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
The Digital electronics collaborative enhanced learning (DECEL) project has recently developed an international collaborative education course. Its main objective is to enhance the digital electronics skills of international students by working on a complex, multidisciplinary applied problem using a mixed digital architecture. We have developed a logic level synthesis and dedicated software layers on the Red Pitaya FPGA platform. The diversity of digital concepts to be implemented, from hardware description language (HDL) to high-level languages such as Python or Matlab, forced the students to work together and rapidly improve their skills. Their motivation was fueled by the curiosity of controlling an ultrasound probe to obtain ultrasound signatures. This particular physics, little known to the students, was an additional source of curiosity. The goal of forming an image in a liquid medium was an additional motivating factor for them. The students reported that they learned a lot from the experiment. Thus, the technical parts and pedagogical results are documented in this work for reproducibility.

Abstract
This tutorial delves into the transformative power of asking effective questions in engineering information systems. We explore how crafting well-defined questions in both the problem space (what issue are we addressing?) and the solution space (how will we approach it?) is paramount for success. The session will unveil the intricate relationship between these questions - how the what shapes the how and vice versa. We move beyond the fear of asking naive questions, demonstrating how these can spark innovation and reveal hidden assumptions. By the end, attendees will have a powerful and easy-to-use technique that removes the fear from questions.

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.