Academic literature on the topic 'Rice Data Simulator'
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Journal articles on the topic "Rice Data Simulator"
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Sarkar, Sukamal, Krishnendu Ray, Sourav Garai, Hirak Banerjee, Krisanu Haldar, and Jagamohan Nayak. "Modelling nitrogen management in hybrid rice for coastal ecosystem of West Bengal, India." PeerJ 11 (February 15, 2023): e14903. http://dx.doi.org/10.7717/peerj.14903.
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Hybrid rice requires adequate nitrogen (N) management in order to achieve good yields from its vegetative and reproductive development. With this backdrop, a field experiment was conducted at Regional Research Station (Coastal Saline Zone), Bidhan Chandra Krishi Viswavidyalaya, Kakdwip, West Bengal (India) to record growth and yield performance of hybrid rice (cv. PAN 2423) under varied N-fertilizer doses. A modelling approach was adopted for the first time in hybrid rice production system under coastal ecosystem of West Bengal (India). In the present study, the Agricultural Production Systems Simulator (APSIM) model was calibrated and validated for simulating a hybrid rice production system with different N rates. The APSIM based crop simulation model was found to capture the physiological changes of hybrid rice under varied N rates effectively. While studying the relationship between simulated and observed yield data, we observed that the equations developed by APSIM were significant with higher R2 values (≥0.812). However, APSIM caused an over-estimation for calibrate data but it was rectified for validated data. The RMSE of models for all the cases was less than respective SD values and the normalized RMSE values were ≤20%. Hence, it was proved to be a good rationalized modelling and the performance of APSIM was robust. On the contrary, APSIM underestimated the calibrated amount of N (kg ha−1) in storage organ of hybrid rice, which was later rectified in case of validated data. A strong correlation existed between the observed and APSIM-simulated amounts of N in storage organ of hybrid rice (R2 = 0.94** and 0.96** for the calibration and validation data sets, respectively), which indicates the robustness of the APSIM simulation study. Scenario analysis also suggests that the optimal N rate will increase from 160 to 200 kg N ha−1 for the greatest hybrid rice production in coming years under elevated CO2 levels in the atmosphere. The APSIM-Oryza crop model had successfully predicted the variation in aboveground biomass and grain yield of hybrid rice under different climatic conditions.
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Balaji, S. Arun, and K. Baskaran. "Feed Forward Back Propagation Neural Network Coupled With Rice Data Simulator for Prediction of Rice Production in Tamilnadu." International Journal of Computer Science, Engineering and Information Technology 4, no. 5 (2014): 11–31. http://dx.doi.org/10.5121/ijcseit.2014.4502.
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Wimalasiri, Eranga M., Ebrahim Jahanshiri, Tengku Adhwa Syaherah Tengku Mohd Suhairi, et al. "Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification." Sustainability 12, no. 18 (2020): 7781. http://dx.doi.org/10.3390/su12187781.
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Data from global soil databases are increasingly used for crop modelling, but the impact of such data on simulated crop yield has not been not extensively studied. Accurate yield estimation is particularly useful for yield mapping and crop diversification planning. In this article, available soil profile data across Sri Lanka were harmonised and compared with the data from two global soil databases (Soilgrids and Openlandmap). Their impact on simulated crop (rice) yield was studied using a pre-calibrated Agricultural Production Systems Simulator (APSIM) as an exemplar model. To identify the most sensitive soil parameters, a global sensitivity analysis was performed for all parameters across three datasets. Different soil parameters in both global datasets showed significantly (p < 0.05) lower and higher values than observed values. However, simulated rice yields using global data were significantly (p < 0.05) higher than from observed soil. Due to the relatively lower sensitivity to the yield, all parameters except soil texture and bulk density can still be supplied from global databases when observed data are not available. To facilitate the wider application of digital soil data for yield simulations, particularly for neglected and underutilised crops, nation-wide soil maps for 9 parameters up to 100 cm depth were generated and made available online.
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Zhu, Meiqing, Yimeng Jiao, Chenchen Wu, et al. "Simulation and Optimization of Double-Season Rice Yield in Jiangxi Province Based on High-Accuracy Surface Modeling–Agricultural Production Systems sIMulator Model." Agriculture 15, no. 10 (2025): 1034. https://doi.org/10.3390/agriculture15101034.
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The accurate estimation of double-season rice yield is critical for ensuring national food security. To address the limitations of traditional crop models in spatial resolution and accuracy, this study innovatively developed the HASM-APSIM coupled model by integrating High-Accuracy Surface Modeling (HASM) with the Agricultural Production Systems sIMulator (APSIM) to simulate the historical yield of double-season rice in Jiangxi Province from 2000 to 2018. The methodological advancements included the following: the localized parameter optimization of APSIM using the Nelder–Mead simplex algorithm and NSGA-II multi-objective genetic algorithm to adapt to regional rice varieties, enhancing model robustness; coarse-resolution yield simulations (10 km grids) driven by meteorological, soil, and management data; and high-resolution refinement (1 km grids) via HASM, which fused APSIM outputs with station-observed yields as optimization constraints, resolving the trade-off between accuracy and spatial granularity. The results showed that the following: (1) Compared to the APSIM model, the HASM-APSIM model demonstrated higher accuracy and reliability in simulating historical yields of double-season rice. For early rice, the R-value increased by 14.67% (0.75→0.86), RMSE decreased by 34.02% (838.50→553.21 kg/hm2), MAE decreased by 31.43% (670.92→460.03 kg/hm2), and MAPE dropped from 11.03% to 7.65%. For late rice, the R-value improved by 27.42% (0.62→0.79), RMSE decreased by 36.75% (959.0→606.58 kg/hm2), MAE reduced by 26.37% (718.05→528.72 kg/hm2), and MAPE declined from 11.05% to 8.08%. (2) Significant spatiotemporal variations in double-season rice yields were observed in Jiangxi Province. Temporally, the simulated yields of early and late rice aligned with statistical yields in terms of numerical distribution and interannual trends, but simulated yields exhibited greater fluctuations. Spatially, high-yield zones for early rice were concentrated in the eastern and central regions, while late rice high-yield areas were predominantly distributed around Poyang Lake. The 1 km resolution outputs enabled the precise identification of yield heterogeneity, supporting targeted agricultural interventions. (3) The growth rate of double-season rice yield is slowing down. To safeguard food security, the study area needs to boost the development of high-yield and high-quality crop varieties and adopt region-specific strategies. The model proposed in this study offers a novel approach for simulating crop yield at the regional scale. The findings provide a scientific basis for agricultural production planning and decision-making in Jiangxi Province and help promote the sustainable development of the double-season rice industry.
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Uchechukwu Moses, Oyibo, and Nosiri Onyebuchi Chikezie. "KPI Deployment for Enhanced Rice Production in a Geo-Location Environment using a Wireless Sensor Network." International Journal of Wireless & Mobile Networks 14, no. 03 (2022): 35–54. http://dx.doi.org/10.5121/ijwmn.2022.14303.
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Rice production plays a significant role in food security in the globe. The automation of rice production remains the paradigm shift to meet up with the consumer demand considering the tremendous increase in consumption rate. The paper aimed at implementing some selected key performance indicators (KPIs) for enhanced rice production by addressing five major challenges that face rice farmers, especially in Nigeria. The Non-availability of water/rain for year-round cultivation, disproportionate application of fertilizer, weed control/prevention, pest/disease control, and rodents and bird’s invasion are outlined as observed constraints. A Zigbee-based Enhanced Wireless Sensor Network (eWSN) was used to model various network scenarios to demonstrate data sensing of different environmental variables in a given farm land. This was achieved by varying network devices at different scenarios using OPNET simulator and understudying the network performances. Each new set of network devices was integrated to a Zigbee Coordinator (ZC) which assigns an address to its members and forms a personal area network (PAN), thus representing data sensing of a particular environmental variable. Three different scenarios were designed and simulated in the study. Each of the temperature and humidity, motion and soil nutrient sensors generated about 29bps of traffic. At the Coordinators, steady stream of traffic was received. The temperature and humidity Coordinators, received a traffic of 64bps each, while the soil nutrient Coordinator received data traffic of 96bps. The outcome of the design demonstrates effective communication between different network components and provides insight on how WSN could be used simultaneously to monitor a number of different environmental variables on a farm field. By implementing the KPIs, the simulation result provided an estimated yield increase from 2.2 to 8.7 metric ton per hectare of a rice farm.
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Oyibo, Uchechukwu Moses and Nosiri Onyebuchi Chikezie. "KPI Deployment for Enhanced Rice Production in a Geo-Location Environment using a Wireless Sensor Network." International Journal of Wireless & Mobile Networks (IJWMN) 14, no. 3 (2022): 35–54. https://doi.org/10.5281/zenodo.6861603.
Full textAbstract:
Rice production plays a significant role in food security in the globe. The automation of rice production remains the paradigm shift to meet up with the consumer demand considering the tremendous increase in consumption rate. The paper aimed at implementing some selected key performance indicators (KPIs) for enhanced rice production by addressing five major challenges that face rice farmers, especially in Nigeria. The Non-availability of water/rain for year-round cultivation, disproportionate application of fertilizer, weed control/prevention, pest/disease control, and rodents and bird’s invasion are outlined as observed constraints. A Zigbee-based Enhanced Wireless Sensor Network (eWSN) was used to model various network scenarios to demonstrate data sensing of different environmental variables in a given farm land. This was achieved by varying network devices at different scenarios using OPNET simulator and understudying the network performances. Each new set of network devices was integrated to a Zigbee Coordinator (ZC) which assigns an address to its members and forms a personal area network (PAN), thus representing data sensing of a particular environmental variable. Three different scenarios were designed and simulated in the study. Each of the temperature and humidity, motion and soil nutrient sensors generated about 29bps of traffic. At the Coordinators, steady stream of traffic was received. The temperature and humidity Coordinators, received a traffic of 64bps each, while the soil nutrient Coordinator received data traffic of 96bps. The outcome of the design demonstrates effective communication between different network components and provides insight on how WSN could be used simultaneously to monitor a number of different environmental variables on a farm field. By implementing the KPIs, the simulation result provided an estimated yield increase from 2.2 to 8.7 metric ton per hectare of a rice farm.
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Afshar, Mehdi H., Timothy Foster, Thomas P. Higginbottom, et al. "Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring." Remote Sensing 13, no. 5 (2021): 924. http://dx.doi.org/10.3390/rs13050924.
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Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.
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Wajid, Aftab, Khalid Hussain, Ayesha Ilyas, Muhammad Habib-ur-Rahman, Qamar Shakil, and Gerrit Hoogenboom. "Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments." Agriculture 11, no. 11 (2021): 1166. http://dx.doi.org/10.3390/agriculture11111166.
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Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha−1 with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments.
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Shaikh, Asad, Alexander Churyumov, Andrey Pozdniakov, and Tatiana Churyumova. "Simulation of the Hot Deformation and Fracture Behavior of Reduced Activation Ferritic/Martensitic 13CrMoNbV Steel." Applied Sciences 10, no. 2 (2020): 530. http://dx.doi.org/10.3390/app10020530.
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This study describes deformation behavior and fracture during compression and tension at high temperatures of ferritic/martensitic 13CrMoNbV steel. Hot compression and tensile tests were carried out in the temperature range of 1100–1275 °C with a thermomechanical simulator Gleeble 3800. The true stress and ultimate tensile strength decrease with an increase in the deformation temperature. The modified Arrhenius-type constitutive model was built for 13CrMoNbV ferritic/martensitic steel using the experimental stress–strain compression data. The modified Rice and Tracy ductile fracture criteria were calculated using finite element simulation of the tensile test at different temperatures. The comparison between experimental and computed force vs. displacement curves shows high predictability of the deformation and fracture models for ferritic/martensitic 13CrMoNbV steel.
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Zhao, Panpan, Yang Zhou, Fengfeng Li, et al. "The Adaptability of APSIM-Wheat Model in the Middle and Lower Reaches of the Yangtze River Plain of China: A Case Study of Winter Wheat in Hubei Province." Agronomy 10, no. 7 (2020): 981. http://dx.doi.org/10.3390/agronomy10070981.
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The middle and lower reaches of the Yangtze River (MLYR) plain represent the second-largest wheat producing area in China; the winter wheat-rice system is one of the main planting systems in this region. The use of the agricultural production system simulator (APSIM)-wheat model to simulate wheat production potential and evaluate the impact of future climate change on wheat production in this region is of great importance. In this study, the adaptability of the APSIM-wheat model in the MLYR was evaluated based on observational data collected in field experiments and daily meteorological data from experimental stations in Wuhan, Jingmen, and Xiangyang in Hubei province. The results showed significant positive relationships between model-predicted wheat growth duration from sowing to anthesis and maturity and the observed values, with coefficients of determination (R2) in ranges of 0.90–0.97 and 0.93–0.96, respectively. The normalized root-mean-square error (NRMSE) of the simulated growth durations and measured values were lower than 1.6%, and the refined index of agreement (dr-values) was in the range of 0.74–0.87. The percent mean absolute relative error (PMARE) was cited here as a new index, with a value below 1.4%, indicating that the model’s rating was excellent. The model’s performance in terms of grain yield and above-ground biomass simulation was also acceptable, although it was not as good as the growth periods simulation. The R2 value was higher than 0.75 and 0.72 for the simulation of grain yield and biomass, respectively. The indices NRMSE and PMARE were lower than 19.8% and 19.9%, and the dr-value was higher than 0.71. According to our results, APSIM-wheat was an effective and accurate model for simulating the phenology and yield production processes of wheat in the MLYR, and the results also provided a theoretical basis and technical support for further research on the yield potential of wheat-rice rotation planting systems with clarification of the key factors limiting the yield gap in this region.
Dissertations / Theses on the topic "Rice Data Simulator"
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Irfan, Kamran. "Adaptation of the generic crop model STICS for rice (Oryza sativa L.) using farm data in Camargue." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4355.
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Le modèle de culture STICS a été adapté pour la culture du riz inondé et la capacité de prédiction du modèle a été évaluée pour la simulation de la biomasse à la récolte et du rendement en grains. La base de données utilisée pour ce travail résulte de la collecte de données au champ sur des parcelles en Camargue (sud-Est de la France) gérées par les agriculteurs. Pour la modélisation, ne disposant que très peu de données d’expérimentation, une procédure originale d’utilisation des données obtenues à la ferme a été développée. Ce travail est composé de trois phases: (i) une analyse de la base de données initiale constituée d’informations sur 472 parcelles, 33 variétés et 11 sols aux propriétés physiques différentes et collectées entre 1984 et 2009 dans toute la Camargue; (ii) la sélection des options et des formalismes pertinents pour la culture du riz, (iii) la préparation du jeu de données pour la modélisation par élimination des parcelles dont les rendements sont limités par des facteurs non pris en compte dans le modèle; (iv) la paramétrisation et la simulation des variables choisies.Les résultats de l’application de STICS au riz sont satisfaisants pour près de 80% des parcelles utilisées pour la base de données de calibration. L’accord entre les simulations et les observations est meilleur lorsque les informations d’entrée du modèle sont complètes. Les simulations de la biomasse et du rendement en grains sont d’une qualité légèrement plus faible pour la base de données de validation que pour la base da calibration<br>The crop model STICS was adapted for the flooded rice and model’s prediction ability was evaluated by the simulation of the plant biomass at harvest as well as the grain yield. The dataset used for this purpose was collected from the fields situated in whole Camargue (Southern France) and managed by the farmers. We introduced an original procedure to use the farm data instead of experimentation for modeling. This work was carried out in three phases, (i) analysis of the initial database of 472 fields, 33 different varieties and 11 physically different soils grown in the whole Camargue between 1984 and 2009, (ii) selection of the options of formalisms relevant to the rice crop, (iii) preparation of dataset for modeling by eliminating the fields in which the yields were limited by the factors not taken into account by the model and (iv) parameterization and the simulation of the selected target variables. The results of the application of STICS to rice crop were satisfactory for almost 80% of the fields of calibration data. Particularly, there was a good agreement between simulations and measurements of the situations with complete information regarding to the inputs. The simulation patterns for both the plant biomass and the grain yield of dataset of validation are similar as that of dataset of calibration exhibiting slightly reduced simulation quality. More discrepancies were observed in the simulations made by the model calculated dates of different phenological stages compared to the simulations run by using the observed dates of same stages
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Girard-Alcindor, Valerian. "Above barrier narrow resonances in fluorine-15." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC209.
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L’étude des noyaux non liés permet de tester notre compréhension du noyau atom- ique dans des conditions extrêmes. Dans cette thèse nous avons analysé deux expériences distinctes réalisées au GANIL au cours desquels nous avons étudié le fluor 15 situé deux neutrons au delà de la drip-line proton. Une des particularités intéressante de ce noyau est la présence d’états de parité négative particulièrement étroits et situés au dessus des barrières Coulombiennes et centrifuges. Nous nous sommes intéressés dans ce travail à ces états, nous avons confirmé l’existence du second état excité 1/2- et avons observé deux nouveaux états, un état 5/2- et un état 3/2- en mesurant leur décroissance par émission d’un ou de deux protons. Nous avons discuté la structure de ces états et avons tenté d’expliquer l’asymétrie observée entre l’état 3/2- du fluor 15 et son analogue dans le carbone 15. Nous avons détaillé aussi la possibilité d’observer des décroissances gammas entre états non-liés<br>The study of unbound nuclei beyond the drip-lines allows us to test our understand- ing of the atomic nucleus under extreme conditions. In this thesis, we have analyzed two separate GANIL experiments in which fluorine 15, located two proton beyond the proton drip-line, was studied. One of the interesting particularity of this nucleus is the presence of particularly narrow negative parity states, above the Coulomb and centrifugal barriers. In this work, we were interested in theses states. We have confirmed the existence of the second excited state 1/2- and have observed two new states, a 5/2- state and a 3/2- state by measuring their decay by emitting either one or two protons. The structure of these states has been discussed and we have tried to explain the origin of the asymmetry observed between the 3/2- state of fluorine 15 and its analogue state in carbon 15. We have also detailed the possibility of gamma transitions between unbound states
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Sehbi, Baljit Singh. "Performance Analysis & Optimization of Well Production in Unconventional Resource Plays." Thesis, 2013. http://hdl.handle.net/1969.1/149482.
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The Unconventional Resource Plays consisting of the lowest tier of resources (large volumes and most difficult to develop) have been the main focus of US domestic activity during recent times. Horizontal well drilling and hydraulic fracturing completion technology have been primarily responsible for this paradigm shift.
The concept of drainage volume is being examined using pressure diffusion along streamlines. We use diffusive time of flight to optimize the number of hydraulic fracture stages in horizontal well application for Tight Gas reservoirs. Numerous field case histories are available in literature for optimizing number of hydraulic fracture stages, although the conclusions are case specific. In contrast, a general method is being presented that can be used to augment field experiments necessary to optimize the number of hydraulic fracture stages. The optimization results for the tight gas example are in line with the results from economic analysis.
The fluid flow simulation for Naturally Fractured Reservoirs (NFR) is performed by Dual-Permeability or Dual-Porosity formulations. Microseismic data from Barnett Shale well is used to characterize the hydraulic fracture geometry. Sensitivity analysis, uncertainty assessment, manual & computer assisted history matching are integrated to develop a comprehensive workflow for building reliable reservoir simulation models. We demonstrate that incorporating proper physics of flow is the first step in building reliable reservoir simulation models. Lack of proper physics often leads to unreasonable reservoir parameter estimates. The workflow demonstrates reduced non-uniqueness for the inverse history matching problem.
The behavior of near-critical fluids in Liquid Rich Shale plays defies the production behavior observed in conventional reservoir systems. In conventional reservoirs an increased gas-oil ratio is observed as flowing bottom-hole pressure is less than the saturation pressure. The production behavior is examined by building a compositional simulation model on an Eagle Ford well. Extremely high pressure drop along the multiple transverse hydraulic fractures and high critical gas saturation are responsible for this production behavior. Integrating pore-scale flow modeling (such as Lattice Boltzmann) to the field-scale reservoir simulation may enable quantifying the effects of high capillary pressure and phase behavior alteration due to confinement in the nano-pore system.
Books on the topic "Rice Data Simulator"
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Institute, International Rice Research. Systems simulation at IRRI. International Rice Research Institute, 1991.
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Real-time rainfall-runoff model of the Carraízo-Reservoir Basin in Puerto Rico. Dept. of the Interior, U.S. Geological Survey, 1996.
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Welberry, T. R. Diffuse X-ray Scattering and Models of Disorder. 2nd ed. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198862482.001.0001.
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Diffuse X-ray scattering is a rich source of local structural information over and above that obtained by conventional crystal structure determination (crystallography). The main aim in the book is to show how computer simulation of a model crystal provides a general method by which diffuse scattering of all kinds and from all types of materials can be interpreted and analysed. Part I gives a description of the experimental methods used to obtain diffuse scattering data including recent improvements both in synchrotron and neutron facilities and latest detectors that are now available. Part II describes a number of stochastic models of disorder, which allows various concepts to be established and enables simple examples to be generated to illustrate key principles. Part III describes example studies of a wide variety of real materials. These examples not only document the development of computer simulation methods for investigating and analysing disorder problems but also provide a resource for helping future researchers recognise the kinds of effects which can occur and for pointing the way to tackling new problems which are encountered. Part IV describes more recent studies in which the increased computational power, coupled with improvements in the quality and quantity of data that is now obtainable, has allowed more quantitative and detailed analyses to be undertaken.
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Bandyopadhyay, Arindam. Basic Statistics for Risk Management in Banks and Financial Institutions. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192849014.001.0001.
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The book provides an engaging account of theoretical, empirical, and practical aspects of various statistical methods in measuring risks of financial institutions, especially banks. In this book, the author demonstrates how banks can apply many simple but effective statistical techniques to analyse risks they face in business and safeguard themselves from potential vulnerability. It covers three primary areas of banking risks—credit, market, and operational risk, and in a uniquely intuitive, step-by-step manner, the author provides hands-on details on the primary statistical tools that can be applied for financial risk measurement and management. The book lucidly introduces concepts of various well-known statistical methods such as correlations, regression, matrix approach, probability and distribution theorem, hypothesis testing, Value at Risk (Vary), and Monte Carlo simulation techniques and provides a hands-on estimation and interpretation of these tests in measuring risks of the financial institutions. The books strike a fine balance between concepts and mathematics to tell a rich story of thoughtful use of statistical methods. The book will be of much interest to academics, risk managers, bankers, and consultants and general readers too. It emphasizes on specific risk measurement tools and techniques with data applications, templates required for data collection and analysis, numerous excel-based illustrations as well as analysis in econometric packages. Excel-based hands-on and use of econometric packages like STATA, EVIEWS, and @RISK will help practitioners, academia, and students to connect theory with application.
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Madden, Anthony P. Informatics and technology for anaesthesia. Edited by Philip M. Hopkins. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0034.
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Health informatics is concerned with the structure, acquisition, and use of health information. Its origins can be traced back to the publication of Bills of Mortality by the parishes of London in the sixteenth century. Interest in health information accelerated during the late nineteenth century with the development of an internationally recognized classification of the causes of death. Further work on the classification of diseases and causes of death has resulted in the ICD-10, while SNOMED CT provides an international thesaurus of medical terms suitable for use in computerized medical record systems. In 1932, Tovell and Dunn described the systematic collection of data about anaesthetics with the aim of identifying areas for improvement. The improvement of healthcare is the main driver for the implementation of electronic patient record systems in hospitals. A natural corollary is the implementation of computerized anaesthetic information management systems. Computerized record systems can automatically store the output of physiological monitors and reduce errors with active and passive decision support. Although the recording and processing of health information in the twenty-first century almost always involves the use of computers, this can give rise to problems with security and inter-operability. Computer technology also has other uses in modern anaesthetic practice. The modelling of physiological processes and the use of simulators in the training of anaesthetists are good examples.
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Vaez-Zadeh, Sadegh. Control of Permanent Magnet Synchronous Motors. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.001.0001.
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This is the first comprehensive, coherent, and up-to-date book devoted solely to the control of permanent magnet synchronous (PMS) motors, as the fastest growing AC motor. It covers a deep and detailed presentation of major PMS motor modeling and control methods. The readers can find rich materials on the fundamentals of PMS motor control in addition to new motor control methods, which have mainly been developed in the last two decades, including recent advancements in the field in a systematic manner. These include extensive modeling of PMS motors and a full range of vector control and direct torque control schemes, in addition to predictive control, deadbeat control, and combined control methods. All major sensorless control and parameter estimation methods are also studied. The book covers about 10 machine models in various reference frames and 70 control and estimation schemes with sufficient analytical and implementation details including about 200 original figures. A great emphasis is placed on energy-saving control schemes. PMS motor performances under different control systems are presented by providing simulation and experimental results. The past, present, and future of the PMS motor market are also discussed. Each chapter concludes with end-chapter problems and focussed bibliographies. It is an essential source for anyone working on PMS motors in academic and industry sectors. The book can be used as a textbook with the first four chapters for a primary graduate course and the final three chapters for an advanced course. It is also a crucial reading for researchers, design engineers, and experts in the field.
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Guía metodológica para zonificación de amenaza por avenidas torrenciales. Servicio Geológico Colombiano, 2021. http://dx.doi.org/10.32685/9789585313156.
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La Guía metodológica para zonificación de amenaza por avenidas torrenciales fue concebida dada la necesidad de contar con un instrumento que proporcionara lineamientos técnicos para la evaluación de procesos como las avenidas torrenciales que, a su vez, permitieran generar un instrumento para la toma de decisiones y la incorporación del riesgo en el ordenamiento territorial, como los mapas de zonificación de amenaza. En el capítulo 1 se describe el marco conceptual en el que se basa la metodología. Este contempla los conceptos utilizados a lo largo de la publicación y el alcance fundamentado en el marco normativo que rige la evaluación de amenaza por avenidas torrenciales. El capítulo 2 aborda la delimitación del área de estudio, las escalas de trabajo, los modelos de elevación digital disponibles y demás insumos para la replicabilidad de la metodología. En el capítulo 3, se presenta el procedimiento para la caracterización de avenidas torrenciales, que involucra la historia fluviotorrencial de la zona a través de análisis geológicos y geomorfológicos. El capítulo 4 describe el procedimiento propuesto para el análisis de detonantes, específicamente las lluvias intensas como principal detonante de las avenidas torrenciales en Colombia. El capítulo 5 hace referencia a la caracterización de la amenaza por avenidas torrenciales a escala 1:25 000, que representa un primer diagnóstico de la amenaza con base en modelaciones fluido-dinámicas y en la susceptibilidad geomorfológica a avenidas torrenciales, de donde resultan las zonas en las que se debe concentrar el estudio detallado. El capítulo 6 corresponde a la evaluación de amenaza por avenidas torrenciales a escala 1:2000, en la que se deben simular los procesos de arrastre y depósito propios de una avenida torrencial que, junto con la información de eventos recientes, permiten obtener la zonificación de amenaza en detalle. Teniendo en cuenta que, dadas las condiciones particulares de los territorios, es posible que se presenten escenarios como represamientos causados por material proveniente de movimientos en masa o material vegetal, que se convierte en detritos leñosos y fallas de diques, entre otros, en el capítulo 7 se reportan las consideraciones para la evaluación de estos escenarios en caso de evidencias que favorezcan la ocurrencia de alguno de ellos. Finalmente, en el capítulo 8 se presentan recomendaciones finales relacionadas con la presentación de los resultados y los perfiles profesionales requeridos para la aplicación de la metodología.
Book chapters on the topic "Rice Data Simulator"
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Vosgien, Thomas, Thomas Nguyen Van, Marija Jankovic, Benoît Eynard, and Jean-Claude Bocquet. "Towards Model-Based System Engineering for Simulation-Based Design in Product Data Management Systems." In Product Lifecycle Management. Towards Knowledge-Rich Enterprises. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35758-9_55.
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Hennig, Jonas, Thomas Meissner, Marcus Krieg, and Felix Przioda. "Business Analysis and Prognosis Regarding the Shared Autonomous Vehicle Market in Germany." In Acceptance and Diffusion of Connected and Automated Driving in Japan and Germany. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-59876-0_4.
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AbstractDriven by the current advancements in autonomous driving (AD) technology, operations of shared autonomous vehicles (SAVs) are increasingly moved from closed test sites to public roads worldwide. Since the market potential of SAV services in Germany remains largely unexplored, this chapter aims to provide a holistic business analysis and prognosis of the German SAV market. To adequately capture the operational complexity of future SAV fleets, a simulation-based approach is used. The simulation model is created utilizing proprietary operational performance and cost data of actual shared vehicle services and pilots in Germany. Three market demand scenarios are simulated and combined with a detailed cost scenario for the year 2035. Results indicate improved operational efficiency indicators for SAV fleets compared to non-autonomous fleets. However, we also found that cost structures of SAV fleets may be highly underestimated in previous studies, resulting in a per VKM cost range of 0.48–0.50 EUR. While customer price levels are significantly lower than the current cost of using ride-hailing services, they may not be low enough to convince a considerable number of passengers to switch from their private cars to SAV services. The results underscore the importance of collaborative efforts between policymakers, fleet operators and manufacturers to harness the benefits of SAV fleets.
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Jayasinghe, Haritha, Tarindu Jayatilaka, Ravin Gunawardena, and Uthayasanker Thayasivam. "Data-Driven Simulation of Ride-Hailing Services Using Imitation and Reinforcement Learning." In Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79457-6_4.
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Pohlkötter, Fabian J., Dominik Straubinger, Alexander M. Kuhn, Christian Imgrund, and William Tekouo. "Unlocking the Potential of Digital Twins." In Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27933-1_18.
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AbstractIncreasing competitive pressure is confronting the automotive industry with major challenges. As a result, conventional reactive maintenance is being transformed into predictive maintenance. In this context, wearing and aging effects no longer lead to plant failure since they are predicted at an earlier stage based on comprehensive data analysis.Furthermore, the evolution towards Smart Factory has given rise to virtual commissioning in the planning phase of production plants. In this process, a Hardware-in-the-Loop (HiL) system combines the real controls (e.g., PLC) and a virtual model of the plant. These HiL systems are used to simulate commissioning activities in advance, thus saving time and money during actual commissioning. The resulting complex virtual models are not further used in the series production.This paper builds upon virtual commissioning models to develop a Digital Twin, which provides inputs for predictive maintenance. The resulting approach is a methodology for building a hybrid predictive maintenance system. A hybrid prediction model combines the advantages of data-driven and physical models. Data-driven models analyse and predict wearing patterns based on real machine data. Physical models are used to reproduce the behaviour of a system. From the simulation of the hybrid model, additional insights for the predictions can be derived.The conceptual methodology for a hybrid predictive maintenance system is validated by the successful implementation in a bottleneck process of the electric engine production for an automotive manufacturer. Ultimately, an outlook on further possible applications of the hybrid model is presented.
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Plumb, Caolan, Farzad Pour Rahimian, Diptangshu Pandit, Hannah Thomas, and Nigel Clark. "A Framework for Realistic Virtual Representation for Immersive Training Environments." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.26.
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As mixed-reality (XR) technology becomes more available, virtually simulated training scenarios have shown great potential in enhancing training effectiveness. Realistic virtual representation plays a crucial role in creating immersive experiences that closely mimic real-world scenarios. With reference to previous methodological developments in the creation of information-rich digital reconstructions, this paper proposes a framework encompassing key components of the 3D scanning pipeline. While 3D scanning techniques have advanced significantly, several challenges persist in the field. These challenges include data acquisition, noise reduction, mesh and texture optimisation, and separation of components for independent interaction. These complexities necessitate the search for an optimised framework that addresses these challenges and provides practical solutions for creating realistic virtual representations in immersive training environments. The following exploration acknowledges and addresses challenges presented by the photogrammetry and laser-scanning pipeline, seeking to prepare scanned assets for real-time virtual simulation in a games-engine. This methodology employs both a camera and handheld laser-scanner for accurate data acquisition. Reality Capture is used to combine the geometric data and surface detail of the equipment. To clean the scanned asset, Blender is used for mesh retopology and reprojection of scanned textures, and attention given to correct lighting details and normal mapping, thus preparing the equipment to be interacted with by Virtual Reality (VR) users within Unreal Engine. By combining these elements, the proposed framework enables realistic representation of industrial equipment for the creation of training scenarios that closely resemble real-world contexts
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Plumb, Caolan, Farzad Pour Rahimian, Diptangshu Pandit, Hannah Thomas, and Nigel Clark. "A Framework for Realistic Virtual Representation for Immersive Training Environments." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.26.
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As mixed-reality (XR) technology becomes more available, virtually simulated training scenarios have shown great potential in enhancing training effectiveness. Realistic virtual representation plays a crucial role in creating immersive experiences that closely mimic real-world scenarios. With reference to previous methodological developments in the creation of information-rich digital reconstructions, this paper proposes a framework encompassing key components of the 3D scanning pipeline. While 3D scanning techniques have advanced significantly, several challenges persist in the field. These challenges include data acquisition, noise reduction, mesh and texture optimisation, and separation of components for independent interaction. These complexities necessitate the search for an optimised framework that addresses these challenges and provides practical solutions for creating realistic virtual representations in immersive training environments. The following exploration acknowledges and addresses challenges presented by the photogrammetry and laser-scanning pipeline, seeking to prepare scanned assets for real-time virtual simulation in a games-engine. This methodology employs both a camera and handheld laser-scanner for accurate data acquisition. Reality Capture is used to combine the geometric data and surface detail of the equipment. To clean the scanned asset, Blender is used for mesh retopology and reprojection of scanned textures, and attention given to correct lighting details and normal mapping, thus preparing the equipment to be interacted with by Virtual Reality (VR) users within Unreal Engine. By combining these elements, the proposed framework enables realistic representation of industrial equipment for the creation of training scenarios that closely resemble real-world contexts
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Zhou, Jun, Lihe Tang, Songyuhao Shi, Wei Li, Pan Hu, and Feng Wang. "Research on Visualization of Power Grid Big Data." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_52.
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AbstractWith the constant improvement of power grid planning and management requirements and the gradual advancement of the urbanization process, the problems that need to be taken into account in the planning process are increasing, especially the demand for big data visualization of the power grid has increased sharply. About 80% of the information that humans obtain from the external environment comes from the visual system. A picture is worth a thousand words. A good visualization platform can monitor the overall operation of the power grid, which is convenient for analyzing and monitoring the operation of power supply companies to provide customers with high-quality services. The platform can complete the interactive simulation of different services, and can display the monitoring and analysis of the power grid through a rich visual interface, which is convenient for people to understand the real-time status of the power grid. This paper uses various advanced visualization technologies and data module algorithms at home and abroad to cooperate with the monitoring network to realize the visualization platform of power grid big data, promote the further development of power grid big data applications, and form a big data standard system for power big data technology research, product research and development, and pilot construction.
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Giovanardi, Matteo, Matteo Trane, and Riccardo Pollo. "Environmental Sensing and Simulation for Healthy Districts: A Comparison Between Field Measurements and CFD Model." In The Urban Book Series. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29515-7_82.
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AbstractAtmospheric Particulate Matter (PM) is considered among the main risk factors for cardiovascular, respiratory, and carcinogenic diseases. Besides, heat waves accounted for 68% of natural hazard-related deaths in Europe between 1980 and 2017 and many climate models project a global rise in climate hazards. Environmental Monitoring (EM) is a key resource to control health determinants, addressing threats arising from unhealthy external conditions. Forecasting models may need data coming from pervasive distributed sensor networks and computational simulations. Moreover, district-scale Environmental Sensing (ES) and Environmental Modelling Simulation (EMS) may identify criticalities and specific strategies to mitigate climate risk affecting physical health. This paper compares the output from ES, by field measurements during a “climate walk” joined by more than 60 people, with EMS, by a Computational Fluid Dynamic software (CFD). The assessment has been performed on a real urban district. For on-site measurements, data were acquired by low-cost IoT-based sensors developed by the authors. For simulations, we used ENVI-met, a prognostic non-hydrostatic CFD. Potential Air Temperature and PM 10-2.5 concentration parameters have been measured and simulated on a specific winter day. Results are presented and discussed through a visualisation matrix making the comparison direct. The analysis of the results pointed out the role of ES and EMS for high-resolution scenarios assessment. Although real-time monitoring needs extensive infrastructure at the urban scale, the use of low-cost sensors and a citizen science approach could provide precise input data to support even more accurate models, towards a healthy district site-specific design perspective. This may finally contribute to achieving the Sustainable Development Goal 11.6, aiming at reducing the adverse environmental impact of cities, thus paying particular attention to air quality.
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Takimoto, Takahiro, Yuji Masutomi, and Makoto Tamura. "Impact Assessment and Adaptation Simulation for Chalky Rice Grain in the Cultivar ‘Koshihikari’ in Japan Using Large Ensemble Climate Projection Data Sets." In Adaptation to Climate Change in Agriculture. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9235-1_2.
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Hojaji, Fazilat, Adam J. Toth, and Mark J. Campbell. "A Machine Learning Approach for Modeling and Analyzing of Driver Performance in Simulated Racing." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_8.
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AbstractThe emerging progress of esports lacks the approaches for ensuring high-quality analytics and training in professional and amateur esports teams. In this paper, we demonstrated the application of Artificial Intelligence (AI) and Machine Learning (ML) approach in the esports domain, particularly in simulated racing. To achieve this, we gathered a variety of feature-rich telemetry data from several web sources that was captured through MoTec telemetry software and the ACC simulated racing game. We performed a number of analyses using ML algorithms to classify the laps into the performance levels, evaluating driving behaviors along these performance levels, and finally defined a prediction model highlighting the channels/features that have significant impact on the driver performance. To identify the optimal feature set, three feature selection algorithms, i.e., the Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) have been applied where out of 84 features, a subset of 10 features has been selected as the best feature subset. For the classification, XGBoost outperformed RF and SVM with the highest accuracy score among the other evaluated models. The study highlights the promising use of AI to categorize sim racers according to their technical-tactical behaviour, enhancing sim racing knowledge and know how.
Conference papers on the topic "Rice Data Simulator"
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Tao, Shixi, Gang Tao, and Lijing Zhang. "Simulation study on elevator-assisted evacuation in high-rise building fires." In 4th International Conference on Electronic Information Engineering and Data Processing (EIEDP 2025), edited by Azlan Bin Mohd Zain and Lei Chen. SPIE, 2025. https://doi.org/10.1117/12.3066934.
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Tiwari, Priyanshu, S. Ambareesh, Soumya Patil, and Rishabh Raj. "Enhancing Fare Prediction Accuracy in Ride Hailing Through Neural Networks and Data Simulation." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10987321.
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Lagad, Vishal V., Michael S. Cayard, and Sridhar Srinivasan. "Prediction and Assessment of Rich Amine Corrosion under Simulated Refinery Conditions." In CORROSION 2010. NACE International, 2010. https://doi.org/10.5006/c2010-10183.
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Abstract The challenges of enhancing amine unit reliability along with demands for increased unit throughput, circulation and treatment capacity, have led to the need for corrosion quantification and improved corrosion prediction capabilities. This paper summarizes some of the experimental results and findings from a joint industry program entitled “Prediction and Assessment of Corrosion in Amine Solvents” conducted over the period from March 2003 to May 2007. The program was jointly sponsored by a group of refining and engineering companies and focused on rich amine corrosion. The impacts of relevant process variables such as acid gas loadings (H2S and CO2), heat stable amine salt (HSAS) concentration, flow velocity/shear stress and temperature on the corrosion of carbon steel, austenitic stainless steel, duplex stainless steel and a nickel-based alloy were quantified using laboratory tests performed under simulated refinery conditions. Data collected were used to develop a software tool to predict corrosion rates for the three amine solvents (mono-ethanolamine (MEA), di-ethanolamine (DEA) and aminoethoxyethanol (DGA)(1)) evaluated in the program. This paper presents an overview of the experimental methods, some of the results and trends observed and comparison of predicted corrosion rates to refinery field data.
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Karpstein, Robin, Victor Luis, and Florian Holzapfel. "Agent-based Simulation of UAV based Logistics Networks with Real World Data." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1407.
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Applications of Unmanned Aerial Vehicles (UAVs) are on the rise. Particularly within the healthcare sector the potential is huge as its cited as the most accepted application. This paper introduces an agent-based simulation to evaluate the network performance of UAV-based logistics networks in healthcare. The simulation is applied to a hypothetical real-world network. During a simulated day, the UAV fleet performs 212 flights, including 97 delivery flights, amounting to 4264 minutes enroute and covering a distance of 5941 kilometers. The analysis reveals average non-idle and mission utilization of 66% and 33%, respectively. The study also calculates annual network costs of EUR 2.23Mn, with a majority of it being direct costs (54.5%). Further sensitivity analysis identifies the biggest influences of battery capacity, C-Rate, and operator-to-UAV ratio on network performance and costs, highlighting these factors as critical for future optimization. Additionally, the benefit of incorporating various different UAV types into the network is only given if each UAV provides a unique value proposition to enhance the network performance.
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Oakes, Jim. "Understanding Atmosphere in Carburizing Applications Using Simulation and Real-Time Carbon Diffusion." In HT 2013, edited by B. Lynn Ferguson. ASM International, 2013. https://doi.org/10.31399/asm.cp.ht2013p0009.
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Abstract In gas carburizing, the source of carbon is a carbon-rich furnace atmosphere produced either from gaseous hydrocarbons or from vaporized hydrocarbon liquids. Using theoretical steps with anticipated process variable inputs allows for the prediction of the carbon available to the steel surface; diffusion can be simulated. Inputs captured during a real-time run can predict the carbon buildup in a part. The simulation and real-time data can be matched up to compare metallurgical results. We will cover principles of atmosphere carburizing, including sensor and control technology. We will discuss the analysis of input variables associated with carburizing applications and understanding the effects the atmosphere, temperature and time have on results. We will look at information using three-gas analysis as opposed to analysis using oxygen probes and review what an atmosphere would look like during a carburizing run. We will review real world scenarios with actual data that allow for a comparison of simulation versus calculated carbon transfer and diffusion against metallurgical lab results.
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Ping, Xu, Rich Burton, and Colin Sargent. "Experimental Implementation of a Neural Simulator." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-1277.
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Abstract This study investigates the use of neural networks to identify transient responses of a nonlinear system, with a long term objective of developing neural simulators of actual systems that are to be incorporated into simulation packages. The study focuses on the experimental work of modeling a hydraulic had sensing pump using an established neural net approach. An experimental system is designed and constructed with particular attention to the design and generation of sufficiently rich input signals. The data sampled at an appropriately chosen sampling rate from the testing of the load sensing pump dynamics are used to train and validate the neural models. The study carefully examines the problems and constraints (not revealed by theoretical or simulation studies) imposed by the actual experimental system The modeling results with two neural model configurations are presented in this paper and establishes, from a practical point of view, the potential of the neural network approach to modeling a real hydraulic component. In addition, it is shown that the training accuracy and the error accumulation are the two most critical factors in examining and interpreting the overall modeling accuracy.
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Mintz, Todd S., Hipolito J. Gonzalez, and Yi-Ming Pan. "An Examination of the Chromium-Rich Passive Film’s Formation on Alloy 22 in Mildly Alkaline Environments." In CORROSION 2008. NACE International, 2008. https://doi.org/10.5006/c2008-08585.
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Abstract The passivity of Alloy 22 is attributed to the presence of a chromium-rich oxide film. However, in some alkaline environments, such as simulated concentrated water, a chromium-rich passive film has not been observed. It is believed that the chromium-rich passive film should be present because of the measured low corrosion rates. A silica/carbon contamination layer noted on the surface of Alloy 22 (UNS N06022) could also contribute to the observed low corrosion rates. A thermodynamic analysis was conducted to analyze the formation of the chromium-rich passive film. Solution compositions examined by other investigators where chromium oxides were not detected have been evaluated with commercial software to develop potential-pH diagrams. The thermodynamic data indicate that a chromium-nickel oxide (NiCr2O4) is thermodynamically stable and may be a likely contributor to the observed low corrosion rates. Failure to observe the film is probably due to it being masked by a thick outer layer on the surface in combination with the dimensions of the film.
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Ota, Masayo, Huy Vo, Claudio Silva, and Juliana Freire. "A scalable approach for data-driven taxi ride-sharing simulation." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363837.
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Zhang, Lu, and Zenghui Ma. "The Rise of Big Data and Land Engineering Disciplines." In 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/cmsa-18.2018.68.
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Guo, Jianmao, Yanghua Gao, Junwei Liu, Dunyue Fei, and Qian Wang. "Simulation of regional rice growth by combination remote sensing data and crop model." In SPIE Asia-Pacific Remote Sensing, edited by Thomas J. Jackson, Jing Ming Chen, Peng Gong, and Shunlin Liang. SPIE, 2014. http://dx.doi.org/10.1117/12.2068302.
Full textReports on the topic "Rice Data Simulator"
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Wilson, D., Vladimir Ostashev, and Max Krackow. Phase-modulated Rice model for statistical distributions of complex signals. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47379.
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The basic Rice model is commonly used to describe complex signal statistics from randomly scattered waves. It correctly describes weak (Born) scattering, as well as fully saturated scattering, and smoothly interpolates between these extremes. However, the basic Rice model is unsuitable for situations involving scattering by random inhomogeneities spanning a broad range of spatial scales, as commonly occurs for sound scattering by turbulence in the atmospheric boundary layer and other scenarios. In such scenarios, the phase variations are often considerably stronger than those predicted by the basic Rice model. Therefore, the basic Rice model is extended to include a random modulation in the signal phase, which is attributable to the influence of the largest, most energetic inhomogeneities in the propagation medium. Various joint and marginal distributions for the complex signal statistics are derived to incorporate the phase-modulation effect. Approximations of the phase-modulated Rice model involving the Nakagami distribution for amplitude, and the wrapped normal and von Mises distributions for phase, are also developed and analyzed. The phase-modulated Rice model and various approximations are shown to greatly improve agreement with simulated data for sound propagation in the near-ground atmosphere.
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Furman, Burford, Laxmi Ramasubramanian, Shannon McDonald, et al. Solar-Powered Automated Transportation: Feasibility and Visualization. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1948.
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A solar-powered automated transportation network (ATN) connecting the North and South campuses of San José State University with three passenger stations was designed, visualized, and analyzed in terms of its energy usage, carbon offset, and cost. The study’s methodology included the use of tools and software such as ArcGIS, SketchUp, Infraworks, Sketchup, Rhinoceros, and Autodesk 3DS Max. ATN vehicle energy usage was estimated using data from the university’s Park & Ride shuttle bus operation and by modeling with SUMOPy, the advanced simulation suite for the micro-traffic simulator SUMO. The energy study showed that an extensive solar photovoltaic (PV) canopy over the guideway and stations is sufficient for the network to run 24/7 in better-than-zero net-metered conditions—even if ridership were to increase 15% above that predicted from SJSU Park & Ride shuttle data. The resulting energy system has a PV-rated output of 6.2 MW, a battery system capacity of 9.8 MWh, and an estimated cost of $11.4 million USD. The solar ATN also produces 98% lower CO2 and PM2.5 emissions compared to the Park & Ride shuttle bus. A team of experts including urban planners, architects, and engineers designed and visualized the conceptual prototype, including a comprehensive video explaining the need for solar ATN and what a typical rider would experience while utilizing the system. This research demonstrates both benefits and challenges for solar-powered ATN, as well as its functionality within the urban built environment to serve diverse San José neighborhoods.
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Ginis, Isaac, Deborah Crowley, Peter Stempel, and Amanda Babson. The impact of sea level rise during nor?easters in New England: Acadia National Park, Boston Harbor Islands, Boston National Historical Park, and Cape Cod National Seashore. National Park Service, 2024. http://dx.doi.org/10.36967/2304306.
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This study examines the potential impact of sea level rise (SLR) caused by climate change on the effects of extratropical cyclones, also known as nor?easters, in four New England coastal parks: Acadia National Park (ACAD), Boston Harbor Islands National Recreation Area (BOHA), Boston National Historical Park (BOST) and Cape Cod National Seashore (CACO). A multi-method approach is employed, including a literature review, observational data analysis, coupled hydrodynamic-wave numerical modeling, 3D visualizations, and communication of findings. The literature review examines previous studies of nor?easters and associated storm surges in New England and SLR projections across the study domain due to climate change. The observational data analysis evaluates the characteristics of nor?easters and their effects, providing a basis for validating the model. Numerical modeling is performed using the Advanced Circulation (ADCIRC) model, coupled with the Simulating Waves in the Nearshore (SWAN) model to simulate storm surges and waves. The model was validated against available observations and demonstrated its ability to simulate water levels, inland inundation, and wave heights in the study area with high accuracy. The validated model was used to simulate three powerful nor?easters (April 2007, January 2018, and March 2018) and each storm was simulated for three sea levels, (1) a baseline mean sea level representative of the year 2020, as well as with a (2) 1 ft of SLR and (3) 1 m of SLR. Analysis of the model output was used to assess the vulnerability of the parks to nor?easters by examining peak impacts in the park areas. Additional simulations were conducted to evaluate the role of waves in predicting peak water levels and the impact of inlet configurations on storm surges within coastal embayments behind the barrier beach systems in the southern Cape Cod region. The project developed maps, three-dimensional visualizations, and an interpretive film to assist the parks in planning for resource management, maintenance, emergency management, visitor access, safety, education, and outreach. These tools provide a better understanding of the potential impacts of nor?easters and SLR and enable the parks to better prepare for future storms.
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Shukla, Indu, Rajeev Agrawal, Kelly Ervin, and Jonathan Boone. AI on digital twin of facility captured by reality scans. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47850.
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The power of artificial intelligence (AI) coupled with optimization algorithms can be linked to data-rich digital twin models to perform predictive analysis to make better informed decisions about installation operations and quality of life for the warfighters. In the current research, we developed AI connected lifecycle building information models through the creation of a data informed smart digital twin of one of US Army Corps of Engineers (USACE) buildings as our test case. Digital twin (DT) technology involves creating a virtual representation of a physical entity. Digital twin is created by digitalizing data collected through sensors, powered by machine learning (ML) algorithms, and are continuously learning systems. The exponential advance in digital technologies enables facility spaces to be fully and richly modeled in three dimensions and can be brought together in virtual space. Coupled with advancement in reinforcement learning and computer graphics enables AI agents to learn visual navigation and interaction with objects. We have used Habitat AI 2.0 to train an embodied agent in immersive 3D photorealistic environment. The embodied agent interacts with a 3D environment by receiving RGB, depth and semantically segmented views of the environment and taking navigational actions and interacts with the objects in the 3D space. Instead of training the robots in physical world we are training embodied agents in simulated 3D space. While humans are superior at critical thinking, creativity, and managing people, whereas robots are superior at coping with harsh environments and performing highly repetitive work. Training robots in controlled simulated world is faster and can increase their surveillance, reliability, efficiency, and survivability in physical space.
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Coster, Pierre. Firms’ Supply Chain Adaptation to Carbon Taxes. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1136.
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This paper investigates how firms adapt their sourcing of clean and dirty inputs in response to changes in climate policy. We use information from the European Union’s Emissions Trading System (EU ETS) and the Carbon Border Adjustment Mechanism (CBAM) to create a new classification of clean and dirty products based on whether they are subject to a domestic or a border carbon tax. We then combine this dataset with French firms’ product-level import data over 2000–2019 and estimate that firms’ propensity to import dirty inputs from non-EU countries increased in the 2010s, reflecting carbon leakage. A heterogeneous firm model is then used to quantify the impact of changes in firms’ sourcing of clean and dirty inputs given the implementation of a carbon tax and a carbon tariff. The simulated ETS carbon tax scenario is able to match leakage observed in the data and leads to a higher price level and a modest decline in emissions. The scenario that further includes the CBAM carbon tariff reverses carbon leakage at the cost of an additional rise in prices. Overall, household welfare declines because the higher costs associated with the carbon policies outweigh the benefits of reduced emissions.
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Perdigão, Rui A. P., and Julia Hall. Empowering Next-Generation Synergies among Models and Data with Information Physical Quantum Technological Intelligence. Synergistic Manifolds, 2024. https://doi.org/10.46337/241209.
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We present and deploy our methodological and technological framework of Information Physical Quantum Technological Intelligence (IPQuTI), to empower next-generation mathematically robust, physically consistent, computationally efficient and operationally scalable synergies among models and data across multisectoral theoretical and applied workflows. Going beyond digital computing platforms, IPQuTI encompasses a richer basis alphabet of fundamental quantum states (information building blocks) and a high-order set of superposition and entanglement functionals (grammar) beyond the state of the art in quantum information itself. These are foundational core underneath our increased ability to encode, analyze, generate and simulate a broader physical language: one that is able to seamlessly treat complex high-dimensional data and model structures, interactions and operations with faster computational speed, higher robustness, physical consistency, information fidelity, spatiotemporal resolution and lead. For example, in the setting of ensemble operations, IPQuTI encapsulates an entire spatiotemporal system of events into a block operation as a coherent universe. Turning lengthy intense computational churning of approximate equations and massive datasets into a post-quantum spatiotemporal pulse of rich spatiotemporal diversity, spanning deterministic and stochastic synergies among models and data into a unified solution. With nonlinear geophysical applications in mind, IPQuTI is explored in key contexts of Data Assimilation, Data Fusion, Machine Learning, Predictability Investigation and Uncertainty Quantification. Firstly, to further optimize gold-standard state-of-art (SoA) solutions, providing them with a new efficient and robust platform to operate. Second, to overcome known SoA challenges, thereby contributing towards their methodological and practical upgrade. Third, to unveil novel features adding methodological and applied value to these areas, including handling sparse records, perturbations and extremes.
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Luo, Hao, Ricardo Chahine, Arianna Rambaram, Elizabeth Theresa Rosenzweig, Konstantina Gkritza, and Hua Cai. Assessing the Travel Demand and Mobility Impacts of Transformative Transportation Technologies in Indiana. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317374.
Full textAbstract:
The rapid development of transformative transportation technologies, such as bike-sharing, shared e-scooters, and ride-hailing systems, is reshaping the transportation landscape. These transformative transportation technologies have the potential to significantly change travel behavior and travel demand and affect transportation agencies’ planning, operations, and decision-making. The objective of this project is to develop a framework and models to quantify the potential travel demand and mobility impacts of transformative transportation technologies in Indiana cities. This project analyzed historical system usage data and conducted survey studies to evaluate the availability and use of transformative transportation technologies in select Indiana cities. The project also proposed a data-driven model to study the relationship between shared micro-mobility and the existing transit system and developed a simulation model to analyze the potential mode choice change under different future development scenarios. Additionally, based on a comprehensive literature review, a list of operations; environmental, health and safety; and accessibility and equity metrics were identified as the Key Performance Indicators to evaluate transformative transportation technologies. Furthermore, as this study was conducted in the midst of the COVID-19 pandemic, the impacts of the pandemic on both traditional and transformative transportation systems were also examined as documented in the literature and stated in our survey.
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Ferreyra, Maria Marta, Carlos Garriga, Juan D. Martin-Ocampo, and Angélica María Sánchez Díaz. Raising College Access and Completion: How Much Can Free College Help? Banco de la República de Colombia, 2021. http://dx.doi.org/10.32468/be.1155.
Full textAbstract:
Free college proposals have become increasingly popular in many countries of the world. To evaluate their potential effects, we develop and estimate a dynamic model of college enrollment, performance, and graduation. A central piece of the model, student effort, has a direct effect on class completion, and an indirect effect in mitigating the risk of not completing a class or not remaining in college. We estimate the model using rich, student-level administrative data from Colombia, and use the estimates to simulate free college programs that differ in eligibility requirements. Among these, universal free college expands enrollment the most, but it does not affect graduation rates and has the highest per-graduate cost. Performance-based free college, in contrast, delivers a slightly lower enrollment expansion yet a greater graduation rate at a lower per-graduate cost. Relative to universal free college, performance-based free college places a greater risk on students but is precisely this feature that delivers better outcomes. Nonetheless, the modest increase in graduation rates suggests that additional, complementary policies might be required to elicit the large effort increase needed to raise graduation rates.
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Stebbing, Nicola, Claire Witham, Frances Beckett, Helen Webster, Lois Huggett, and David Thomson. © Crown copyright 2024, Met Office Page 1 of 43 Can we improve plume dispersal modelling for fire related emergency response operations by utilising short-range dispersion schemes? Met Office, 2024. http://dx.doi.org/10.62998/wnnr5415.
Full textAbstract:
Large fires that produce plumes of smoke and other contaminants can cause harm to both people and the environment. To support UK emergency responders, the Met Office Environmental Monitoring and Response Centre (EMARC) provides dedicated weather advice and forecasts of the plume in the form of CHEmical METeorological (CHEMET) reports. The plume’s expected location, extent and relative air concentrations of pollutants are predicted using the Numerical Atmospheric-dispersion Modelling Environment (NAME), which simulates the transport and dispersion of pollutants using numerical weather prediction data. During major fires, air quality monitoring equipment is deployed to confirm the presence of elevated concentrations of contaminants. We use ground-level air concentration measurements from multiple events to evaluate the operational set-up of NAME. We investigate both the output averaging depth used to calculate air concentrations and the use of three optional NAME schemes that are designed to improve the representation of short-range dispersal dynamics: the near-source scheme, the plume-rise scheme, and the urban scheme. We find that using the current operational output averaging depth of 100 m produces model air concentrations that compare best to point observations at the surface, and that using the near-source and urban schemes further improves the fit. However, using these more computationally expensive schemes has little impact on the modelled location and extent of the plume, suggesting they may offer no advantage over using the current operational set-up to produce CHEMETs. Using the plume-rise scheme strongly influences the predicted plume location, extent and surface concentrations. Further work is needed to understand whether its application is appropriate for simulating plumes from fires. We conclude that the current operational set-up can be maintained while the significance of the impact the optional schemes have on CHEMET plume dispersal forecasts is considered further.
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Magdalinos, Tassos, and Katerina Petrova. Uniform Inference with General Autoregressive Processes. Federal Reserve Bank of New York, 2025. https://doi.org/10.59576/sr.1151.
Full textAbstract:
A unified theory of estimation and inference is developed for an autoregressive process with root in (-∞, ∞) that includes the stationary, local-to-unity, explosive and all intermediate regions. The discontinuity of the limit distribution of the t-statistic outside the stationary region and its dependence on the distribution of the innovations in the explosive regions (-∞, -1) ∪ (1, ∞) are addressed simultaneously. A novel estimation procedure, based on a data-driven combination of a near-stationary and a mildly explosive artificially constructed instrument, delivers mixed-Gaussian limit theory and gives rise to an asymptotically standard normal t-statistic across all autoregressive regions. The resulting hypothesis tests and confidence intervals are shown to have correct asymptotic size (uniformly over the space of autoregressive parameters and the space of innovation distribution functions) in autoregressive, predictive regression and local projection models, thereby establishing a general and unified framework for inference with autoregressive processes. Extensive Monte Carlo simulation shows that the proposed methodology exhibits very good finite sample properties over the entire autoregressive parameter space (-∞, ∞) and compares favorably to existing methods within their parametric (-1, 1] validity range. We demonstrate how our procedure can be used to construct valid confidence intervals in standard epidemiological models as well as to test in real-time for speculative bubbles in the price of the Magnificent Seven tech stocks.