Search results

Using the industrial data between 2000 and 2016, this study analysed the process of coupling and coordinated development of technological innovation and standardisation. Accordingly, the study considered the high-tech industry (five sub-sectors) in China as the research object.

Based on the summary of innovation and standardisation literature review, this study constructed a theoretical model of the influence of technological innovation and standardisation on industrial development from the perspective of the coupling system. Furthermore, the study employed multivariate linear regression analysis to explore coupling coordination relationships.

The study results revealed that high coupling coordination between technological innovation and standardisation is highly conducive to industrial development. Moreover, requirements for standardisation levels differ owing to different stages and characteristics in each segmented industry.

This study primarily contributes to the literature by using a bibliometrics tool to summarise related literature on innovation and standardisation and provides a new perspective of reviewing, and it also offers new evidence on the coupling coordination relationship between innovation and standardisation in the high-tech industry.

AI is an emerging tool in HRM practices that has drawn increasing attention from HRM researchers and HRM practitioners. While there is little doubt that AI-enabled HRM exerts positive effects, it also triggers negative influences. Gaining a better understanding of the dark side of AI-enabled HRM holds great significance for managerial implementation and for enriching related theoretical research.

In this study, the authors conducted a systematic review of the published literature in the field of AI-enabled HRM. The systematic literature review enabled the authors to critically analyze, synthesize and profile existing research on the covered topics using transparent and easily reproducible procedures.

In this study, the authors used AI algorithmic features (comprehensiveness, instantaneity and opacity) as the main focus to elaborate on the negative effects of AI-enabled HRM. Drawing from inconsistent literature, the authors distinguished between two concepts of AI algorithmic comprehensiveness: comprehensive analysis and comprehensive data collection. The authors also differentiated instantaneity into instantaneous intervention and instantaneous interaction. Opacity was also delineated: hard-to-understand and hard-to-observe. For each algorithmic feature, this study connected organizational behavior theory to AI-enabled HRM research and elaborated on the potential theoretical mechanism of AI-enabled HRM's negative effects on employees.

Building upon the identified secondary dimensions of AI algorithmic features, the authors elaborate on the potential theoretical mechanism behind the negative effects of AI-enabled HRM on employees. This elaboration establishes a robust theoretical foundation for advancing research in AI-enable HRM. Furthermore, the authors discuss future research directions.

Studies of the antecedents of daily abusive supervision have mainly focused on work stressors and family stressors, ignoring the potential importance of commuting stressors that are encountered enroute to work. Based in affective events theory, the authors propose a daily, within-person model to examine how the commuting stressors faced by supervisors affect their propensity to engage in abusive supervision behavior and the mechanisms underlying this effect.

Using experience-sampling methodology, the authors collected data from 49 supervisors in China who responded to two daily surveys for 10 working days.

The authors found that daily morning commuting anger mediates the link between daily morning commuting stressors and subsequent abusive supervision. The authors also found that trait-displaced aggression moderates this relationship, such that the mediating effect occurs only when supervisors' trait-displaced aggression is high rather than low.

This study enriches the antecedents of daily abusive supervision and extends the commuting literature to the leadership context.

The purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the direction-of-arrival (DOA) estimation performance of coherent signals at low signal-to-noise ratio (SNRs).

An improved multiple-Toeplitz matrices reconstruction method is proposed via quadratic spatial smoothing processing. Our proposed method takes advantage of the available information contained in the auto-covariance matrices of individual Toeplitz matrices and the cross-covariance matrices of different Toeplitz matrices, which results in a higher noise suppression ability.

Theoretical analysis and simulation results show that, compared with the existing Toeplitz matrix processing methods, the proposed method improves the DOA estimation performance in cases with a low SNR. Especially for the cases with a low SNR and small snapshot number as well as with closely spaced sources, the proposed method can achieve much better performance on estimation accuracy and resolution probability.

The study investigates the possibility of reusing pre-existing designs for the DOA estimation of the coherent signals. The proposed technique enables achieve good estimation performance at low SNRs.

The paper includes implications for the DOA problem at low SNRs in communication systems.

The proposed method proved to be useful for the DOA estimation at low SNR.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

There are three double bonds in the chemical structure of diallyl maleate. The purpose of this study is that the acrylate is modified with diallyl maleic anhydride to make the propionate resin present a spatial network structure, thereby improving the performance of the acrylate resin.

Methyl methacrylate (MMA) and butyl acrylate(BA) were used as were used as main monomers. Diallyl maleate (DAM) was used as crosslinking monomer and dodecafluoroheptyl methacrylate (DFMA) was used as fluoromonomer. Potassium persulfate (KPS) was used as thermal decomposition initiator, sodium lauryl sulfate (AS) and sodium dodecyl sulfonate (SDS) were used as anionic emulsifiers, and EFS-470 (Alkyl alcohol polyether type nonionic emulsifier) was a non-ionic emulsifier.

Through optimizing the reaction conditions, the uniform and stable latex is obtained. The polymer of structure was characterized by Fourier transform infrared spectroscopy (FTIR). Thermogravimetric analysis (TGA) and contact angle (CA) were tested on latex films. The particle size and distribution range of emulsion were tested with nano particle size analyzer.

The experimental results showed that the thermal decomposition temperature of the acrylic coating film increased by 20.56°C after modification. In addition, the effect of cross-linking density on the water contact angle of the fluorocarbon groups in DFMA when they migrate to the surface of the latex film during drying has been explored. The experimental results show that a higher degree of cross-linking will hinder the migration of fluorocarbon groups to the surface of the resin film.

The paper aims to identify a suitable generic brake force distribution ratio (β) corresponding to optimal brake design attributes in a diminutive driving range, where road conditions do not exhibit excessive variations. This will intend for an appropriate allocation of brake force distribution (BFD) to provide dynamic stability to the vehicle during braking.

Two techniques are presented (with and without wheel slip) to satisfy both brake stability and performance while accommodating variations in load sharing and road friction coefficient. Based on parametric optimization of the design variables of hydraulic brake using evolutionary algorithm, taking into account both the laden and unladen circumstances simultaneously, this research develops an improved model for computing and simulating the BFD applied to commercial and passenger vehicles.

The optimal parameter values defining the braking system have been identified, resulting in effective β = 0.695 which enhances the brake forces at respective axles. Nominal slip of 3.42% is achieved with maximum deceleration of 5.72 m/s² maintaining directional stability during braking. The results obtained from both the methodologies are juxtaposed and assessed governing the vehicle stability in straight line motion to prevent wheel lock.

Optimization results establish the practicality, efficacy and applicability of the proposed approaches. The findings provide valuable insights for the design and optimization of hydraulic drum brake systems in modern automobiles, which can lead to safer and more efficient braking systems.

The growing demand for halal products has attracted small and large companies looking to secure their market share in the lucrative global halal market. However, it is difficult for resource-constrained firms, such as small and medium enterprises (SMEs), to compete internationally. Therefore, drawing from a resource-based view, this paper aims to examine how intangible resources affect the export performance of Malaysian SMEs exporting halal food and beverages (F&B) products.

This study used a purposive sampling technique, and respondents were reached out by mail. Out of 517 local SMEs exporting halal-certified F&Bs contacted, 193 firms responded, and only 188 responses were eligible to be used for data analysis. The partial least squares structural equation modelling technique was used to conduct the analysis. The data underwent measurement and structural model evaluation to confirm the hypotheses postulated.

Based on the data analysis conducted, it was discovered that intangible resources, namely, international orientation and marketing capability, significantly influence the export performance of Malaysian SMEs exporting halal F&B. However, the influence of cultural intelligence on export performance could not be demonstrated.

This paper fills the gap of the need for more attention to SMEs in developing countries, especially in the halal industry. This research paper also contributes to international business and halal studies by promoting an understanding of intangible resources as strategic resources for SMEs to create competitive advantages and elevate their export performance in the emerging global halal market.