Search results

Although tourism destination image is a widely studied subject, the perspective of local players is generally neglected, albeit its relevance for informing the positioning and brand management strategies of the places is recognized. This chapter aims to determine the perceptions of key local public organizations from the historical-cultural and heritage sectors and companies linked to commerce and tourism (private sector) about the historic center of a United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage site in Brazil. The results of this investigation contribute to the development of marketing and tourism development strategies in historic towns, especially those classified as World Heritage by UNESCO.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use of solar panels is becoming widespread, and control problems are increasing. Physical control of the solar panels is critical in obtaining electrical power. Controlling solar panel power plants and rooftop panel applications installed in large areas can be difficult and time-consuming. Therefore, this paper designs a system that aims to panel detection.

This paper designed a low-cost AI-based unmanned aerial vehicle to reduce the difficulty of the control process. Convolutional neural network based AI models were developed to classify solar panels as damaged, dusty and normal. Two approaches to the solar panel detection model were adopted: Approach 1 and Approach 2.

The training was conducted with YOLOv5, YOLOv6 and YOLOv8 models in Approach 1. The best F1 score was 81% at 150 epochs with YOLOv5m. In total, 87% and 89% of the best F1 score and mAP values were obtained with the YOLOv5s model at 100 epochs in Approach 2 as a proposed method. The best models at Approaches 1 and 2 were used with a developed AI-based drone in the real-time test application.

The AI-based low-cost solar panel detection drone was developed with an original data set of 1,100 images. A detailed comparative analysis of YOLOv5, YOLOv6 and YOLOv8 models regarding performance metrics was realized. Gaussian, salt-pepper noise addition and wavelet transform noise removal preprocessing techniques were applied to the created data set under the proposed method. The proposed method demonstrated expressive and remarkable performance in panel detection applications.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

This paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.

The approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.

The study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.

This paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.

This study aims to look into and evaluate a sustainability-based destination loyalty model that takes into account how people perceive the urban destination in alignment with represented concept of sustainable tourism.

Using a convenience sampling technique, 414 questionnaires were sent out to nonresident tourists (outside Bandung city) in Indonesia using online survey platforms and analyzed with the structural equation model method.

Socioeconomic image, cultural image and environmental image all have a beneficial effect on tourists’ satisfaction levels. Furthermore, tourist satisfaction has a direct impact on destination loyalty. When it comes to tourists’ loyalty to a particular destination, only the degree to which they were satisfied moderated the influence of socioeconomic, cultural and environmental images.

The research sample exclusively comprised tourists hailing from Indonesia, a developing nation. Subsequent studies may evaluate tourists from various nations to obtain a more precise comprehension of the tourist population.

Authorities and destination organizations should periodically examine tourists to get a feel for how they feel about a destination’s sustainability, so they can adjust policies as needed to keep tourism there viable over the long term.

This study aims to fill a significant gap in the existing literature by examining the impact of sustainable practices and initiatives on tourists’ satisfaction to an urban destination. The examination of the relationship between efforts to promote sustainability and destination loyalty can offer valuable insights for destination managers and policymakers who aim to improve long-term tourist relationships.

Sectoral brands are umbrella brands created to represent all companies’ products belonging to a country’s economic industry abroad to enhance their export performance. This study aims to explore the development of a sectoral brand model through the optic of the social constructionist perspective. Besides, this study also proposes to apply the model to a sectoral brand case in the business-to-business market.

The authors have developed a systematic qualitative literature review to provide a theoretical basis for the attributes chosen to compose the social constructionist sectoral brand management (SCSBM) model. To apply the model, the authors have conducted a series of 17 in-depth semi-structured interviews with the association’s managers that constitute the sectoral brand development, the director of the branding consultancy firm and specialists on place branding.

The authors present the SCSBM model, highlighting that sectoral branding should be seen as a dynamic and continuous process with the integrated participation of all industry stakeholders. Moreover, the authors have applied the model to the Brazil Fashion System brand.

The main contribution to theory is the link between sectoral brand management and the social constructionist approach, being the first study, to the best of the authors’ knowledge, to propose this connection. SCSBM model extends previous work on sectoral brands by adopting a social constructionist view.

The SCSBM model might contribute to marketing professionals willing to develop sectoral brands across multiple economic sectors and geographies.

The study’s originality lies in developing the first model, which adopts a social constructionist approach to sectoral brands.

Additive manufacturing (AM) has been one of the most highlighted processes of the last few years. AM prints complex parts and items from 3D files regarding different materials, such as polymers. Moreover, there are different AM techniques available for polymers, such as selective laser sintering. In the SLS technology, polyamides 11 and 12 lead 88% of the market. These materials are high-cost and use an average of 50% of virgin material at each printing. It is possible to use lower rates of virgin material, but at least 30% is recommended. Low rates of virgin material decrease mechanical properties.

This study aims to evaluate the influence on the mechanical properties of the percentage of reused PA12 in parts manufactured by the SLS process. The specimens of PA12 were manufactured with a percentage of virgin/reused polymer of 50/50, 40/60, 30/70, 20/80 and 10/90. We considered three distinct printing directions to compare the mechanical properties of the specimens: horizontal, perpendicular and vertical.

The results showed that when the percentage of reused material increases, the tensile strength limit (TSL), flexural strength limit and Shore D hardness decrease. Another aspect visualized was the fragile behavior presented in the vertical specimens. In addition, DSC analysis indicated a 2% reduction of crystallinity. Scanning electron microscopy images revealed spherical voids and unfused particles of PA12 at the fracture of tensile test specimens. The material thermal history and unfused particles could decrease the material properties.

We observed that the mechanical properties, such as the TSL, flexural strength limit and hardness, decrease as the percentage of reused material increases. In addition, the process presented a printing-direction dependence, where the vertical direction presented as the more brittle between the ones used.

The purpose of this study is to develop a magnetic resonance imaging (MRI)-safe iron-free electrical actuator for MR-guided surgical interventions.

The paper deals with the design of an MRI compatible electrical actuator. Three-dimensional electromagnetic and thermal analytical models have been developed to design the actuator. These models have been validated through 3D finite element (FE) computations. The analytical models have been inserted in an optimization procedure that uses genetic algorithms to find the optimal parameters of the actuator.

The analytical models are very fast and precise compared to the FE models. The computation time is 0.1 s for the electromagnetic analytical model and 3 min for the FE one. The optimized actuator does not perturb imaging sequence even if supplied with a current 10 times higher than its rated one. Indeed, the actuator’s magnetic field generated in the imaging area does not exceed 1 ppm of the B₀ field generated by the MRI scanner. The actuator can perform up to 25 biopsy cycles without any risk to the actuator or the patient since he maximum temperature rise of the actuator is about 20°C. The actuator is compact and lightweight compared to its pneumatic counterpart.

The MRI compatible actuator uses the B₀ field generated by scanner as inductor. The design procedure uses magneto-thermal coupled models that can be adapted to the design of a variety actuation systems working in MRI environment.