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
Rising temperatures, extreme precipitation events such as excessive or insufficient rainfall, increasing levels of carbon dioxide, and associated climatic factors will persistently impact crop growth and agricultural production. The warming temperatures have reduced the agricultural crop yields. Rice (Oryza sativa L.) is the major food crop, which is particularly susceptible to the effects of climate change. It is very important to accurately evaluate the impacts of climate change on rice growth and rice yield. In this study, the rice growth during 1981-2018 (baseline period) and 2041-2100 (future period) were separately simulated and compared within the CERES-Rice model (v4.6) using high-quality weather data, soil, and field experimental data at six agro-meteorological stations in Hainan Province. For the climate data of the future period, the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios were applied, with carbon dioxide (CO2) fertilization effects considered. The adaptation strategies such as adjusting planting dates and switching rice cultivars were also assessed. The simulation results indicated that the early rice yields in the 2050s, 2070s, and 2090s were projected to decrease by 6.2%, 11.8%, and 20.0% when the CO2 fertilization effect was not considered, compared with the results of the baseline period, respectively, while late rice yields would decline by 9.9%, 23.4%, and 36.3% correspondingly. When accounting for the CO2 fertilization effect, the yields of early rice and late rice in the 2090s increased 16.9% and 6.2%, respectively. Regarding adaptation measures, adjusting planting dates and switching rice cultivars could increase early rice yields by 22.7% and 43.3%, respectively, while increasing late rice yields by 20.2% and 34.2% correspondingly. This study holds substantial scientific importance for elucidating the mechanistic pathways through which climate change influences rice productivity in tropical agro-ecosystems, and provides a critical foundation for formulating evidence-based adaptation strategies to mitigate climate-related risks in a timely manner. Cultivar substitution and temporal shifts in planting dates constituted two adaptation strategies for attenuating the adverse impacts of anthropogenic climate change on rice.

Abstract
Ensuring food quality and safety is a growing challenge in the food industry, where early detection of contamination or spoilage is crucial. Using gas sensors combined with Artificial Intelligence (AI) offers an innovative and effective approach to food identification, improving quality control and minimizing health risks. This study aims to evaluate food identification strategies using supervised learning techniques applied to data collected by the BME Development Kit, equipped with the BME688 sensor. The dataset includes measurements of temperature, pressure, humidity, and, particularly, gas composition, ensuring a comprehensive analysis of food characteristics. The methodology explores two strategies: a neural network model trained using Bosch BME AI-Studio software, and a more flexible, customizable approach that applies multiple predictive algorithms, including DT, LR, kNN, NB, and SVM. The experiments were conducted to analyze the effectiveness of both approaches in classifying different food samples based on gas emissions and environmental conditions. The results demonstrate that combining electronic noses (E-Noses) with machine learning (ML) provides high accuracy in food identification. While the neural network model from Bosch follows a structured and optimized learning approach, the second methodology enables a more adaptable exploration of various algorithms, offering greater interpretability and customization. Both approaches yielded high predictive performance, with strong classification accuracy across multiple food samples. However, performance variations depend on the characteristics of the dataset and the algorithm selection. A critical analysis suggests that optimizing sensor calibration, feature selection, and consideration of environmental parameters can further enhance accuracy. This study confirms the relevance of AI-driven gas analysis as a promising tool for food quality assessment.

Abstract
The Eighth International Workshop on Narrative Extraction from Texts (Text2Story'25) was held on April 10 th , 2025, in conjunction with the 47 th European Conference on Information Retrieval (ECIR 2025) in Lucca, Italy. During this half-day event, more than 30 attendees engaged in discussions and presentations focused on recent advancements in narrative representation, extraction, and generation. The workshop featured a keynote address and a mix of oral presentations and poster sessions covering nineteen papers. The workshop proceedings are available online 1 . Date: 10 April 2025. Website: https://text2story25.inesctec.pt/.