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1. Trends in Remote Sensing Technologies in Olive Cultivation

Author:
Evangelos Anastasiou; Athanasios T. Balafoutis; Spyros Fountas
Source:
Smart agricultural technology 2023 v.3 pp. 100103
ISSN:
2772-3755
Subject:
data collection,   etc   ; olives; vigor;     Show all 3 Subjects
Abstract:
... Various Remote Sensing (RS) technologies and platforms have been widely used in olive cultivation studies over the last 16 years. These technologies and platforms have been applied throughout the olive cultivation cycle, providing significant insights into olive growth and productivity. The goal of this review was to determine the importance of RS technologies and platforms in specific agronomic f ...
DOI:
10.1016/j.atech.2022.100103

2. State of health estimation of lithium-ion battery with improved radial basis function neural network

Author:
Ji Wu; Leichao Fang; Guangzhong Dong; Mingqiang Lin
Source:
Energy 2023 v.262 pp. 125380
ISSN:
0360-5442
Subject:
data collection,   etc   ; algorithms; energy; lithium batteries;     Show all 4 Subjects
Abstract:
... Accurate state of health (SOH) estimation for lithium-ion batteries is crucial to ensure the safety and reliability of electric vehicles. However, traditional neural network algorithms to estimate SOH often focus on fitting nonlinear fluctuation and is weak in the overall tracking trend. This paper thus proposes an improved radial basis function neural network (IRBFNN) to estimate the SOH with the ...
DOI:
10.1016/j.energy.2022.125380

3. Tikhonov regularization stabilizes multi-parameter estimation of geothermal heat exchangers

Author:
Yufang Du; Min Li; Yong Li; Alvin CK. Lai
Source:
Energy 2023 v.262 pp. 125479
ISSN:
0360-5442
Subject:
data collection,   etc   ; algorithms; energy; heat; models; temperature;     Show all 6 Subjects
Abstract:
... Parameter estimation from thermal response tests (TRTs) becomes unreliable when testing time reduces or the number of estimated parameters increases because of low identifiability and ill-posed mathematical feature. To overcome this challenge, this paper reports an inversion algorithm integrating a short-time temperature response model and the zero-order Tikhonov regularization strategy. We applie ...
DOI:
10.1016/j.energy.2022.125479

4. Hierarchical clustered multiclass discriminant analysis via cross-validation

Author:
Kei Hirose; Kanta Miura; Atori Koie
Source:
Computational statistics & data analysis 2023 v.178 pp. 107613
ISSN:
0167-9473
Subject:
data collection,   etc   ; algorithms; data analysis; discriminant analysis; prediction;     Show all 5 Subjects
Abstract:
... Linear discriminant analysis (LDA) is a well-known method for multiclass classification and dimensionality reduction. However, in general, ordinary LDA does not achieve high prediction accuracy when observations in some classes are difficult to be classified. A novel cluster-based LDA method is proposed that significantly improves prediction accuracy. Hierarchical clustering is adopted, and the di ...
DOI:
10.1016/j.csda.2022.107613

5. Spatial segmentation of mass spectrometry imaging data featuring selected principal components

Author:
Yuchen Zou; Weiwei Tang; Bin Li
Source:
Talanta 2023 v.253 pp. 123958
ISSN:
0039-9140
Subject:
data collection,   etc   ; mass spectrometry; mice; principal component analysis; rats;     Show all 5 Subjects
Abstract:
... Spatial segmentation aims to find homogeneous/heterogeneous subgroups of spectra or ion images in mass spectrometry imaging (MSI) data. The maps it generated inform researchers of vital characteristics of the data and thus provide the basis for strategizing further biological analysis. Dimensional reduction and clustering are two basic steps of segmentation. Due to the variations in the quality, r ...
DOI:
10.1016/j.talanta.2022.123958

6. Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization

Author:
Jiaqi Shi; Chenxi Li; Xiaohe Yan
Source:
Energy 2023 v.262 pp. 125295
ISSN:
0360-5442
Subject:
data collection,   etc   ; artificial intelligence; energy; mathematical theory; models; prediction;     Show all 6 Subjects
Abstract:
... State-of-art artificial intelligence (AI) has made great breakthroughs in various industries. Ensemble learning mixed with various predictors provides a considerable solution for electric load forecasting in power system. In our paper, the generalization error of ensemble learning is statistically decomposed to exhibit the significance of base-learner diversity. A diversity regularized Stacking le ...
DOI:
10.1016/j.energy.2022.125295

7. Smoothed tensor quantile regression estimation for longitudinal data

Author:
Baofang Ke; Weihua Zhao; Lei Wang
Source:
Computational statistics & data analysis 2023 v.178 pp. 107609
ISSN:
0167-9473
Subject:
data collection,   etc   ; air quality; algorithms; data analysis; regression analysis; China;     Show all 6 Subjects
Abstract:
... As extensions of vector and matrix data with ultrahigh dimensionality and complex structures, tensor data are fast emerging in a large variety of scientific applications. In this paper, a two-stage estimation procedure for linear tensor quantile regression (QR) with longitudinal data is proposed. In the first stage, we account for within-subject correlations by using the generalized estimating equ ...
DOI:
10.1016/j.csda.2022.107609

8. Controlled product testing in the home - implications for sample size

Author:
Anne Hasted; Jean A. McEwan; Mary Jenner
Source:
Food quality and preference 2023 v.103 pp. 104688
ISSN:
0950-3293
Subject:
data collection,   etc   ; COVID-19 infection; food quality; meta-analysis; sample size;     Show all 5 Subjects
Abstract:
... Consumer product testing in the laboratory or using a Central Location Test (CLT) is a common approach to collect consumer responses to multiple products. The Covid-19 pandemic has challenged companies to adapt, with both sensory and consumer testing in home becoming a common way of working. Moving to the home for controlled product tests brings with it both practical and statistical consideration ...
DOI:
10.1016/j.foodqual.2022.104688

9. Pesticides monitoring in biological fluids: Mapping the gaps in analytical strategies

Author:
Francesco Fama; Matteo Feltracco; Giulia Moro; Elena Barbaro; Marco Bassanello; Andrea Gambaro; Chiara Zanardi
Source:
Talanta 2023 v.253 pp. 123969
ISSN:
0039-9140
Subject:
data collection,   etc   ; bioaccumulation; chromatography; environmental monitoring; humans; mass spectrometry; pests; toxicity;     Show all 8 Subjects
Abstract:
... Pesticides play a key-role in the development of the agrifood sector allowing controlling pest growth and, thus, improving the production rates. Pesticides chemical stability is responsible of their persistency in environmental matrices leading to bioaccumulation in animal tissues and hazardous several effects on living organisms. The studies regarding long-term effects of pesticides exposure and ...
DOI:
10.1016/j.talanta.2022.123969

10. State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression

Author:
Haiyan Jin; Ningmin Cui; Lei Cai; Jinhao Meng; Junxin Li; Jichang Peng; Xinchao Zhao
Source:
Energy 2023 v.262 pp. 125503
ISSN:
0360-5442
Subject:
data collection,   etc   ; batteries; energy; geometry; management systems; models; normal distribution; seeds;     Show all 8 Subjects
Abstract:
... State-of-Health (SOH) estimation is crucial for the safety and reliability of battery-based applications. Data-driven methods have shown their promising potential in battery SOH estimation, yet creating a high-performance model with a compact structure is still a grand challenge. This paper focuses on constructing the elastic feature to formulate auto-configurable Gaussian Process Regression (GPR) ...
DOI:
10.1016/j.energy.2022.125503

11. Using an integrated feature-based molecular network and lipidomics approach to reveal the differential lipids in yak shanks and flanks

Author:
Weili Li; Jiaqi Wang; Chengxue Zhang; Ning Wang; Cong Zhang; Wuzhi Chen; Tao Wu
Source:
Food chemistry 2023 v.403 pp. 134352
ISSN:
0308-8146
Subject:
data collection,   etc   ; food chemistry; lipid composition; lipidomics; meat; phosphatidylethanolamines; sphingomyelins; yaks;     Show all 8 Subjects
Abstract:
... Yak shanks and flanks are often used as food ingredients, but the lipid composition of these two parts may differ significantly. These meat parts were subjected to a lipidomics analysis using UHPLC-Q-Obitrap. Several computational tools, including feature-based molecular networks, ms-dial, and lipidone, were used to perform deep mining on the entire dataset. The analysis annotated 355 lipid specie ...
DOI:
10.1016/j.foodchem.2022.134352

12. Data-driven model predictive control for precision irrigation management

Author:
Erion Bwambale; Felix K. Abagale; Geophrey K. Anornu
Source:
Smart agricultural technology 2023 v.3 pp. 100074
ISSN:
2772-3755
Subject:
data collection,   etc   ; Internet; climate; irrigation canals; prediction; soil water; water potential;     Show all 7 Subjects
Abstract:
... The future of agriculture faces a threat from a changing climate and a rapidly growing population. This has put enormous pressure on water and land resources as more food is expected from less inputs. Advancement in smart agriculture through the use of the Internet of Things and improvement in computational power has enabled extensive data collection from agricultural ecosystems. This review intro ...
DOI:
10.1016/j.atech.2022.100074

13. Machine learning application in forecasting tire wear particles emission in China under different potential socioeconomic and climate scenarios with tire microplastics context

Author:
Xinyi Zhou; Zhuanxi Luo; Haiming Wang; Yinchai Luo; Ruilian Yu; Shufeng Zhou; Zhenhong Wang; Gongren Hu; Baoshan Xing
Source:
Journal of hazardous materials 2023 v.441 pp. 129878
ISSN:
0304-3894
Subject:
data collection,   etc   ; climate; economic sustainability; education; energy; microplastics; temperature; traffic; China;     Show all 9 Subjects
Abstract:
... Little information is available on different contribution of TMPs from tire wear particles (TWPs), recycled tire crumbs (RTCs) and tire repair-polished Debris (TRDs) in the environment at national scale and their potential tendency. In this study, the TWPs were predicted using machine learning method of CNN (Convolutional Neural Networks) algorithms under different potential socioeconomic and clim ...
DOI:
10.1016/j.jhazmat.2022.129878

14. Spatio-temporally differentiated impacts of temperature inversion on surface PM2.5 in eastern China

Author:
Min Shao; Xiaoying Xu; Yutong Lu; Qili Dai
Source:
Science of the total environment 2023 v.855 pp. 158785
ISSN:
0048-9697
Subject:
data collection,   etc   ; air quality; environment; pollution; statistical analysis; temperature inversion; China;     Show all 7 Subjects
Abstract:
... Temperature inversion (TI) is one of the meteorological conditions that significantly affect regional air quality. Knowledge gap regarding the impacts of TI on surface PM₂.₅ in different topographies still existed. In the present study, the occurrence frequency, temperature lapse rate (TLR), depth, and the diurnal variations of TI, surface-based TI (SBTI), elevated TI (ElTI), and multiple layers o ...
DOI:
10.1016/j.scitotenv.2022.158785

15. Discrimination method of biomass slagging tendency based on particle swarm optimization deep neural network (DNN)

Author:
Yingxin Bi; Chunxiang Chen; Xiaodong Huang; Haokun Wang; Guangsheng Wei
Source:
Energy 2023 v.262 pp. 125368
ISSN:
0360-5442
Subject:
data collection,   etc   ; algorithms; ash content; biofuels; biomass; combustion; combustion efficiency; energy; neural networks;     Show all 9 Subjects
Abstract:
... The slagging problem that occurs during the combustion of biomass fuels is difficult to deal with, affecting the safe operation of the boiler and reducing the combustion efficiency. Predicting the slagging tendency of biomass combustion can guide the selection of fuel, reduce the experimental cost and improve the production efficiency of the boiler. In this paper, 114 kinds of biomass are selected ...
DOI:
10.1016/j.energy.2022.125368

16. A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction

Author:
Hao Wang; Jingzhen Ye; Linxuan Huang; Qiang Wang; Haohua Zhang
Source:
Energy 2023 v.262 pp. 125428
ISSN:
0360-5442
Subject:
data collection,   etc   ; Canis lupus; Passeriformes; algorithms; energy; forests; hybrids; neural networks; prediction; wind power;     Show all 10 Subjects
Abstract:
... Offshore wind power prediction is the basis for safe operation and grid dispatch. However, it is difficult due to the high volatility. Aiming at the three shortcomings of current methods: lack of analysis of the impact of multiple variables; the reconstruction method of decomposition components often adopts the summation method; the traditional machine learning prediction methods are not accurate ...
DOI:
10.1016/j.energy.2022.125428

17. Fast Bayesian inference on spectral analysis of multivariate stationary time series

Author:
Zhixiong Hu; Raquel Prado
Source:
Computational statistics & data analysis 2023 v.178 pp. 107596
ISSN:
0167-9473
Subject:
data collection,   etc   ; Bayesian theory; electroencephalography; models; spectral analysis; time series analysis; wind speed; California;     Show all 8 Subjects
Abstract:
... Spectral analysis discovers trends, periodic and other characteristics of a time series by representing these features in the frequency domain. However, when multivariate time series are considered, and the number of components increases, the size of the spectral density matrix grows quadratically, making estimation and inference rather challenging. The proposed novel Bayesian framework considers ...
DOI:
10.1016/j.csda.2022.107596

18. A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools

Author:
Aanis Ahmad; Dharmendra Saraswat; Aly El Gamal
Source:
Smart agricultural technology 2023 v.3 pp. 100083
ISSN:
2772-3755
Subject:
data collection,   etc   ; disease control; disease diagnosis; disease severity; farmers; humans; image analysis; precision agriculture; surveys;     Show all 9 Subjects
Abstract:
... Several factors associated with disease diagnosis in plants using deep learning techniques must be considered to develop a robust system for accurate disease management. A considerable number of studies have investigated the potential of deep learning techniques for precision agriculture in the last decade. However, despite the range of applications, several gaps within plant disease research are ...
DOI:
10.1016/j.atech.2022.100083

19. A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery

Author:
L.G. Divyanth; Aanis Ahmad; Dharmendra Saraswat
Source:
Smart agricultural technology 2023 v.3 pp. 100108
ISSN:
2772-3755
Subject:
data collection,   etc   ; corn; disease control; leaf area; leaf blight; leaf spot; management systems; models; technology;     Show all 9 Subjects
Abstract:
... It is important to develop accurate disease management systems to identify and segment corn disease lesions and estimate their severity under complex field conditions. Although deep learning techniques are becoming increasingly popular to identify singular diseases, access to robust models for identifying multiple diseases and segmenting lesion areas for severity estimation under field conditions ...
DOI:
10.1016/j.atech.2022.100108

20. Implementing a novel deep learning technique for rainfall forecasting via climatic variables: An approach via hierarchical clustering analysis

Author:
Shah Fahad; Fang Su; Sufyan Ullah Khan; Muhammad Rashid Naeem; Kailei Wei
Source:
Science of the total environment 2023 v.854 pp. 158760
ISSN:
0048-9697
Subject:
data collection,   etc   ; agricultural industry; air quality; environment; meteorological data; models; people; prediction; rain; regression analysis; temperature; Pakistan;     Show all 12 Subjects
Abstract:
... Variations in rainfall negatively affect crop productivity and impose severe climatic conditions in developing regions. Studies that focus on climatic variations such as variability in rainfall and temperature are vital, particularly in predominant rainfed areas. Forecasting rainfall is very essential in the agriculture sector due to the dependence of many people, while it is very complex to accur ...
DOI:
10.1016/j.scitotenv.2022.158760