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Journal articles on the topic 'Scale estimation ECTS'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

Khmelnychyі, L., V. Vechorka, and S. Khmelnychyi. "The life expectancy of ukrainian brown dairy breed cows depending on linear estimation of the udder morphological characteristics." Tehnologìâ virobnictva ì pererobki produktìv tvarinnictva, no. 1(156) (May 25, 2020): 29–38. http://dx.doi.org/10.33245/2310-9270-2020-157-1-29-38.

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The Estimation of dairy cattle cows by morphological characteristics of the udder is an important element in the system of linear classifi cation of dairy cattle. This is due to the existence of a correlation between the linear characteristics of the udder and indicators of milk productivity and longevity of cows. Studies on the impact of assessing the linear characteristics of the exterior on the life expectancy of dairy cows are motivated and relevant. The exterior type of fi rst-born cows was evaluated according to the method of linear classifi cation in the leading farms of Sumy region for

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Li, Xin, Xincheng Wang, Yuanfeng Gao, et al. "Comparison of Different Important Predictors and Models for Estimating Large-Scale Biomass of Rubber Plantations in Hainan Island, China." Remote Sensing 15, no. 13 (2023): 3447. http://dx.doi.org/10.3390/rs15133447.

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Rubber (Hevea brasiliensis Muell.) plantations are among the most critical agricultural ecosystems in tropical regions, playing a vital role in regional carbon balance. Accurate large-scale biomass estimation for these plantations remains a challenging task due to the severe signal saturation problem. Recent advances in remote sensing big data, cloud platforms, and machine learning have facilitated the precise acquisition of key physiological variables, such as stand age (A) and canopy height (H), which are critical parameters for biomass estimation but have been underutilized in prior studies

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Özcan, Seyit Emre, and Hayri Arabaci. "Calculation vs. estimation: Impact of internal resistance determination on SOC estimation accuracy in lithium-ion batteries." Energy Storage and Conversion 3, no. 2 (2025): 3249. https://doi.org/10.59400/esc3249.

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The electrical equivalent circuit model (ECM) is widely employed for state of charge (SOC) estimation in lithium-ion batteries. Among ECM-based approaches, the Thevenin equivalent circuit model (TECM) is particularly favored due to its computational efficiency and ease of parameter identification. TECM can be implemented in various configurations, with 1RC, 2RC, and 3RC structures being the most common. In these configurations, each RC unit consists of a resistor (R) and a capacitor (C) connected in parallel and incorporated into the circuit branch in series. As the number of RC branches incre

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Umsura, Bunyarat, Kamonlak Chaidee, Kingkan Puansurin, et al. "Enhancing Smart Farming Capabilities for Small-Scale Cattle Farmers in Chiang Rai, Thailand." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 18, no. 1 (2024): 1–13. http://dx.doi.org/10.37936/ecti-cit.2024181.253823.

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This research aims to develop an IoT-driven smart farming system for beef cattle management in Chiang Rai Province, Thailand. The system empowers small-scale farmers by enabling precise criteria for cattle care, optimized feeding, growth monitoring, breeding analysis, and cost estimation through WSN and cloud-based platforms. The sensors gather raw data on consumption from the feeding troughs and then transmit it to the cloud-based platform. Consumption data is then analyzed using Linear Regression Analysis. Key findings indicate a substantial correlation (0.995) between feed quantity and catt

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Liu, Xia, Marek E. Bialkowski, and Feng Wang. "Investigation into the Effects of Channel Properties on Training-based MIMO Channel Estimation." ECTI Transactions on Electrical Engineering, Electronics, and Communications 8, no. 2 (2008): 173–78. http://dx.doi.org/10.37936/ecti-eec.201082.172093.

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This paper reports on investigations into the effect of channel properties on training-based MIMO channel estimation. Here, the channel's properties are represented by eigenvalues of the complex channel correlation matrix. The influence of these eigen values is assessed for two training based channel estimation methods, Scaled Least Square (SLS) method and Minimum Mean Square Error (MMSE) method. It is shown that for a given transmitted power to noise ratio in the training mode, the performance of the two estimation methods is governed by the sum of eigen values of the channel correlation matr

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Borzì, Iolanda, Brunella Bonaccorso, and Giuseppe Tito Aronica. "Evaluating the Influence of DEM Resolution and Potential Evapotranspiration Assessment on Groundwater Resources Estimation with a Reverse Hydrogeological Balance Method." Proceedings 48, no. 1 (2019): 25. http://dx.doi.org/10.3390/ecws-4-06420.

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Quantifying groundwater resources is important for effective water resource planning and management at the river basin scale, and it has to take into account all the natural and anthropogenic components of the water balance, i.e., rainfall and runoff processes, as well as mutual interactions between surface water and groundwater, but also artificial groundwater recharges (i.e., from irrigation) and groundwater extractions. In the present study, a reverse hydrogeological balance model was applied to estimate the active mean annual recharge of the northern Etna groundwater system within the Alca

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Cheong, Yuk Fai, Randall P. Fotiu, and Stephen W. Raudenbush. "Efficiency and Robustness of Alternative Estimators for Two- and Three-level Models: The Case of NAEP." Journal of Educational and Behavioral Statistics 26, no. 4 (2001): 411–29. http://dx.doi.org/10.3102/10769986026004411.

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This article investigates the efficiency and robustness of alternative estimators of regression coefficients for three-level data. To study student achievement, researchers might formulate a standard regression model or a hierarchical model with a two- or three-level structure. Having chosen the model, the researchers might employ either a model-based or a robust estimator of the standard errors. A simulation study showed that, as expected, the hierarchical model analyses produced more efficient point estimates than did analyses that ignored the covariance structure in the data, even when the

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Fieuzal, Remy, Vincent Bustillo, David Collado, and Gerard Dedieu. "Estimation of Sunflower Yields at a Decametric Spatial Scale—A Statistical Approach Based on Multi-Temporal Satellite Images." Proceedings 18, no. 1 (2019): 7. http://dx.doi.org/10.3390/ecrs-3-06203.

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Recent advances in sensors onboard harvesting machines allow accessing the intra-plot variability of yields, spatial scale fully compatible with numerous on-going satellite missions. The aim of this study is to estimate the sunflower yield at the intra-plot spatial scale using the multi-temporal images provided by the Landsat-8 and Sentinel-2 missions. The proposed approach is based on a statistical algorithm, testing different sampling strategies to partition the dataset into independent training and testing sets: A random selection (testing different ratio), a systematic selection (focusing

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Thonglek, Kundjanasith, Norawit Urailertprasert, Patchara Pattiyathanee, and Chantana Chantrapornchai. "Damaged Vehicle Parts Detection Platforms using Deep Learning Technique." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 15, no. 3 (2021): 313–23. http://dx.doi.org/10.37936/ecti-cit.2021153.223151.

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   Automatic vehicle damage detection platform can increase the market value of car insurance. The es- timation process is usually manual and requires hu- man experts and their time to evaluate the damage cost. Intelligent Vehicle Accident Analysis (IVAA) system provides an artificial intelligence as a service (AIaaS) for building a system that can automatically assess vehicle parts’ damage and severity level. The insurance company can adopt our service to build the application to speedup the claiming process. There are four main elements in the service system which support f

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Xiao, Shuai, Jun Xia, and Lei Zou. "Evaluation of Multi-Satellite Precipitation Products and Their Ability in Capturing the Characteristics of Extreme Climate Events over the Yangtze River Basin, China." Water 12, no. 4 (2020): 1179. http://dx.doi.org/10.3390/w12041179.

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Against the background of global climate change and anthropogenic stresses, extreme climate events (ECEs) are projected to increase in both frequency and intensity. Precipitation is one of the main climate parameters for ECE analysis. However, accurate precipitation information for extreme climate events research from dense rain gauges is still difficult to obtain in mountainous or economically disadvantaged regions. Satellite precipitation products (SPPs) with high spatial and temporal resolution offer opportunities to monitor ECE intensities and trends on large spatial scales. In this study,

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11

Nagel, Claudia, Giorgio Luongo, Luca Azzolin, Steffen Schuler, Olaf Dössel, and Axel Loewe. "Non-Invasive and Quantitative Estimation of Left Atrial Fibrosis Based on P Waves of the 12-Lead ECG—A Large-Scale Computational Study Covering Anatomical Variability." Journal of Clinical Medicine 10, no. 8 (2021): 1797. http://dx.doi.org/10.3390/jcm10081797.

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The arrhythmogenesis of atrial fibrillation is associated with the presence of fibrotic atrial tissue. Not only fibrosis but also physiological anatomical variability of the atria and the thorax reflect in altered morphology of the P wave in the 12-lead electrocardiogram (ECG). Distinguishing between the effects on the P wave induced by local atrial substrate changes and those caused by healthy anatomical variations is important to gauge the potential of the 12-lead ECG as a non-invasive and cost-effective tool for the early detection of fibrotic atrial cardiomyopathy to stratify atrial fibril

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Chaikovsky, Illya A., Anton O. Popov, Anatolii M. Kravchenko, and Tetiana M. Ryzhenko. "POSSIBILITIES OF USING ARTIFICIAL INTELLIGENCE TO PERFORM ECG ANALYSIS DURING POPULATION SCREENING." Ukrainian educational and scientific medical space, no. 1 (December 1, 2023): 54–64. http://dx.doi.org/10.31612/3041-1548.1.2023.07.

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The aim of this study is to discuss the evolution and possibilities of modern methods of electrocardiogram analysis, that is, methods based on the mathematical transformation of the ECG signal, first of all a modern method of machine learning, which is called the anomaly detection method. Materials and methods: Five different groups were examined (in total 1211 persons): patients with proven coronary heart disease, military personnel with landmine and explosive injuries, those who suffered from COVID-19, as well as two subgroups that involved participants of a large-scale screening in one of t

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McLeod, S., C. Thompson, B. Borgundvaag, et al. "LO21: Consistency of CTAS scores by presenting complaint pre and post eCTAS implementation in 35 emergency departments across Ontario." CJEM 22, S1 (2020): S14—S15. http://dx.doi.org/10.1017/cem.2020.77.

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Introduction: eCTAS is a real time electronic triage decision-support tool designed to improve patient safety and quality of care by standardizing the application of the Canadian Triage and Acuity Scale (CTAS). The tool dynamically calculates a recommended CTAS score based on the presenting complaint, vital signs and selected clinical modifiers. The primary objective was to assess consistency of CTAS score distributions across 35 emergency departments (EDs) by 16 presenting complaints pre and post eCTAS implementation. Methods: This retrospective cohort study used population-based administrati

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14

Ahmed, Moin, Zhiyu Mao, Yunpeng Liu, Aiping Yu, Michael Fowler, and Zhongwei Chen. "Comparative Analysis of Computational Times of Lithium-Ion Battery Management Solvers and Battery Models Under Different Programming Languages and Computing Architectures." Batteries 10, no. 12 (2024): 439. https://doi.org/10.3390/batteries10120439.

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With the global rise in consumer electronics, electric vehicles, and renewable energy, the demand for lithium-ion batteries (LIBs) is expected to grow. LIBs present a significant challenge for state estimations due to their complex non-linear electrochemical behavior. Currently, commercial battery management systems (BMSs) commonly use easier-to-implement and faster equivalent circuit models (ECMs) than their counterpart continuum-scale physics-based models (PBMs). However, despite processing more mathematical and computational complexity, PBMs are attractive due to their higher accuracy, high

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Klein, Alexander, and Guy Tchuente. "Spatial differencing for sample selection models with ‘site-specific’ unobserved local effects." Econometrics Journal, November 28, 2023. http://dx.doi.org/10.1093/ectj/utad025.

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Abstract This paper proposes an estimator which combines spatial differencing with a two-step sample selection estimator. We derive identification, estimation, and inference results from ’site-specific’ unobserved effects. These effects operate at a spatial scale that cannot be captured by administrative borders. Therefore, we use spatial differencing. We show that under justifiable assumptions, the estimator is consistent and asymptotically normal. A Monte Carlo experiment illustrates the small sample properties of our estimator. We apply our procedure to the estimation of a female wage offer

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16

Breidenbach, Philipp, and Timo Mitze. "Large-scale sport events and COVID-19 infection effects: evidence from the German professional football ‘experiment’." Econometrics Journal, July 9, 2021. http://dx.doi.org/10.1093/ectj/utab021.

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Summary This paper studies the effects of large-scale sport events with live spectators on COVID-19 infection trends at the local population level. Specifically, we compare the development of incidence rates in 41 German Nomenclature of Territorial Units for Statistics level 3 (NUTS-3) districts hosting a professional football match with at least 1,000 spectators vis-à-vis similar districts without hosting a match. Our empirical analysis builds on difference-in-difference and dynamic event study estimation for panel data. Synthetic control method is applied as a robustness check. While our fin

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17

Mastromarco, Camilla, Léopold Simar, and Ingrid Van Keilegom. "Estimating nonparametric conditional frontiers and efficiencies: A new approach." Econometrics Journal, January 3, 2025. https://doi.org/10.1093/ectj/utae025.

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Abstract In production theory, conditional frontiers and conditional efficiency measures are flexible and appealing tools to investigate the role of environmental variables on the production process. Direct approaches estimate non-parametrically conditional distribution functions requiring smoothing techniques and the use of bandwidths. Traditional methods for selecting bandwidths provide bandwidths with order that may not be optimal when estimating the boundary of the distribution function. In this paper we suggest an approach that avoids this problem, by eliminating in a first step, with fle

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18

Andreu, Ana, William P. Kustas, Maria Jose Polo, Arnaud Carrara, and Maria P. González-Dugo. "Modeling Surface Energy Fluxes over a Dehesa (Oak Savanna) Ecosystem Using a Thermal Based Two Source Energy Balance Model (TSEB) II—Integration of Remote Sensing Medium and Low Spatial Resolution Satellite Images." April 2, 2018. https://doi.org/10.3390/rs10040558.

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Dehesas are highly valuable agro-forestry ecosystems, widely distributed over Mediterranean-type climate areas, which play a key role in rural development, basing their productivity on a sustainable use of multiple resources (crops, livestock, wildlife, etc.). The information derived from remote sensing based models addressing ecosystem water consumption, at different scales, can be used by institutions and private landowners to support management decisions. In this study, the Two-Source Energy Balance (TSEB) model is analyzed over two Spanish dehesa areas integrating multiple satellites (MODI

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19

Zink, M. D., M. Lueken, S. Leonhardt, et al. "Factors for incorrect automated atrial fibrillation identification in large-scale screening." European Heart Journal 44, Supplement_2 (2023). http://dx.doi.org/10.1093/eurheartj/ehad655.318.

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Abstract Introduction Correct heart rhythm diagnosis in single-lead electrocardiogram (ECG) screening for atrial fibrillation (AF) is paramount for large-scale implementation. Aim Examine the accuracy of automated AF detection and identify patient factors and ECG signal quality leading to incorrect measurements based on human overread and a machine learning approach. Methods Screening for AF using a single-lead ECG device (MyDiagnostick, Biomedical Systems, Netherlands) was performed on 7295 customers in community pharmacies in Germany. ECG rhythm strips were blinded for automatic heart rhythm

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20

Chiaramonti, David, and Calliope Panoutsou. "Policy measures for sustainable sun ower cropping in EU-MED marginal lands amended by biochar: case study in Tuscany, Italy." Biomass and Bioenergy, May 28, 2018. https://doi.org/10.1016/j.biombioe.2019.04.021.

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The aim of this study is to evaluate economic support measures based on current EU policies a ecting the pro tability of large-scale deployment of biochar for sun ower cultivation in dry marginal lands in Italy, paving the way to large scale carbon sequestration in the EU Mediterranean region. Two cases were considered: i) straight biochar use and ii) biochar in combination with compost (COMBI: 20% biochar and 80% compost mass fraction), at application rates of 5 and 10Mgha−1 respectively. Based on realistic estimations of achievable crop-yield performances by biochar and COMBI addition

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Antoun, Ibrahim, Ahmed Abdelrazik, Mahmoud Eldesouky, et al. "Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations." Frontiers in Cardiovascular Medicine 12 (June 24, 2025). https://doi.org/10.3389/fcvm.2025.1596574.

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Atrial fibrillation (AF) is the most prevalent sustained arrhythmia and a major contributor to stroke and heart failure. Despite progress in management, challenges persist in early detection, risk stratification, and personalised treatment. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has emerged as a transformative tool in AF care. This scoping review examines the applications of AI across key domains: detection, risk prediction, treatment optimisation, and remote monitoring. AI-driven models enhance AF detection by analysing ECGs and wearable device

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Khurshid, Shaan, Timothy W. Churchill, Nathaniel Diamant, et al. "Deep learned representations of the resting 12-lead electrocardiogram to predict V̇O2 at peak exercise." European Journal of Preventive Cardiology, October 5, 2023. http://dx.doi.org/10.1093/eurjpc/zwad321.

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Abstract Aim To leverage deep learning on the resting 12-lead ECG to estimate peak oxygen consumption (V̇O2 PEAK) without cardiopulmonary exercise testing (CPET). Methods V̇O2 PEAK estimation models were developed in 1,891 individuals undergoing CPET at Massachusetts General Hospital (age 45±19 years, 38% female) and validated in a separate test set (MGH Test, n=448) and external sample (BWH Test, n=1,076). Three penalized linear models were compared: a) age, sex, and body mass index (“Basic”), b) Basic plus standard ECG measurements (“Basic + ECG Parameters”), and c) Basic plus 320 deep learn

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