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This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021.

Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices.

The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities.

This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities.

The primary aim of this study is to identify and analyze the key factors that impact the intentions of Jordanian tourists to visit and revisit destinations using the Jannah Jo app.

A self-administered questionnaires via Google Forms was employed comprising a sample of 401 Jordanian tourists who have the Jannah Jo app. Partial least squares-structural equation modeling approach was applied for hypotheses testing.

The present investigation has revealed that the constructs of perceived ease of use (PEU), perceived usefulness (PU) and perceived value (PV) exerted a significant and positive impact on electronic word of mouth (e-WOM). Additionally, e-WOM was observed to wield a positive and significant influence on the attitudes of consumers' decision-making, thereby ultimately affecting the intentions of Jordanian tourists with regard to their decisions to visit and revisit destinations. Nevertheless, it is noteworthy that the results indicated that neither augmented reality nor content quality exhibited any statistically significant positive relationship with e-WOM.

Tourism agencies striving to encourage the adoption of smart applications must grasp the relevance of e-WOM within the contemporary digital milieu. Additionally, they should acknowledge the significance of tourists' intentions concerning both revisiting and initial visits. This research contends that such agencies ought to take into account the substantial influence exerted by PEU, PU and PV in shaping the favorable e-WOM discourse.

By integrating the technology acceptance model in conjunction with other relevant variables, this research strives to develop a comprehensive model that advances the comprehension of the intricate determinants affecting tourists' engagements with mobile applications. Furthermore, it is noteworthy that this study represents the initial investigation conducted in the Middle East, specifically in Jordan, on this subject matter.

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

The purpose of this paper is to introduce the simple synthesis process of thermal-insulation coating by using three different nanoparticles, namely, nano-zinc oxide (ZnO), nano-tin dioxide (SnO₂) and nano-titanium dioxide (TiO₂), which can reduce the temperature of solar cells.

The thermal-insulation coating is designed using sol-gel process. The aminopropyltriethoxysilane/methyltrimethoxysilane binder system improves the cross-linking between the hydroxyl groups, -OH of nanoparticles. The isopropyl alcohol is used as a solvent medium. The fabrication method is a dip-coating method.

The prepared S1B1 coating (20 Wt.% of SnO₂) exhibits high transparency and great thermal insulation property where the surface temperature of solar cells has been reduced by 13°C under 1,000 W/m² irradiation after 1 h. Meanwhile, the Z1B2 coating (20 Wt.% of ZnO) reduced the temperature of solar cells by 7°C. On the other hand, the embedded nanoparticles have improved the fill factor of solar cells by 0.2 or 33.33%.

Findings provide a significant method for the development of thermal-insulation coating by a simple synthesis process and low-cost materials.

The thermal-insulation coating is proposed to prevent exterior heat energy to the inside solar panel glass. At the same time, it can prevent excessive heating on the solar cell’s surface, later improves the efficiency of solar cell.

This study presents a the novel method to develop and compare the thermal-insulation coating by using various nanoparticles, namely, nano-TiO₂, nano-SnO₂ and nano-ZnO at different weight percentage.

In power electronics, there are various metallic material systems used as die attachments. The complete understanding of the thermomechanical behavior of such interconnections is very important. Therefore, this paper aims to examine the thermomechanical response of four famous die attach materials, including sintered silver, sintered nano-copper particles, gold-tin solders and silver-tin transient liquid phase (TLP) bonds, using nonlinear finite element analysis.

During the study, the mechanical properties of all die attach systems, including elastic and viscoplasticity parameters, are obtained from literature studies and hence incorporated into the numerical analysis. Subsequently, the bond stress–strain relationships, stored inelastic strain energies and equivalent plastic strains are thoroughly examined.

The results showed that the silver-tin TLP bonds are more likely to develop higher inelastic strain energy densities, while the sintered silver and copper interconnects would possess higher plastic strains and deformations. Suggesting higher damage to such metallic die attachments. The expensive gold-based solders have developed least inelastic strain energy densities and least plastic strains as well. Thus, they are expected to have improved fatigue performance compared to other bonding configurations.

This paper extensively investigates and compares the mechanical and thermal response of various metallic die attachments. In fact, there are no available research studies that discuss the behavior of such important die attachments of power electronics when exposed to mechanical and thermomechanical loads.

The purpose of this paper is to present an optimisation of four-point star-shaped structures produced through additive manufacturing (AM) polylactic acid (PLA). The study also aims to investigate the compression failure mechanism of the structure.

A Taguchi L₉ orthogonal array design of the experiment is adopted in which the input parameters are resolution (0.06, 0.15 and 0.30 mm), print speed (60, 70 and 80 mm/s) and bed temperature (55°C, 60°C, 65°C). The response parameters considered were printing time, material usage, compression yield strength, compression modulus and dimensional stability. Empirical observations during compression tests were used to evaluate the load–response mechanism of the structures.

The printing resolution is the most significant input parameter. Material length is not influenced by the printing speed and bed temperature. The compression stress–strain curve exhibits elastic, plateau and densification regions. All the samples exhibit negative Poisson’s ratio values within the elastic and plateau regions. At the beginning of densification, the Poisson’s ratios change to positive values. The metamaterial printed at a resolution of 0.3 mm, 80 mm/s and 60°C exhibits the best mechanical properties (yield strength and modulus of 2.02 and 58.87 MPa, respectively). The failure of the structure occurs through bending and torsion of the unit cells.

The optimisation study is significant for decision-making during the 3D printing and the empirical failure model shall complement the existing techniques for the mechanical analysis of the metamaterials.

To the best of the authors’ knowledge, for the first time, a new empirical model, based on the uniaxial load response and “static truss concept”, for failure mechanisms of the unit cell is presented.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.