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This paper aims to propose a stable and precise recognition and pose estimation method to deal with the difficulties that industrial parts often present, such as incomplete point cloud data due to surface reflections, lack of color texture features and limited availability of effective three-dimensional geometric information. These challenges lead to less-than-ideal performance of existing object recognition and pose estimation methods based on two-dimensional images or three-dimensional point cloud features.

In this paper, an image-guided depth map completion method is proposed to improve the algorithm's adaptability to noise and incomplete point cloud scenes. Furthermore, this paper also proposes a pose estimation method based on contour feature matching.

Through experimental testing on real-world and virtual scene dataset, it has been verified that the image-guided depth map completion method exhibits higher accuracy in estimating depth values for depth map hole pixels. The pose estimation method proposed in this paper was applied to conduct pose estimation experiments on various parts. The average recognition accuracy in real-world scenes was 88.17%, whereas in virtual scenes, the average recognition accuracy reached 95%.

The proposed recognition and pose estimation method can stably and precisely deal with the difficulties that industrial parts present and improve the algorithm's adaptability to noise and incomplete point cloud scenes.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

Because brand addiction is one of the most intense forms of consumer–brand relationships, this type of behavior has recently attracted the attention of researchers in the field of consumer behavior management. The purpose of this paper is to investigate the antecedents and consequences of brand addiction among Iranian consumers.

This research used a cross-sectional study based on a questionnaire to collect data from a sample of 401 people in Iran. Structural equation modeling was used to test the hypotheses.

This study found that brand love and brand involvement significantly influence brand addiction. Brand addiction was also positively associated with contingent self-esteem (CSE), compulsive shopping and resilience to negative information. Additionally, the results revealed a positive and significant effect of brand involvement on brand love and a similar positive and significant effect of CSE on compulsive shopping.

By examining the antecedents and consequences of brand addiction in an Islamic country, this research offers valuable insights that can enrich the literature on brand addiction in consumer behavior and brand management.

The primary objective of this study was to evaluate the influence of greenery on residential mobility within purpose-built student housing facilities in Northern Ghana.

This research employed a structured questionnaire and utilized an experimental block design, encompassing 124 comparative greened and non-greened student housing facilities, with a total of 995 resident participants. The impact of greenery on residential mobility was analyzed using a repeated sales model and t-test analysis.

Results revealed that residential mobility was significantly higher in non-greened student housing facilities than their greened counterparts. The study further indicated that the presence of greenery had a substantial effect on residential mobility, attributed to residents' preferences for the ecological, social and economic benefits associated with greenery, rather than merely infrastructure considerations.

Enhancing the aesthetic appeal, economic viability, safety, security and health benefits of greened student housing facilities while managing the influence of greenery on infrastructure was found to affect residential mobility. The findings suggest that improving occupancy rates in these facilities through the incorporation of greenery could yield higher rental income and better cash flows for investors involved in student housing operations.

This study highlights the ecological, social and economic advantages of greenery for residents. While the benefits of greenery in residential contexts are increasingly recognized, the specific impact of greenery on residential mobility within the Sub-Saharan African context represents a novel contribution. The application of neighborhood effects theory to the examination of greenery benefits and residential mobility in this region adds a new dimension to existing research.

Service quality in academics is imperatively crucial. Therefore, the purpose of the current research is to examine the effect of academic service quality factors in higher education institutions which mainly focuses on students' satisfaction and the extent of recommending the same institution to other students.

This research evaluated the effect of service quality on student satisfaction and word of mouth (WOM). Data were collected from 200 respondents from various business schools, and hypotheses were tested through a quantitative method using partial least square-structural equation modelling (PLS-SEM) with the help of SmartPLS to validate the measurement model.

The findings of the current research revealed that all three components of service quality including information quality, teaching quality and service efficiency have a significant impact on student satisfaction. Results also indicated that student satisfaction is significantly linked with WOM which means satisfied students spread positive WOM and recommend the same institution to other students.

This research provides an extension towards the body of knowledge on the issue, which will be used in future detailed and critical examinations. The focus of the research was on SERVQUAL and how, when and why business schools may spot possible applications that can have an influence on their admissions by WOM.

Online learning has come to stay in a technologically advancing world with increasing populations. The search for ways to make online learning more efficient and effective in some developing countries continues as the accompanying issues in developing country contexts abound. This paper explores the issues that underlie online learning management in a developing country context, focusing on the Moodle, Sakai and Zoom platforms used in some selected public universities in Ghana.

The study relied on the qualitative approach to data collection and a descriptive design for analysis. Using the social constructivism theory, the paper discussed the critical issues students, and to a lesser extent, instructors encounter in their engagement on these platforms for academic learning.

The study found that the inefficient use of these platforms is due to several factors including the high cost of data, poor perception of the quality of online learning compared to traditional in-person contacts, poor attitude of students, low participation in online learning, lack of computers and poor internet connectivity among others.

It is important to mention that this study was limited to some selected public universities in Ghana. Data could have been collected from a wider sample including other Ghanaian tertiary institutions or some other developing countries. The similarity of study outcomes in other developing countries, however, suggests that similar results would have been obtained in an international sample.

Despite the robustness of the Learning Management systems in place, the evidence suggests that their utilisation is far less than optimal. However, with relevant policies and the provision of needed technical support, training, provision of equipment like computers for use by both students and instructors and efficient internet connectivity, the LMS platforms could be more efficient for online learning.

The authors conducted this research using original data from interviews in the selected public universities in Ghana. The data was meant to inform the discussion on some of the critical issues that underlie online learning in a developing country context. Although the study relied on data from selected public universities in a single country, its outcomes reflect fundamental issues of online learning in a developing country context which find relevance in available study outcomes.

The purpose of this study is to explore learner-centered emerging technologies, methods of learning with artificial intelligence (AI), student engagement and concerns related to the assessment process at public and private universities in Bangladesh.

The study employed a qualitative research approach to investigate innovations in learning, student engagement and assessment concerns. Respondents were categorized into three clusters: Cluster-A comprised students from various academic levels, Cluster-B included faculty members with online teaching experience and Cluster-C consisted of information and communication technology (ICT) experts from university information technology (IT) departments. Qualitative data were collected via in-depth interviews.

The research findings indicate the widespread acceptance and utilization of learner-centered technologies, with online and hybrid learning offering flexibility, accessibility, self-paced learning and better student engagement opportunities. While AI platforms like ChatGPT and Gemini are valued for clarifying theoretical concepts, concerns exist regarding their ethical use, particularly in completing assignments. The assessment process faces challenges related to online exams, with concerns over cheating prompting calls for AI-supported solutions. However, there is a gap between commitment and action in regulating unethical AI usage, highlighting the need for clearer guidance and investment in technological solutions for an effective assessment process.

Limitations of this study include the inability to quantify the satisfaction level with the emergence of learner-centered technologies, which could be better explained through a quantitative study.

This study’s originality and value stem from its thorough investigation of innovations in learner-centered education considering the emergence of AI and student engagement, uniquely focusing on the perspective of a developing country.

In this study, the researchers explored the predictive powers of corporate cultural factors and self-efficacy on Pakistan's public sector bank employees' organizational commitment.

The researchers designed a co-relational study based on cross-sectional data using a questionnaire to collect the data from the Pakistan public sector banks' managers, assistant managers and operational managers. Consequently, the researchers based this study's findings on the 270 valid responses to the questionnaire.

This study's findings reveal that, except for teamwork, together with self-efficacy, the corporate cultural factors comprising organizational communication, training and development and reward and recognition have positive and significant impacts on organizational commitment. More specifically, self-efficacy plays a mediating role in terms of the relationships between organizational commitment and organizational communication, training and development and reward and recognition.

From establishing the most relevant corporate cultural factors, the researchers consider that this study's findings are helpful to policymakers and organizations in developing organizational commitment among employees. More practically in the case of Pakistan's public sector banks, the employees can improve employees' performance by recognizing the significance of the corporate cultural factors on employees' organizational commitment. In addition, the researchers consider that this study's findings can improve managerial efficiency which, in turn, can lead to the organizations becoming more successful.

In the context of Pakistan's public sector banks, this study's findings provide empirical insights to the relationships between the corporate cultural factors and organizational commitment. In addition, the findings provide insights to the role played by self-efficacy in mediating these relationships.

The distinct characteristics of slow tourism compared to mass tourism determine the differences between slow travelers and mass travelers. This transformation is essential for potential tourists to transition into slow travelers when promoting slow tourism to them. Can travel vlogs, a “fast” way of promoting, achieve such a “slow” transformation? To address this question, the purpose of this paper is to introduce the resonance theory and investigate the impact of resonance with slow tourism vlogs on potential travelers’ destination image and travel intention.

Through the distribution of online questionnaires on site, this study collected a total of 349 valid questionnaires. Subsequently, the researcher used partial least squares structural equation modeling to analyze the data and test the hypotheses.

The research findings indicate that cognitive resonance only positively affected cognitive image, while emotional resonance was positively associated with both cognitive image and affective image. In addition, it was observed that cognitive image and affective image played a positive mediating role between resonance with slow tourism vlogs and travel intention.

This research is pioneering in quantitatively validating the structure linking resonance, destination image and travel intention in the context of slow tourism. By validating the second-order structure of cognitive and emotional resonance, this study underscores the crucial role of resonance in shaping tourists’ transition toward a slow mindset in slow tourism.