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To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small.

The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power spectrum and the pitch acceleration power spectrum, which is calculated using ADAMS finite element software. Adaboost Backward Propagation algorithm is used in each restricted Boltzmann machine deep neural network classification model for fine-tuning given its performance of global searching. The algorithm is first applied to road spectrum detection and experiments indicate that the algorithm is suitable for detecting pavement roughness.

The detection rate of RBM deep neural network algorithm based on Adaboost Backward Propagation is up to 96 per cent, and the false positive rate is below 3.34 per cent. These indices are both better than the other supervised algorithms, which also performs better in extracting the intrinsic characteristics of data, and therefore improves the classification accuracy and classification quality. Additionally, the classification performance is optimized. The experimental results show that the algorithm can improve performance of restricted Boltzmann machine deep neural networks. The system can be used for detecting pavement roughness.

This paper presents an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation for identifying the road roughness. Through the restricted Boltzmann machine, it completes pre-training and initializing sample weights. The entire neural network is fine-tuned through the Adaboost Backward Propagation algorithm, verifying the validity of the algorithm on the MNIST data set. A quarter vehicle model is used as the foundation, and the vertical acceleration spectrum of the vehicle center of mass and pitch acceleration spectrum were obtained by simulation in ADAMS as the input samples. The experimental results show that the improved algorithm has better optimization ability, improves the detection rate and can detect the road roughness more effectively.

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.

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.

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.

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.

In view of the significant changes in the capital structure of China’s real estate industry and enterprises in recent years, this chapter employs financial indicators and the linear regression function to analyze the relationship between corporate debt ratio and the performance of 111 A-share listed real estate enterprises in China. This study finds that the corporate debt ratio of China’s real estate enterprises in the past decade has a significant negative impact on enterprises’ performance. The study also finds that among China’s real estate companies, the corporate debt ratio has a more significant negative impact on the performance of non-state-owned enterprises than state-owned enterprises. In addition, a high debt ratio has a more significant negative impact on return on equity (ROE) than on return on assets (ROA). However, when Tobin’s Q serves as a proxy for firm performance, the negative impact of the corporate debt ratio becomes insignificant in the presence of the firm size factor. The research results of this chapter can provide some reference for subsequent policy-making and investment decisions in the Chinese real estate market.

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.

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).

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.