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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 investigate whether brand positioning strategies influence individuals’ conformity in product choices and identifies the mediator and boundary condition of this relationship.

To test the hypotheses, three experiments were conducted, with data collected using an online platform.

The results indicate that local (vs global) brand positioning promotes consumers’ tendencies to conform in their product choice. Furthermore, this effect is sequentially driven by their perceived similarity with such positioning and the feeling of social connectedness. The influence of local (vs global) brand positioning on consumer conformity diminishes among consumers with a focus on similarity.

This study expands the consumer conformity literature by identifying a new antecedent of consumer conformity. It also introduces a novel downstream consequence of local (vs global) brand positioning on consumer behavior and provides a broader theoretical basis for understanding the psychological connotations underlying local (vs global) brands.

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.

Capacitive six-axis force/torque (F/T) sensors require various configurations to fulfill diverse performance requirements; however, a systematic method to assess the feasibility of any new configuration is lacking. This study aims to propose three criteria for evaluating the rationality of these configurations, enabling a quick determination of the feasibility of the initial structure of the sensor.

This study used sensitivity isotropy as a performance metric. By examining the signal conversion process from F/T to displacement using the compliance transformation matrix, the authors identified Criterion 1: the symmetry condition. By analyzing the decoupling process of the sensor, the authors discovered Criterion 2: the capacitor arrangement condition. Through the optimization of analog sensors, this study derived Criterion 3: the range and structural parameters conditions. Ultimately, this study designed and fabricated a sensor that fulfills these criteria, thereby demonstrating the feasibility of the approach through its performance.

By analogy with capacitive six-axis F/T sensors that have demonstrated exceptional performance in recent years, the authors have found that they all meet the criteria proposed in this paper. Furthermore, the sensor designed and fabricated in this study achieves an accuracy of 0.64% FS, surpassing both the accuracy and sensitivity of the commercially available high-performance ATI industrial automation (Gamma) sensor. This underscores the feasibility of this study’s criteria.

By following the configuration guidelines presented in this paper, designers can quickly assess whether a new configuration will perform well at the early stages of the design process. This makes it easier to consider other requirements while meeting the basic performance needs, thereby significantly enhancing design efficiency.

The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions.

A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data.

The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models.

Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety.

The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.

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.