July 31 · Article · PeerJ Computer Science · Building and Construction
Circular economy and sustainability have both seen rapid growth in academic literature, often leading to ambiguity and the overuse of these terms. This obscures their true objectives and makes it challenging to discern their distinct intentions. Manually analyzing the vast body of recent publications to understand how these concepts connect to environmentally beneficial practices is laborious and time-consuming. This study aims to compare and analyze existing literature on sustainable and circular construction using natural language processing (NLP) techniques to elucidate the similarities and overlaps between these concepts within the construction industry. To achieve this, we employed three NLP methods: (1) TextRank, a graph-based ranking algorithm that extracts key structural relationships between terms in a document; (2) term frequency–inverse document frequency, a statistical measure that identifies the most significant terms based on their frequency and uniqueness within the corpus; and (3) semantic annotation (Wikifier), a method that links text tokens to structured knowledge bases such as Wikipedia for better contextual understanding. These methods are used to analyze a dataset of 480 academic articles focusing on sustainability and circular economy in the construction sector. Our analysis revealed that circular construction is more specific and practical, emphasizing resource efficiency, waste management, and industry-specific processes, targeting the operational aspects of recycling and resource recovery. In contrast, sustainable construction encompasses a broader and more holistic scope, including urban planning, community development, and long-term environmental impacts. This study demonstrates how NLP methods can systematically disentangle closely related frameworks in construction literature, providing a replicable methodological framework for future data-driven investigations. By clarifying the distinctions and overlaps between the terms “circular construction” and “sustainable construction”, our research offers enhanced understanding for policymakers, industry practitioners, and academics aiming to integrate sustainable and circular principles effectively within the construction sector.
IAIskander AkhmetovOpen access
July 31 · Article · Applied Sciences · Environmental Engineering
The diffusion coefficient (DC) of CO2 in brine is a key parameter in geological carbon sequestration and CO2-Enhanced Oil Recovery (EOR), as it governs mass transfer efficiency and storage capacity. This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. The dataset, comprising 176 data points, spans pressures from 0.10 to 30.00 MPa, temperatures from 286.15 to 398.00 K, salinities from 0.00 to 6.76 mol/L, and DC values from 0.13 to 4.50 × 10−9 m2/s. The data was split into 80% for training and 20% for testing to ensure reliable model evaluation. Model performance was assessed using R2, RMSE, and MAE. The RF model demonstrated the best performance, with an R2 of 0.95, an RMSE of 0.03, and an MAE of 0.11 on the test set, indicating high predictive accuracy and generalization capability. In comparison, GBR achieved an R2 of 0.925, and XGBoost achieved an R2 of 0.91 on the test set. Feature importance analysis consistently identified temperature as the most influential factor, followed by salinity and pressure. This study highlights the potential of ML models for predicting CO2 diffusion in brine, providing a robust, data-driven framework for optimizing CO2-EOR processes and carbon storage strategies. The findings underscore the critical role of temperature in diffusion behavior, offering valuable insights for future modeling and operational applications.
PPPeyman PourafsharyOpen access
July 31 · Article · Sechenov Medical Journal · Infectious Diseases
Aim. To study the prevalence of pneumonia features associated with 2019 coronavirus disease (COVID-19) in cancer patients based on chest computed tomography (CT) data using an artificial intelligence (AI) algorithm.Materials and methods. A cross-sectional study was conducted as part of the ARILUS project. Using multitarget AI, CT images of 1148 patients examined at the Arkhangelsk Clinical Oncology Dispensary from 01.04.2020 to 31.12.2021 were analyzed. Patients were divided into groups: without signs of pneumonia (n = 592, 51.6%) and with signs of pneumonia (n = 556, 48.4%). In 95.3% of patients with pneumonia, the lesion volume was less than 25% (CT-1). Using multivariate Poisson regression, adjusted prevalence ratios (aPR) with 95% confidence intervals (CI) were calculated.Results. For demographic characteristics such as gender, age, place of residence, no relationship with the presence of signs of COVID-19 pneumonia was established. Topography of neoplasm is associated with the presence of signs of COVID-19 pneumonia (reference group – cancers of the female genital organs): lung cancer – aPR 1.87; 95% CI: 1.40–2.49; head and neck cancers – aPR 1.85; 95% CI: 1.32–2.58; upper gastrointestinal tract – aPR 1.51; 95% CI: 1.12–2.04; breast cancer – aPR: 1.38; 95% CI: 1.00–1.90; p < 0.01. The presence of pulmonary emphysema is associated with signs of COVID-19 pneumonia: aPR 1.25; 95% CI: 1.09–1.45, p = 0.002. With an increase in the Agatston score (AS) reflecting coronary artery calcification (reference group absence of calcification), the association with the presence of signs of COVID-19 pneumonia increased – for AS 1–99: aPR 1.24; 95% CI: 1.05–1.47; AS 100– 299: aPR 1.58; 95% CI: 1.33–1.87; AS 300 and above: aPR 1.61; 95% CI: 1.36–1.90; p < 0.001 for a linear trend.Conclusion. Factors associated with the detection of COVID-19 pneumonia among cancer patients include the localization of neoplasms in the lungs, head and neck organs, upper gastrointestinal tract, breast, and as well as the presence of signs of emphysema and coronary calcification according to CT data
AGAndrej GrjibovskiOpen access
