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Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN).

Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition.

The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN.

In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.

This study aimed to investigate the relationship between spirituality, including religious spirituality (i.e. supernatural beliefs) and secular spirituality (i.e. social beliefs), and mental health among police trainees.

Participants in this study were police trainees of a police academy. An online survey was conducted to measure spirituality and mental health among these police trainees. The association between spirituality and mental health was analyzed using hierarchical linear regression and hierarchical logistic regression with demographic variables (i.e. gender and age) controlled for.

The results revealed that the police trainees with stronger secular spirituality tended to have better general mental health. Higher levels of secular spirituality were significantly associated with lower levels of mental illness risk and suicidal ideation. By contrast, religious spirituality was not significantly related to police trainees' mental health.

The present study is the first to empirically investigate the relationship between spirituality and mental health among police trainees. The findings may be enlightening for future research on the mental health of police officers and trainees, and provide novel perspectives and pragmatic implications for the development of spirituality-based prevention strategies and intervention programs for enhancing the mental health and well-being of the police.

This study aims to understand Chinese consumers’ perceived barriers to using peer-to-peer (P2P) accommodation before and after the outbreak of COVID-19 and the negotiation strategies they applied in overcoming the barriers and enabling consumption.

A qualitative research design with 28 semi-structured interviews was used. Data were analysed by content analysis.

Five psychological barriers and four functional barriers were found to inhibit consumers from using P2P accommodation both before and after the COVID-19 outbreak. In overcoming the perceived barriers, consumers applied both behavioural negotiation strategies, including seeking information, behavioural adaptation, selective choice and seeking social support, and cognitive negotiation strategies, including cognitive adaptation and trusting agents. COVID-19 was found to serve as both a barrier and a facilitator for using P2P accommodation. A barriers–negotiation framework was developed in the context.

Theoretically, this study advances consumer resistance and perceived barriers literature by integrating negotiation and developing a barriers–negotiation framework of P2P accommodation usage. This study also offers insights for practitioners in the P2P accommodation industry.

This study showcases the role of negotiation in understanding barriers to using P2P accommodation, paving the way to extend relevant knowledge to advance consumer resistance research, which is an emerging topic in the broader management domain.

A case study of Lilongwe University of Agriculture and Natural Resources (LUANAR) in Malawi explores its contribution to improving food security and nutrition using varied genetic resources and plant-based diets. The chapter articulates specific examples of research and outreach activities conducted to improve availability, access, and consumption of safe and quality food to reduce undernutrition. Malawi, together with other countries, adopted the global 2030 sustainable development goals (SDGs) during the United Nations General Assembly in September 2015 to transform the world, end poverty and inequality, protect the planet, and ensure that all people enjoy health, justice, and prosperity. SDG2 is on ending hunger, achieving food security, improving nutrition, and promoting sustainable agriculture. Malawi has made significant progress and is on track to achieving SDG number 2 by 2030, and LUANAR has contributed to this achievement in multiple ways. The university has academic programmes and carries out research in various areas of agriculture and natural resources that relate directly to SGD 2. The faculty of Food and Human Sciences champions training, research, and innovation on food and nutrition at the university. The chapter concludes by reiterating that government leadership, support from development partners, and collaboration with the academic, research, and private sectors are key to success. The research models, impact, and challenges presented in the chapter have relevance and potential for wider application in the developing world.

Sandwich structures with well-designed cellular cores exhibit superior shock resistance compared to monolithic structures of equal mass. This study aims to develop a comprehensive analytical model for predicting the dynamic response of cellular-core sandwich structures subjected to shock loading and investigate their application in protective design.

First, an analytical model of a clamped sandwich beam for over-span shock loading was developed. In this model, the incident shock-wave reflection was considered, the clamped face sheets were simplified using two single-degree-of-freedom (SDOF) systems, the core was idealized using the rigid-perfectly-plastic-locking (RPPL) model in the thickness direction and simplified as an SDOF system in the span direction. The model was then evaluated using existing analytical models before being employed to design the sandwich-beam configurations for two typical engineering applications.

The model effectively predicted the dynamic response of sandwich panels, especially when the shock-loading pulse shape was considered. The optimal compressive cellular-core strength increased with increasing peak pressure and shock-loading impulse. Neglecting the core tensile strength could result in an overestimation of the optimal compressive cellular-core strength.

A new model was proposed and employed to optimally design clamped cellular-core sandwich-beam configurations subjected to shock loading.

This paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.

We deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.

The approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.

This work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.

This work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.

In this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).

This study aims to investigate whether students would accept augmented reality technology in Al Ain University (AAU) libraries as part of digital library services.

This study used a modified technology acceptance model–based survey instrument for data collection. Data was collected through an online questionnaire, which was sent to 400 students via email in March 2023. Out of the total participants, 176 students completed the questionnaire.

This study found that AAU students have a positive perception of augmented technology use in the library. They believe that augmented technology will be useful and easy to use, and students are willing to use it to access library resources and services.

This study contributes to the digital library perspectives in academic libraries.