Search Results (11,555)

The water quality monitoring of large water masses using robotic vehicles is a complex task highly developed in recent years. The main approaches utilize adaptative informative path planning of fleets of autonomous surface vehicles and computer learning methods. However, water monitoring is characterized by a highly dynamic and unknown environment. Thus, the characterization of the water quality state of a water mass becomes a challenge. This paper proposes a variational autoencoder structure, trained in a model-free manner, that aims to provide a dynamic contamination model from partial observations of a homogeneous fleet of autonomous surface vehicles. To train the proposed approach, an oil spillage simulator based on heuristics is provided for world building. The proposed variational autoencoder was tested in three different environments characterized by different oil spill movements and twp different fleets equipped with different sensors. The results show accurate future contamination distribution predictions with a mean squared error ranging from 3 to 9% of the baseline at validation, defined as the static approach. Further tests addressed the overfit of the proposed neural network, showing a high robustness against unseen scenarios, and the effects of the gathered monitoring information in the variational autoencoder performance. Full article

The development of perianal fistulas leads to a significant decrease in the quality of patients’ lives. The onset of this condition is dependent on many factors, including inflammation or trauma. In the occurrence of Crohn’s disease-associated fistulas, numerous molecular factors and metabolic pathways are involved. To integrate the current knowledge on the biochemical, genetic, and epigenetic factors taking part in the development of perianal fistulas, we conducted a literature review. We gathered and analyzed 45 articles on this subject. The pathophysiology of fistulas associated with Crohn’s disease (CD) involves epithelial–mesenchymal transition (EMT) and matrix remodeling enzymes, with key regulators including transforming growth factor β (TGF-β), tumor necrosis factor α (TNFα), and interleukin-13 (IL-13). Genetic factors, such as mutations in receptor-interacting serine/threonine-protein kinase 1 (RIPK1), interleukin-10 receptor (IL-10R), and the MEFV gene, contribute to the onset and severity of perianal fistulas, suggesting potential therapeutic targets. Understanding the complex interplay of molecular pathways and genetic predispositions offers insights into personalized treatment strategies for this challenging condition. Further research is necessary to elucidate the intricate mechanisms underlying the pathogenesis of perianal fistulas and to identify new therapeutic interventions. Full article

Factors related to catering distribution are typically characterized by local changes, but few studies have quantitatively investigated the inherent spatial nonstationarity correlations. In this study, a multiscale geographically weighted regression (MGWR) model was adopted to locally examine the impact of various factors on catering distribution, which were obtained through a novel method incorporating GeoDetector analysis and exploratory factor analysis (EFA) using point of interest (POI) data. GeoDetector analysis was used to identify the effective variables that truly contribute to catering distribution, and EFA was adopted to extract interpretable latent factors based on the underlying structure of the effective variables and thus eliminate multicollinearity. In our case study in Nanjing, China, four primary factors, namely commuting activities, shopping activities, tourism activities, and gathering activities, were retained from eight categories of POIs with respect to catering distribution. The results suggested that GeoDetector working in tandem with EFA could improve the representativeness of factors and infer POI configuration patterns. The MGWR model explained the most variations (adj. R²: 0.903) with the lowest AICc compared to the OLS regression model and the geographically weighted regression (GWR) model. Mapping MGWR parameter estimates revealed the spatial variability of relationships between various factors and catering distribution. The findings provide useful insights for guiding catering development and optimizing urban functional spaces. Full article

This article, based on systems thinking, explores how community organizations in Montreal providing newcomers support through the various stages of their settlement process operate within a local municipal system and a broader provincial system, both promoting integration and intercultural relations. On a local scale, the City of Montreal has set itself the goal of raising public awareness of the benefits of cultural diversity and wishes to encourage positive interactions in the public space. For those interviewed during our research, this municipal model of integration does not necessarily align with Quebec’s unique and unofficial integration model, interculturalism, which can be perceived as a political project supporting the French-speaking majority’s interests and which may seem incompatible with the social justice values espoused by community organizations. This article is based on verbatim excerpts gathered from individual and group in-depth interviews conducted with 37 community workers in the spring of 2023. Full article

Recent AI-assisted coding tools, such as GitHub Copilot and Cursor, have enhanced developer productivity through real-time snippet suggestions. However, these tools primarily assist with isolated coding tasks and lack a structured approach to automating complex, multi-step software development workflows. This paper introduces a workflow-centric AI framework for end-to-end automation, from requirements gathering to code generation, validation, and integration, while maintaining developer oversight. Key innovations include automatic context discovery, which selects relevant codebase elements to improve LLM accuracy; a structured execution pipeline using Prompt Pipeline Language (PPL) for iterative code refinement; self-healing mechanisms that generate tests, detect errors, trigger rollbacks, and regenerate faulty code; and AI-assisted code merging, which preserves manual modifications while integrating AI-generated updates. These capabilities enable efficient automation of repetitive tasks, enforcement of coding standards, and streamlined development workflows. This approach lays the groundwork for AI-driven development that remains adaptable as LLM models advance, progressively reducing the need for human intervention while ensuring code reliability. Full article

The shift to sustainable energy requires a thorough understanding of the elements affecting policy adoption, especially regarding political and economic dynamics. Current approaches, such as the technology acceptance model (TAM), theory of planned behavior (TPB), and unified theory of acceptance and use of technology (UTAUT), mainly emphasize individual behavioral aspects, often neglecting macro-level implications. This research uses the hybrid model for energy policy adoption (HMEPA) to bridge this gap, including economic and political factors with behavioral theories to evaluate energy policy acceptability. We propose that social impact, attitudes toward the policy, and financial and political considerations substantially affect stakeholders’ acceptance intentions. We gathered 421 valid answers from people in Taiwan using a questionnaire survey and analyzed the data using structural equation modeling (SEM). The findings demonstrate that whereas effort expectation and enabling circumstances have little impact, social influence and attitude are the most significant determinants of policy adoption intention. Moreover, political variables influence attitudes and social dynamics, while economic policy impacts performance expectations, perceived behavioral control, and enabling circumstances. These results underscore the need to synchronize policy plans with political and economic realities. Policymakers may use these findings to formulate stakeholder-oriented policies that promote sustainable energy transitions. Full article

The rapid advancement of digital technologies in education is revolutionizing learning environments and influencing the future of educational methodologies. This study seeks to determine the parameters affecting students’ satisfaction with e-learning platforms utilized during the COVID-19 pandemic, within a multimodal educational framework. A Structural Equation Modeling (SEM) approach was used to examine the contributions of different components to students’ views of e-learning tools and the inter-relationships between them. Data were gathered from 314 students via a questionnaire, with the dependent variable being student satisfaction with e-learning platforms and the independent variables comprising the perceived benefits and disadvantages, ease of use, prior experience, perceptions of the platforms, perceived risks, and communication efficiency between students and professors. The results indicated that 78% of the variance in student satisfaction was explained by these parameters (R-squared = 0.78), underscoring the substantial impact of these features on the digital learning experience. This study enhances the comprehension of the influence of e-learning platforms within a multimodal educational framework on students’ learning experiences, especially with the challenges presented by the pandemic. The collected insights can guide the development of more effective, accessible, and user-focused educational tools. Full article

The accelerating digitalization of the public and private sectors has made information technologies (IT) indispensable in modern life. As services shift to digital platforms and technologies expand across industries, the complexity of legal, regulatory, and technical requirement documentation is growing rapidly. This increase presents significant challenges in managing, gathering, and analyzing documents, as their dispersion across various repositories and formats hinders accessibility and efficient processing. This paper presents the development of an automated repository designed to streamline the collection, classification, and analysis of cybersecurity-related documents. By harnessing the capabilities of natural language processing (NLP) models—specifically Generative Pre-Trained Transformer (GPT) technologies—the system automates text ingestion, extraction, and summarization, providing users with visual tools and organized insights into large volumes of data. The repository facilitates the efficient management of evolving cybersecurity documentation, addressing issues of accessibility, complexity, and time constraints. This paper explores the potential applications of NLP in cybersecurity documentation management and highlights the advantages of integrating automated repositories equipped with visualization and search tools. By focusing on legal documents and technical guidelines from Portugal and the European Union (EU), this applied research seeks to enhance cybersecurity governance, streamline document retrieval, and deliver actionable insights to professionals. Ultimately, the goal is to develop a scalable, adaptable platform capable of extending beyond cybersecurity to serve other industries that rely on the effective management of complex documentation. Full article

The domain of Archaea has gathered significant interest for its ecological and biotechnological potential and its role in helping us to understand the evolutionary history of Eukaryotes. In comparison to the bacterial domain, the number of adequately described members in Archaea is relatively low, with less than 1000 species described. It is not clear whether this is solely due to the cultivation difficulty of its members or, indeed, the domain is characterized by evolutionary constraints that keep the number of species relatively low. Based on molecular evidence that bypasses the difficulties of formal cultivation and characterization, several novel clades have been proposed, enabling insights into their metabolism and physiology. Given the extent of global sampling and sequencing efforts, it is now possible and meaningful to question the magnitude of global archaeal diversity based on molecular evidence. To do so, we extracted all sequences classified as Archaea from 500 thousand amplicon samples available in public repositories. After processing through our highly conservative pipeline, we named this comprehensive resource the ‘Global Archaea Diversity’ (GAD), which encompassed nearly 3 million molecular species clusters at 97% similarity, and organized it into over 500 thousand genera and nearly 100 thousand families. Saline environments have contributed the most to the novel taxa of this previously unseen diversity. The majority of those 16S rRNA gene sequence fragments were verified by matches in metagenomic datasets from IMG/M. These findings reveal a vast and previously overlooked diversity within the Archaea, offering insights into their ecological roles and evolutionary importance while establishing a foundation for the future study and characterization of this intriguing domain of life. Full article

The current, continuous increase in attention toward preservation of the environment and natural resources is forcing resource-intensive industries like steelworks to investigate new solutions to improve resource efficiency and promote the growth of a circular economy. In this context, electric steelworks, which inherently implement circularity principles, are spending efforts to enhance valorization of their main by-product, namely slags. A reliable characterization of the slag’s composition is crucial for the identification of the best valorization pathway, but, currently, slag monitoring is often discontinuous. Furthermore, in the current period of transformation of steel production, preliminary knowledge of the effect of modifications of operating practices on slags composition is crucial to assessing the viability of these modifications. In this paper, a stationary flowsheet model of the electric steelmaking route is presented; this model enables joint monitoring of key variables related to process, steel and slags. For the estimation of the content of most compounds in slags, the average relative percentage error is below 20% for most of the considered steel families. Thus, the tool can be considered suitable for scenario analyses supporting slag valorization. Higher performance is achievable by exploiting more reliable data for model tuning. These data can be obtained via novel devices that gather more numerous and representative data on the amount and composition of slags. Full article

This study examined the wetland ecology, institutional frameworks, and governance mechanisms for managing self-recruiting small indigenous fishes (SIFs) across four wetlands in the lower Gangetic plain, a region bridging the Himalayan and Indo-Burma biodiversity hotspots. Using a mixed-method approach, data were gathered through semi-structured interviews with 100 respondents from the fisher community, focus group discussions, unpublished records, and direct observations. The findings revealed a lack of systematic institutional mechanisms in three wetlands, possibly due to their small size, which fostered informal regulations among community members. The Chamardaha (35.813) wetland received a low score in an Ecosystem Health Index (EHI; range: 0–100) and the others, viz., Beledanga (53.813), Kumil (45.237), and Panchita (54.989), received a medium score. A wide range of significant (p < 0.05) effect sizes (β = −0.20 to 0.65) was found for the different governance parameters on sustainability and average per capita income of fisher society. Our investigation showed that 90% to 76% of the harvested SIFs were sold and the rest were consumed within the fisher community to meet part of their nutritional needs. According to the fishers’ perception, a reduction of more than 50% in the availability of the SIF population was observed compared to its previous levels. The proposed governance model emphasizes women’s roles in the fisher community and aims to improve economic outcomes, nutritional security, biodiversity conservation, and ecological services. This is the first study to document SIF utilization patterns and their link to local governance in the lower Gangetic ecoregion’s inland open waters. The findings are expected to advance wetland fisheries governance research. Full article

Lichens are an important vegetative component of the Antarctic terrestrial ecosystem and present a wide diversity. Recent advances in omics technologies have allowed for the identification of lichen microbiomes and the complex symbiotic relationships that contribute to their survival mechanisms under extreme conditions. The preservation of biodiversity and genetic resources is fundamental for the balance of ecosystems and for human and animal health. In order to assess the current knowledge on Antarctic lichens, we carried out a systematic review of the international applied research published between January 2019 and February 2024, using the PRISMA model (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Articles that included the descriptors “lichen” and “Antarctic” were gathered from the web, and a total of 110 and 614 publications were retrieved from PubMed and ScienceDirect, respectively. From those, 109 publications were selected and grouped according to their main research characteristics, namely, (i) biodiversity, ecology and conservation; (ii) biomonitoring and environmental health; (iii) biotechnology and metabolism; (iv) climate change; (v) evolution and taxonomy; (vi) reviews; and (vii) symbiosis. Several topics were related to the discovery of secondary metabolites with potential for treating neurodegenerative, cancer and metabolic diseases, besides compounds with antimicrobial activity. Survival mechanisms under extreme environmental conditions were also addressed in many studies, as well as research that explored the lichen-associated microbiome, its biodiversity, and its use in biomonitoring and climate change, and reviews. The main findings of these studies are discussed, as well as common themes and perspectives. Full article

The COVID-19 pandemic had a significant impact on the economy and the stability of financial markets, creating challenges and financial risks for companies. This study analyzes the financial reports of companies listed on Euronext Lisbon with the aim of examining financial risk disclosures and calculating their determinants. For this purpose, data was collected from the Euronext Lisbon website as well as the companies’ own websites. Once the data were gathered, 16 companies were analyzed over a five-year period, from 2018 to 2022. Using panel data regression techniques (e.g., fixed effects regression models), it was observed that profitability, capital structure, and size have a positive but not statistically significant relationship with interest risk. Conversely, size and capital structure they have a positive and significant relationship with liquidity risk. Profitability has a positive and significant relationship with insolvency risk. Macroeconomic variables do not exhibit consistent signs across all models. This research provides insights into how the determinants of financial risks influence risks during a pandemic period. Full article

Predicting default risk in commercial bills for small and medium-sized enterprises (SMEs) is crucial, as these enterprises represent one of the largest components of a nation’s economic structure, and their financial stability can impact systemic financial risk. However, data on the commercial bills of SMEs are scarce and challenging to gather, which has impeded research on risk prediction for these businesses. This study aims to address this gap by leveraging 38 multi-dimensional, non-financial features collected from 1972 real SMEs in China to predict bill default risk. We identified the most influential factors among these 38 features and introduced a novel prompt-based learning framework using large language models for risk assessment, benchmarking against seven mainstream machine learning algorithms. In the experiments, the XGBoost algorithm achieved the best performance on the Z-Score standardized dataset, with an accuracy of 81.42% and an F1 score of 80%. Additionally, we tested both the standard and fine-tuned versions of the large language model, which yielded accuracies of 75% and 82.1%, respectively. These results indicate that the proposed framework has significant potential for predicting risks in SMEs and offers new insights for related research. Full article

The adoption of building information modeling (BIM) in the operational and maintenance phase remains limited, with many buildings still managed through paper-based processes. While BIM has the potential to optimize various facility management (FM) areas—such as energy performance, security, administration, and space management—most studies only provide global analyses of adoption barriers. This study aims to identify and analyze area-specific barriers to BIM adoption in FM, highlighting the need for tailored integration strategies rather than a one-size-fits-all approach. By taking a novel approach, it investigates these barriers and demonstrates that BIM implementation cannot be uniformly applied across all FM areas. The methodology involves a multi-step process: first, a literature review is conducted to identify generic barriers to BIM implementation. Subsequently, FM areas are classified to provide a structured framework for analysis. Based on this classification, an interview structure is developed to gather expert insights on area-specific barriers. The research proposes that barriers should be assessed based on their impact. While contextual barriers or knowledge areas may be addressed through a global approach, ensuring BIM adoption across all areas requires consideration of specific characteristics. This approach will ultimately facilitate broader implementation in every domain. Full article