Search Results (54)

Three-dimensional concrete printing (3DCP) enables the fabrication of wall sections with internal cavities that are impractical to form with conventional methods. Although individual studies demonstrate that cavity geometry influences thermal resistance, the magnitude of this effect has not been quantified on a common basis across the literature. This paper introduces the Thermal Effectiveness Index (TEI), defined as the ratio of the theoretical U-value of a solid concrete wall (calculated using the material’s actual thermal conductivity) to the reported U-value, in order to isolate the purely geometric contribution to thermal performance. A structured extraction from 24 Scopus-indexed articles yielded an analysis dataset of 255 variants from 23 articles (after the exclusion of one study whose composite mix made the geometric effect inseparable from material substitution), spanning 17 typology categories. Results show that standard rectangular air cavities perform worse than solid printed counterparts of the same material (median TEI $=0.56$ versus $1.00$ for solid walls) due to internal convection and thermal bridging, whereas sinusoidal infills with post-printed insulation reach a median TEI of 8.9 (18 variants from a single study, so the magnitude requires independent replication). These patterns are descriptive; an article-level sensitivity check indicates that the number of independent studies per wall-type group is not yet sufficient for confirmatory statistical inference. A compliance analysis against five building energy codes showed that only 22% of variants meet the $0.30$ $\mathrm{W}$/($\mathrm{m}$² $\mathrm{K}$) threshold, in line with the component-based U-value criteria specified in the Passive House Institute’s EnerPHit Standard for building retrofits and warm-climate classifications, as opposed to the more stringent $\le 0.15 \mathrm{W}/({\mathrm{m}}^2 \mathrm{K})$ requirement applicable to new construction in temperate climates. Full article

The 2025 update of the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 27701 standard offers a major advantage by enabling organizations to implement a Privacy Information Management System (PIMS) autonomously while maintaining alignment with the General Data Protection Regulation (GDPR). However, it remains unclear to what extent privacy legal frameworks in developing jurisdictions, particularly in Latin American countries, align with this new standard. At the same time, the traditional method for assessing the alignment between privacy legal frameworks and ISO/IEC 27701 continues to rely on manual mapping between the standard’s subclauses and privacy regulatory articles, a process that is time-consuming, costly, and error-prone. More critically, no method exists to quantitatively assess the reliability of such mappings, leaving alignment assessments largely subjective. To address these limitations, this paper proposes a novel method based on an OpenAI Generative Pre-trained Transformer (GPT) combined with a Chain-of-Thought (CoT) reasoning strategy to quantitatively assess the alignment between privacy legal frameworks and ISO/IEC 27701:2025. By leveraging GPT’s logarithmic probabilities (logprobs) and the standard’s subclause definitions as classification categories, the method enables confidence-based evaluation of legal–standard alignment. The proposed method is then applied to analyze the privacy legal frameworks of Paraguay, Chile, Ecuador, México, Colombia, and Perú, examining how effectively they promote the standard’s guidelines. A suitable confidence threshold is then selected by assessing the GDPR and comparing the results with the reference mappings reported in Annex D of the standard. Finally, the method identifies the number of compliant subclauses per clause, the regulatory articles influencing the resulting logprobs, and the underlying privacy gaps for reduced alignment across the analyzed privacy legal frameworks. Overall, our results indicate that while Latin American privacy legal frameworks mandate protective measures by promoting a suitable operation and continuous improvement of a PIMS, they do not explicitly demand adequate risk management and sufficient preventive safeguards for citizens’ Personally Identifiable Information (PII) in dynamic contexts. Full article

Media coverage of disasters frequently frames self-interested behavior in contrast to collective responsibility and coordinated response. This study aims to explore how such behavior is emotionally constructed in disaster-related media, using a carefully selected corpus of 12 text-centered news articles focusing on selfish behavior. The analysis combines transformer-based sentence-level emotion classification using the tweetnlp RoBERTa model, which predicts 11 emotion categories, with Latent Dirichlet Allocation topic modeling across single-sentence and three-sentence windows in a small purposively selected corpus. Emotion–topic relationships are quantified by weighting emotion probabilities by topic distributions and visualized using bar charts, network graphs, and heatmaps. The findings suggest that fear and disgust dominate portrayals of self-interested behavior, while anticipation appears in projections of harm and anger is linked to inequality and institutional accountability. Two discursive configurations emerge: Responsibility Across Individuals and Institutions, emphasizing public accountability and authority, and Collective Fear and Self-Protective Practices, reflecting affect-driven responses under uncertainty. Although negative emotions predominate, optimism appears conditionally, signaling coordination and recovery. Overall, disaster reporting constructs selfishness through integrated emotional–semantic patterns that position individual actions within broader social risk and collective responsibility. Full article

Artificial intelligence is often framed as a neutral technical tool that enhances efficiency and consistency in institutional decision-making. This article challenges that framing by showing that automated systems now operate as social and institutional actors that reshape recognition, opportunity, and public trust in everyday life. Focusing on employment screening, welfare administration, and digital platforms, the study examines how algorithmic systems mediate social relations and reorganise how individuals are evaluated, classified, and legitimised. Drawing on regulatory and policy materials, platform governance documents, technical disclosures, and composite vignettes synthesised from publicly documented evidence, the article analyses how automated judgement acquires institutional authority. It advances three core contributions. First, it develops a sociological framework explaining how delegated authority, automated classification, and procedural opacity transform institutional power and individual standing. Second, it demonstrates a dual logic of inequality: automated systems both reproduce historical disadvantage through patterned data and generate new forms of exclusion through data abstraction and optimisation practices that detach individuals from familiar legal, social, and moral categories. Third, it shows that automation destabilises procedural justice by eroding relational recognition, producing trust deficits that cannot be resolved through technical fairness or explainability alone. The findings reveal that automated systems do not merely support institutional decisions; they redefine how institutions perceive individuals and how individuals interpret institutional legitimacy. The article concludes by outlining governance reforms aimed at restoring intelligibility, accountability, inclusion, and trust in an era where automated judgement increasingly structures social opportunity and public authority. Full article

This article proposes a new method for bearing fault diagnosis based on sparse representation classification to address the challenges of fault identification under complex working conditions with different degrees of damage. The core of this method lies in directly using the original vibration signal to construct an overcomplete dictionary without the need for signal denoising or manual feature extraction in advance, thus avoiding the information loss and subjective bias introduced by denoising and feature engineering in traditional methods. Firstly, all training samples are used as a dictionary to sequentially solve for sparse coefficients for each test sample. Secondly, the corresponding parts of each category in the sparse coefficients are filtered out. Then, the category error is calculated based on the sparse coefficients corresponding to each category. Finally, the fault classification of bearings is carried out by comparing the category errors. The experimental results show that this method can maintain high diagnostic accuracy and robustness in complex scenarios with various working conditions and damage levels, verifying its effectiveness and universality for bearing fault diagnosis. Full article

A substantial amount of data is generated day-to-day, to which news articles are a major contributor. Most of this data is not well-structured, highlighting the need for efficient ways to manage, process, and analyze said data. One useful approach involves the categorization of the data. The work “News Article Category Classification” develops a Long Short-Term Memory (LSTM) model for classifying news articles into 14 categories. LSTM networks are suitable for text classification tasks, as they efficiently capture contextual and sequential dependencies. They have a special ability to retain long-term information which makes them perfect for understanding the meaning of news articles. Full article

In Chile, human activity is a key factor in the occurrence and impact of wildfires in the wildland–urban interface, as more than 95% of such events are anthropogenic in origin. The 2024 Valparaíso megafire represents the most severe incident in the past three decades, with significant consequences for both the affected population and local infrastructure. In disaster contexts, the media play a crucial role in shaping social representations by establishing analytical categories within society. Therefore, the objective of this paper is to describe how Chilean media outlets addressed this megafire during the wildfire management process, considering the agenda during the whole wildfire season in Chile. The methodological approach is based on a multi-stage strategy for news classification based on the wildfire lifecycle: prevention (before), response (during), and recovery (after). We have employed a mixed-method design that integrates manual and computational techniques (topic analysis) as a triangulation technique on the same social network data. This study automatically collects articles related to the Valparaiso megafire, which occurred in 2024, from 140 Chilean media sources, considering print, radio, and television media. The main finding indicates that news coverage predominantly frames the Valparaiso megafire as a particular event in a short period of time. The media coverage does not focus on wildfire concepts such as nature, state management, policy, and the relationship between state and citizen. Finally, the automated analysis of emerging topics in the articles belonging to each manual category provides a consistent description of the social representations identified through manual analysis. Full article

As a well-known World Cultural Heritage Site, the Historic Centre of Macao’s historical buildings possess a wealth of decorative patterns. These patterns contain cultural esthetics, geographical environment, cultural traditions, and other elements from specific historical periods, deeply reflecting the evolution of religious rituals and political and economic systems throughout history. Through long-term research, this article constructs a dataset of 11,807 images of local decorative patterns of historical buildings in Macau, and proposes a fine-grained image classification method using the ConvNeXt-L model. The ConvNeXt-L model is an efficient convolutional neural network that has demonstrated excellent performance in image classification tasks in fields such as medicine and architecture. Its outstanding advantages lie in limited training samples, diverse image features, and complex scenes. The most typical advantage of this model is its structural integration of key design concepts from a Transformer, which significantly enhances the feature extraction and generalization ability of samples. In response to the objective reality that the decorative patterns of historical buildings in Macau have rich levels of detail and a limited number of functional building categories, ConvNeXt-L maximizes its ability to recognize and classify patterns while ensuring computational efficiency. This provides a more ideal technical path for the classification of small-sample complex images. This article constructs a deep learning system based on the PyTorch 1.11 framework and compares ResNet50, EfficientNet-B7, ViT-B/16, Swin-B, RegNet-Y-16GF, and ConvNeXt series models. The results indicate a positive correlation between model performance and structural complexity, with ConvNeXt-L being the most ideal in terms of accuracy in decorative pattern classification, due to its fusion of convolution and attention mechanisms. This study not only provides a multidimensional exploration for the protection and revitalization of Macao’s historical and cultural heritage and enriches theoretical support and practical foundations but also provides new research paths and methodological support for artificial intelligence technology to assist in the planning and decision-making of historical urban areas. Full article

Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines using TF-IDF and Word2Vec to advanced transformer-based models such as BERT and GPT-4 with zero-shot, instruction, and few-shot prompting strategies. The results reveal that LLMs, particularly GPT-4 with few-shot prompts, achieve a competitive performance (F1 = 0.81) approaching that of the best classical model (BERT + SVM; F1 = 0.86), all without the need for training data. Moreover, LLMs exhibit a more balanced performance across imbalanced risk categories, showcasing their adaptability in data-sparse settings. These findings contribute theoretically by positioning LLMs as scalable plug-and-play alternatives to NLP pipelines, offering practical value by highlighting how LLMs can support early-stage project planning and risk assessment in contexts where labeled data and expert resources are limited. Full article

The energy transition has now been recognised by European Union (EU) member states as a necessary condition for their long-term development. The process of energy transformation is predicated on the simultaneous implementation of the Sustainable Development Goals, which present a considerable challenge for modern economies and impose significant restrictions on their functioning. The objective of this article is to evaluate the transformation of EU member states in the field of sustainable energy development and to categorise them based on their alignment with Sustainable Development Goal No. 7 of the United Nations Agenda 2030, concerning affordable and clean energy, in 2015 and 2023. The monitoring of the progress of the energy transition, as well as the examination of its temporal trends and spatial characteristics, can provide a fundamental analysis framework for the strategic development of energy policy in the EU at national and regional levels. An important approach for this endeavour is the indicator-based assessment of trends. The correspondence analysis proposed in this study and the hierarchical classification, which was intended to help link categories in a two-dimensional space, have the potential to facilitate a more comprehensive assessment of the degree to which EU member states are developing sustainable energy. The study results confirmed significant heterogeneity among the EU-27 countries in terms of energy sustainability. In both 2015 and 2023, the groups of EU countries that achieved a certain level of energy sustainability were identified. However, it should be noted that the composition of each group changed in 2023 compared to 2015. Moreover, no group of EU countries was without its own particular strengths and weaknesses. The results provide opportunities for their interpretation, both in terms of analysing changes in individual indicators and in terms of the global assessment of sustainable development in individual countries. The adoption of an original research approach, divergent from previous studies, for the new timeframe contributes to the resolution of the research gap in empirical studies concerning the classification of EU member states in terms of SDG7 implementation. Full article

The spread of misinformation during the COVID-19 pandemic raised widespread concerns about public health communication and media reliability. In this study, we focus on these issues as they manifested in Romanian-language media and employ Large Language Models (LLMs) to classify misinformation, with a particular focus on super-narratives—broad thematic categories that capture recurring patterns and ideological framings commonly found in pandemic-related fake news, such as anti-vaccination discourse, conspiracy theories, or geopolitical blame. While some of the categories reflect global trends, others are shaped by the Romanian cultural and political context. We introduce a novel dataset of fake news centered on COVID-19 misinformation in the Romanian geopolitical context, comprising both annotated and unannotated articles. We experimented with multiple LLMs using zero-shot, few-shot, supervised, and semi-supervised learning strategies, achieving the best results with an LLaMA 3.1 8B model and semi-supervised learning, which yielded an F1-score of 78.81%. Experimental evaluations compared this approach to traditional Machine Learning classifiers augmented with morphosyntactic features. Results show that semi-supervised learning substantially improved classification results in both binary and multi-class settings. Our findings highlight the effectiveness of semi-supervised adaptation in low-resource, domain-specific contexts, as well as the necessity of enabling real-time misinformation tracking and enhancing transparency through claim-level explainability and fact-based counterarguments. Full article

Background/Objectives: The definitions of feeding disorders of infants and young children were historically based on a dichotomic organic/non-organic vision. Since 2019, a new definition of pediatric feeding disorders (PFDs) has reshaped the understanding of these disorders with a global vision. The aim of this study is to obtain a better understanding of the diagnostic criteria for general practice, both by exploring the evolution of classifications and by clearing the actual definition of PFDs and their possible treatments. Methods: We conducted a narrative review of the literature, including 36 articles about PFDs, excluding adolescents, anorexia nervosa, bulimia, pica, rumination, and specific neurodevelopmental or chronic pediatric disorders. We summarized these studies in three parts: the specific classifications for children before puberty, the current definition, and the clinical guidelines. Results: Concerning the history of the classifications, we summarized the studies of Chatoor and Kerzner and the older pediatric vision of failure to thrive. For the definition of pediatric feeding disorders, we presented this new category involving at least one out of four domains: medical, nutritional, feeding skills, or psychosocial. For the main clinical guidelines, we presented recommendations for both severe and common PFDs in each altered domain for use in daily practice. Conclusions: The new definition promotes a transdisciplinary vision of childhood feeding disorders, which considers each of the intricate domains of PFDs. Using common terminology for PFDs could help all healthcare providers, families, and researchers to better understand and address PFDs. Full article

In the present article, the impacts that arise in electrical grids due to the integration of distributed energy resources (DER) are identified and analyzed, aiming to provide a basis from which the effects of these new technologies can be considered. To conduct this research, information was collected and analyzed, which was classified according to each of the impacts evidenced in the literature, such as technical, economic, social, environmental, sectoral, and political. Considering the classification of impacts by category, the corresponding advantages and disadvantages were highlighted, and based on this, a qualitative evaluation of the information found was conducted along with respective analyses. Thus, based on the development of this article, it can be concluded that DER influences many aspects, and according to the qualitative evaluation, clarity is provided regarding the contribution of each impact within electrical grids. It was found that, out of 100% of the impacts identified, those with the highest percentage of contribution are the technical impacts. Full article

The traditional approach to curating and disseminating information about agricultural injuries relies heavily on manual input and review, resulting in a labor-intensive process. While the unstructured nature of the material traditionally requires human reviewers, the recent proliferation of Large Language Models (LLMs) has introduced the potential for automation. This study investigates the feasibility and implications of filling the role of a human reviewer with an LLM in analyzing information about agricultural injuries from news articles and investigation reports. Multiple language models were tested for accuracy in extracting relevant incident and victim information, and these models include OpenAI’s ChatGPT 3.5 and 4 and an open-source fine-tuned version of Llama 2. To measure accuracy, each LLM was given prompts to gather relevant data from a set of randomly selected online news articles already cataloged by human reviewers, such as the use of drugs or alcohol, time of day, or other information about the victim(s). Results showed that the fine-tuned Llama2 was the most proficient model with an average accuracy of 93% and some categories reaching 100%. ChatGPT-4 also performed well with around 90% accuracy. Additionally, we found that the fine-tuned Llama2 model was somewhat proficient in coding injuries using the OIICS classification scheme, achieving 48% accuracy when predicting the first digit. Though none of the models are perfectly accurate, the methodology and results prove that LLMs are promising in streamlining workflows in order to reduce human and financial resources and increase the efficiency of data analysis. Full article

Model-Based Systems Engineering (MBSE) has demonstrated importance in the aerospace field. However, the MBSE modeling process is often tedious and heavily reliant on specialized knowledge and experience; thus, a new modeling method is urgently required to enhance modeling efficiency. This article focuses on the MBSE modeling in space science mission phase 0, during which the mission requirements are collected, and the corresponding dataset is constructed. The dataset is utilized to fine-tune the BERT pre-training model for the classification of requirements pertaining to space science missions. This process supports the subsequent automated creation of the MBSE requirement model, which aims to facilitate scientific objective analysis and enhances the overall efficiency of the space science mission design process. Based on the characteristics of space science missions, this paper categorized the requirements into four categories: scientific objectives, performance, payload, and engineering requirements, and constructed a requirements dataset for space science missions. Then, utilizing this dataset, the BERT model is fine-tuned to obtain a space science mission requirements classification model (SSMBERT). Finally, SSMBERT is compared with other models, including TextCNN, TextRNN, and GPT-2, in the context of the space science mission requirement classification task. The results indicate that SSMBERT performs effectively under Few-Shot conditions, achieving a precision of 95%, which is at least 10% higher than other models, demonstrating superior performance and generalization capabilities. Full article