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This research aimed to capture undergraduate students' experiences in the transition from face-to-face (F2F) learning to online learning. This study explored their perceptions regarding the effectiveness of online learning in their academic lives, challenges encountered and suggestions for enhancing online learning in the post-COVID-19 era.

This study employed a concurrent mixed-methods research design and selected 118 undergraduate students using a multistage random sampling technique from four colleges in Assam. Standardized questionnaires and open-ended interview schedules were used.

Undergraduate students reported a positive attitude and satisfaction with online learning, valuing its adaptability to their schedules, its role as a motivating factor for self-learning, its effect on making them more technically proficient and enhancing their communication skills to articulate their thoughts. However, the challenges identified by the students have the potential to overshadow the promises of online learning. This research provided more constructive suggestions under the themes of “content delivery”, “systemic and infrastructural issues”, “pedagogy” and “capacity building” to enhance their experiences with online learning.

Our research findings would assist educational institutions in adopting innovative approaches for simpler and more efficient online learning experiences post-COVID-19 pandemic. Institutions should prepare themselves and design dual-mode courses for F2F and online learning.

The paper addressed a relevant topic in this era of online learning by examining undergraduate students’ viewpoints that added complementary information to the current body of literature on online learning in rural India. The insights gleaned from their experiences would be beneficial for the development of best practices for online learning in the coming decades.

This research aims to investigate the leadership strategies employed by two higher education institutions in Malaysia as they navigated the shift to online delivery of their computer science programs in response to the demands of Education 4.0.

A phenomenological, comparative case study approach was used to delve into the leadership and management practices of these institutions during the transition to online learning. Data were collected through interviews and document analysis.

This study explores the leadership strategies employed by two higher education institutions in Malaysia during their transition to online learning due to the COVID-19 pandemic. Five key themes emerged from the data: leadership and team coordination, training and skill development, adaptation to new assessment methods, resource management and work culture and environment. Both institutions demonstrated effective leadership, continuous training and adaptability in assessment methods. However, differences were noted in resource management and work culture. Institution A's leader had to liaise with various departments and personally invest in equipment, while Institution B was already well-equipped. The work culture at Institution A demonstrated flexibility and mutual understanding, while Institution B used key performance indicators to measure progress. Despite these differences, both leaders successfully managed the shift to online teaching, underscoring the importance of effective leadership, continuous training, flexibility, resource management and a supportive work culture in managing change. The study also highlighted the distinct roles of curriculum leaders in both institutions, with Institution A's leader focusing on multiple activities, while Institution B's leader was able to focus solely on curriculum change due to their institution's preparedness.

This study provides a rich, qualitative exploration of the strategies and challenges faced by program leaders in managing the shift to online teaching during the COVID-19 pandemic. Future research could build on these findings by conducting similar studies in other educational contexts or countries to compare and contrast the strategies and challenges faced by program leaders. Additionally, future research could also employ quantitative methods to measure the effectiveness of different strategies in managing the shift to online teaching. This could provide a more comprehensive understanding of the factors that contribute to successful change management in educational institutions.

This study provides valuable insights for program leaders, educators and policymakers in managing change in educational institutions. The themes identified in this study – effective leadership, continuous training and skill development, flexibility in adapting to new assessment methods, effective resource management and a supportive work culture and environment – can serve as a guide for program leaders in managing future changes in their institutions. Moreover, the strategies employed by the program leaders in this study, such as forming a powerful coalition, providing training on online tools and prioritizing student welfare, can be adopted or adapted by other program leaders in managing change.

This study presents a unique contribution to the existing literature by offering a comparative analysis of change management strategies in two distinct educational institutions during the shift to online teaching due to the COVID-19 pandemic. It uncovers the nuanced differences in leadership styles, resource management and pedagogical adaptations, providing a rich, context-specific understanding of the change process. The study fills a research gap by examining the practical application of Kotter's 8-Step Change Model and the McKinsey 7S Model in real-world educational settings. The findings offer valuable insights for other institutions navigating similar changes, thereby extending the practical and theoretical understanding of change management in higher education.

This research explores support vector machine (SVM) with Gaussian radial basis function kernel (RBF) as the model and Analysis of Variance (ANOVA) for forecasting the invalidation re-examination decisions of China invention patents, it is beneficial to support patent monetization for corporate intellectual capital.

There were 8,666 China invention patents with their existing invalidation re-examination decisions during 2000∼2021 chosen to conduct classification model training and prediction for the accuracy of invalidation re-examination decisions through SVM with RBF. Statistical significance was performed by ANOVA to identify indicators for these invention patents selected in this research. These selected 8,666 China invention patents were divided into two groups based on their invalidation re-examination decisions during 2000∼2021 in Table 1, which Group 1 included 5,974 invention patents with all valid or partially valid claims, and Group 0 included 2,692 invention patents with all invalid claims. Thereafter, each group was further divided into sub-groups based on 13 major regions where the applicants filed invalidation re-examination. The training sets for Group 1, Group 0 and the sub-groups were selected based on the patent issued in January, February, April, May, July, August, October and November; while the prediction sets were selected from the invention patents issued in March, June, September and December.

The training and prediction accuracies were compared to the existing invalidation re-examination decisions. Accuracies of training sets were ranged from 100% in region 7 (Beijing) and region 9 (Shanghai) to 95.95% in region 1 (US), and the average accuracy of invalidation re-examination decisions was 98.95%. While the accuracies of prediction sets for Group 1 were ranged from 100.00% in region 7 (Beijing) to 90.78% in region 13 (Overseas-others), and the average accuracy of classification was 95.96%, this research’s outcomes confirmed the purpose of applying SVM with RBF to predict the patentability sustainability.

This research developed an empirical method through SVM with RBF to predict patentability sustainability which is crucial for corporate intellectual capital on patents. In particular, the investments on patents are huge, including the patent cultivation and maintenance, developments into products or services, patent litigations and dispute managements. Therefore, this research is beneficial not only for corporation, but also for research organisations to perform cost-effective and profitable patent strategies on intellectual capital.

The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.

Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

Given the unique cultural-political context of China, this paper aims to investigate two research questions: What has been the development trajectory of policy-making on consumer privacy protection in China, and what factors have shaped its development over the years?

This paper adopts a historical approach and examines the development of Chinese consumer privacy policy during four periods: 1980s, 1990s, 2000s and 2010-present.

Chinese policy-making on consumer privacy protection has made steady advancement in the past few decades due to factors such as technological development, elite advocacy and emulation of other markets; however, the effects of these factors are conditioned by local forces.

To date, most studies of consumer privacy issues have focused on Western countries, especially the European Union and the USA. A better understanding of how consumer privacy policy has developed in China provides important lessons on the promotion of consumer privacy protection in other developing countries.

The purpose of this paper is to review the criminal enforcement of insider dealing cases in People's Republic of China's (PRC) securities market and to provide feasible suggestions for improvement for a more coherent and streamlined insider dealing regulatory framework in the PRC during the enforcement of China's new Securities Law (SL 2020) in March 2020.

Through analysing the previous literature on public interest theories and economic theories of regulation, this paper examines the necessity to regulate insider dealing in China with criminal law to ensure fairness and avoid monopolies in its securities market. The paper reviews the criminalising of severe insider dealing cases in China from the Nanking National Government in the 1920s to the inception of the securities market of the PRC in the 1990s to the present day. The investigation, prosecution, enforcement and trial of criminal offences of insider dealing in China are thoroughly examined.

The paper finds a tendency for over reliance on the investigation and the administrative judgement of the China Securities Regulatory Commission in criminal investigation, prosecution and trial in the PRC.

To the best of the authors’ knowledge, this paper is one of the first papers to critically and thoroughly analyse the criminal enforcement of insider dealing in China following the recent enforcement of China’s new Securities Law in March 2020.

Purpose and methodology – Focusing on the policy contexts of gender education in Taiwan, this chapter uses data from interviews with elite policymakers and policy documents to examine how feminist activists sought to legitimatize gender equity in education in the wake of the comprehensive social and educational reforms of the 1990s and early years of this decade.

Findings – The embedding of gender in education did not follow a smooth path in terms of policy formulation. Feminist activists drove the process of reform by retaining control over the naming of the legislation, and its wording, thus preserving the language and imperatives of gender equity.

Social implications – In this chapter, I examine the formation of the Gender Equity Education Law, detailing the struggles, contentions, and negotiations that underlay the eventual approval of gender reform in education.

Originality/value of chapter – The chapter contributes significantly by identifying the necessity to recognize the nature of the state and its relations with society in order to research gender in education in Taiwan.

Asteroids have the characteristics of noncooperative, irregular gravity and complex terrain on the surface, which cause difficulties in successful landing for conventional landers. The purpose of this paper is to study the trajectory tracking problem of a multi-node flexible lander with unknown flexible coefficient and space disturbance.

To facilitate the stability analysis, this paper constructs a simplified dynamic model of the multi-node flexible lander. By introducing the nonlinear transformation, a concurrent learning-based adaptive trajectory tracking guidance law is designed to ensure tracking performance, which uses both real-time information and historical data to estimate the parameters without persistent excitation (PE) conditions. A data selection algorithm is developed to enhance the richness of historical data, which can improve the convergence rate of the parameter estimation and the guidance performance.

Finally, Lyapunov stability theory is used to prove that the unknown parameters can converge to their actual value and, meanwhile, the closed-loop system is stable. The effectiveness of the proposed algorithm is further verified through simulations.

This paper provides a new design idea for future asteroid landers, and a trajectory tracking controller based on concurrent learning and preset performance is first proposed.