Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals

Training artificial neural networks using self-organizing migrating algorithm for skin segmentation

This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network's accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks.

Finding common development paths in voluntary national reviews reporting on sustainable development goals using aspect-based sentiment analysis

Voluntary National Reviews (VNRs) provide a platform for participating countries to share their experiences, failures, and successes in achieving the United Nations (UN) Sustainable Development Goals (SDGs). The objective of this study is to gain a deeper understanding of the narrative elements, particularly the sentiment, in VNRs in order to more effectively assess and support global SDG progress. A total of 232 VNRs from 166 countries are analyzed using Aspect-Based Sentiment Analysis (ABSA) to extract each country's sentiment toward the 17 SDGs. The sentiment scores are then compared to the corresponding official UN SDG scores, and countries are grouped by their sentiment toward all 17 SDGs to identify potential common development pathways. The analysis uncovers a notable positive correlation between the reported sentiment and official SDG scores for SDG 2 (zero hunger) and SDG 11 (sustainable cities and communities), and a negative correlation for SDG 5 (gender equality). Conversely, this relationship is not significant for the majority of SDGs, suggesting that VNR narratives may not directly reflect actual progress. A t-distributed stochastic neighbor embedding (t-SNE) approach indicates a consistent sentiment score among developed countries. In contrast, there are greater differences in reporting sentiment among Emerging Markets, Frontier Markets, and Least Developed Countries (LDCs), where there is greater dispersion (especially among LDCs) and sentiment in reporting on SDG progress that appears to have changed from one reporting year to another. These findings highlight the need to interpret VNRs in the context of each country's unique situation and challenges specific to each country.

Using novel data and ensemble models to improve automated labeling of Sustainable Development Goals

A number of labeling systems based on text have been proposed to help monitor work on the United Nations (UN) Sustainable Development Goals (SDGs). Here, we present a systematic comparison of prominent SDG labeling systems using a variety of text sources and show that these differ considerably in their sensitivity (i.e., true-positive rate) and specificity (i.e., true-negative rate), have systematic biases (e.g., are more sensitive to specific SDGs relative to others), and are susceptible to the type and amount of text analyzed. We then show that an ensemble model that pools SDG labeling systems alleviates some of these limitations, exceeding the performance of the individual SDG labeling systems considered. We conclude that researchers and policymakers should care about the choice of the SDG labeling system and that ensemble methods should be favored when drawing conclusions about the absolute and relative prevalence of work on the SDGs based on automated methods.

Sustainable education and youth confidence as pillars of future civil society

While sustainability is at the centre of many government agendas, there is a great risk of entrusting strategic decisions to those lacking in sustainability expertise. It is therefore necessary to ensure that universities are the green engines of sustainable communities. The present study administered a questionnaire to students enrolled in a Management Engineering programme at an Italian university, to collect their perceptions of and opinions on sustainability and energy issues. Students completed the questionnaire twice: once prior to beginning and once at the end of term. The results showed that students held more sustainable attitudes at the end of term, and perceived sustainable education and youth confidence as the building blocks of future society. They also observed that decarbonisation of the Italian energy system and national energy independence would require the significant development of renewable systems and interventions to promote energy efficiency. In addition, they recognised subsidies for green production, energy communities, differentiated waste collection and professional skills training as crucial. The sustainable university should support younger generations by encouraging student engagement in real-world projects and the development of long-term, structured teacher-student relationships.

Authorship identification using ensemble learning

With time, textual data is proliferating, primarily through the publications of articles. With this rapid increase in textual data, anonymous content is also increasing. Researchers are searching for alternative strategies to identify the author of an unknown text. There is a need to develop a system to identify the actual author of unknown texts based on a given set of writing samples. This study presents a novel approach based on ensemble learning, DistilBERT, and conventional machine learning techniques for authorship identification. The proposed approach extracts the valuable characteristics of the author using a count vectorizer and bi-gram Term frequency-inverse document frequency (TF-IDF). An extensive and detailed dataset, “All the news” is used in this study for experimentation. The dataset is divided into three subsets (article1, article2, and article3). We limit the scope of the dataset and selected ten authors in the first scope and 20 authors in the second scope for experimentation. The experimental results of proposed ensemble learning and DistilBERT provide better performance for all the three subsets of the “All the news” dataset. In the first scope, the experimental results prove that the proposed ensemble learning approach from 10 authors provides a better accuracy gain of 3.14% and from DistilBERT 2.44% from the article1 dataset. Similarly, in the second scope from 20 authors, the proposed ensemble learning approach provides a better accuracy gain of 5.25% and from DistilBERT 7.17% from the article1 dataset, which is better than previous state-of-the-art studies.