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The study aims to study the effect of non-cognitive ability in human capital on the wages of rural migrant workers in China. The study also examines the mechanisms by which career choice, career development and social capital influence.

Based on the new human capital theory, this paper empirically investigates the effects and mechanisms of rural migrant workers' non-cognitive ability on wages using the 2018 China Family Panel Studies database and Stata 17.0 for construct validation and hypothesis testing.

The results showed that non-cognitive ability has a significant positive effect on rural migrant workers' wages. Subsequently, the mechanism of non-cognitive ability was examined. In further analysis, the study found that non-cognitive ability has a greater effect on the wages of vulnerable individuals (females, low and medium skills) among the rural migrant workers.

The originality of this study is to break through the existing research perspectives, overcome the limitations of scholars' existing research perspectives focusing on the employment and competitiveness of rural migrant workers in China and explore the factors affecting the rural migrant workers' wages from the perspective of non-cognitive ability as a new entry point by combining psychology. At the same time, the study design is more rigorous, avoiding the measurement error of variables.

Consumers’ digital engagement can bring various benefits to both brands and consumers. Besides, few studies investigated the outcomes of engagement with restaurant brands on Instagram. Therefore, this study aims to examine the effect of consumer engagement (CE) with restaurant brands on consumer-related factors (namely, consumer’s brand knowledge, perceived enjoyment and consumer social interaction) and brand-related factors (namely, e-WOM and brand reputation), as well as the mediating role of consumer-related factors.

The sample consisted of 394 Instagram followers of restaurant/coffee shop brands, and covariance-based structural equation modeling and bootstrapping were used to assess the hypothesized relationships.

The results show that CE with restaurant brands on Instagram enhances brand-related outcomes as well as consumer-related outcomes. Moreover, consumer-related factors partially mediate these relationships.

The findings of this study provide insights for restaurant managers and digital marketers to stimulate consumer-brand engagement.

To the best of the authors’ knowledge, this study is among the first that examines the effect of CE with restaurant brands on consumer- and brand-related outcomes on Instagram. The context of the study is Iran, which adds to the literature on CE that mainly focuses on developed countries.

Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem.

The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries.

It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries.

The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data.

The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time.

According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

This study aims to introduce a novel technique for nonlinear sensor time constant estimation and sensor dynamic compensation in hot-bar soldering using an extended Kalman filter (EKF) to estimate the temperature of the thermocouple.

Temperature optimal control is combined with a closed-loop proportional integral differential (PID) control method based on an EKF. Different control methods for measuring the temperature of the thermode in terms of temperature control, error and antidisturbance are studied. A soldering process in a semi-industrial environment is performed. The proposed control method was applied to the soldering of flexible printed circuits and circuit boards. An infrared camera was used to measure the top-surface temperature.

The proposed method can not only estimate the soldering temperature but also eliminate the noise of the system. The performance of this methodology was exemplary, characterized by rapid convergence and negligible error margins. Compared with the conventional control, the temperature variability of the proposed control is significantly attenuated.

An EKF was designed to estimate the temperature of the thermocouple during hot-bar soldering. Using the EKF and PID controller, the nonlinear properties of the system could be effectively overcome and the effects of disturbances and system noise could be decreased. The proposed method significantly enhanced the temperature control performance of hot-bar soldering, effectively suppressing overshoot and shortening the adjustment time, thereby achieving precise temperature control of the controlled object.

Construction sustainability (CS) is a strategic reaction to the sustainability expectations of the construction industry's external stakeholders. The extant literature has viewed the environmental, social and economic dimensions of CS as having independent effects on financial performance. Due to the influence of common stakeholders, however, interactions in these dimensions will be present in their effect on financial performance. Accordingly, this study identifies the mechanisms of the interactions between the three CS dimensions and how they jointly affect financial performance.

Content analysis of GRI reports of 60 large construction organisations, followed by a hierarchical regression analysis was used to identify the interactions between environmental, social and economic CS in their effect on financial performance.

Economic CS was found to indirectly, and not directly, affect financial performance, the effect being mediated by both environmental and social CS. Environmental CS was found to have a strong negative effect on financial performance, whilst social CS was found to have a strongly significant positive effect on financial performance.

The motivation for engaging in CS is that investment in economic CS will have a positive effect on both environmental and social CS outcomes, which, in turn can have a combined effect on financial performance.

This is one of the first studies investigating the effect of interactions between the environmental, social and economic CS dimensions on the financial performance of construction organisations. It is also one of the first studies that applies a sociotechnical framework to this relationship.

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

The purpose of this study is to identify and prioritize capabilities and practices to ensure a resilient supply chain during an unexpected disruption. In addition, this study ranks maturity factors that influence the main capabilities identified.

This paper is conducted in three stages. First, capabilities and practices are extracted through a literature review. Second, capabilities and practices are ranked using the analytical hierarchical process method. Third, a gray technique for order preference by similarity to ideal solution method is used to rank maturity factors influencing capabilities.

The findings indicate that responsiveness, readiness, flexibility and adaptability are the most important capabilities for supply chain resilience. Also, commitment and communication are the highest maturity factors influencing resilience capabilities.

The findings provide a hierarchical vision of capabilities and practices for industries to increase resilience. Limitations of the paper are related to capabilities, practices and number of experts consulted.

This paper highlights the importance of high-maturity practices in resilience capability adoption. The findings of this study will encourage decisions-makers to increase maturity practices to build resilience against disruption.

The paper reveals that developing powerful capabilities, good practices and a high level of maturity improve supply chain resilience.

This study aims to investigate the individual electrochemical transients arising from local anodic events on stainless steel, to uncover the potential mechanisms producing different types of transients and to derive appropriate parameters indicative of the corrosion severity of such transient events.

An equivalent circuit model was used for the transient analysis, which was performed using a local current allocation rule based on the relative instant cathodic resistance of the coupled electrodes, as well as the kinetic parameters derived from the electrochemical polarization measurement.

The shape and size of the electrochemical current transients arising from SS 316 L were influenced by the film stability, local anodic dissolution kinetics and the symmetry of the cathodic kinetics between the coupled electrodes, where the ultralong transient might correspond to the propagation of film damage with a slow anodic dissolution rate. The dynamic cathodic resistance during the final stage of transient current growth can serve as a characteristic parameter that reflects the loss of passive film protection.

Estimation of the local anodic current trace opens a new way for individual electrochemical transient analysis associated with the charges involved, local current densities and changes in film resistance throughout localized corrosion processes.

This study aims to investigate the influence mechanism of brazing and aging on the strengthening and corrosion behavior of novel multilayer sheets (AA4045/AA7072/AA3003M/AA4045).

Polarization curve tests, immersion experiments and transmission electron microscopy analysis were used to study the corrosion behavior and tensile properties of the sheets before and after brazing and aging.

The strength of the sheet is weakened after brazing due to brittle eutectic phases, and recovered after aging due to enhanced precipitation strengthening in the AA7072 interlayer. The core of nonbrazed sheets cannot be protected due to the significant galvanic coupling effect between the intermetallic particles and the substrate. Brazing and aging treatments promote the redissolved of second phased and limit corrosion along the eutectic region in the clad, allowing the core to be protected.

AA7xxx alloy was added to conventional brazed sheets to form a novel Al alloy composite sheet with AA4xxx/AA7xxx/AA3xxx structure. The strengthening and corrosion mechanism of the sheet was proposed. The added interlayer can sacrificially protect the core from corrosion and improves strength after aging treatment.

Although technologies such as blockchain (BCT) hold great potential to improve global food supply chains (GFSCs), our understanding of BCT use in GFSCs remains limited. The purpose of this study is to broaden BCT discussions by exploring its benefits and challenges across the entire GFSC.

Qualitative interviews with 23 industry experts were used to identify and comprehend the nuanced issues with BCT application in GFSCs.

The study identifies 21 perceived benefits and challenges with BCT use in GFSCs, including the benefit of broader data incentives beyond BCT use and the challenge of reluctance to assume dominant roles among the GFSC actors.

While prior studies mostly focused on BCT use for traceability and food safety in the GFSC midstream, this study extends the scope to include upstream and midstream actors. It highlights socio-economic benefits for traditionally disadvantaged farmers in the upstream and normative challenges to its adoption in the GFSC midstream and downstream. It also identifies three paradoxes emerging with BCT use in the GFSCs including the paradoxes of food technology, transparency and de-centralization.