Abstract
The Digital Twin concept involves the transition to digital representations of factory floor equipment, the computerized simulation of processes and the visualization of data in real time. This type of digital transformations can be considered radical, encountering barriers in its implementation either due to resistance to change by the different elements that make up the industry or due to the disruption it can cause in the production process. The start of production on an assembly line is usually preceded by a checking procedure of parameters/conditions of the equipment present on the assembly line, using a sheet of paper containing the list of items to check and validate. In this article we describe the adoption of a paperless checklist to verify the configuration of assembly line equipment at production bootstrapping. A training program to coach the employees for a successful digital transition is also presented and discussed. Both the digital checklist and the training program are validated in a real-world industrial scenario. The results highlight the advantages of the digital approach given to the checklist with a multi-access viewing and maintenance of data for later analysis, with the training plan demonstrating effectiveness in breaking down barriers and resistance to the adoption of a new working method.

Abstract
JavaScript is a widely used programming language initially designed to make the Web more dynamic in the 1990s. In the last decade, though, its scope has extended far beyond the Web, finding utility in backend development, desktop applications, and even IoT devices. To circumvent the needs of modern programming, JavaScript has undergone a remarkable evolution since its inception, with the groundbreaking release of its sixth version in 2015 (ECMAScript 6 standard). While adopting modern JavaScript features promises several benefits (such as improved code comprehension and maintenance), little is known about which modern features of the language have been used in practice (or even ignored by the community). To fill this gap, in this paper, we report the results of an empirical study that aims to understand the adoption trends of modern JavaScript features, and whether or not developers conduct rejuvenation efforts to replace legacy JavaScript constructs and idioms with modern ones in legacy systems. To this end, we mined the source code history of 158 JavaScript open-source projects, identified contributions to rejuvenate legacy code, and used time series to characterize the adoption trends of modern JavaScript features. The results of our study reveal extensive use of JavaScript modern features which are present in more than 80% of the analyzed projects. Our findings also reveal that (a) the widespread adoption of modern features happened between one and two years after the release of ES6 and, (b) a consistent trend toward increasing the adoption of modern JavaScript language features in open-source projects and (c) large efforts to rejuvenate the source code of their programs.

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
Monodic folk music has traditionally been preserved in physical documents. It constitutes a vast archive that needs to be digitized to facilitate comprehensive analysis using AI techniques. A critical component of music score digitization is the transcription of lyrics, an extensively researched process in Optical Character Recognition (OCR) and document layout analysis. These fields typically require the development of specific models that operate in several stages: first, to detect the bounding boxes of specific texts, then to identify the language, and finally, to recognize the characters. Recent advances in vision language models (VLMs) have introduced multimodal capabilities, such as processing images and text, which are competitive with traditional OCR methods. This paper proposes an end-to-end system for extracting lyrics from images of handwritten musical scores. We aim to evaluate the performance of two state-of-the-art VLMs to determine whether they can eliminate the need to develop specialized text recognition and OCR models for this task. The results of the study, obtained from a dataset in a real-world application environment, are presented along with promising new research directions in the field. This progress contributes to preserving cultural heritage and opens up new possibilities for global analysis and research in folk music.

Abstract
Supervised Machine Learning (ML) models require large amounts of labeled data for training. However, this becomes challenging when dealing with resource- and network-constrained Internet of Things (IoT) devices that collect data. Furthermore, in scenarios where the acquired data is fast-changing and highly temporal, continuous and online learning becomes necessary. In this paper, we address the problem of efficiently training ML models using data from IoT nodes. We specifically focus on two aspects: i) selecting the nodes that provide data for the re/training, and ii) determining the optimal amounts of data to be acquired from these nodes, considering network and time constraints, while minimizing learning errors. To tackle this optimization problem, we propose ONDS: an Optimum Node and Data Selection algorithm with linear complexity in the worst-case. ONDS offers a model-agnostic solution applicable to different data modalities and ML architectures. To evaluate the performance of ONDS, we conduct experiments using various models and real-world datasets. The results demonstrate the effectiveness of ONDS, as it outperforms existing alternatives in both classification and regression tasks.

Abstract
Ultra-wideband (UWB) systems are critical for indoor positioning in robotics, industrial tracking, and asset management due to their accuracy in multipath-prone environments. Like GPS satellites requiring precise orbital data, UWB systems depend on well-calibrated anchors-fixed reference points whose positional accuracy directly impacts location estimates. We systematically evaluate and compare computational calibration methods, such as Genetic Algorithms, Maximum Likelihood, and the Extended Kalman Filter, using synthetic data, assessing both efficiency and error reduction in calibration and location. Nonlinear Least Squares (NLS) outperformed other approaches from this review as well as state-of-the-art methods, reducing anchor calibration errors to 10.7cm (86.03% improvement from 1-meter initial uncertainty) and tag localization errors to 5.6cm (88.35% reduction). NLS maintained computational efficiency (mean execution time of 0.011s, proving ideal for real-world deployments where efficiency and accuracy are critical.