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The combination of improved PSO (IPSO) algorithm and artificial neural network (ANN) model for intelligent monitoring of the bearing performance of the hydrostatic turntable.

This paper proposes an artificial neural network model based on IPSO algorithm for intelligent monitoring of hydrostatic turntables.

The theoretical model proposed in this paper improves the accuracy of the working performance of the static pressure turntable and provides a new direction for intelligent monitoring of the static pressure turntable. Therefore, the theoretical research in this paper is novel.

Theoretical novelties: an ANN model based on the IPSO algorithm is designed to monitor the load-bearing performance of a static pressure turntable intelligently; this study show that the convergence accuracy and convergence speed of the IPSO-NN model have been improved by 52.55% and 10%, respectively, compared to traditional training models; and the proposed model could be used to solve the multidimensional nonlinear problem in the intelligent monitoring of hydrostatic turntables.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0081/

This study aims to report the development and experimental evaluation of two kinds of PANI@semiconductor based photocathodic anti-corrosion coating, for application on stainless steel substrates.

PANI was in situ chemical polymerized on TiO₂ and BiVO₄ particles, and FT-IR and SEM/EDS were used to understand the characteristics and elemental distribution of the composite particles. Composite coatings, which consisted of epoxy, PANI@TiO₂ or PANI@BiVO₄ and graphene, were prepared on the 304L stainless steel. Photoelectrochemical response measurement, electrochemical tests and immersion tests were used to assess the anti-corrosion performance of the prepared coatings in 45°C 3.5 wt.% NaCl solution. And the corrosion protection mechanism was further explained by combining with surface observation.

The photoelectrochemical response tests revealed the good photocathodic effect of the coatings, and the reversible oxidation-reduction properties of PANI (pseudocapacitive effect) leading to the repeated usage of the coatings. Consequently, the anti-corrosion mechanism of the composite coating is attributed to the physical barrier effect of the coating, the anodic protection effect of PANI and the photocathodic and energy store effect.

These kind coatings could prevent corrosion from day to night for stainless steel, which has great engineering application prospects on stainless steel corrosion protection.

Social commerce (s-commerce) has gained widespread popularity as a social platform where customers engage in resource-sharing activities such as information exchange, advice-seeking and expressing their opinions on mutual interests. However, existing studies have not fully comprehended the drivers of electronic customer-to-customer interaction (eCCI) and how such behavior contributes to the customer “stick” on s-commerce sites. This study develops the Motivation–Opportunity–Ability (MOA) theory and investigates the impact of MOA factors on eCCI, which in turn affects customer stickiness.

A survey was used to acquire data from 455 valid respondents, and the research employed a combination of fuzzy-set qualitative comparative analysis (fsQCA) and structural equation modeling.

The results revealed associations between perceived self-efficacy, intrinsic motivation, tie strength with other customers, eCCI and customer stickiness.

Considering the limited availability of complete eCCI frameworks in existing scholarly works, the authors present valuable perspectives on the role of consumer characteristics as both antecedents and consequences of eCCI. Additionally, this study proposes a research agenda for the field of eCCI on s-commerce sites.

Smallholders are essential in ensuring food security; however, smallholder-dominated food production often involves high resource-environmental costs. This study analyzed the factors that differentiate horticultural practices, willingness to adopt technology and social networks between optimized practices (OPT) and farmer practices (FP) to provide localized and systematic solutions for the sustainable apple production.

To explore the approach of smallholder-dominated sustainable apple production, 257 apple producers in the Bohai Bay region, a major apple planting area in China, were investigated. Life cycle assessment (LCA), emergy analysis and social network analysis methods were used for evaluation.

The results showed that the net economic profit and emergy sustainability index (ESI) in OPT was 15.8 × 104 RMB·ha-1 and 1.2, respectively, which were 126.9 and 128.0% higher than FP. In contrast, greenhouse gas (GHG) emissions under OPT was 29.3% lower than those under FP. OPT has a higher percentage of adoption of scientific fertilizer application and water-saving irrigation technologies compared to FP. OPT has strong learning abilities, more social resources (such as technical training and sharing technical experience with others) and connections with stakeholders in the apple supply chain. Optimizing smallholders' social capital, willingness to adopt technology, behavioral willingness and technological awareness can promote sustainable apple production.

Considering the horticultural practices employed by smallholders in conjunction with their social networks, these factors contributed to the transition of smallholder-led apple production toward sustainability. The findings provided viable options and a theoretical basis for smallholder-dominated crop production to move toward sustainability, with significant implications for policymakers.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

This study aims to explore the factors that affect online knowledge sharing among the scientific performance of academics at the University of Mosul, Republic of Iraq.

Data were collected by using a random sample by means of an electronic questionnaire distributed to 211 academics at the University of Mosul via online platforms. Structural equation modeling was used to test hypotheses using AMOS 24 software.

The results showed that collaboration, perceived flexibility and willingness to share had a significant effect on online knowledge sharing behavior and that which in turn had an effect on the academic performance of respondents, while the results showed no influence of knowledge self-efficacy and communication on the behavior of online knowledge sharing.

In this research, the effects of five factors on online knowledge sharing behavior were studied, in addition to the effect of the latter on academic performance. The research did not study the effect of the participants’ personal characteristics on the behavior of knowledge sharing online, which could be an area of future investigation. The study was limited to online knowledge sharing for academics working in public universities, and the authors suggest conducting a future study from the perspective of medical staff working in hospitals and its impact on their performance.

Based on the findings of the current study, this study suggests that the academic staff at the University of Mosul and similar universities pay attention to scientific cooperation between researchers and teaching staff, by creating online groups to carry out joint research projects that contribute to sharing knowledge among participants through social media in general and scientific platforms in particular. Establishing a scientific research portal could serve this well for example.

Most previous studies have focused on areas related to knowledge sharing in higher education institutions from the traditional perspective and therefore studies that examine the factors affecting online knowledge sharing among academics are limited, so this study fills this gap. This paper focuses on identifying the scientific benefits for academics through knowledge exchange and the use of electronic platforms at the University of Mosul. The study is expected to contribute to enhance the exchange of knowledge online between academics in similar universities in Asia and Africa due to the convergence of cultures and the nature of the work of these universities, as well as the possibility of generalization due to the research’s use of the deductive method.

Although the literature recognizes that coopetition plays a significant role in the success of international construction joint ventures (ICJVs), the impacts of coopetition on the performance outcomes of ICJVs remain largely unknown. This study extends this line of research by theorizing coopetition from three dimensions, i.e. coopetition intensity, coopetition balance and coopetition structure, and examining the relationships between coopetition and ICJV performance outcomes from both the contingency and configuration perspectives.

The hypotheses were tested using survey data from a sample of 188 ICJVs. Structural equation modelling was employed for the contingency approach to estimate the relationships between the three dimensions of coopetition and performance. For the configuration approach, cluster analysis was utilized to identify coopetition patterns. Subsequently, an analysis of variance was employed to analyse the relationships between these coopetition patterns and performance.

The contingency results indicate that while coopetition intensity is positively related to all types of performance, coopetition balance is only positively related to project performance and partner performance. Moreover, coopetition structure is only related to partner performance and socioenvironmental performance. The configuration approach identifies six patterns of coopetition, manifesting different levels of project, partner and socioenvironmental performance.

These findings, therefore, contribute to the ICJV literature by extending the understanding of how coopetition dimensions individually and jointly influence ICJV performance.

Fast fashion brands in Asia are increasingly incorporating corporate social responsibility (CSR) into their business strategies in response to the mounting environmental and social concerns. The study aims to provide insights into the current landscape of CSR practices and communication within the Asian context through focusing on two prominent Asian brands to understand how these fashion brands articulate and convey their CSR activities on their respective websites.

This study used a qualitative content analysis to critically evaluate the CSR communication used by Uniqlo and Shein, with the incorporation of signalling theory as a theoretical framework to discuss the observed elements on their websites.

The results indicate that the two Asian fast fashion brands actively engage in CSR’s environmental and social dimensions. Notably, the social dimension is more prominently expressed on both brands’ websites than on other dimensions. Through a nuanced interplay of colour, imagery and keywords, these Asian fast fashion brands strategically signify their commitment to environmental stewardship and stakeholder well-being.

This study fills a gap in the literature by examining CSR programs on Asian-owned fast fashion brands’ websites through the application of signalling theory. The research contributes to the theoretical foundation of CSR communication outcome research, offering actionable insights for marketers entering the Asian fashion market to enhance brand reputation and legitimacy.

This paper aims to optimize the air pressure regulation scheme of the aeroengine pressure test bench.

Based on the requirements of pressure regulation process and the operating mechanism of aeroengine pressure test bench, a grey performance evaluation index system is constructed. The combination of principal component analysis and grey theory is employed to assign weights to grey indexes. The grey target evaluation model is introduced to evaluate the performance of historical regulation processes, and the evaluation results are analyzed to derive optimization mechanism for pressure regulating schemes.

A case study based on monitoring data from nearly 300 regulation processes verifies the feasibility of the proposed method. On the one hand, the improved principal component analysis method can achieve rational weighting for grey indexes. On the other hand, the method comparison intuitively shows that the proposed method performs better.

The pressure test bench is a fundamental technical equipment in the aviation industry, serving the development and testing of aircraft engines. Due to the complex system composition, the pressure and flow adjustment of the test bench heavily rely on manual experience, leading to issues such as slow adjustment speed and insufficient accuracy. This paper proposes a performance evaluation method for the regulation process of pressure test bench, which can draw knowledge from historical regulation processes, provide guidance for the pressure regulation of test benches, and ultimately achieve the goal of reducing equipment operating costs.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.