This paper evaluates the performance of different small area estimation methods using model and design-based simulation experiments. Design-based simulation experiments are carried out using the Mexican Intra Censal survey as a census of roughly 3.9...
In this paper, we propose an improved Nested Error Regression model in which the random effects for each area are given a prior distribution using the Poisson–Dirichlet Process. Based on this model, we mainly investigate the construction of the...
We predict the finite population proportion of a small area when individual-level data are available from a survey and more extensive household-level (not individual-level) data (covariates but not responses) are available from a census. The census a...
Metamaterials, which are not found in nature, are used to increase the performance of antennas with their extraordinary electromagnetic properties. Since metamaterials provide unique advantages, performance improvements have been made with many optim...
In order to solve the problem that the existing optimal operation model of reservoirs cannot coordinate the contradiction between long-term and short-term benefits, the paper nested the long-term optimal operation and mid-long-term optimal operations...
The modes through which individuals disperse prior to reproduction has important consequences for gene flow in populations. In honey bees (Apis sp.), drones (males) reproduce within a short flight range of their natal nest, leaving and returning each...
Traditional electrical resistivity tomography (ERT) technology confronts bottlenecks such as the volume effect in the detection of termite nests in levees, while the ERT based on deep learning has insufficient interpretation accuracy due to small sam...
With the aging of the social population structure, the number of empty-nesters is also increasing. Therefore, it is necessary to manage empty-nesters with data mining technology. This paper proposed an empty-nest power user identification and power c...
We explore the impact of time series behavior on model errors when working under an elliptically contoured distribution. By adopting a time series approach aligned with the realistic dependence between errors under such distributions, this perspectiv...
When unmanned platforms perform precise target detection, the configuration of detection nodes will significantly impact accuracy. Aiming to obtain the minimum dilution of precision (DOP), this paper innovatively proposes an optimal detection configu...
A simple and computationally efficient method is presented for creating a high-resolution regional (child) model nested within a coarse-resolution, good-quality data-assimilating (parent) model. The method, named Nesting with Downscaling and Data Ass...
Recently developed super nested array families have drawn much attention owing to their merits on keeping the benefits of the standard nested arrays while further mitigating coupling in dense subarray portions. In this communication, a new mutual cou...
Leaf-cutting ants of the genus Atta are an important insect pest in forest plantations in many countries of South America. The objective of this work was to evaluate the potential for using Landsat-8 images, with medium spatial resolution and distrib...
This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts...
The passive semi-strapdown roll stabilized platform is an inertial platform, which can isolate the rolling of a projectile body by a special mechanical device. In the passive semi-strapdown roll stabilized platform, the bearing device plays an import...
Cavity limitations and interspecific competition render large macaws valuable models for elucidating the integration of parental care, pair-bond maintenance, and nest defense across reproductive stages. Through continuous video monitoring of a single...
This paper proposes a Bayesian RC-frame finite element model updating (FEMU) and damage state estimation approach using the nonlinear acceleration time history based on nested sampling. Numerical RC-frame finite element model (FEM) parameters are sel...
Cooperative MIMO communication systems play an important role in the development of future sixth-generation (6G) wireless systems incorporating new technologies such as massive MIMO relay systems, dual-polarized antenna arrays, millimeter-wave commun...
In this paper we propose a maximum entropy estimator for the asymptotic distribution of the hedging error for options. Perfect replication of financial derivatives is not possible, due to market incompleteness and discrete-time hedging. We derive the...
Reconfigurable miniature modular robotic swarms realize flexible payload transportation by the serial assembly of micro vehicle modules. Strong dynamic coupling and unsynchronized motion among interconnected modules bring prominent challenges to the...
Although sea turtles are formidable prey as adults, their nests are highly vulnerable to terrestrial predation. Along the Southeastern coast of the United States, a primary predator of sea turtle nests is the red fox (Vulpes vulpes). Examining the re...
Digitized counter-diabatic (CD) optimization algorithms have been proposed and extensively studied to enhance performance in quantum computing by accelerating adiabatic processes while minimizing energy transitions. While adding approximate counter-d...
Knowledge of temperature variation within and across beach-nesting bird habitat, and how such variation may affect the nesting success and survival of these species, is currently lacking. This type of data is furthermore needed to refine predictions...
For the traditional uniform linear array (ULA) direction of arrival (DOA) estimation method with a limited array aperture, a non-circular signal off-grid sparse Bayesian DOA estimation method based on nested arrays is proposed. Firstly, the extended...
This work investigates how surrogate-model behavior changes with increasing input dimensionality, focusing on Gaussian process regression (GPR), polynomial chaos expansion (PCE), and deep neural networks (DNNs). The central objective is to find out h...
This paper describes an intelligent, CAD-based decision support and optimization framework for predicting fabric waste directly from geometry attributes of apparel pattern segments. It is applicable during the early design stage, providing a pre-scre...
Contact-angle prediction from literature data is vulnerable to target leakage when solid surface-free-energy descriptors are reconstructed using the liquid, which is later treated as the target. We developed a target-masked workflow that removes the...
Machine learning is increasingly used to predict machining and process quality from experimental data, yet such data are typically small, generated by designed experiments, and hierarchically organized, with several measurements taken on the same spe...
The fundamental modeling of energy systems through individual unit commitment decisions is crucial for energy system planning. However, current large-scale models are not capable of including uncertainties or even risk-averse behavior arising from fo...
Distractors might display discriminatory power with respect to the construct of interest (e.g., intelligence), which was shown in recent applications of nested logit models to the short-form of Raven’s progressive matrices and other reasoning t...
In this study, the general impact of high-resolution moving nesting domains on tropical cyclone (TC) intensity and track forecasts was verified, for a total of 107 forecast cases of 33 TCs, using the Weather Research and Forecasting (WRF) model. The...
The expansion of digital technologies in dairy farming (precision dairy farming) has created new opportunities for the systematic use of data, which can lead to more efficient production processes. This study aimed to develop and evaluate models for...
The urinary sodium-to-potassium (Na/K) ratio is a clinically established predictor of blood pressure and cardiovascular risk. This study aimed to develop and rigorously evaluate machine learning models for estimating the urinary Na/K ratio using four...
The contribution of contact-aligned kinematics to landing-force estimation requires evaluation against simple task-based predictions. We analyzed 2191 trials from 43 participants across six jump-landing tasks and two fatigue conditions. The outcome w...
Heart rate variability (HRV) changes nonlinearly across the lifespan, and such nonlinearity is often taken to require machine learning (ML). Distinguishing explanatory from predictive modeling, we tested three propositions using 24 h Holter recording...
Reliable prediction of the factor of safety (Fs) of circular-failure soil slopes is critical to geotechnical practice. Data-driven models developed on small slope-stability datasets are, however, prone to overfitting, data leakage, and optimistic bia...
In data-sparse areas, due to the lack of hydrogeological data, numerical groundwater models have some uncertainties. In this paper, a nested model and a multi-index calibration method are used to improve the reliability of a numerical groundwater mod...
This paper examines the efficiency of a recently developed Nesting with Data Assimilation (NDA) method at mitigating errors in heat and momentum fluxes at the ocean surface coming from external forcing. The analysis uses a set of 19 numerical simulat...
24 August 2023
Numerical weather prediction models exhibit errors while simulating atmospheric processes. To provide alerts for weather hazards, early warning systems are fed with forecast data from these models. The success of such an early warning system requires...
This paper establishes a multi-scale nested sub-modeling finite element simulation framework for the deformation and damage analysis of large-scale structures under complex loading conditions. By sequentially transferring displacement solutions from...
Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and op...
Crops cultivated on trellis systems, such as kiwifruit, grapes, et al., create a partially structured workspace; this environment is highly suitable for Cartesian robotic harvesting. However, limited extension range of current Cartesian manipulators...
This paper re-examines the instrumental variable (IV) approach to estimating returns to education by use of compulsory school law (CSL) in the US. We show that the IV-approach amounts to a change in model specification by changing the causal status o...
Negative Poisson’s ratio materials show great potential in aerospace, automotive engineering, and military protection owing to their unique deformation behavior and superior mechanical properties. Nevertheless, current negative Poisson’s...
High countermovement-jump (CMJ) height prediction may reflect mechanical reconstruction rather than independent neuromuscular information. This study separated mechanically proximal variables from mechanically restricted force-expression, movement-or...
Condition-specific liquid chromatographic retention time prediction remains challenging because retention depends on both molecular structure and experimental conditions. This study compared feature-selected quantitative structure–retention relations...
Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamical downscaling in representing a high-resolution regional climate. Two distinct strategies of ESD are employed here to reconstruct near-surface winds...
Force measurement is crucial in numerous engineering applications, while traditional force sensors often face problems such as elevated expenses or significant measurement errors. To tackle this issue, we propose an innovative force sensor employing...
With the continuous deterioration of air quality and the increasingly serious environmental problem of air pollution, accurate air quality prediction is of great significance for environmental governance. Air quality index (AQI) prediction based on d...
The Ramsey regression equation specification error test (RESET) furnishes a diagnostic for omitted variables in a linear regression model specification (i.e., the null hypothesis is no omitted variables). Integer powers of fitted values from a regres...
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