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This research sought to determine the impact of the management of electronic customer relationships through applying 5IS model on the mental image of Umniah Mobile Network Operator Company’s customers in Amman City. To fulfill the goals of the study, the researcher adopted the descriptive, analytic method. He developed an instrument to collect the data through a questionnaire, which was distributed through the simple, random sampling method over 700 customers of Umniah Company, in the City of Amman, out of which 400 analyzable questionnaires were retrieved. The researcher further employed the convenient statistical methods applying SPSS 22 Program for data analysis. The study concluded many results such as there is statistically significant impact at the significance level (α < 0.05) for the administration of consumer relationships through the use of 5IS model on the mental image of the provided services of Umniah Mobile Network Operator Company in Amman city. In this concern, the integration component is the most influential in the mental image with the customers of Umniah Company. Therefore, the study recommended the need Umniah Telecom Company has to search for the best means to positively influence shaping the mental image of its products, especially the means through which it can provide more information about its services.

This paper aims to critically evaluate the role of advanced artificial intelligence (AI)-enhanced image fusion techniques in lung cancer diagnostics within the context of AI-driven precision medicine.

We conducted a systematic review of various studies to assess the impact of AI-based methodologies on the accuracy and efficiency of lung cancer diagnosis. The focus was on the integration of AI in image fusion techniques and their application in personalized treatment strategies.

The review reveals significant improvements in diagnostic precision, a crucial aspect of the evolution of AI in healthcare. These AI-driven techniques substantially enhance the accuracy of lung cancer diagnosis, thereby influencing personalized treatment approaches. The study also explores the broader implications of these methodologies on healthcare resource allocation, policy formation, and epidemiological trends.

This study is notable for both emphasizing the clinical importance of AI-integrated image fusion in lung cancer treatment and illuminating the profound influence these technologies have in the future AI-driven healthcare systems.

This study aims to investigate how image-presentation-order influences mental imagery (MI) processing and purchase intentions. This study also examines the moderating effect of a series of images on the relationship between image-presentation-order and MI processing.

This research conducted two studies using an experimental approach.

Two studies showed that MI processing was higher, when an apparel product image worn by a model with a background was shown after rather than before a simple product image (SPI), indicating the recency effect. In contrast, examining a series of images, consumers were more engaged in MI processing, when product image(s) worn by a model with a background were presented first, followed by the four SPIs, than the reversed order (primacy effect). The level of MI in two studies subsequently increased purchase intentions.

Results of this study have the potential to provide guidance to online retailers for how to best order their product images on a website to help consumers form elaborated MI about the product and thus increase purchasing intentions.

Although past research has examined presentation-order effect using textual information, very limited studies have explored presentation-order effect of pictorial information. To the best of the authors’ knowledge, this research is in the forefront of investigations about the joint effect of image-presentation-order and the number of images on individuals’ perceptions.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

This study aims to explore the impact of athlete brand image on fans’ social media engagement, purchase intentions, and also examines the mediating role of emotional attachment on these relationships, as well as the moderating role of perceived price value between emotional attachment and purchase intentions.

The data are based on an online survey conducted in China (N = 572). The PLS-SEM (partial least squares structural equation model) and regression-based estimation method (PROCESS) are employed to test the hypotheses.

The results indicate a positive relationship between athletes’ athletic performance and fans’ social media engagement as well as purchase intentions. The impact of athletes’ attractive appearance and marketable lifestyle on fans’ purchase intentions is sequentially mediated by emotional attachment and social media engagement. Moreover, the mediation effect of athletes’ off-field image and purchase intentions is contingent upon fans’ perceived price value.

Athletes and marketers could integrate and leverage both the on-field and off-field attributes to cultivate emotional connections with fans. Sports organizations and managers need to pay attention to fans’ social media engagement and provide content that increases engagement and converts into transactional behavioural intentions.

The study provides empirical evidence of the mediating role of emotional attachment between athlete brand image and fans’ purchase intentions. The explanatory mechanisms involving emotional attachment and social media engagement (non-transactional behavioural intentions) are anticipated to be a noteworthy addition to the traditional fan transactional behavioural intentions framework. Moreover, the research introduces and confirms perceived price value as a crucial moderating factor influencing the relationship between emotional attachment and purchase intentions.

This paper aims to examine the image realism effect, studying how changing the front-of-package visual affects the acceptance of an insect-based food by consumers. By comparing reactions to realistic and less realistic images of an insect as an ingredient, this research investigated how visual imagery can affect consumers’ responses, reducing perceived disgust or increasing expected taste.

Three experiments studied the impact of realistic (photo) versus less realistic (drawing) images for two types of insects (mealworm, cricket) on consumers’ psychological distance from the image, perceived disgust, expected taste, willingness to eat, purchase intention and food choice.

Study 1 demonstrates that using a less realistic insect image reduces perceived disgust, with psychological distance from this image and perceived disgust mediating realism effect on willingness to eat. Study 2 shows that a less realistic insect image, perceived as more remote, improves expected taste and willingness to eat. Study 3 confirms the results by measuring behavior: consumers were more likely to choose the product with the less realistic image.

The research focused on one kind of product and two ways of depicting this product, limiting the generalizability of the findings for other visual representations and product categories.

The findings suggest how brand managers can use the image realism effect on the packaging of novel, sustainable products to influence consumers, reducing their disgust and increasing their expected taste.

This research breaks new ground by explaining how visual cues on packaging affect the acceptance of insect-eating, drawing on construal level theory.

In the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments.

Prior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments.

The average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors.

This paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

Using computer technology to realize ferrographic intelligent fault diagnosis technology is fundamental research to inspect the operation of mechanical equipment. This study aims to effectively improve the technology of deep learning technology in the field of ferrographic image recognition.

This paper proposes a binocular image classification model to solve ferrographic image classification problems.

This paper creatively proposes a binocular model (BesNet model). The model presents a more extreme situation. On the one hand, the model is almost unable to identify cutting wear particles. On the other hand, the model can achieve 100% accuracy in identifying Chunky and Nonferrous wear particles. The BesNet model is a bionic model of the human eye, and the used training image is a specially processed parallax image. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university. The BesNet model developed in this article is a brand new system for ferrographic image recognition. The BesNet model adopts a method of imitating the eyes to view ferrography images, and its image processing method is also unique. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0150/

The paper examines the role and significance of gastronomic manifestations in the context of destination branding, within the framework of image transfer mechanisms and the Associative Network Memory Model.

The research was conducted on a sample of 53 gastronomic events in the tourist destination of Vojvodina.

The results indicate that gastronomic manifestations image has a positive impact on the brand image and brand identity of the destination, as well as the destination's overall image. Furthermore, the study found that the food experience has a positive influence on the image of gastronomic events and the destination.

The study contributes to the advancement of research on tourist destination branding.

The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult.

In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition.

The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition.

A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal.