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Aqueous zinc-ion battery has broad application prospects in smart grid energy storage, power tools and other fields. Co₃O₄ is one of the ideal cathode materials for water zinc-ion batteries due to their high theoretical capacity, simple synthesis, low cost and environmental friendliness. Many studies were concentrated on the synthesis, design and doping of cathodes, but the effect of process parameters on morphology and performance was rarely reported.

Herein, Co₃O₄ cathode material based on carbon cloth (Co₃O₄/CC) was prepared by different temperatures hydrothermal synthesis method. The temperatures of hydrothermal reaction are 100°C, 120°C, 130°C and 140°C, respectively. The influence of temperatures on the microstructures of the cathodes and electrochemical performance of zinc ion batteries were investigated by X-ray diffraction analysis, scanning electron microscopy, cyclic voltammetry curve, electrochemical charging and discharging behavior and electrochemical impedance spectroscopy test.

The results show that the Co₃O₄/CC material synthesized at 120°C has good performance. Co₃O₄/CC nanowire has a uniform distribution, regular surface and small size on carbon cloth. The zinc-ion battery has excellent rate performance and low reaction resistance. In the voltage range of 0.01–2.2 V, when the current density is 1 A/g, the specific capacity of the battery is 108.2 mAh/g for the first discharge and the specific capacity of the battery is 142.6 mAh/g after 60 charge and discharge cycles.

The study aims to investigate the effect of process parameters on the performance of zinc-ion batteries systematically and optimized applicable reaction temperature.

This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

The interaction between evaluators is underestimated in legitimacy literature. This study aims to examine the impact of CEO celebrity on initial public offerings (IPOs) underpricing in Strategic Emerging Industries (SEIs). Based on legitimacy and limited attention effect, this study introduces a new antecedent to the asset pricing literature under a particular sample.

This paper illustrates how CEO celebrity promotes IPO underpricing by enhancing the legitimacy and then explores how the CEO characteristics can moderate this relationship. Using 1,128 IPO companies in China SEIs from 2010 to 2019, cross-section data is used to build a multiple linear regression model to test the hypotheses.

The result indicates that CEO celebrity is positively related to IPO underpricing. Founder CEO and CEO duality amplify the relationship. Further analysis shows that the relationship between CEO celebrity and IPO underpricing is more pronounced in firms with high Baidu search and low market sentiment.

This study provides insights into how CEO celebrity as notable internal information shapes the formation of investors' preliminary impressions of firms. The evidence consists of legitimacy and limited attention perspective by showing how investors favor, follow and hype the stocks with celebrity CEOs. The results extend the knowledge about how CEO characteristics influence information frictions in asset pricing during IPO.

In response to the navigation challenges faced by coal mine tunnel inspection robots in semistructured underground intersection environments, many current studies rely on structured map-based planning algorithms and trajectory tracking techniques. However, this approach is highly dependent on the accuracy of the global map, which can lead to deviations from the predetermined route or collisions with obstacles. To improve the environmental adaptability and navigation precision of the robot, this paper aims to propose an adaptive navigation system based on a two-dimensional (2D) LiDAR.

Leveraging the geometric features of coal mine tunnel environments, the clustering and fitting algorithms are used to construct a geometric model within the navigation system. This not only reduces the complexity of the navigation system but also optimizes local positioning. By constructing a local potential field, there is no need for path-fitting planning, thus enhancing the robot’s adaptability in intersection environments. The feasibility of the algorithm principles is validated through MATLAB and robot operating system simulations in this paper.

The experiments demonstrate that this method enables autonomous driving and optimized positioning capabilities in harsh environments, with high real-time performance and environmental adaptability, achieving a positioning error rate of less than 3%.

This paper presents an adaptive navigation system for a coal mine tunnel inspection robot using a 2D LiDAR sensor. The system improves robot attitude estimation and motion control accuracy to ensure safe and reliable navigation, especially at tunnel intersections.

With the development of a new generation of digital technologies, digital transformation (DT) has become an inevitable trend for enterprises to achieve development in various industries. Nevertheless, the contradiction between the “fragmented” use of digital technologies and the “systematic” transformation of the industry leads to the underperformance of DT in the construction industry. Whilst previous studies have examined why DT is needed and how separate digital technologies can be used in construction projects, they failed to specify effective tools that can help enterprises identify key resources that facilitate DT from the organisational perspective.

This study established an objective assessment framework for evaluating the digital transformation capability (DTC) of construction enterprises in identifying limitations in their transformation efforts. This study also established a management entropy quantitative model and a comprehensive capability evaluation model of DT to analyse the DT performance of construction enterprises from the internal and external perspectives. Data were collected from 95 listed enterprises in China’s construction industry in 2020 as a case study.

This study concluded that enterprise profitability provides a strong endogenous driving force for DT. Research and development capabilities and DT proficiency of enterprises are the most critical factors in facilitating DT. In addition, China’s construction enterprises' DT was characterised by uneven development and low orderliness. The lack of a unified digital integration platform is key to cracking the dilemma.

This paper systematically identified key DTC in construction enterprises and proposed an objective framework for measuring DTC to enhance the DT performance of these enterprises.

The purpose of this paper is to empirically analyze the impact of directors’ and officers’ (D&O) liability insurance on enterprise strategic change. It also explores the mediating role of litigation risk, the moderating roles of enterprise science and technology level and precipitation organizational slack between them. In addition, it examines the joint moderating roles of the top management team (TMT) external social network and enterprise science and technology level, and enterprise scale and precipitation organizational slack.

Using the unbalanced panel data of A-share listed companies in the Shanghai and Shenzhen stock exchanges of China from 2002 to 2020 as the research sample, this paper uses the ordinary least square method and fixed-effect model to study the relationship between D&O liability insurance and enterprise strategic change. The study also focuses on the mediating mechanism and moderating mechanisms between them.

The authors find that D&O liability insurance has an “incentive effect,” which can significantly promote enterprise strategic change. Litigation risk plays a partial mediating role between D&O liability insurance and enterprise strategic change. Enterprise science and technology level and precipitation organizational slack negatively moderate the relationship between D&O liability insurance and enterprise strategic change. TMT external social network and enterprise science and technology level, and enterprise-scale and precipitation organizational slack have joint moderating effects on the relationship between D&O liability insurance and enterprise strategic change.

This paper confirms the “incentive effect hypothesis” of the impact of D&O liability insurance on enterprise strategic change, which not only broadens the research perspective of enterprise strategic management but also further expands the research scope of D&O liability insurance. Besides, this paper thoroughly explores the influencing mechanisms between D&O liability insurance and enterprise strategic change, providing incremental contributions to the research literature in the field of enterprise risk management and corporate governance. The findings have practical guiding significance for expanding the coverage of D&O liability insurance, promoting the implementation of strategic changes and improving the level of corporate governance of Chinese enterprises.

This research paper aims to provide a comprehensive overview of the determinants influencing organisations decisions to adopt crowdsourcing. By synthesising existing literature, it seeks to identify critical factors that act as enablers or inhibitors in the adoption process and propose a framework for understanding crowdsourcing adoption within organisational contexts.

This study employed a systematic literature review methodology to examine the determinants influencing organisations' decisions to adopt crowdsourcing. The review encompassed research articles from the Web of Science and Scopus databases, spanning 2006 to 2021. Additionally, morphological analysis was conducted to categorise the identified determinants into three distinct contexts: technological, organisational and environmental. This methodological approach facilitated a comprehensive exploration of the factors shaping crowdsourcing adoption within organisational settings, allowing for a nuanced understanding of the phenomenon across different dimensions.

The study identifies 12 determinants influencing crowdsourcing adoption, categorised into technological, organisational and environmental dimensions. These determinants include technological compatibility, organisational readiness, top management support, crowd readiness and availability of third-party platforms. While some determinants primarily act as enablers, others exhibit dual roles or serve as inhibitors depending on contextual factors.

The findings offer valuable insights for scholars, practitioners, and organisational leaders seeking to leverage crowdsourcing as a strategic tool for innovation and competitiveness. The assessment scale of drivers and barriers developed in this research offers a systematic approach for evaluating the factors influencing crowdsourcing adoption, providing a nuanced understanding of innovation adoption dynamics. Theoretical implications include advancements in morphological analysis methodology and a nuanced understanding of innovation adoption dynamics. Managerial implications highlight strategies for enhancing organisational readiness, leveraging leadership support and mitigating adoption risks. Overall, the study provides a foundation for future empirical research and practical guidance for organisations planning to adopt crowdsourcing initiatives.

This research contributes significantly to crowdsourcing by presenting an integrated and theoretically grounded framework. By consolidating adoption determinants from diverse contexts, this study clarifies the understanding of crowdsourcing adoption. The framework offers practical value to managers and decision-makers, equipping them with a structured approach to assess and navigate the challenges associated with effectively adopting crowdsourcing. As such, this study contributes to advancing crowdsourcing practices and supports more informed managerial decision-making in innovation and knowledge sourcing.

This study aims to enhance the effectiveness of knowledge markets and overall knowledge management (KM) practices within organisations. By addressing the challenge of internal knowledge stickiness, it seeks to demonstrate how machine learning and AI approaches, specifically a text-based AI method for personality assessment and regression trees for behavioural analysis, can automate and personalise knowledge market incentivisation mechanisms.

The research employs a novel approach by integrating machine learning methodologies to overcome the limitations of traditional statistical methods. A natural language processing (NLP)-based AI tool is used to assess employees’ personalities, and regression tree analysis is applied to predict and categorise behavioural patterns in knowledge-sharing contexts. This approach is designed to capture the complex interplay between individual personality traits and environmental factors, which traditional methods often fail to adequately address.

Cognitive style was confirmed as a key predictor of knowledge-sharing, with extrinsic motivators outweighing intrinsic ones in market-based platforms. These findings underscore the significance of diverse combinations of environmental and individual factors in promoting knowledge sharing, offering key insights that can inform the automatic design of personalised interventions for community managers of such platforms.

This research stands out as it is the first to empirically explore the interaction between the individual and the environment in shaping actual knowledge-sharing behaviours, using advanced methodologies. The increased automation in the process extends the practical contribution of this study, enabling a more efficient, automated assessment process, and thus making critical theoretical and practical advancements in understanding and enhancing knowledge-sharing behaviours.

This study aims to conduct a bibliometric assessment of existing literature in the fields of health communication and social media in the Asian context.

Using 265 Scopus-indexed papers, a comprehensive bibliometric study was performed, incorporating both performance and science mapping analyses.

The results reveal an increasing trend in the publication of this topic. This study also identified the top author, country, articles and author collaboration clusters. Four primary themes emerged from the publications: “Papillomavirus” and “the COVID-19 pandemic” were categorized as niche themes; “gender and cohort” was identified as a basic theme; and “behavioral intention” was classified as an emerging or declining theme. These can serve as the foundations for future research directions.

This research used only the Scopus database as its data source. However, future bibliometric research could investigate other databases.

This paper has practical implications for researchers, health communication managers, government and policymakers. It provides valuable information that can guide researchers in conducting new studies, fostering collaborations and conducting further bibliometric analyses. Health communication managers can use this paper to design and manage social media-based health communication programs. The government could leverage these findings to support evidence-based policy implementation in the field of health communication.

This study, to the best of the authors’ knowledge, marks the first bibliometric analysis focused on the literature in the field of health communication and social media in the Asian context.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.