Abstract
The intrinsic challenges of contemporary marketing encourage discovering new approaches to engage and retain customers effectively. As the main channels of interactions between customers and brands pivot between the physical and the digital world, analyzing the outcome behavioral patterns must be achieved dynamically with the stimulus performed in both poles. This systematic review investigates the collaborative impact of adopting multidisciplinary fields of Affective Computing to evaluate current marketing strategies, upholding the process of using multimodal information from consumers to perform and integrate Sentiment Analysis tasks. The adjusted representation of modalities such as textual, visual, audio, or even psychological indicators enables prospecting a more precise assessment of the advantages and disadvantages of the proposed technique, glimpsing future applications of Multimodal Artificial Intelligence in Marketing. Embracing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as the research method to be applied, this article warrants a rigorous and sequential identification and interpretation of the synergies between the latest studies about affective computing and marketing. Furthermore, the robustness of the procedure is deepened in knowledge-gathering concerning the current state of Affective Computing in the Marketing area, their technical practices, ethical and legal considerations, and the potential upcoming applications, anticipating insights for the ongoing work of marketers and researchers.

Abstract
Great advances in stitching high-quality retinal images have been made in recent years. On the other hand, very few studies have been carried out on low-resolution retinal imaging. This work investigates the challenges of low-resolution retinal images obtained by the D-EYE smartphone-based fundus camera. The proposed method uses homography estimation to register and stitch low-quality retinal images into a cohesive mosaic. First, a Siamese neural network extracts features from a pair of images, after which the correlation of their feature maps is computed. This correlation map is fed through four independent CNNs to estimate the homography parameters, each specializing in different corner coordinates. Our model was trained on a synthetic dataset generated from the Microsoft Common Objects in Context (MSCOCO) dataset; this work added an important data augmentation phase to improve the quality of the model. Then, the same is evaluated on the FIRE retina and D-EYE datasets for performance measurement using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The obtained results are promising: the average PSNR was 26.14 dB, with an SSIM of 0.96 on the D-EYE dataset. Compared to the method that uses a single neural network for homography calculations, our approach improves the PSNR by 7.96 dB and achieves a 7.86% higher SSIM score.

Abstract

Abstract
A blue jeans brand committed to the environmental cause could position itself as unique and socially responsible and attract environmentally driven consumers. This research study examines the relationship between brand love and consumers’ environmental cause knowledge and their willingness to recommend and pay a premium for sustainable blue jeans. To this end, this cross-sectional study comprises a snowball convenience sample of 978 Portuguese respondents, whose data were collected from December 2022 to January 2023. Positive associations between self-expression, brand love, loyalty, environmental cause knowledge, positive word-of-mouth, and willingness to pay a premium for sustainable blue jeans stand out. There are differences in the willingness to pay a premium among generations, education levels, and consumers who are aware of sustainable line extensions and those who are not. The results may be helpful for brands, suggesting their communication should focus on creating increased proximity to consumers by enhancing their values and seeking to link their brands to intrinsic benefits and environmental stakes. This is the first study to incorporate knowledge of the environmental cause into a model linking brand love, brand loyalty, positive word-of-mouth, and willingness to pay a premium for sustainable blue jeans.

Abstract
Precision agriculture (PA) has advanced agricultural practices, offering new opportunities for crop management and yield optimization. The use of unmanned aerial vehicles (UAVs) in PA enables high-resolution data acquisition, which has been adopted across different agricultural sectors. However, its application for decision support in chestnut plantations remains under-represented. This study presents the initial development of a methodology for segmenting chestnut burrs from UAV-based imagery to estimate its productivity in point cloud data. Deep learning (DL) architectures, including U-Net, LinkNet, and PSPNet, were employed for chestnut burr segmentation in UAV images captured at a 30 m flight height, with YOLOv8m trained for comparison. Two datasets were used for training and to evaluate the models: one newly introduced in this study and an existing dataset. U-Net demonstrated the best performance, achieving an F1-score of 0.56 and a counting accuracy of 0.71 on the proposed dataset, using a combination of both datasets during training. The primary challenge encountered was that burrs often tend to grow in clusters, leading to unified regions in segmentation, making object detection potentially more suitable for counting. Nevertheless, the results show that DL architectures can generate masks for point cloud segmentation, supporting precise chestnut tree production estimation in future studies.

Abstract
Precision spraying in agriculture is crucial for optimizing the application of pesticides while minimizing environmental impact. Despite significant advancements in control models for spraying systems, predictive control algorithms were not used. This paper addresses this gap by proposing a real-time control framework that integrates predictive control strategies to ensure consistent pressure output in a trailer sprayer. Based on information from various sensors, the framework anticipates and adapts to dynamic environmental conditions, enhancing accuracy and sustainability in spraying practices. A methodology is developed to define a proportional valve model. Based on this valve model, the predictive control model optimizes valve movements to minimize errors between predicted and reference pressures, thereby improving spraying efficiency. This study demonstrates the viability of predictive control in improving precision spraying systems applicable to autonomous robots, encouraging future advances in agricultural spraying technologies.