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Autonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.

The tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.

The containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.

The state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.

Online learning has come to stay in a technologically advancing world with increasing populations. The search for ways to make online learning more efficient and effective in some developing countries continues as the accompanying issues in developing country contexts abound. This paper explores the issues that underlie online learning management in a developing country context, focusing on the Moodle, Sakai and Zoom platforms used in some selected public universities in Ghana.

The study relied on the qualitative approach to data collection and a descriptive design for analysis. Using the social constructivism theory, the paper discussed the critical issues students, and to a lesser extent, instructors encounter in their engagement on these platforms for academic learning.

The study found that the inefficient use of these platforms is due to several factors including the high cost of data, poor perception of the quality of online learning compared to traditional in-person contacts, poor attitude of students, low participation in online learning, lack of computers and poor internet connectivity among others.

It is important to mention that this study was limited to some selected public universities in Ghana. Data could have been collected from a wider sample including other Ghanaian tertiary institutions or some other developing countries. The similarity of study outcomes in other developing countries, however, suggests that similar results would have been obtained in an international sample.

Despite the robustness of the Learning Management systems in place, the evidence suggests that their utilisation is far less than optimal. However, with relevant policies and the provision of needed technical support, training, provision of equipment like computers for use by both students and instructors and efficient internet connectivity, the LMS platforms could be more efficient for online learning.

The authors conducted this research using original data from interviews in the selected public universities in Ghana. The data was meant to inform the discussion on some of the critical issues that underlie online learning in a developing country context. Although the study relied on data from selected public universities in a single country, its outcomes reflect fundamental issues of online learning in a developing country context which find relevance in available study outcomes.

Research focusing on psychological capital (PsyCap) has been mainly conducted at the individual level. However, recent research has expanded investigations to the collective level with a greater focus on team-level PsyCap. Although, as demonstrated by recent systematic reviews and meta-analyses, the relationships between individual-level PsyCap and the desirable/undesirable outcomes are fairly established in the literature, less is known about such relationships for team-level PsyCap. One of these important, yet least investigated, research areas is the research stream that focuses on the relationship between team-level PsyCap and the outcomes of health, Well-Being, and safety. This chapter aims to highlight the role of individual-level PsyCap as an important predictor of employees’ health, Well-Being, and safety outcomes, but also to go beyond that to provide insights into the potential role of team-level PsyCap in predicting such outcomes at both individual and team levels. To do so, the chapter first draws upon relevant theories to discuss the empirical research findings focusing on the relationship between individual-level PsyCap and the outcomes of health, Well-Being, and safety. It then focuses on team-level PsyCap from theoretical, conceptualization, and operationalization perspectives and provides insights into how team-level PsyCap might be related to health, Well-Being, and safety outcomes at both individual and team levels. Thus, this chapter proposes new research directions in an area of PsyCap that has been left unexplored.

Previous studies may have overstated the benefits of entrepreneurial resilience while neglecting its potential adverse effects, especially in the context of team entrepreneurship. This study focuses on the structural characteristics of resilience, entrepreneurial team resilience diversity, to delve into the potential dark side effects of entrepreneurial resilience. Drawn upon the similarity-attraction theory, this study investigates the detrimental impact of entrepreneurial team resilience diversity on entrepreneurial performance from the team-member exchange (TMX) perspective, which describes the reciprocal relationships within a team and the moderating effect of environmental hostility.

The study collected data by conducting a two-wave survey. With the assistance of officials from entrepreneurship service agencies, entrepreneurship parks and entrepreneurship training institutions in southeast China, this study collected survey data from 361 participants of 91 entrepreneurial teams. The current research conducted empirical tests with SPSS and PROCESS macro to test the hypotheses.

Empirical results from a two-wave survey of 91 entrepreneurial teams in China reveal the detrimental effects of entrepreneurial resilience. In the context of team entrepreneurship, the diversity of a team’s entrepreneurial resilience negatively impacts entrepreneurial performance by impairing the quality of TMX. Moreover, the indirect effect of TMX is strengthened in more hostile environments.

Existing studies have mainly focused on the positive side of resilience. However, based on the similarity-attraction theory, this study explores the potential adverse effects of the structural characteristics of entrepreneurial team resilience from the perspective of team-member exchange (TMX). This study enriches the literature on resilience by demonstrating how resilience functions in new venture teams and challenges the assumption that resilience is universally beneficial to entrepreneurs. Introducing team members with high resilience in a hostile environment may not always benefit the team, deepening the understanding of entrepreneurial resilience when confronting adversities.

The purpose of this paper is to test if the digital economy improves the quality of life of our residents. Furthermore, if this finding is confirmed, what would be the mechanism behind its effect? Does the impact of the digital economy on quality of life vary according to its level of development?

A comprehensive index of the digital economy, income gap and quality of life was constructed empirically based on data from 220 cities in China from 2011–2020. A multi-dimensional empirical analysis was conducted in this paper.

The analysis of the pathways of action shows that narrowing the income gap is an important mechanism through which the digital economy actively contributes to the quality of life. The results of the threshold model show that the “marginal effect” of the digital economy on quality of life is non-linear and increasing. The results show that after a series of robustness tests, including instrumental variables, the digital economy still significantly enhances people’s quality of life.

This paper reveals the intrinsic link between the digital economy and quality of life and provides a theoretical basis for further improving people’s well-being.

Encouraging the development of the digital economy is a useful way to improve the quality of life by narrowing the income gap.

Data analysis of the digital economy from 2011–2020 in China to get an insight into what would be the mechanism behind the digital economy improving the quality of life of our residents.

The purpose of this paper is to study the aerodynamic characteristics and uplift force tendencies of pantographs within the operational height span of 1,600–2,980 mm, aiming to offer valuable insights for research concerning the adaptability of pantograph-catenary systems on double-stack high container transportation lines.

Eight pantograph models were formulated based on lines with the contact wire of 6,680 mm in height. The aerodynamic calculations were carried out using the SST k-ω separated vortex model. A more improved aerodynamic uplift force method was also presented. The change rule of the aerodynamic uplift force under different working heights of the pantograph was analyzed according to the transfer coefficients of the aerodynamic forces and moments.

The results show that the absolute values of the aerodynamic forces and moments of the upper and lower frame increase with the working height, whereas those of the collector head do not change. The absolute values of the transfer coefficients of the lower frame and link arm were significantly larger than those of the upper frame. Therefore, the absolute value of the aerodynamic uplift force increased and then decreased with the working height. The maximum value occurred at a working height of 2,400 mm.

A new method for calculating the aerodynamic uplift force of pantographs is proposed. The specifical change rule of the aerodynamic uplift force of the pantograph on double-stack high container transportation lines was determined from the perspective of the transfer coefficients of the aerodynamic forces and moments.

This study aims to explore the key challenges and drawbacks of smart contracts (SCs) and how they impact digital resilience within small and medium enterprises (SMEs). Whilst this type of technology is seen as a step forward in terms of traceability, transparency and immutability to increase digital resilience, we argue that it should be approached with trepidation.

In developing this paper, the authors conduct a systematic literature search using the Scopus database. Through this, we identified 931 relevant articles, of which 30 were used as the focus of this article. Thematic analysis was used as the analytical approach to develop themes and meaning from the data.

In this paper, there is an emphasis on the importance of understanding the potential risks associated with SC implementation, as well as identifying appropriate strategies for mitigating any negative impact. In our findings, we puts forward three key themes, namely legality, security and human error, which we argue are key smart contract challenges that impact SME digital resilience.

In this paper, we propose the notion of “centralised control in decentralised solutions”. This comes from the research highlighting SC weaknesses in digital resilience for SMEs. We argue that there is a need for standards, regulations and legislation to address these issues, advocating, ironically, a centralised approach to decentralised technology.

Purpose: Green consumerism is on the rise in the 21st century, impelling businesses to prioritise environmental awareness and expand eco-products to keep up with the growing demand. This research examines how social media (SM) and moral obligations (MO) affect consumer views and their propensity to make eco-friendly choices.

Methodology: Data were gathered from 508 participants using an adaptive questionnaire. The proposed model was tested using ‘structural equation modelling’.

Findings: The results show that electronic word-of-mouth (EWOM) and the intent to acquire green goods favourably impact consumer behaviour. MO positively influences attitudes and intentions to make green purchases (GPI), with attitudes acting as a mediator between MO and GPI.

Implications: This research is of utmost importance for marketers wanting to enhance their SM communication strategies to influence consumers’ opinions of green products and raise the possibility that they would make environmentally conscious purchases.

The purpose of this study is to empirically examine the cause–effect relationships between workplace friendship and knowledge-sharing behavior, from a static perspective. Furthermore, it investigates the bi-directional relationship between the increase in both workplace friendship and knowledge-sharing behavior over same time periods, and also endeavors to identify whether there is a significant negative lagged effect of the increase in both workplace friendship on knowledge-sharing behavior, and vice versa, across time from a dynamic perspective.

The study conducts a three-wave questionnaire survey to test the research model. A latent change score approach was used to test the direct relationship between changes in workplace friendship and changes in knowledge-sharing behavior.

The findings reveal that knowledge-sharing behavior fosters workplace friendship and workplace friendship promotes the emergence of knowledge-sharing behavior. An increase in workplace friendship promotes an increase in knowledge-sharing behavior over same time periods. However, an increase in workplace friendship will lead to a lagged decrease of knowledge-sharing behavior across time, and vice versa.

The time interval in this study is a little short to capture the full changes in workplace friendship. Some important control factors and mediating mechanisms are not included in the research model.

This study guides managers to focus on various motivators to better strengthen workplace friendship and knowledge-sharing behavior and to consider and effectively respond to the negative side of workplace friendship and knowledge-sharing behavior across time.

This study emphasizes the predictivity of one important interaction patterns, namely, knowledge-sharing behavior on friendship at the workplace, from a static perspective. This study also shows the benefits of an increase in workplace friendship for the development of knowledge-sharing behavior in the same time period. Furthermore, the study presents a counterintuitive finding when taking the lag effect into consideration in exploring the relationship between changes both in workplace friendship and knowledge-sharing behavior, and identifies a negative side of both when viewed over longer periods.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.