303 Results Found

Atmospheric fine particles (${\mathrm{PM}}_{2.5}$) have been found to be harmful to the environment and human health. Recently, remote sensing technology and machine learning models have been used to monitor ${\mathrm{PM}}_{2.5}$ concentrations. Partial dependence plots (PDP) were...

The relationship between structure and properties is fundamental in materials science, particularly for aliovalently doped perovskites, where structural changes significantly influence material performance. Accurate prediction of key structural param...

Accurately mapping soil organic carbon (SOC) is conducive to evaluating carbon storage and soil quality. However, the high spatial heterogeneity of SOC caused by river-related factors and agricultural management brings challenges to digital soil mapp...

Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors...

Accurate prediction of the state-of-charge (SOC) of batteries is essential for reliable energy management and safety in electric vehicles (EVs). Although machine learning (ML) models have shown strong predictive performance for SOC estimation, their...

Accurate prediction of the Langelier Saturation Index (LSI), an indicator of water’s scaling and corrosive potential, is vital for water treatment and infrastructure maintenance. In this study, five machine learning models (Ridge Regression, Su...

Headed shear studs are an essential interfacial connection for precast steel–concrete structures to ensure composite action; hence, the accurate prediction of the shear capacity of headed studs is of pivotal significance. This study first estab...

Topographic features impact biomass and other agriculturally relevant observables. However, conventional tools for processing digital elevation model (DEM) data in geographic information systems have severe limitations. Typically, 3-by-3 window sizes...

Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow Universi...

Waste glass powder (WGP) faces challenges in recycling and regeneration, which is used as a partial substitute for concrete components, with its macro-mechanical properties being investigated. This study aims to elucidate the extent to which various...

The hemispherical temperature (HT) is the most important indicator representing ash fusion temperatures (AFTs) in the Polish industry to assess the suitability of coal for combustion as well as gasification purposes. It is important, for safe operati...

This study proposes an explainability-driven feature selection framework for phishing website detection using a large-scale, heterogeneous dataset collected from four independent sources. The combined dataset contains approximately 500,000 samples, i...

Fused deposition modeling (FDM) is one of the most widely deployed additive manufacturing methods, and the mechanical performance of FDM-printed parts is governed by a small set of strongly coupled process parameters. Silk PLA, a PLA-based filament e...

In the present study, a thorough examination of nanoindentation-based mechanical properties of a refractory MoTaNbVW high-entropy alloy (RHEA) was conducted. Basic mechanical properties, such as the indentation modulus of elasticity, indentation hard...

The energy sector plays a pivotal role in economic development, societal progress, and environmental sustainability, yet heavy reliance on fossil fuels remains a major challenge for achieving climate neutrality. Within this context, the European Unio...

In recent years, the field of construction engineering has experienced a significant paradigm shift, embracing the integration of machine learning (ML) methodologies, with a particular emphasis on forecasting the characteristics of steel-fiber-reinfo...

Wildfires are a major natural hazard causing extensive ecological damage and endangering human survival. Previous studies on wildfires in China have mostly focused on specific regions or individual drivers, with limited systematic assessments at the...

Artificial Intelligence (AI) has achieved immense progress in recent years across a wide array of application domains, with biomedical imaging and sensing emerging as particularly impactful areas. However, the integration of AI in safety-critical fie...

The contents of soil available phosphorus (AVP) and potassium (AVK) in karstic mountainous agricultural areas have changed rapidly in recent decades. This temporal variation displays strong spatial heterogeneity due to these areas’ complex topo...

This study analyzed the spatiotemporal changes in grassland NDVI from 2000 to 2020 in the eastern route of the China–Mongolia–Russia Economic Corridor, a region with frequent ecological–economic interactions, and explained the main...

Tablets are the most common dosage form of pharmaceutical products. While tablets represent the majority of marketed pharmaceutical products, there remain a significant number of patients who find it difficult to swallow conventional tablets. Such di...

The growing reliance on solar energy necessitates robust and interpretable forecasting models for stable grid management. Current research frequently employs Explainable AI (XAI) to glean insights from complex black-box models, yet the reliability an...

Background: Iron ore tailings (IOTs) are a mine waste product used as road materials and suffer from a lack of sufficient strength, which should be improved through stabilization. Unconfined compressive strength (UCS) is a crucial parameter for deter...

Mitigating nitrogen dioxide (NO₂) pollution is a critical objective for enhancing urban environmental quality. The spatial structure of urban tree canopies plays a crucial role in influencing NO₂ diffusion and deposition. However, previous studies ha...

Accurate photovoltaic (PV) power forecasting is important for grid dispatch, energy-storage management, and electricity-market trading. This paper presents a PV power forecasting framework that combines multi-model ensemble learning with multi-perspe...

Industrial steel property prediction is often constrained by limited labelled data, reducing the effectiveness of conventional machine learning models. This study investigated whether metallurgy-informed feature engineering enhances predictive perfor...

Existing machine learning (ML) models often encounter challenges in accurately predicting the shear strength of steel fiber reinforced concrete (SFRC) beams, mainly due to a lack of generalization. This study introduces an advanced stacked ensemble M...

Susceptibility mapping represents a modern tool to support forest protection plans and to address fuel management. With the present work, we continue with a research framework developed in a pioneristic study at the local scale for Liguria (Italy) an...

Machine learning algorithms are being used for multiple real-life applications and in research. As a consequence of digital technology, large structured and georeferenced datasets are now more widely available, facilitating the use of these algorithm...

Land abandonment is one of the main drivers of land use/land cover (LULC) change across Europe, which has already led to a significant loss of open habitats, threatening species hosted in them. We investigated LULC changes for a period of 70 years in...

The prediction of traffic accident severity is essential for traffic safety management and control. To achieve high prediction accuracy and model interpretability, we propose a hybrid model that integrates random forest (RF) and Bayesian optimization...

Forest mensuration is important to gain knowledge and information about forest stands. Because tree height often proves more difficult to measure than diameter, different statistical models are used for their estimation instead. In this paper, the da...

The relationships between land cover characteristics in riparian areas and the biological integrity of rivers and streams are critical in riparian area management decision-making. This study aims to evaluate such relationships using the Trophic Diato...

The relationship between urban waterfront spaces and health is widely recognized. However, the relative importance and nonlinear associations of specific attributes with residents’ physical health remain unclear. Drawing on survey data from 375...

Above-ground biomass (AGB) is affected by numerous factors, including topography, climate, land use, or tree/forest attributes. Investigating the distribution and driving factors of AGB within the managed forests in Nepal is crucial for developing ef...

This study investigated the relationship between the degree of satisfaction with the pedestrian environments in their neighborhoods and the degree of neighborhood satisfaction in Seoul, South Korea. This study employed proportional odds logistic regr...

Partial discharge (PD) detection is a standardized technique to qualify electrical insulation in machines and power cables. Several techniques that analyze the waveform of the pulses have been proposed to discriminate noise from PD activity. Among th...

Background: although China’s total health expenditure has been dramatically increased so that the country can cope with its aging population, inequalities among individuals in terms of their medical expenditures (relative to their income level)...

Prediction of post-stroke functional outcomes is crucial for allocating medical resources. In this study, a total of 577 patients were enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program, and 77 predictors were collected at admi...

A quantitative understanding of the relationship between comprehensive health levels, such as healthy life expectancy and their related factors, through a highly explanatory model is important in both health research and health policy making. In this...

The high prevalence of food insecurity experienced by northern First Nations partially results from dependence on an expensive import-based food system that typically lacks nutritional quality and further displaces traditional food systems. In the pr...

Lungs and kidneys are two vital and frequently injured organs among critically ill patients. In this study, we attempt to develop a weaning prediction model for patients with both respiratory and renal failure using an explainable machine learning (X...

Significant models predicting Soil Organic Carbon (SOC) and other chemical and biological indicators of soil health in an experimental farm with semi-arid Mediterranean Calcisol have been obtained by partial least squares (PLS) regression, with mid-i...

Dryland riparian forests depend on river-derived water in arid landscapes, but the relative associations of regional climate, local soil moisture, and biodiversity with ecosystem multifunctionality (EMF) remain uncertain. We analysed data from 516 pl...

Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is the most common cause of chronic liver disease in developed countries. Certain conditions, including mild inflammation biomarkers, dyslipidemia, and in...

A Mediterranean holm oak forest was subjected to experimental partial rainfall exclusion during 21 consecutive years to study the effects of the expected decrease in water availability for Mediterranean vegetation in the coming decades. Allocation in...

Windthrows in the forestry fund, which have become more frequent due to the increase in extreme weather events, have had, and continue to have, mostly negative economic and ecological effects, making them a pressing issue in forestry research. The ma...

In this paper, we study the pressure-driven thin film flow of an inhomogeneous incompressible fluid in which its viscosity depends on the density. The constitutive response of this class of fluids can be derived using a thermodynamical framework put...

Artificial intelligence is gaining interest among clinicians, but its results are difficult to be interpreted, especially when dealing with survival outcomes and censored observations. Explainable machine learning (XAI) has been recently extended to...

As more machine learning and deep learning models are applied in studying the quantitative relationship between the climate and terrestrial vegetation growth, the uncertainty of these advanced models requires clarification. Partial dependence plots (...