Forecasting estuarine circulation is a hot topic, especially in densely populated regions, like Santos (Brazil). This paper aims to improve a water-level forecasting system for the Santos estuary, particularly the physical forcing determining the res...
This paper discusses Bayesian model averaging (BMA) in Stochastic Frontier Analysis and investigates inference sensitivity to prior assumptions made about the scale parameter of (in)efficiency. We turn our attention to the “standard” prio...
This paper presents and parameterizes an equivalent circuit model of an all-solid-state lithium-sulfur battery cell, filling a gap in the literature associated with low computational intensity models suitable for embedded battery management applicati...
This paper presents an integrated modelling and decision-support reanalysis of a published 24-run experiment on diamond-spark grinding (electrical-discharge diamond grinding) of two hard alloys—the tungsten-free cermet TN-20 and the WC–Ti...
The rapid advancement of machine learning techniques has led to their widespread application in various domains, including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a...
Data-driven models for embedded control must account for acquisition filtering and experimental uncertainty while retaining dependence among identified parameters. This work develops a framework connecting identification in filtered-state coordinates...
In today’s world, where sustainable energy is essential for the planet’s survival, accurate solar energy forecasting is crucial. This study focused on predicting short-term Global Horizontal Irradiance (GHI) using minute-averaged data fro...
Environmental influences on Hurricane Maria in the Antilles Islands are analyzed at the large-scale (1–25 September) and at the meso-scale (17–20 September 2017). The storm intensified rapidly prior to landfall in Dominica, going from cat...
To evaluate the stochastic characteristics of ultimate strength for spherical shells subjected to external pressure, uncertainty analysis is conducted in this study. Experimental measurements and result analysis of dimensions, as well as ultimate str...
An initial condition that closely represents the true atmospheric state can minimize errors that propagate into the future, and could theoretically lead to improvements in the forecast. This study aims to evaluate and understand the impacts of 3DVAR...
Automated generation of geometric models from point cloud data holds significant importance in the field of computer vision and has expansive applications, such as shape modeling and object recognition. However, prevalent methods exhibit accuracy iss...
An ensemble of model runs with the COAMPS© regional model is compared to observations in the central Arctic for August 2001 from the Arctic Ocean Experiment 2001 (AOE-2001). The results are from a 6-km horizontal resolution 2nd, inner, nest of the mo...
Accurate estimation of forest carbon storage is fundamental to climate change mitigation. However, the highly heterogeneous site conditions, together with the biological limitations of conventional linear and polynomial models, have posed considerabl...
Fine-scale shortwave-infrared (SWIR) imagery can support detailed land-surface mapping, but submetre satellites generally acquire only blue, green, red and near-infrared (RGBN) bands. Combining these data with Sentinel-2 SWIR requires an observation...
It is widely recognized that regions with complex heterogeneous topography and land-use properties produce a variety of diurnal mesoscale and microscale flows, which can be modified or even masked by significant large-scale synoptic forcing. These fl...
In this work, we develop the Single-Input Multi-Output U-Net (SIMOU-Net), a hybrid network for foetal brain segmentation inspired by the original U-Net fused with the holistically nested edge detection (HED) network. The SIMOU-Net is similar to the o...
Improvements in the short-term predictability of irradiance in numerical weather prediction models can assist grid operators in managing intermittent solar-generated electricity. In this study, the performance of the Weather Research and Forecasting...
We compare three modern Bayesian approaches, Hamiltonian Monte Carlo (HMC), Variational Bayes (VB), and Integrated Nested Laplace Approximation (INLA), for two classic spatial econometric specifications: the spatial lag model and spatial error model....
Educational data mining (EDM) often reports classifiers and feature selectors fitted to large-scale assessments without respecting how these surveys are designed. This study specifies an evaluation protocol for such data—a published predictor a...
Background/Objectives: Accurate preoperative detection of locally advanced breast cancer is essential for neoadjuvant therapy planning. We developed and validated gradient-boosting models using structured BI-RADS mammographic features to simultaneous...
Background: Symmetric sigmoidal models with four parameters based on an idealized S/Z-shaped curve are commonly used to analyze the optical parameters of thermochromic materials. However, our experimental findings show that this approach leads to sys...
Background/Objectives: Machine-learning models may support gallstone status classification using heterogeneous clinical and body-composition variables; however, ensemble methods often rely on predefined model combinations or arbitrarily weighted perf...
Background/Objectives: Hospitalization among older adults is a growing challenge in Mexico due to the high prevalence of chronic diseases and limited public healthcare resources. This study aims to develop a predictive model for hospitalization using...
Condition monitoring of components in internal combustion engines is an essential tool for increasing engine durability and avoiding critical engine operation. If lubrication at the crankshaft main bearings is insufficient, metal-to-metal contacts be...
Periodic conductive metasurfaces provide a compact means of controlling frequency-selective electromagnetic transmission and shielding; however, their design is commonly performed using topology-specific parametric optimization, limiting the transfer...
New towns often experience a structural transit gap in early stages, where transport supply lags behind population growth. Demand-responsive transit (DRT) has emerged as a promising complementary solution; however, most studies rely on MNL-based ICLV...
Perceptual evaluation remains the clinical standard for postoperative cleft-palate speech, but objective patient-level assessment of overall speech intelligibility remains limited. We developed a patient-level three-class classification model using f...
Steam injection is an energy-intensive enhanced-oil-recovery method for high-viscosity reservoirs, and its performance is controlled by coupled heat delivery, steam condensation, temperature-dependent viscosity reduction, mobility change and reservoi...
Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The W...
Markerless pose estimation offers a practical route to field-based biomechanical assessment, but its ability to predict distinct dimensions of football kicking performance remains uncertain. This study evaluated 60 youth football players (48 male, 12...
This paper extends the data-driven computational mechanics paradigm to nonlinear materials characterized by the Duncan–Chang Elastic-Bulk (E-B) constitutive model. Unlike in linear elastic systems, geotechnical media exhibit stress-dependent ta...
This paper develops and evaluates HQER-SFA, a likelihood-based stochastic frontier specification that embeds a hybrid quantile-expectile error layer into the bilateral noise component while preserving the standard one-sided inefficiency structure. Th...
Identifying induction-motor equivalent-circuit parameters from nameplate data is challenging without the IEEE Std 112 no-load and locked-rotor tests. This study reformulates the task from constraint-based curve fitting to population-calibrated maximu...
Assessment of multi-stage mathematics tasks requires explicit links between student work and evaluation criteria. This study presents a large language model (LLM)-supported hybrid rubric framework combining rule-based correctness checks, categorical...
We propose a nested ensemble learning framework that utilizes Topological Data Analysis (TDA) to extract and integrate topological features from graph data, with the goal of improving performance on classification and regression tasks. Our approach c...
This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model conf...
Historical data on chinstrap penguin (Pygoscelis antarctica) breeding population sizes are sparse and sometimes highly uncertain, making it hard to estimate true population trajectories. Yet, information on population trends is desirable as changes i...
The pronounced spatial heterogeneity of underlying surface characteristics within the parallel ridge–valley system of eastern Sichuan necessitated hydrological discretization of the watershed into nested subdomains comprising inter-ridge valley...
Based on big data, we can build a regression model between a temperature field and a temperature-induced deflection to provide a control group representing the service performance of bridges, which has a positive effect on the full life cycle mainten...
Machine-learning models used to inform building thermal-environment design and operation are often assessed by random record splits, whereas application in a new building needs an unseen-building test. We analyzed 49,231 thermal-sensation records fro...
Introduction: Optical coherence tomography (OCT) is the gold standard for retinal nerve fibre layer (RNFL) assessment; its high cost and limited accessibility hinder widespread use. This study aims to develop deep learning models that generate RNFL t...
Balancing path-following accuracy and error convergence with graceful motion in steering control is challenging due to the competing nature of these requirements, especially across a range of operating speeds and conditions. This paper demonstrates t...
Nowadays, the steel industry is seeking to reduce its carbon footprint without affecting productivity or profitability. This challenge needs to be supported by continuous improvements in equipment, methods, sensors and models. The present work expose...
Service providers need to meet their service level objectives (SLOs) to ensure better client experiences. Predicting tail sojourn times of applications is an essential step to combat long tail latency. Therefore, as an attempt to further unravel the...
Traditional rural landscape evaluations have generally relied on ground-level photographs or videos. However, these approaches have limitations in spatial continuity, depth cues, and interactivity. Unmanned Aerial Vehicle (UAV) photogrammetry and imm...
Recently, deep unfolding networks have been widely used in direction of arrival (DOA) estimation because of their improved estimation accuracy and reduced computational cost. However, few have considered the existence of a nested array (NA) with off-...
A high-resolution Weather Research and Forecasting (WRF) model for a heavy rainfall case is configured and the performance of the precipitation forecasting is evaluated. Sensitivity tests were carried out by changing the model configuration, such as...
High-renewable virtual power plants (VPPs) exhibit asymmetric operational risks stemming from net-load forecast errors: positive errors require upward reserve capacity, while negative errors can cause grid export saturation and renewable curtailment....
This paper analyzes how a robust and dynamic forecasting system was designed and implemented to predict material volumes for the inbound logistics network of an international automotive company. The system aims to reduce transportation logistics cost...
In a Cassegrain optical system, the surface precision of the primary mirror is an important factor in the quality of the image. The design of a lightweight primary mirror with a high-quality optical surface is crucial. In this thesis, an integrated m...
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