – framework. For such industries to operate within the fourth industrial revolution – Industry 4.0 – that is only possible if there is significantly well-developed industrial infrastructure, availability of engineering talent, stable commercial partnerships, demand from the marketplace and transactional relationship with customers.

This study’s limitations include: firstly, Industry 4.0 is an emerging area; this resulted in limited theoretical underpinnings in the Tanzanian perspectives. Secondly, the studied industries may not suffice the need to generalise the findings for the entire country, thus needing another study.

Although Industry 4.0 conceptual frameworks have been on trial in several industries since 2011, this is amongst the first empirical research on Industry 4.0 in the Tanzanian apparel industry that assesses the digitalisation levels.

To develop electrode materials for supercapacitor with superior electrochemical performance and simple preparation process, the purpose of this study is to prepare flexible CC/NiS/a-NiS electrodes with self-supporting structure by loading hydrothermally synthesized a-NiS particles along with nano-NiS on carbon cloth by electroplating method.

The effects of current densities, temperatures and pH values on the loading amount and uniformity of the active substances during the plating process were investigated on the basis of optimization of surface morphology, crystalline structure and electrochemical evaluation as the cyclic voltammetry curves, constant current charge–discharge curves and AC impedance.

The a-NiS particles on CC/NiS/a-NiS were mostly covered by the plated nano-NiS, which behaved as a bulge and provided a larger specific surface area. The CC/NiS/a-NiS electrode prepared with the optimized parameter exhibited a specific capacitance of 115.13 F/g at a current density of 1 A/g and a Coulomb efficiency of 84% at 5 A/g, which is superior to that of CC/NiS electrode prepared by electroplating at a current density of 10 mA/cm², a temperature of 55°C and a pH of 4, demonstrating its fast charge response of the electrode and potential application in wearable electronics.

This study provides an integrated solution for the development of specifically structured NiS-based electrode for supercapacitor with simple process, low cost and high electrochemical charge/discharge performance, and the simple and easy-to-use method is also applicable to other electrochemically active composites.

The major objective of this research work was to evaluate various physico-chemical characteristics, such as, chemical composition, antioxidant capacity, objective color and texture profile analysis (TPA) of the wheat flour/chickpea flour (CF) blends, so that nutritious baked products could be consumed by the type-2 diabetic persons.

Wholegrain wheat flour (WGF) and white wheat flour (WWF) were substituted with CF at 0 to 40% levels. These wheat flour/CF blends were analyzed for proximate composition, the prepared dough and baked breads were tested for objective color, antioxidant capacity as trolox equivalent antioxidant capacity (TEAC), malondialdehyde (MDA) and total phenolic content (TPC) and TPA.

WGF had the highest TEAC (117.42 mM/100g) value, followed by WWF (73.98 mM/100g) and CF (60.67 mM/100g). TEAC, MDA and TPC values varied significantly among all the three flour samples.

Inclusion of whole chickpea (without dehulling) flour in such type of blends would be another interesting investigation during the future research studies.

These research findings have a great potential for the production of these baked products for human consumption on an industrial scale.

Production of breads using wheat flour and CF blends would benefits the consumers.

Production of Arabic and pan breads using wheat flour and CF blends would, therefore, combine the benefits of both the needed proteins of plant origin and the health-promoting bioactive compounds, in a most sustainable way for the consumers.

This study aims to explore the opposite effects of single-category versus multi-category products information diversity on consumer decision making. Further, the authors investigate the moderating role of three categories of visitors – direct, hesitant and hedonic – in the relationship between product information diversity and consumer decision making.

The research utilizes a sample of 1,101,062 product click streams from 4,200 consumers. Visitors are clustered using the k-means algorithm. The diversity of information recommendations for single and multi-category products is characterized using granularity and dispersion, respectively. Empirical analysis is conducted to examine their influence on the two-stage decision-making process of heterogeneous online visitors.

The study reveals that the impact of recommended information diversity on consumer decision making differs significantly between single-category and multiple-category products. Specifically, information diversity in single-category products enhances consumers' click and purchase intention, while information diversity in multiple-category products reduces consumers' click and purchase intention. Moreover, based on the analysis of online visiting heterogeneity, hesitant, direct and hedonic features enhance the positive impact of granularity on consumer decision making; while direct features exacerbate the negative impact of dispersion on consumer decision making.

First, the article provides support for studies related to information cocoon. Second, the research contributes evidence to support the information overload theory. Third, the research enriches the field of precision marketing theory.

Ulam is a traditional salad that contains high levels of antioxidants and is commonly consumed in raw form. However, the average ulam consumption among the low-income Malaysian population was only ½ serving daily. Thus, this study aimed to explore the motivators and barriers to ulam consumption among residents of low-cost housing areas (household income RM4849 or $1015.50) in Kuala Lumpur, Malaysia.

Six focus group discussions were conducted with 27 Malay residents aged 18–59 years in Kuala Lumpur, Malaysia. The researchers asked the participants a series of semi-structured questions. All the interviews were audio-recorded and transcribed verbatim. Two researchers coded the transcripts independently, and several themes were identified. The data were analysed using Nvivo version 12 software.

Three main factors for ulam consumption were identified in this study: personal, environmental and behavioural. The motivators and barriers were identified based on nine main themes and 16 sub-themes.

The results of this study identified potential areas for an effective intervention to increase ulam intake among residents in low-cost housing areas.

This work has the potential to identify the factors that have an impact on consumers' ulam preferences to help fulfil daily vegetable recommendations.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

The purpose of this study is to investigate when and why supervisor negative feedback is associated with employees' job performance via two different pathways (i.e. emotion-focused coping and problem-focused coping) and to introduce proactive personality as a moderator.

Time-lagged data were collected using a field survey research design. Participants included 389 dyads of employees and their direct supervisors from five companies in China.

Supervisor negative feedback can lead to employees' emotion-focused coping, which in turn impairs their job performance. Meanwhile, supervisor negative feedback can trigger employees’ problem-focused coping, which subsequently promotes their job performance. Furthermore, proactive personality moderates the indirect effect of supervisor negative feedback on employee performance through emotion-focused coping.

This study explored the double-edged effects of supervisor negative feedback on employee job performance from a coping strategy perspective and investigated how proactive personality influences the choice of coping strategies.