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Artículos de revistas sobre el tema "Peak prediction"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

Gerber, Brandon S., James L. Tangler, Earl P. N. Duque, and J. David Kocurek. "Peak and Post-Peak Power Aerodynamics from Phase VI NASA Ames Wind Turbine Data." Journal of Solar Energy Engineering 127, no. 2 (2005): 192–99. http://dx.doi.org/10.1115/1.1862260.

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Constant speed/pitch rotor operation lacks adequate theory for predicting peak and post-peak power. The objective of this study was to identify and quantify how measured blade element performance characteristics from the Phase VI NASA Ames 24m×36m80ft×120ft wind tunnel test of a two-bladed, tapered, twisted rotor relate to the prediction of peak and post-peak rotor power. The performance prediction code, NREL’s Lifting Surface Prescribed Wake code (LSWT), was used to study the flow physics along the blade. Airfoil lift and drag coefficients along the blade were derived using the predicted angl

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2

Keith, David, and Juan Moreno-Cruz. "Pitfalls of coal peak prediction." Nature 469, no. 7331 (2011): 472. http://dx.doi.org/10.1038/469472b.

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3

Tangka, George Morris William, and Lidya Chitra Laoh. "Deep Learning for Peak Load Duration Curve Forecasting." CogITo Smart Journal 10, no. 1 (2024): 603–12. http://dx.doi.org/10.31154/cogito.v10i1.694.603-612.

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As the energy landscape changes towards renewable energy sources and smart grid technologies, accurate prediction of peak load duration curve (PLDC) becomes crucial to ensure power system stability. The background to this research is the urgent need for more effective prediction methods to manage increasingly complex energy loads. This research presents a leading-edge approach to PLDC prediction, leveraging Deep Learning, a subsection of artificial intelligence. Focusing on data from the Taiwan State Electric Company, this study uses a Long Short-Term Memory (LSTM) network to capture complex l

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4

Soroka, Juliana, Larry Grenkow, Héctor Cárcamo, Scott Meers, Shelley Barkley, and John Gavloski. "An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera: Curculionidae) on the Canadian Prairies." Canadian Entomologist 152, no. 1 (2019): 110–29. http://dx.doi.org/10.4039/tce.2019.71.

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AbstractThis study examined the use of degree-day models to predict alfalfa weevil Hypera postica (Gyllenhal) (Coleoptera: Curculionidae) population development on the Canadian prairies. Air temperatures, alfalfa weevil abundance, and instar data were collected in 2013 and 2014 from 13 alfalfa (Medicago sativa Linnaeus; Fabaceae) fields across Alberta, Saskatchewan, and Manitoba. We coupled three alfalfa weevil population prediction models with three temperature data sources to determine which combination most closely aligned with results observed. Our objective was to find the best prediction

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5

Li, Haitao, Guo Yu, Yizhu Fang, Yanru Chen, Chenyu Wang, and Dongming Zhang. "Studies on natural gas reserves multi-cycle growth law in Sichuan Basin based on multi-peak identification and peak parameter prediction." Journal of Petroleum Exploration and Production Technology 11, no. 8 (2021): 3239–53. http://dx.doi.org/10.1007/s13202-021-01212-3.

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AbstractResearch on predicting the growth trend of natural gas reserves will help provide theoretical guidance for natural gas exploration in Sichuan Basin. The growth trend of natural gas reserves in Sichuan Basin is multi-cycle and complex. The multi-cyclic peak is screened by the original multi-cyclic peak judgment standard. Metabolically modified GM(1,3) gray prediction method is used to predict the multi-cycle model parameters. The multi-cycle Hubbert model and Gauss model are used to predict the growth trend of natural gas reserves. The research results show that: (1) The number of cycle

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6

Zhao, Mengchen, Santiago Gomez-Rosero, Hooman Nouraei, Craig Zych, Miriam A. M. Capretz, and Ayan Sadhu. "Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning." Energies 17, no. 7 (2024): 1672. http://dx.doi.org/10.3390/en17071672.

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Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the imple

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7

Zhang, Yang. "Peak Traffic Prediction Using Nonparametric Approaches." Advanced Materials Research 378-379 (October 2011): 196–99. http://dx.doi.org/10.4028/www.scientific.net/amr.378-379.196.

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How to accurately predict peak traffic is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the technique and analyze the forecast performance in the domain. For comparison purpose, other two non-parametric predictors are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals

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8

Duncan, Michael J., Joanne Hankey, Mark Lyons, Rob S. James, and Alan M. Nevill. "Peak Power Prediction in Junior Basketballers." Journal of Strength and Conditioning Research 27, no. 3 (2013): 597–603. http://dx.doi.org/10.1519/jsc.0b013e31825d97ac.

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9

Ramesh, S., Bhaskar Natarajan, and Gopika Bhagat. "Peak load prediction using weather variables." Energy 13, no. 8 (1988): 671–79. http://dx.doi.org/10.1016/0360-5442(88)90097-7.

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10

Kim, Seunghawk, Gwangseob Kim, and Kyeong-Eun Lee. "Rainfall peak prediction using deep learning." Journal of the Korean Data And Information Science Society 34, no. 4 (2023): 607–17. http://dx.doi.org/10.7465/jkdi.2023.34.4.607.

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11

Oukaira, Aziz, Amrou Zyad Benelhaouare, Dariush Amirkhani, Jamal Zbitou, and Ahmed Lakhssassi. "Silicon Die Transient Thermal Peak Prediction Approach." ITM Web of Conferences 48 (2022): 02007. http://dx.doi.org/10.1051/itmconf/20224802007.

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It is well known that Field Programmable Gate Arrays (FPGA) are good platforms for implementing embedded systems because of their configurable nature. However, the temperature of FPGAs is becoming a serious concern. Improvements in manufacturing technology led to increased logic density in integrated circuits as well as higher clock frequencies. As logic density increases, so do power density, which in turn increases the temperature, FPGAs follow the same path. A prediction of the thermal state of the Altera Cyclone V System-on-Chip (SoC) is presented in this work. The prediction study employs

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12

Veras, Lucas, Daniela Oliveira, Florêncio Diniz-Sousa, et al. "External Validation of Accelerometry-Based Mechanical Loading Prediction Equations." Applied Sciences 14, no. 22 (2024): 10292. http://dx.doi.org/10.3390/app142210292.

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Accurately predicting physical activity-associated mechanical loading is crucial for developing and monitoring exercise interventions that improve bone health. While accelerometer-based prediction equations offer a promising solution, their external validity across different populations and activity contexts remains unclear. This study aimed to validate existing mechanical loading prediction equations by applying them to a sample and testing conditions distinct from the original validation studies. A convenience sample of 49 adults performed walking, running, and jumping activities on a force

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13

Schmitt, Thomas, Tobias Rodemann, and Jürgen Adamy. "The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing." Energies 14, no. 9 (2021): 2569. http://dx.doi.org/10.3390/en14092569.

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Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability to respect the forecasts of loads and generation of renewable energies. However, while there are lots of approaches to accounting for uncertainties in these forecasts, their impact is rarely analyzed systematically. Here, we use a simplified linear state space model of a commercial building including a photovoltaic (PV) plant and real-world data from a 30 day period in 2020. PV predictions are derived from weather forecasts and industry peak pricing is assumed. The effect of prediction accuracy o

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14

Latifoğlu, Levent, and Emre Altuntaş. "Deep Learning Approaches for Stream Flow and Peak Flow Prediction: A Comparative Study." European Journal of Research and Development 4, no. 1 (2024): 61–84. http://dx.doi.org/10.56038/ejrnd.v4i1.422.

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Stream flow prediction is crucial for effective water resource management, flood prevention, and environmental planning. This study investigates the performance of various deep neural network architectures, including LSTM, biLSTM, GRU, and biGRU models, in stream flow and peak stream flow predictions. Traditional methods for stream flow forecasting have relied on hydrological models and statistical techniques, but recent advancements in machine learning and deep learning have shown promising results in improving prediction accuracy. The study compares the performance of the models using compre

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15

Xie, Lianku, Qinglei Yu, Jiandong Liu, Chunping Wu, and Guang Zhang. "Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm." Applied Sciences 14, no. 9 (2024): 3839. http://dx.doi.org/10.3390/app14093839.

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Obtaining accurate basic parameters for long hole blasting is challenging, and the resulting vibration damage significantly impacts key surface facilities. Predicting ground vibration velocity accurately and mitigating the harmful effects of blasting are crucial aspects of controlled blasting technology. This study focuses on the prediction of ground vibration velocity induced by underground long hole blasting tests. Utilizing the fitting equation based on the US Bureau of Mines (USBM) formula as a baseline for predicting peak particle velocity, two machine learning models suitable for small s

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16

Zeng, Qinghui, Xiaolin Yu, Haobo Ni, et al. "Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm." PLOS Neglected Tropical Diseases 17, no. 6 (2023): e0011418. http://dx.doi.org/10.1371/journal.pntd.0011418.

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Predicting the specific magnitude and the temporal peak of the epidemic of individual local outbreaks is critical for infectious disease control. Previous studies have indicated that significant differences in spatial transmission and epidemic magnitude of dengue were influenced by multiple factors, such as mosquito population density, climatic conditions, and population movement patterns. However, there is a lack of studies that combine the above factors to explain their complex nonlinear relationships in dengue transmission and generate accurate predictions. Therefore, to study the complex s

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17

Nilsson, Lars-Olof. "Chloride profiles with a peak – why and what are the consequences for predictions?" MATEC Web of Conferences 364 (2022): 02024. http://dx.doi.org/10.1051/matecconf/202236402024.

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Chloride ingress profiles do almost always have a peak at some depth but most prediction models are missing this peak. Some prediction models, such as the fib model, simply “cut off” a slice of the concrete up to the peak in further predictions. Other prediction models use data only from the profiles beyond the peak but include the concrete up to the peak as if it has the same properties as the rest of the concrete. A physical model has been developed to quantify the local changes because of leaching and the consequences of these changes with time. The model uses Fick’s 1st law for chloride di

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18

Jones, Trevor H., and N. Brad Willms. "A critique of Hubbert’s model for peak oil." FACETS 3, no. 1 (2018): 260–74. http://dx.doi.org/10.1139/facets-2017-0097.

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In 1956, Shell Oil Company geologist M. King Hubbert published a model for the growth and decline over time of the production rates of oil extracted from the land mass of the continental US. Employing an estimate for the amount of ultimately recoverable oil and a logistic curve for the oil production rate, he accurately predicted a peak in US oil production for 1970. His arguments and the success of his prediction have been much celebrated, and the original paper has 1400 publication citations to date. The theory of “peak oil” (and subsequently, of natural resource scarcity in general) has con

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19

Qiao, Liang, and Gang Qin. "Flare Set-Prediction Transformer: A Transformer-Based Set-Prediction Model for Detailed Solar Flare Forecasting." Universe 11, no. 6 (2025): 174. https://doi.org/10.3390/universe11060174.

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Solar flare prediction models typically use classification, predicting only the probability of categorized events within a time window. This misses critical information, such as how many flares occur, their precise timings, and their intensities. To address this, we propose a paradigm shift to set prediction, directly forecasting a variable-sized set of flare events with detailed characteristics. We demonstrate this approach with FSPT (Flare Set-Prediction Transformer), a transformer-based model adapted from object detection principles. FSPT predicts sets containing individual flare start, pea

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20

Kim, Eunhye, Tsatsral Amarbayasgalan, and Hoon Jung. "Efficient Weighted Ensemble Method for Predicting Peak-Period Postal Logistics Volume: A South Korean Case Study." Applied Sciences 12, no. 23 (2022): 11962. http://dx.doi.org/10.3390/app122311962.

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Demand prediction for postal delivery services is useful for managing logistic operations optimally. Particularly for holiday periods, namely the Lunar New Year and Korean Thanksgiving Day (Chuseok) in South Korea, the logistics service increases sharply compared with the usual period, which makes it hard to provide reliable operation in mail centers. This study proposes a Multilayer Perceptron-based weighted ensemble method for predicting the accepted parcel volumes during special periods. The proposed method consists of two main phases: the first phase enriches the training dataset via synth

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21

Glanz, James. "Bold Prediction Downplays the Sun's Next Peak." Science 275, no. 5302 (1997): 927. http://dx.doi.org/10.1126/science.275.5302.927.

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22

van derVeen, C. J. "Reevaluating Hubbert's prediction of U.S. peak oil." Eos, Transactions American Geophysical Union 87, no. 20 (2006): 199. http://dx.doi.org/10.1029/2006eo200003.

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23

Hefke, Frederik, Roland Schmucki, and Peter Güntert. "Prediction of peak overlap in NMR spectra." Journal of Biomolecular NMR 56, no. 2 (2013): 113–23. http://dx.doi.org/10.1007/s10858-013-9727-9.

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24

Nakashima, Toshihisa, Takayuki Ohno, Keiichi Koido, Hironobu Hashimoto, and Hiroyuki Terakado. "Accuracy of predicting the vancomycin concentration in Japanese cancer patients by the Sawchuk–Zaske method or Bayesian method." Journal of Oncology Pharmacy Practice 26, no. 3 (2019): 543–48. http://dx.doi.org/10.1177/1078155219851834.

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Background In cancer patients treated with vancomycin, therapeutic drug monitoring is currently performed by the Bayesian method that involves estimating individual pharmacokinetics from population pharmacokinetic parameters and trough concentrations rather than the Sawchuk–Zaske method using peak and trough concentrations. Although the presence of malignancy influences the pharmacokinetic parameters of vancomycin, it is unclear whether cancer patients were included in the Japanese patient populations employed to estimate population pharmacokinetic parameters for this drug. The difference of p

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25

Yıldırım, Ahmet, Orhan Cicek, and Yavuz Selim Genç. "Can AI-Based ChatGPT Models Accurately Analyze Hand–Wrist Radiographs? A Comparative Study." Diagnostics 15, no. 12 (2025): 1513. https://doi.org/10.3390/diagnostics15121513.

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Background/Aims: The aim of this study was to evaluate the effectiveness of large language model (LLM)-based chatbot systems in predicting bone age and identifying growth stages, and to explore their potential as practical, infrastructure-independent alternatives to conventional methods and convolutional neural network (CNN)-based deep learning models. Methods: This study evaluated the performance of three ChatGPT-based models (GPT-4o, GPT-o4-mini-high, and GPT-o1-pro) in predicting bone age and growth stage using 90 anonymized hand–wrist radiographs (30 from each growth stage—pre-peak, peak,

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26

Xie, Shijie, Rubing Yao, Yatao Yan, Hang Lin, Peilei Zhang, and Yifan Chen. "Hybrid Machine-Learning-Based Prediction Model for the Peak Dilation Angle of Rock Discontinuities." Materials 16, no. 19 (2023): 6387. http://dx.doi.org/10.3390/ma16196387.

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The peak dilation angle is an important mechanical feature of rock discontinuities, which is significant in assessing the mechanical behaviour of rock masses. Previous studies have shown that the efficiency and accuracy of traditional experimental methods and analytical models in determining the shear dilation angle are not completely satisfactory. Machine learning methods are popular due to their efficient prediction of outcomes for multiple influencing factors. In this paper, a novel hybrid machine learning model is proposed for predicting the peak dilation angle. The model incorporates supp

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27

Sun, Rui, Wanwan Qi, Tong Zheng, and Jinlei Qi. "Explainable Machine-Learning Predictions for Peak Ground Acceleration." Applied Sciences 13, no. 7 (2023): 4530. http://dx.doi.org/10.3390/app13074530.

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Peak ground acceleration (PGA) prediction is of great significance in the seismic design of engineering structures. Machine learning is a new method to predict PGA and does have some advantages. To establish explainable prediction models of PGA, 3104 sets of uphole and downhole seismic records collected by the KiK-net in Japan were used. The feature combinations that make the models perform best were selected through feature selection. The peak bedrock acceleration (PBA), the predominant frequency (FP), the depth of the soil when the shear wave velocity reaches 800 m/s (D800), and the bedrock

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28

Sun, Jianan, Xiaofei Ye, Xingchen Yan, Tao Wang, and Jun Chen. "Multi-Step Peak Passenger Flow Prediction of Urban Rail Transit Based on Multi-Station Spatio-Temporal Feature Fusion Model." Systems 13, no. 2 (2025): 96. https://doi.org/10.3390/systems13020096.

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Accurate prediction of station passenger flow is crucial for optimizing rail transit efficiency, but peak passenger flow in urban rail transit (URT) is often disrupted by random events, making predictions challenging. In this paper, in order to solve this challenge, the Bi-graph Graph Convolutional Spatio-Temporal Feature Fusion Network (BGCSTFFN)-based model is introduced to capture complex spatio-temporal correlations. A combination of a graph convolutional neural network and a Transformer is used. The model separately inputs land use (point of interest, POI) and station adjacency informatio

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29

Arhin, Stephen, Babin Manandhar, and Hamdiat Baba-Adam. "Predicting Travel Times of Bus Transit in Washington, D.C. Using Artificial Neural Networks." Civil Engineering Journal 6, no. 11 (2020): 2245–61. http://dx.doi.org/10.28991/cej-2020-03091615.

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This study aimed to develop travel time prediction models for transit buses to assist decision-makers improve service quality and patronage. Six-months’ worth of Automatic Vehicle Location and Automatic Passenger Counting data for six Washington Metropolitan Area Transit Authority bus routes operating in Washington, DC was used for this study. Artificial Neural Network (ANN) models were developed for predicting travel times of buses for different peak periods. The analysis included variables such as length of route between stops, average dwell time and number of intersections between bus stops

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Zhang, Zhaohui, Qiuwen Liu, Ligong Chen, and Pengwei Wang. "A Peak Prediction Method for Subflow in Hybrid Data Flow." Scientific Programming 2020 (February 14, 2020): 1–13. http://dx.doi.org/10.1155/2020/2548351.

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Subflow prediction is required in resource active elastic scaling, but the existing single flow prediction methods cannot accurately predict the peak variation of subflow in hybrid data flow. These do not consider the correlation between subflows. The difficulty is that it is hard to calculate the correlation between different data flows in hybrid data flow. In order to solve this problem, this paper proposes a new method DCCSPP (subflow peak prediction of hybrid data flow based on delay correlation coefficients) to predict the peak value of hybrid data flow. Firstly, we establish a delay corr

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31

Wang, Yu, and Ling Dong. "Research on Carbon Peak Prediction of Various Prefecture-Level Cities in Jiangsu Province Based on Factors Influencing Carbon Emissions." Sustainability 16, no. 16 (2024): 7105. http://dx.doi.org/10.3390/su16167105.

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Jiangsu Province is a region with a high concentration of economy and population in China, as well as a spatial unit with relatively concentrated carbon emissions. It is also the pioneer in achieving carbon peak. Analyzing the factors influencing carbon emissions and predicting the peak year of carbon emissions will help Jiangsu Province clarify the direction of carbon reduction and take the lead in achieving carbon peak. This article selects relevant data from Jiangsu Province from 2005 to 2020, uses the STIRPAT model to analyze the influencing factors of carbon emissions in Jiangsu Province,

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32

Mutuku, Vincent, Joshua Mwema, and Mutwiri Joseph. "Time-Series Prediction of Gamma-Ray Counts Using XGB Algorithm." Open Journal for Information Technology 5, no. 1 (2022): 33–40. http://dx.doi.org/10.32591/coas.ojit.0501.03033m.

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Radioactivity is spontaneous and thus not easy to predict when it will occur. The average number of decay events in a given interval can lead to accurate projection of the activity of a sample. The possibility of predicting the number of events that will occur in a given time using machine learning has been investigated. The prediction performance of the Extreme gradient boosted (XGB) regression algorithm was tested on gamma-ray counts for K-40, Pb-212 and Pb-214 photo peaks. The accuracy of the prediction over a six-minute duration was observed to improve at higher peak energies. The best per

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33

Gupta, R. N., P. Pal Roy, and B. Singh. "Prediction of peak particle velocity and peak air pressure generated by buried explosion." International Journal of Mining and Geological Engineering 6, no. 1 (1988): 15–26. http://dx.doi.org/10.1007/bf00881024.

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Gupta, R. N., P. Pal Roy, and B. Singh. "Prediction of peak particle velocity and peak air pressure generated by buried explosion." International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 26, no. 2 (1989): 78. http://dx.doi.org/10.1016/0148-9062(89)90222-2.

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Nakubulwa, Susan Kiwanuka, K. Baisley, and J. Levin. "Prediction of peak expiratory flow rate in a Ugandan population." South African Respiratory Journal 21, no. 4 (2015): 96. http://dx.doi.org/10.7196/sarj.2015.v21i4.36.

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<p>Background. Peak expiratory ow rate (PEFR) measurement is one of the commonly used methods for assessing lung function in general practice<br />consultations. e reference values for use by this method are mainly from Caucasian populations; data for African populations are limited. e<br />existence of ethnic and racial dierences in lung function necessitates further generation of PEFR reference values for use in African populations.<br />Objective. To generate equations for predicting PEFR in a Ugandan population.<br />Methods. e PEFR study was cross-sectional a

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Zhao, Wuchao, Jiang Qian, and Pengzhao Jia. "Peak Response Prediction for RC Beams under Impact Loading." Shock and Vibration 2019 (January 22, 2019): 1–12. http://dx.doi.org/10.1155/2019/6813693.

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In this paper, a novel and simple method for predicting the peak response of RC beams subjected to impact loading is proposed. The theoretical basis for calculating the peak impact force originates from the contact law, the principle of conservation of energy, the impulse-momentum theorem, and the wave theory. Additionally, the conventional beam theory, in conjunction with the well-known layered-section approach, is utilized to obtain the force-deflection relationship of the RC beam. Subsequently, by taking into account the strain rate effect, the maximum midspan deflection of RC beams under i

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37

Brenneis, Marco, Niklas Thewes, Jana Holder, Felix Stief, and Sebastian Braun. "Validation of central peak height method for final adult height predictions on long leg radiographs." Bone & Joint Open 4, no. 10 (2023): 750–57. http://dx.doi.org/10.1302/2633-1462.410.bjo-2023-0105.r1.

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AimsAccurate skeletal age and final adult height prediction methods in paediatric orthopaedics are crucial for determining optimal timing of growth-guiding interventions and minimizing complications in treatments of various conditions. This study aimed to evaluate the accuracy of final adult height predictions using the central peak height (CPH) method with long leg X-rays and four different multiplier tables.MethodsThis study included 31 patients who underwent temporary hemiepiphysiodesis for varus or valgus deformity of the leg between 2014 and 2020. The skeletal age at surgical intervention

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38

Jampole, Ezra, Eduardo Miranda, and Gregory G. Deierlein. "Predicting earthquake-induced sliding displacements using effective incremental ground velocity." Earthquake Spectra 36, no. 1 (2020): 378–99. http://dx.doi.org/10.1177/8755293019878200.

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This article evaluates a pulse intensity measure, the effective incremental ground velocity ( EIGV), for predicting sliding displacements induced by real ground motions. EIGV is based on computing the additional incremental velocity of a pulse after a system begins to slide. Predictions of peak sliding displacements are made using multiple ground motion and pulse intensity measures, and it is found that at high friction levels, defined here as friction coefficient above 0.15, EIGV is a very effective parameter with a lognormal standard deviation of predicted displacements around 0.5, despite i

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SHARMA, NISHA, RAMAN NARANG, POONAM RATWAN, et al. "Prediction of first lactation 305-days lactation milk yield from peak yield and test day milk yields in crossbred cattle." Indian Journal of Animal Sciences 89, no. 2 (2019): 200–203. http://dx.doi.org/10.56093/ijans.v89i2.87340.

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Lactation yield (LY) of dairy animals have high relationship with peak yield and test day milk yield. The present study was carried out for prediction of 305 days lactation yield based on peak yield and test day milk yield. Data on 384 first lactation test day milk yield records of crossbred cattle maintained at GADVASU dairy farm over a period of 25 years from 1991â€“2015 were utilized for the study. Test day milk records were taken at 30 days interval. The simple regression analysis using peak yield (PY) revealed that peak yield alone could predict the lactation yield with 47% accuracy. Pred

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40

Bommer*, Julian J., and John E. Alarcon*. "THE PREDICTION AND USE OF PEAK GROUND VELOCITY." Journal of Earthquake Engineering 10, no. 1 (2006): 1–31. http://dx.doi.org/10.1080/13632460609350586.

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41

Lu, Peng. "Cost heterogeneity and peak prediction in collective actions." Expert Systems with Applications 79 (August 2017): 130–39. http://dx.doi.org/10.1016/j.eswa.2017.02.009.

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Sciannameo, Veronica, Danila Azzolina, Corrado Lanera, et al. "Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models." Healthcare 11, no. 16 (2023): 2363. http://dx.doi.org/10.3390/healthcare11162363.

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The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, an

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Kahl, Jonathan D. W. "Forecasting Peak Wind Gusts Using Meteorologically Stratified Gust Factors and MOS Guidance." Weather and Forecasting 35, no. 3 (2020): 1129–43. http://dx.doi.org/10.1175/waf-d-20-0045.1.

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Abstract Gust prediction is an important element of weather forecasting services, yet reliable methods remain elusive. Peak wind gusts estimated by the meteorologically stratified gust factor (MSGF) model were evaluated at 15 locations across the United States during 2010–17. This model couples gust factors, site-specific climatological measures of “gustiness,” with wind speed and direction forecast guidance. The model was assessed using two forms of model output statistics (MOS) guidance at forecast projections ranging from 1 to 72 h. At 11 of 15 sites the MSGF model showed skill (improvement

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Elsberry, Russell L., Hsiao-Chung Tsai, Wei-Chia Chin, and Timothy P. Marchok. "Predicting Rapid Intensification Events Following Tropical Cyclone Formation in the Western North Pacific Based on ECMWF Ensemble Warm Core Evolutions." Atmosphere 12, no. 7 (2021): 847. http://dx.doi.org/10.3390/atmos12070847.

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When the environmental conditions over the western North Pacific are favorable for tropical cyclone formation, a rapid intensification event will frequently follow formation. In this extension of our combined three-stage 7-day Weighted Analog Intensity Pacific prediction technique, the European Centre for Medium-range Weather Prediction ensemble predictions of the warm core magnitudes of pre-tropical cyclone circulations are utilized to define the Time-to-Formation (35 knots) and to estimate the Likely Storm Category. If that category is a Typhoon, the bifurcation version of our technique is m

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Lee, Kyung Hyun, and Sangwon Byun. "Age Prediction in Healthy Subjects Using RR Intervals and Heart Rate Variability: A Pilot Study Based on Deep Learning." Applied Sciences 13, no. 5 (2023): 2932. http://dx.doi.org/10.3390/app13052932.

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Autonomic cardiac regulation is affected by advancing age and can be observed by variations in R-peak to R-peak intervals (RRIs). Heart rate variability (HRV) has been investigated as a physiological marker for predicting age using machine learning. However, deep learning-based age prediction has rarely been performed using RRI data. In this study, age prediction was demonstrated in a healthy population based on RRIs using deep learning. The RRI data were extracted from 1093 healthy subjects and applied to a modified ResNet model to classify four age groups. The HRV features were evaluated usi

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Leandri, Pietro, and Massimo Losa. "Peak Friction Prediction Model Based on Surface Texture Characteristics." Transportation Research Record: Journal of the Transportation Research Board 2525, no. 1 (2015): 91–99. http://dx.doi.org/10.3141/2525-10.

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This paper proposes a new model for predicting the speed gradient of peak friction values on asphalt pavements on the basis of surface characteristics. The innovative feature of the proposed model is the reliable estimation of peak friction values experienced by vehicles equipped with an antilock brake system at a certain vehicle speed. To define the experimental model, several types of dense asphalt concrete surface layers with various surface characteristics were analyzed by in situ tests. Friction was measured with the Skiddometer BV11 and the British pendulum tester, and texture properties

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Safari, Alireza, and F. De Smedt. "Improving WetSpa model to predict streamflows for gaged and ungaged catchments." Journal of Hydroinformatics 16, no. 4 (2013): 758–71. http://dx.doi.org/10.2166/hydro.2013.216.

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In the second phase of the Distributed Model Intercomparison Project (DMIP2), the WetSpa model is applied to simulate flows at basin and subbasin scales. Parent basins and their nested subbasins are modeled as gaged and ungaged basins, respectively. Available observations in the subbasins were only used to validate the model predictions. Gaged basins simulation results show that the predictions and observations are in good agreement. However, major peaks are underestimated, as is often the case in runoff modeling. Underestimation of high flows and in particular peak flows indicates that the pr

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Wu, Zhenfen, Zhe Wang, Qiliang Yang, and Changyun Li. "Prediction Model of Electric Power Carbon Emissions Based on Extended System Dynamics." Energies 17, no. 2 (2024): 472. http://dx.doi.org/10.3390/en17020472.

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In response to global climate change, China has committed to peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, commonly known as the “30–60 Dual Carbon”. Under the background of “30–60 Dual Carbon”, this article takes the electric power industry, which is the main industry contributing to China’s carbon emission, as the research object, explores the time and peak value of the carbon peak of the electric power industry, and analyzes whether carbon neutrality can be realized under the peak method, so as to get the carbon neutrality path of the electric power industry and

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Dangar, Nikhil S., and Pravin H. Vataliya. "Prediction of Lifetime Milk Yield using Principal Component Analysis in Gir Cattle." Indian Journal of Veterinary Sciences & Biotechnology 18, no. 4 (2022): 92–96. http://dx.doi.org/10.48165/ijvsbt.18.4.19.

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The objective of the research was to investigate the relationship among production traits i.e., lactation milk yield, lactation length and lactation peak milk yield of the first three lactations using principal component analysis and formulation of prediction equation to predict lifetime milk production in Gir cattle. Data were from multiparous dairy cows of the University farm. Principal component analysis with correlation matrix was used to find the relationship among lactation milk yield, lactation length and lactation peak milk yield of first three lactation and other fixed effects, includ

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Li, Peng, Qin, and Chao. "A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra." Applied Sciences 9, no. 8 (2019): 1699. http://dx.doi.org/10.3390/app9081699.

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In the study, a new correction method was applied to reduce error during Raman spectral detection on mixed pesticide residue in apples. Combined with self-built pesticide residues detection system by Raman spectroscopy and the application of surface enhancement technology, rapid real-time qualitative and quantitative analysis of deltamethrin and acetamiprid residues in apples could be applied effectively. In quantitative analysis, compared with the intensity value of characteristic peaks of single pesticide with same concentration, the intensity value of characteristic peaks of the two pestici

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