Abstract
Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two primary types. While previous studies have addressed diverse methods and transportation issues, this research integrates pedestrian modeling with MCDA and ML approaches. This paper examines how MCDA and ML can be combined to enhance decision-making in pedestrian dynamics. Drawing on a review of 1574 papers published from 1999 to 2023, this study identifies prevalent themes and methodologies in MCDA, ML, and pedestrian modeling. The MCDA methods are categorized into weighting and ranking techniques, with an emphasis on their application to complex transportation challenges involving both qualitative and quantitative criteria. The findings suggest that hybrid MCDA algorithms can effectively evaluate ML performance, addressing the limitations of traditional methods. By synthesizing the insights from the existing literature, this review outlines key methodologies and provides a roadmap for future research in integrating MCDA and ML in pedestrian dynamics. This research aims to deepen the understanding of how informed decision-making can enhance urban environments and improve pedestrian safety.

Abstract
Precision agriculture seeks to optimize crop yields while minimizing resource use. A key challenge is achieving uniform pesticide spraying to prevent crop damage and environmental contamination. Water-sensitive paper (WSP) is a common tool used for assessing spray quality, as it visually registers droplet impacts through color change. This work introduces a smartphone-based solution for capturing WSP images within vegetation, offering a tool for farmers to assess spray quality in real-world conditions. To achieve this, two approaches were explored: classical computer vision techniques and machine learning (ML) models (YOLOv8, Mask-RCNN, and Cellpose). Addressing the challenges of limited real-world data and the complexity of manual annotation, a programmatically generated synthetic dataset was employed to enable sim-to-real transfer learning. For the task of WSP segmentation within vegetation, YOLOv8 achieved an average Intersection over Union of 97.76%. In the droplet detection task, which involves identifying individual droplets on WSP, Cellpose achieved the highest precision of 96.18%, in the presence of overlapping droplets. While classical computer vision techniques provided a reliable baseline, they struggled with complex cases. Additionally, ML models, particularly Cellpose, demonstrated accurate droplet detection even without fine-tuning.

Abstract
A comparative analysis of Ka and D-band unit cells is presented using a Waveguide Simulator and infinite array models with a Floquet port. Initially, a single-unit cell design is employed with a tapered transition section. Subsequently, a 1 × 2-unit cell is designed and integrated into standard rectangular waveguides WR-34 and WR-7. For the Ka-band, the results obtained from both models exhibit excellent agreement in terms of magnitude and phase. In the D-band, the 1 × 2-unit cell demonstrated low loss for both techniques, and the phase responses were reasonably accurate with differences of less than 40 degrees. At such high frequencies (145-175 GHz), the Waveguide Simulator offers a viable solution for assessing the behavior of the unit cell without the need for a full array. © 2025 European Association on Antennas and Propagation.

Abstract
Non-adherence to medication is a pervasive issue worldwide, affecting 50% of prescription users, resulting in suboptimal therapy outcomes and premature death. One of the key factors contributing to non-adherence is the complexity associated with managing medication regimens. To address this challenge, an automated pill dispenser “SelfMed, your medication partner” has been proposed. This study focuses on studying determinants of medication non-adherence, its ramifications and alternatives available in the market in order to increase medication adherence among adults aged 60 and over. The overarching goal of this research is to evaluate whether digital solutions like SelfMed are required for addressing non-compliance issues in Portugal while assessing their effectiveness over time for our target audience within the marketplace. The research was conducted using primary data collected through a questionnaire distributed to users and care institutions/companies in Portugal (Northern area). According to the analysis, 40% of users and 40% of care companies are interested in obtaining SelfMed to simplify the complex medication management and prescription regimen for the end user. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
The aim of this study was to conduct a qualitative analysis research that will support the internal and external analysis, as well as the strategic options to be developed for a Marketing Plan for the Polytechnic Institute of Viana do Castelo. The methodology used was through exploratory interviews using a script based on nine key dimensions for creating a marketing plan. The script was applied through face-to-face interviews with the president of the Polytechnic Institute of Viana do Castelo. We obtained six interviews that could be analyzed in the nine dimensions. It was possible to understand that the elements suggest some changes in the different dimensions of the script. One of the points with the most observations for improvement was communication, mostly internal. However, all the improvements and growth that the institute has achieved over the years were acknowledged. It was also possible to realize that the six people interviewed have the same perspective and make similar observations on the different questions asked. With the interviews, it will be possible to outline the best structure and path for the strategic marketing plan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
The presence of missing data poses a common challenge for time series analysis in general since the most usual requirement is that the data is equally spaced in time and therefore imputation methods are required. For time series of counts, the usual imputation methods which usually produce real valued observations, are not adequate. This work employs Bayesian principles for handling missing data within time series of counts, based on first-order integer-valued autoregressive (INAR) models, namely Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms. The methodologies are illustrated with synthetic and real data and the results indicate that the estimates are consistent and present less bias when the percentage of missing observations decreases, as expected. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.