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The purpose of this paper is to investigate the effect of perceived product similarity and comparative ad claims on brand responses.

This study uses a two (similarity between the target product and the comparison product: relatively similar vs dissimilar) by three (product attributes of the target product: common to the comparison product, distinct from the comparison product, and a combination of common and distinct attributes) between-subjects factorial design with 300 Thai undergraduate students.

It finds that when perceived similarity between the products is high, a combination of superiority (distinct) and parity (common) ad claims lead to the best brand responses. When perceived similarity is low, superiority claims bring about the best brand responses.

It extends comparative advertising and category-substitution research by addressing the research gaps in perceived similarity and claim type.

Companies should emphasize a product’s superior attributes in general but a combination of common and superior attributes when the product is relatively similar to other products in comparative advertising.

This study provides new evidence that perceived product similarity moderates the effect of comparative ad claims on brand responses.

The purpose of this paper is to provide the historical background of genealogical records and analyze the value of Chinese genealogical research through the study of names and genealogical resources.

The paper examines the historical evolution and value of Chinese genealogical records, with the focus on researching the Islamic Chinese names used by the people living in Guilin. The highlight of this paper includes the analysis and evolution of the Islamic Chinese names commonly adopted by the local people in Guilin. It concludes with the recommendations on emphasizing and making the best use of genealogical records to enhance the research value of Chinese overseas studies.

The paper covers the history of Islam and describes how the religion was introduced into China, as well as Muslims' ethnicity and identity. It also places focus on the importance of building a research collection in Asian history and Chinese genealogy.

This research study has a strong subject focus on Chinese genealogy, Asian history, and Islamic Chinese surnames. It is a narrow field that few researchers have delved into.

The results of this study will assist students, researchers, and the general public in tracing the origin of their surnames and developing their interest in the social and historical value of Chinese local history and genealogies.

The study of Chinese surnames is, by itself, a particular field for researching the social and political implications of contemporary Chinese society during the time the family members lived.

Very little research has been done in the area of Chinese local history and genealogy. The paper would be of value to researchers such as historians, sociologists, ethnologists and archaeologists, as well as students and anyone interested in researching a surname origin, its history and evolution.

China's defense industry is analyzed by comparing the technical level of the military and civilian products manufactured by China's nuclear, space, aviation, shipbuilding, ordnance, and electronics industries with their advanced counterparts. Generally, China's defense industry is about 20 years behind the global leaders. Thus, it is inappropriate to declare China's emergence as the world’s second military power. However, if it continues on its current development trajectory, it will attain that status in the near future.

The purpose of this paper is to improve the precision of gangue detection. In the real production environment, some gangue features are not obvious, and it is difficult to distinguish between coal and gangue. The color of the conveyor belt is similar to the gangue, the background noise also brings challenge to gangue detection. To address the above problems, we propose a feature aggregation method based on optical flow (FAOF).

An FAOF is proposed. First, to enhance the feature representation of the current frame, FAOF applies the timing information of video stream, propagates the feature information of the past few frames to the current frame by optical flow. Second, the coordinate attention (CA) module is adopted to suppress the noise impact brought by the background of convey belt. Third, the Mish activation function is used to replace rectified linear unit to improve the generalization capability of our model.

The experimental results show that the gangue detection model proposed in this paper improve 4.3 average precision compared to baseline. This model can effectively improve the accuracy of gangue detection in real production environment.

The key contributions are as follows: this study proposes an FAOF; this study adds CA module and Mish to reduce noise from the background of the conveyor belt; and this study also constructs a large gangue data set.

This paper examines the impact of residents’ human capital investment inequality on the urban–rural income gap, using China’s provincial panel data from 1997 to 2013. The results show that, at the national level as well as at the regional level, residents’ overall human capital investment inequality has a positive significant impact on the urban–rural income gap. In addition, the impact of overall human capital investment inequality increased monotonically from the eastern region inward to the western region. In terms of the relative impact of each component of human capital investment inequality on the urban–rural income gap, migration investment inequality appears to have the greatest impact at the national level, whereas health investment inequality has the greatest impact on the urban–rural income gap in the eastern region, and education investment inequality exhibits the greatest impact in the central and western regions. We also investigate the impact of human capital investment inequality on the urban–rural income gap over different periods. The results show that residents’ overall human capital investment inequality had a positive impact on the urban–rural income gap in the period 1997–2008, but the impact rapidly shrunk in 2009–2013. Furthermore, the impact of residents’ health investment inequality on the urban–rural income gap shows a downward trend, and the impact of residents’ education investment inequality trended slightly upward from 1997 to 2008, and then rapidly shrunk in 2009–2013. Finally, the impact of residents’ migration investment inequality was only significant in 1997–2002.

In order to solve the current imbalance of academic resources within the discipline, this article builds a three-dimensional talent evaluation model based on the topic–author–citation based on the z index and proposes the ZAS index to evaluate scholars on different research topics within the discipline.

Based on the sample data of the CSSCI journals in the discipline of physical education in the past five years, the keywords were classified into 13 categories of research topics including female sports. The ZAS index of scholars on topic of female sports and so on was calculated, and quantitative indexes such as h index p index and z index were calculated. Comparative analysis of the evaluation effect was performed.

It is found that compared with the h index and p index, the z index achieves a better balance between the quantity, quality and citation distribution of scholars' results and effectively recognizes that the citation quality is higher and the number of citations of each paper is more balanced. In addition, compared to the z index, this article is based on a ZAS index model with an improved three-dimensional topic–author–citation relationship in research fields such as female sports.

It can identify some outstanding scholars who are engaged in small-scale or emerging topic research such as female sports and are excellent in different research areas. Talents create an objective and fair evaluation environment. At the same time, the ranking ability of ZAS indicators in the evaluation of talents is the strongest, and it is expected to be used in practical evaluations.

This paper aims to provide a better understanding of the challenges and potential solutions in Visual Simultaneous Localization and Mapping (SLAM), laying the foundation for its applications in autonomous navigation, intelligent driving and other related domains.

In analyzing the latest research, the review presents representative achievements, including methods to enhance efficiency, robustness and accuracy. Additionally, the review provides insights into the future development direction of Visual SLAM, emphasizing the importance of improving system robustness when dealing with dynamic environments. The research methodology of this review involves a literature review and data set analysis, enabling a comprehensive understanding of the current status and prospects in the field of Visual SLAM.

This review aims to comprehensively evaluate the latest advances and challenges in the field of Visual SLAM. By collecting and analyzing relevant research papers and classic data sets, it reveals the current issues faced by Visual SLAM in complex environments and proposes potential solutions. The review begins by introducing the fundamental principles and application areas of Visual SLAM, followed by an in-depth discussion of the challenges encountered when dealing with dynamic objects and complex environments. To enhance the performance of SLAM algorithms, researchers have made progress by integrating different sensor modalities, improving feature extraction and incorporating deep learning techniques, driving advancements in the field.

To the best of the authors’ knowledge, the originality of this review lies in its in-depth analysis of current research hotspots and predictions for future development, providing valuable references for researchers in this field.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

This paper traces the timeline and milestones of Chinese graduate students in agricultural economics and related fields at foreign universities, with an emphasis on North American universities, since the early 1980s, and assesses the contributions of Chinese doctoral recipients from foreign universities to agricultural economic research and education in North America and China.

Data from department and college websites, associations of agricultural economics, university libraries and databases of theses and dissertations and selected agricultural economic journals in English and Chinese are used to attain the above purposes through graphical and bibliometric analyses.

First, the numbers of Chinese doctoral recipients and tenure-track and tenured faculty in agricultural economics at North American universities have increased steadily and significantly. Second, Chinese scholars in North America have achieved tremendous success in agricultural economic research via high-quality publications, prestigious awards, editorship of top journals, leadership in professional organizations, etc. Third, more Chinese doctoral recipients overseas have increasingly returned to China and are playing important roles in China’s agricultural economic research, education and international collaboration. Fourth, the publications of overseas Chinese scholars in Chinese journals and those of their counterparts working in China on topics beyond China are relatively limited and more collaboration may enhance the global impacts of Chinese agricultural economists.

This research is limited by data availability and quality and the data problems are discussed in the paper.

This is likely the first study to assess the contributions of Chinese doctoral recipients from foreign universities to agricultural economic research and education in China and abroad.

By modelling China’s property price changes and their effect on GDP, this study aims to develop a more general model of the costs and benefits driving price bubbles.

The authors develop a five-sector dynamic model (using data from China and seven other comparator jurisdictions), resulting in a bubble risk factor. The authors then correlate this risk factor with changes in property prices and resulting changes in GDP.

The authors find that economic structures (the way GDP, property prices and other variables change relative to each other) can change during/after a financial crisis. The authors also find that price disequilibria can help predict the risk of a property price fall – which thus reverberates into GDP change.

To the best of the authors’ knowledge, no dynamic models of price bubbles exist (though many exist of financial bubbles). The authors provide both theoretical novelties (such as providing a model of risk using non-linear differential equations) and practical ones (showing when we can expect Chinese GDP to fall).