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Journal articles on the topic 'Physical Predictions'
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Nijland, Rinske H. M., Erwin E. H. van Wegen, Barbara C. Harmeling-van der Wel, and Gert Kwakkel. "Accuracy of Physical Therapists' Early Predictions of Upper-Limb Function in Hospital Stroke Units: The EPOS Study." Physical Therapy 93, no. 4 (April 1, 2013): 460–69. http://dx.doi.org/10.2522/ptj.20120112.
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Background Early prediction of outcome after stroke is becoming increasingly important, as most patients are discharged from hospital stroke units within several days after stroke. Objectives The primary purposes of this study were: (1) to determine the accuracy of physical therapists' predictions at hospital stroke units regarding upper-limb (UL) function, (2) to develop a computational prediction model (CPM), and (3) to compare the accuracy of physical therapists' and the CPM's predictions. Secondary objectives were to explore the impact of timing on the accuracy of the physical therapists' and CPM's predictions and to investigate the direction of the difference between predicted and observed outcomes. Finally, this study investigated whether the accuracy of physical therapists' predictions was affected by their experience in stroke rehabilitation. Design A prospective cohort study was conducted. Methods Physical therapists made predictions at 2 time points—within 72 hours after stroke onset (T72h) and at discharge from the hospital stroke unit (Tdischarge)—about UL function after 6 months in 3 categories, derived from the Action Research Arm Test. At the same time, clinical variables were measured to derive a CPM. The accuracy of the physical therapists' and CPM's predictions was evaluated by calculating Spearman rank correlation coefficients (rs) between predicted and observed outcomes. Results One hundred thirty-one patients and 20 physical therapists participated in the study. For the T72h assessment, the rs value between predicted and observed outcomes was .63 for the physical therapists' predictions and .75 for the CPM's predictions. For the Tdischarge assessment, the rs value for the physical therapists' predictions improved to .75, and the rs value for the CPM's predictions improved slightly to .76. Limitations Physical therapists administered a test battery every 3 days, which may have enhanced the accuracy of prediction. Conclusions The accuracy of the physical therapists' predictions at T72h was lower than that of the CPM's predictions. At Tdischarge, the physical therapists' and CPM's predictions are about equally accurate.
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Zhao, Bo. "Studying on the Fiber Diameter of Polypropylene (PP) Spunbonding Fabric by Means of Artificial Neural Network Model and Physical Model." Key Engineering Materials 426-427 (January 2010): 356–60. http://dx.doi.org/10.4028/www.scientific.net/kem.426-427.356.
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In this work, the artificial neural network model and physical model are established and utilized for predicting the fiber diameter of polypropylene(PP) spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the physical model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.
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Hafri, Alon, Michael Bonner, and Chaz Firestone. "Visual predictions from physical relations." Journal of Vision 20, no. 11 (October 20, 2020): 1615. http://dx.doi.org/10.1167/jov.20.11.1615.
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Hou, Yuanyuan, Shiyu Wang, Bing Bai, H. C. Stephen Chan, and Shuguang Yuan. "Accurate Physical Property Predictions via Deep Learning." Molecules 27, no. 5 (March 3, 2022): 1668. http://dx.doi.org/10.3390/molecules27051668.
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Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further. Here, we proposed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. It is based on data augmentation and SMILES tokenization technology without relying on auxiliary knowledge, such as complex spatial structure. In addition, our model takes the advantages of the long- and short-term memory network (LSTM) in sequence processing. The embedded channel and spatial attention modules in turn specifically identify the prime factors in the SMILES sequence for predicting properties. The model was further improved by Bayesian optimization. In this work, we demonstrate that the trained BSCA model is capable of predicting aqueous solubility. Furthermore, our proposed method shows noticeable superiorities and competitiveness in predicting oil–water partition coefficient, when compared with state-of-the-art graphs models, including graph convoluted network (GCN), message-passing neural network (MPNN), and AttentiveFP.
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Bianco, Valentina, Alessandra Finisguerra, Sonia Betti, Giulia D’Argenio, and Cosimo Urgesi. "Autistic Traits Differently Account for Context-Based Predictions of Physical and Social Events." Brain Sciences 10, no. 7 (July 1, 2020): 418. http://dx.doi.org/10.3390/brainsci10070418.
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Autism is associated with difficulties in making predictions based on contextual cues. Here, we investigated whether the distribution of autistic traits in the general population, as measured through the Autistic Quotient (AQ), is associated with alterations of context-based predictions of social and non-social stimuli. Seventy-eight healthy participants performed a social task, requiring the prediction of the unfolding of an action as interpersonal (e.g., to give) or individual (e.g., to eat), and a non-social task, requiring the prediction of the appearance of a moving shape as a short (e.g., square) or a long (e.g., rectangle) figure. Both tasks consisted of (i) a familiarization phase, in which the association between each stimulus type and a contextual cue was manipulated with different probabilities of co-occurrence, and (ii) a testing phase, in which visual information was impoverished by early occlusion of video display, thus forcing participants to rely on previously learned context-based associations. Findings showed that the prediction of both social and non-social stimuli was facilitated when embedded in high-probability contexts. However, only the contextual modulation of non-social predictions was reduced in individuals with lower ‘Attention switching’ abilities. The results provide evidence for an association between weaker context-based expectations of non-social events and higher autistic traits.
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Bock, Frederic E., Sören Keller, Norbert Huber, and Benjamin Klusemann. "Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions." Materials 14, no. 8 (April 10, 2021): 1883. http://dx.doi.org/10.3390/ma14081883.
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Within the fields of materials mechanics, the consideration of physical laws in machine learning predictions besides the use of data can enable low prediction errors and robustness as opposed to predictions only based on data. On the one hand, exclusive utilization of fundamental physical relationships might show significant deviations in their predictions compared to reality, due to simplifications and assumptions. On the other hand, using only data and neglecting well-established physical laws can create the need for unreasonably large data sets that are required to exhibit low bias and are usually expensive to collect. However, fundamental but simplified physics in combination with a corrective model that compensates for possible deviations, e.g., to experimental data, can lead to physics-based predictions with low prediction errors, also despite scarce data. In this article, it is demonstrated that a hybrid model approach consisting of a physics-based model that is corrected via an artificial neural network represents an efficient prediction tool as opposed to a purely data-driven model. In particular, a semi-analytical model serves as an efficient low-fidelity model with noticeable prediction errors outside its calibration domain. An artificial neural network is used to correct the semi-analytical solution towards a desired reference solution provided by high-fidelity finite element simulations, while the efficiency of the semi-analytical model is maintained and the applicability range enhanced. We utilize residual stresses that are induced by laser shock peening as a use-case example. In addition, it is shown that non-unique relationships between model inputs and outputs lead to high prediction errors and the identification of salient input features via dimensionality analysis is highly beneficial to achieve low prediction errors. In a generalization task, predictions are also outside the process parameter space of the training region while remaining in the trained range of corrections. The corrective model predictions show substantially smaller errors than purely data-driven model predictions, which illustrates one of the benefits of the hybrid modelling approach. Ultimately, when the amount of samples in the data set is reduced, the generalization of the physics-related corrective model outperforms the purely data-driven model, which also demonstrates efficient applicability of the proposed hybrid modelling approach to problems where data is scarce.
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Bratholm, Lars A., Will Gerrard, Brandon Anderson, Shaojie Bai, Sunghwan Choi, Lam Dang, Pavel Hanchar, et al. "A community-powered search of machine learning strategy space to find NMR property prediction models." PLOS ONE 16, no. 7 (July 20, 2021): e0253612. http://dx.doi.org/10.1371/journal.pone.0253612.
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The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.
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Chen, Zhewu, Zhanda Huang, Yong Guo, and Guibing Li. "Prediction of Mechanical Properties of Thin-Walled Bar with Open Cross-Section under Restrained Torsion." Coatings 12, no. 5 (April 21, 2022): 562. http://dx.doi.org/10.3390/coatings12050562.
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Thin-walled bars with an open cross-section are widely used in mechanical structures where weight and size control are particularly required. Thus, this paper attempts to propose a theoretical model for predicting the mechanical properties of a thin-walled bar with an open cross-section under restrained torsion. Firstly, a theoretical model with predictions of shear stress, buckling normal stress, and secondary shear stress of the thin-walled bar with open cross-section under the condition of restrained torsion was developed based on torsion theory. Then, physical test and finite element modeling data were employed to validate the theoretical predictions. The results indicate that the theoretical predictions show good agreements with data of finite element modeling and experiments. Therefore, the proposed theoretical model could be used for the prediction of the mechanical response of a thin-walled bar with an open annular section under restrained torsion.
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Oh, Kyoungcheol, Eui-Jong Kim, and Chang-Young Park. "A Physical Model-Based Data-Driven Approach to Overcome Data Scarcity and Predict Building Energy Consumption." Sustainability 14, no. 15 (August 2, 2022): 9464. http://dx.doi.org/10.3390/su14159464.
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Predicting building energy consumption needs to be anticipated to save building energy and effectively control the predictions. This study depicted the target building as a physical model to improve the learning performance in a data-scarce environment and proposed a model that uses simulation results as the input for a data-driven model. Case studies were conducted with different quantities of data. The proposed hybrid method proposed in this study showed a higher prediction accuracy showing a cvRMSE of 22.8% and an MAE of 6.1% than using the conventional data-driven method and satisfying the tolerance criteria of ASHRAE Guideline 14 in all the test cases.
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Feig, Michael, and Charles L. Brooks. "Evaluating CASP4 predictions with physical energy functions." Proteins: Structure, Function, and Genetics 49, no. 2 (September 3, 2002): 232–45. http://dx.doi.org/10.1002/prot.10217.
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PALOPOLI, LUIGI, and GIORGIO TERRACINA. "CooPPS: A SYSTEM FOR THE COOPERATIVE PREDICTION OF PROTEIN STRUCTURES." Journal of Bioinformatics and Computational Biology 02, no. 03 (September 2004): 471–95. http://dx.doi.org/10.1142/s0219720004000697.
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Predicting the three-dimensional structure of proteins is a difficult task. In the last few years several approaches have been proposed for performing this task taking into account different protein chemical and physical properties. As a result, a growing number of protein structure prediction tools is becoming available, some of them specialized to work on either some aspects of the predictions or on some categories of proteins; however, they are still not sufficiently accurate and reliable for predicting all kinds of proteins. In this context, it is useful to jointly apply different prediction tools and combine their results in order to improve the quality of the predictions. However, several problems have to be solved in order to make this a viable possibility. In this paper a framework and a tool is proposed which allows: (i) definition of a common reference applicative domain for different prediction tools; (ii) characterization of prediction tools through evaluating some quality parameters; (iii) characterization of the performances of a team of predictors jointly applied over a prediction problem; (iv) the singling out of the best team for a prediction problem; and (v) the integration of predictor results in the team in order to obtain a unique prediction. A system implementing the various steps of the proposed framework (CooPPS) has been developed and several experiments for testing the effectiveness of the proposed approach have been carried out.
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Shin, Ju-Young, Bu-Yo Kim, Junsang Park, Kyu Rang Kim, and Joo Wan Cha. "Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms." Remote Sensing 12, no. 18 (September 19, 2020): 3076. http://dx.doi.org/10.3390/rs12183076.
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Leaf wetness duration (LWD) and plant diseases are strongly associated with each other. Therefore, LWD is a critical ecological variable for plant disease risk assessment. However, LWD is rarely used in the analysis of plant disease epidemiology and risk assessment because it is a non-standard meteorological variable. The application of satellite observations may facilitate the prediction of LWD as they may represent important related parameters and are particularly useful for meteorologically ungauged locations. In this study, the applicability of geostationary satellite observations for LWD prediction was investigated. GEO-KOMPSAT-2A satellite observations were used as inputs and six machine learning (ML) algorithms were employed to arrive at hourly LW predictions. The performances of these models were compared with that of a physical model through systematic evaluation. Results indicated that the LWD could be predicted using satellite observations and ML. A random forest model exhibited larger accuracy (0.82) than that of the physical model (0.79) in leaf wetness prediction. The performance of the proposed approach was comparable to that of the physical model in predicting LWD. Overall, the artificial intelligence (AI) models exhibited good performances in predicting LWD in South Korea.
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Shankar, Karthik H., Inder Singh, and Marc W. Howard. "Neural Mechanism to Simulate a Scale-Invariant Future." Neural Computation 28, no. 12 (December 2016): 2594–627. http://dx.doi.org/10.1162/neco_a_00891.
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Predicting the timing and order of future events is an essential feature of cognition in higher life forms. We propose a neural mechanism to nondestructively translate the current state of spatiotemporal memory into the future, so as to construct an ordered set of future predictions almost instantaneously. We hypothesize that within each cycle of hippocampal theta oscillations, the memory state is swept through a range of translations to yield an ordered set of future predictions through modulations in synaptic connections. Theoretically, we operationalize critical neurobiological findings from hippocampal physiology in terms of neural network equations representing spatiotemporal memory. Combined with constraints based on physical principles requiring scale invariance and coherence in translation across memory nodes, the proposition results in Weber-Fechner spacing for the representation of both past (memory) and future (prediction) timelines. We show that the phenomenon of phase precession of neurons in the hippocampus and ventral striatum correspond to the cognitive act of future prediction.
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Burke, Amanda, Nathan Snook, David John Gagne II, Sarah McCorkle, and Amy McGovern. "Calibration of Machine Learning–Based Probabilistic Hail Predictions for Operational Forecasting." Weather and Forecasting 35, no. 1 (January 23, 2020): 149–68. http://dx.doi.org/10.1175/waf-d-19-0105.1.
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Abstract In this study, we use machine learning (ML) to improve hail prediction by postprocessing numerical weather prediction (NWP) data from the new High-Resolution Ensemble Forecast system, version 2 (HREFv2). Multiple operational models and ensembles currently predict hail, however ML models are more computationally efficient and do not require the physical assumptions associated with explicit predictions. Calibrating the ML-based predictions toward familiar forecaster output allows for a combination of higher skill associated with ML models and increased forecaster trust in the output. The observational dataset used to train and verify the random forest model is the Maximum Estimated Size of Hail (MESH), a Multi-Radar Multi-Sensor (MRMS) product. To build trust in the predictions, the ML-based hail predictions are calibrated using isotonic regression. The target datasets for isotonic regression include the local storm reports and Storm Prediction Center (SPC) practically perfect data. Verification of the ML predictions indicates that the probability magnitudes output from the calibrated models closely resemble the day-1 SPC outlook and practically perfect data. The ML model calibrated toward the local storm reports exhibited better or similar skill to the uncalibrated predictions, while decreasing model bias. Increases in reliability and skill after calibration may increase forecaster trust in the automated hail predictions.
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Field, Edward H., and Klaus H. Jacob. "Monte-Carlo Simulation of the Theoretical Site Response Variability at Turkey Flat, California, Given the Uncertainty in the Geotechnically Derived Input Parameters." Earthquake Spectra 9, no. 4 (November 1993): 669–701. http://dx.doi.org/10.1193/1.1585736.
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In the weak-motion phase of the Turkey Flat blind-prediction effort, it was found that given a particular physical model of each sediment site, various theoretical techniques give similar estimates of the site response. However, it remained to be determined how uncertainties in the physical model parameters influence the theoretical predictions. We have studied this question by propagating the physical parameter uncertainties into the theoretical site-response predictions using monte-carlo simulations. The input-parameter uncertainties were estimated directly from the results of several independent geotechnical studies performed at Turkey Flat. While the computed results generally agree with empirical site-response estimates (average spectral ratios of earthquake recordings), we found that the uncertainties lead to a high degree of variability in the theoretical predictions. Most of this variability comes from poor constraints on the shear-wave velocity and thickness of a thin (∼2m) surface layer, and on the attenuation of the sediments. Our results suggest that in site-response studies which rely exclusively on geotechnically based theoretical predictions, it will be important that the variability resulting from input-parameter uncertainties is recognized and accounted for.
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Nooteboom, Peter D., Qing Yi Feng, Cristóbal López, Emilio Hernández-García, and Henk A. Dijkstra. "Using network theory and machine learning to predict El Niño." Earth System Dynamics 9, no. 3 (July 23, 2018): 969–83. http://dx.doi.org/10.5194/esd-9-969-2018.
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Abstract. The skill of current predictions of the warm phase of the El Niño Southern Oscillation (ENSO) reduces significantly beyond a lag time of 6 months. In this paper, we aim to increase this prediction skill at lag times of up to 1 year. The new method combines a classical autoregressive integrated moving average technique with a modern machine learning approach (through an artificial neural network). The attributes in such a neural network are derived from knowledge of physical processes and topological properties of climate networks, and they are tested using a Zebiak–Cane-type model and observations. For predictions up to 6 months ahead, the results of the hybrid model give a slightly better skill than the CFSv2 ensemble prediction by the National Centers for Environmental Prediction (NCEP). Interestingly, results for a 12-month lead time prediction have a similar skill as the shorter lead time predictions.
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Gustavsson, Linda, Jörgen I. Johnsson, and Tobias Uller. "Mixed Support for Sexual Selection Theories of Mate Preferences in the Swedish Population." Evolutionary Psychology 6, no. 4 (October 1, 2008): 147470490800600. http://dx.doi.org/10.1177/147470490800600404.
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Evolutionary theory predicts the existence of relatively stable sex differences in partner preferences with, for example, males being more concerned with traits predicting high fertility and females with traits predicting high resource availability. We tested three predictions using personal advertisements from both traditional newspapers and internet dating services. In accordance with predictions, men offered resources more often than did women, and women requested resources more often than did men. Males in all age-categories preferred younger partners. Young females preferred older males, but the pattern was reversed for the majority of females past their fertile period. In contrast to predictions, there was no difference between males and females in the degree to which they offered, or asked for, physical attractiveness. Based on our results and a review of previous studies, we suggest that sex differences in factual or advertised preference for physical attractiveness may be more labile than sex differences in preference for resources and status across societies.
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Coquerel, G. "Thermodynamic Predictions of Physical Properties – Prediction of Solid Solutions in Molecular Solutes Exhibiting Polymorphism." Chemical Engineering & Technology 29, no. 2 (February 2006): 182–86. http://dx.doi.org/10.1002/ceat.200500377.
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Saha, Koustuv, Ted Grover, Stephen M. Mattingly, Vedant Das swain, Pranshu Gupta, Gonzalo J. Martinez, Pablo Robles-Granda, Gloria Mark, Aaron Striegel, and Munmun De Choudhury. "Person-Centered Predictions of Psychological Constructs with Social Media Contextualized by Multimodal Sensing." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (March 19, 2021): 1–32. http://dx.doi.org/10.1145/3448117.
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Personalized predictions have shown promises in various disciplines but they are fundamentally constrained in their ability to generalize across individuals. These models are often trained on limited datasets which do not represent the fluidity of human functioning. In contrast, generalized models capture normative behaviors between individuals but lack precision in predicting individual outcomes. This paper aims to balance the tradeoff between one-for-each and one-for-all models by clustering individuals on mutable behaviors and conducting cluster-specific predictions of psychological constructs in a multimodal sensing dataset of 754 individuals. Specifically, we situate our modeling on social media that has exhibited capability in inferring psychosocial attributes. We hypothesize that complementing social media data with offline sensor data can help to personalize and improve predictions. We cluster individuals on physical behaviors captured via Bluetooth, wearables, and smartphone sensors. We build contextualized models predicting psychological constructs trained on each cluster's social media data and compare their performance against generalized models trained on all individuals' data. The comparison reveals no difference in predicting affect and a decline in predicting cognitive ability, but an improvement in predicting personality, anxiety, and sleep quality. We construe that our approach improves predicting psychological constructs sharing theoretical associations with physical behavior. We also find how social media language associates with offline behavioral contextualization. Our work bears implications in understanding the nuanced strengths and weaknesses of personalized predictions, and how the effectiveness may vary by multiple factors. This work reveals the importance of taking a critical stance on evaluating the effectiveness before investing efforts in personalization.
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dos Santos, Marcos Alex, Quirijn de Jong van Lier, Jos C. van Dam, and Andre Herman Freire Bezerra. "Benchmarking test of empirical root water uptake models." Hydrology and Earth System Sciences 21, no. 1 (January 26, 2017): 473–93. http://dx.doi.org/10.5194/hess-21-473-2017.
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Abstract. Detailed physical models describing root water uptake (RWU) are an important tool for the prediction of RWU and crop transpiration, but the hydraulic parameters involved are hardly ever available, making them less attractive for many studies. Empirical models are more readily used because of their simplicity and the associated lower data requirements. The purpose of this study is to evaluate the capability of some empirical models to mimic the RWU distribution under varying environmental conditions predicted from numerical simulations with a detailed physical model. A review of some empirical models used as sub-models in ecohydrological models is presented, and alternative empirical RWU models are proposed. All these empirical models are analogous to the standard Feddes model, but differ in how RWU is partitioned over depth or how the transpiration reduction function is defined. The parameters of the empirical models are determined by inverse modelling of simulated depth-dependent RWU. The performance of the empirical models and their optimized empirical parameters depends on the scenario. The standard empirical Feddes model only performs well in scenarios with low root length density R, i.e. for scenarios with low RWU compensation. For medium and high R, the Feddes RWU model cannot mimic properly the root uptake dynamics as predicted by the physical model. The Jarvis RWU model in combination with the Feddes reduction function (JMf) only provides good predictions for low and medium R scenarios. For high R, it cannot mimic the uptake patterns predicted by the physical model. Incorporating a newly proposed reduction function into the Jarvis model improved RWU predictions. Regarding the ability of the models to predict plant transpiration, all models accounting for compensation show good performance. The Akaike information criterion (AIC) indicates that the Jarvis (2010) model (JMII), with no empirical parameters to be estimated, is the best model. The proposed models are better in predicting RWU patterns similar to the physical model. The statistical indices point to them as the best alternatives for mimicking RWU predictions of the physical model.
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Hall, E. J. "Aerodynamic modelling of multistage compressor flow fields Part 2: Modelling deterministic stresses." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 212, no. 2 (February 1, 1998): 91–107. http://dx.doi.org/10.1243/0954410981532162.
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The primary purpose of this study was to investigate improved numerical techniques for predicting flows through multistage compressors. The vehicle chosen for this study was the Pennsylvania State University Research Compressor (PSRC). The PSRC facility consists of a 3 1/2-stage axial flow compressor which shares design features which are consistent with embedded stages of modern gas turbine engine axial flow compressors. In Part 2 of this two-part paper, time-dependent predictions of rotor- stator-rotor aerodynamic interactions are employed to quantify the levels and distribution of deterministic stresses resulting from the average-passage flow field description. Details of the spanwise and blade-to- blade distributions of the velocity correlations are examined and compared with results based on physical deterministic flow structures such as blade wakes and clearance flows. The predicted ‘apparent’ wake profile decay resulting from the interaction of the wake through a downstream blade row is presented and compared with test data. This ‘apparent’ wake profile decay is employed to define a simplified model for deterministic stress correlations in a steady state flow field prediction scheme which retains the ‘mixing- plane’ methodology. Calculations based on this proposed model are described and predicted results are compared with both time-dependent predictions and test data. The resulting prediction strategy is computationally efficient and also contains sufficient physical realism to permit its use in design studies.
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Zhao, Bo. "Predicting the fiber diameter of spunbonding nonwovens fabrics by means of physical model, statistical method and artifical neural network theory." International Journal of Clothing Science and Technology 27, no. 2 (April 20, 2015): 262–71. http://dx.doi.org/10.1108/ijcst-01-2014-0015.
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Purpose – The purpose of this paper is to establish three modeling methods (physical model, statistical model, and artificial neural network (ANN) model) and use it to predict the fiber diameter of spunbonding nonwovens from the process parameters. Design/methodology/approach – The results show the physical model is based on the inherent physical principles, it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter. Findings – By analyzing the results of the physical model, the effects of process parameters on fiber diameter can be predicted. The ANN model has good approximation capability and fast convergence rate, it can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical model. Originality/value – The effects of process parameters on fiber diameter are also determined by the ANN model. Excellent agreement is obtained between these two modeling methods.
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Sih, Bryant L., and Charles H. Negus. "Physical Training Outcome Predictions With Biomechanics, Part I: Army Physical Fitness Test Modeling." Military Medicine 181, no. 5S (May 2016): 77–84. http://dx.doi.org/10.7205/milmed-d-15-00168.
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Torvi, D. A., and J. D. Dale. "A Finite Element Model of Skin Subjected to a Flash Fire." Journal of Biomechanical Engineering 116, no. 3 (August 1, 1994): 250–55. http://dx.doi.org/10.1115/1.2895727.
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A variable property, multiple layer finite element model was developed to predict skin temperatures and times to second and third degree burns under simulated flash fire conditions. A sensitivity study of burn predictions to variations in thermal physical properties of skin was undertaken using this model. It was found that variations in these properties over the ranges used in multiple layer skin models had minimal effects on second degree burn predictions, but large effects on third degree burn predictions. It was also found that the blood perfusion source term in Pennes’ bioheat transfer equation could be neglected in predicting second and third degree burns due to flash fires. The predictions from this model were also compared with those from the closed form solution of this equation, which has been used in the literature for making burn predictions from accidents similar to flash fires.
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Arnborg, T. "Performance predictions of scaled BiCMOS gates using physical simulation." IEEE Journal of Solid-State Circuits 27, no. 5 (May 1992): 754–60. http://dx.doi.org/10.1109/4.133162.
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WANG, M., and N. PAN. "Predictions of effective physical properties of complex multiphase materials." Materials Science and Engineering: R: Reports 63, no. 1 (December 20, 2008): 1–30. http://dx.doi.org/10.1016/j.mser.2008.07.001.
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Whitteker, J. H. "Physical optics and field-strength predictions for wireless systems." IEEE Journal on Selected Areas in Communications 20, no. 3 (April 2002): 515–22. http://dx.doi.org/10.1109/49.995510.
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Doebeli, Michael. "Evolutionary predictions from invariant physical measures of dynamic processes." Journal of Theoretical Biology 173, no. 4 (April 1995): 377–87. http://dx.doi.org/10.1006/jtbi.1995.0070.
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Bañares-Alcántara, René, Arthur W. Westerberg, and Michael D. Rychener. "Development of an expert system for physical property predictions." Computers & Chemical Engineering 9, no. 2 (January 1985): 127–42. http://dx.doi.org/10.1016/0098-1354(85)85003-1.
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Zhou, Yanlai, Shenglian Guo, Chong-Yu Xu, Hua Chen, Jiali Guo, and Kairong Lin. "Probabilistic prediction in ungauged basins (PUB) based on regional parameter estimation and Bayesian model averaging." Hydrology Research 47, no. 6 (January 5, 2016): 1087–103. http://dx.doi.org/10.2166/nh.2016.058.
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Predictions in ungauged basins (PUB) are widely considered to be one of the fundamentally challenging research topics in the hydrological sciences. This paper couples a regional parameter transfer module with a probabilistic prediction module in order to obtain probabilistic PUB. Steps in the proposed probabilistic PUB include: (1) variable infiltration capacity-three layers (VIC-3L) model description; (2) three regional parameter transfer schemes for ungauged basins, i.e., regression analysis, spatial proximity, and physical similarity; (3) probabilistic PUB using Bayesian model averaging (BMA); and (4) performance evaluation for probabilistic PUB. The study is performed on 12 sub-basins in the Hanjiang River basin, China. The results demonstrate that the mean prediction of BMA is much closer to the observed data compared with its associated individual parameter transfer scheme (physical similarity approach), and the probabilistic predictions of BMA can effectively reduce the uncertainty in runoff PUB better than any associated individual parameter transfer schemes for two ungauged sub-basins.
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Landgraff, Nancy C., Susan L. Whitney, Diane Wrisley, and Jamie Berlin. "Physical Therapist Prediction Accuracy of Discharge Placement from Acute Care." Stroke 32, suppl_1 (January 2001): 380–81. http://dx.doi.org/10.1161/str.32.suppl_1.380-e.
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P228 Background: The clinical impression of the Physical Therapist is requested in the determination of patient placement after an acute stroke. There is little evidence to determine if physical therapists are accurately making these recommendations. Also, there is little consensus regarding factors physical therapists consider when making these decisions. It is unknown if years of clinical experience affects placement judgment. The purpose of this retrospective chart review was to address the following questions: How accurate were the physical therapists in predicting placement and did experience affect the accuracy of the prediction? Methods: A retrospective chart review of 64 medical records of persons admitted to The University of Pittsburgh Medical Center Stroke Institute during 1999 was conducted. Data collected included client demographics, elements of the medical and social history, and measures obtained from the initial evaluation and the therapist s recommendation for discharge placement. Results: Spearman rank correlation coefficients were calculated between the predicted and actual discharge location, and to determine the association of the accuracy of prediction and other factors from the client s history or evaluation.An accurate prediction was made 73 % of the time. A moderate correlation was found between the predicted and actual discharge location. (r =.655). Fair but significant correlations were found between the accuracy of prediction and the following factors: the extent of brain damage (r=-.423), bowel and bladder function (r= .357), National Institutes of Health Stroke Scale score (r=.391), and the client s abilities to perform balance (r=.399), sit to stand (r=.483), and locomotion (r=.411). No significance was found between the therapist s years of experience and correct placement decisions. Physical therapists with a minimum of 3 years of experience evaluated 77% of the patients.. Conclusions: In this retrospective chart review, physical therapists were accurate in their predictions of placement location for persons with an acute stroke. The physical therapists years of experience did not appear to significantly affect accuracy
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Mansfield, Theodore J., and Jacqueline MacDonald Gibson. "Health Impacts of Increased Physical Activity from Changes in Transportation Infrastructure: Quantitative Estimates for Three Communities." BioMed Research International 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/812325.
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Recently, two quantitative tools have emerged for predicting the health impacts of projects that change population physical activity: the Health Economic Assessment Tool (HEAT) and Dynamic Modeling for Health Impact Assessment (DYNAMO-HIA). HEAT has been used to support health impact assessments of transportation infrastructure projects, but DYNAMO-HIA has not been previously employed for this purpose nor have the two tools been compared. To demonstrate the use of DYNAMO-HIA for supporting health impact assessments of transportation infrastructure projects, we employed the model in three communities (urban, suburban, and rural) in North Carolina. We also compared DYNAMO-HIA and HEAT predictions in the urban community. Using DYNAMO-HIA, we estimated benefit-cost ratios of 20.2 (95% C.I.: 8.7–30.6), 0.6 (0.3–0.9), and 4.7 (2.1–7.1) for the urban, suburban, and rural projects, respectively. For a 40-year time period, the HEAT predictions of deaths avoided by the urban infrastructure project were three times as high as DYNAMO-HIA’s predictions due to HEAT’s inability to account for changing population health characteristics over time. Quantitative health impact assessment coupled with economic valuation is a powerful tool for integrating health considerations into transportation decision-making. However, to avoid overestimating benefits, such quantitative HIAs should use dynamic, rather than static, approaches.
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Hansen, Maiken B., Lone Ross Nylandsted, Morten A. Petersen, Mathilde Adsersen, Leslye Rojas-Concha, and Mogens Groenvold. "Patient-reported symptoms and problems at admission to specialized palliative care improved survival prediction in 30,969 cancer patients: A nationwide register-based study." Palliative Medicine 34, no. 6 (March 18, 2020): 795–805. http://dx.doi.org/10.1177/0269216320908488.
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Background: Large, nationally representative studies of the association between quality of life and survival time in cancer patients in specialized palliative care are missing. Aim: The aim of this study was to investigate whether symptoms/problems at admission to specialized palliative care were associated with survival and if the symptoms/problems may improve prediction of death within 1 week and 1 month, respectively. Setting/participants: All cancer patients who had filled in the EORTC QLQ-C15-PAL at admission to specialized palliative care in Denmark in 2010–2017 were included through the Danish Palliative Care Database. Cox regression was used to identify clinical variables (gender, age, type of contact (inpatient vs outpatient), and cancer site) and symptoms/problems significantly associated with survival. To test whether symptoms/problems improved survival predictions, the overall accuracy (area under the receiver operating characteristic curve) for different prediction models was compared. The validity of the prediction models was tested with data on 5,508 patients admitted to palliative care in 2018. Results: The study included 30,969 patients with an average age of 68.9 years; 50% were women. Gender, age, type of contact, cancer site, and most symptoms/problems were significantly associated with survival time. The predictive value of symptoms/problems was trivial except for physical function, which clearly improved the overall accuracy for 1-week and 1-month predictions of death when added to models including only clinical variables. Conclusion: Most symptoms/problems were significantly associated with survival and mainly physical function improved predictions of death. Interestingly, the predictive value of physical function was the same as all clinical variables combined (in hospice) or even higher (in palliative care teams).
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Ma, Meiyi, John Stankovic, Ezio Bartocci, and Lu Feng. "Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems." ACM Transactions on Embedded Computing Systems 20, no. 5s (October 31, 2021): 1–25. http://dx.doi.org/10.1145/3477032.
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Predictive monitoring—making predictions about future states and monitoring if the predicted states satisfy requirements—offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named Signal Temporal Logic with Uncertainty (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on whether all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world CPS datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines.
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Chrispell, John C., Eleanor W. Jenkins, Kathleen R. Kavanagh, and Matthew D. Parno. "Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework." Modelling 2, no. 4 (December 15, 2021): 753–75. http://dx.doi.org/10.3390/modelling2040040.
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Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather patterns may be. The effort described in this work is geared towards a flexible mathematical and software framework for simulating the impact of meteorological variability on future crop yield. Our framework is data driven and can easily be applied to any location with suitable historical observations. This will enable site-specific studies that are needed for rigorous risk assessments and climate adaptation planning. The framework combines a physics-based model of crop yield with stochastic process models for meteorological inputs. Combined with techniques from uncertainty quantification, global sensitivity analysis, and machine learning, this hybrid statistical–physical framework allows studying the potential impacts of meteorological uncertainty on future agricultural yields and identify the environmental variables that contribute the most to prediction uncertainty. To highlight the utility of our general approach, we studied the predicted yields of multiple crops in multiple scenarios constructed from historical data. Using global sensitivity analysis, we then identified the key environmental factors contributing to uncertainty in these scenarios’ predictions.
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Bautista-Sánchez, Rogelio, Liliana Ibeth Barbosa-Santillan, and Juan Jaime Sánchez-Escobar. "Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation." Applied Sciences 11, no. 5 (March 9, 2021): 2429. http://dx.doi.org/10.3390/app11052429.
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The prediction of vessel maritime navigation has become an exciting topic in the last years, especially considering economics, commercial exchange, and security. In addition, vessel monitoring requires better systems and techniques that help enterprises and governments to protect their interests. Specifically, the prediction of vessel movements is essential for safety and tracking. However, the applications of prediction techniques have a high cost related to computational efficiency and low resource saving. This article presents a sample method to select historical data on vessel-specific routes to optimize the computational performance of the prediction of vessel positions and route estimation in real-time. These historical navigation data can help to estimate a complete path and perform vessel position predictions through time. This Select Best AIS Data in Prediction Vessel Movements and Route Estimation (PreMovEst) method works in a Vessel Traffic Service database to save computational resources when predictions or route estimations are executed. This article discusses AIS data and the artificial neural network. This work aims to present a prediction model that correctly predicts the physical movement in the route. It supports path planning for the Vessel Traffic Service. After testing the method, the results obtained for route estimation have a precision of 76.15%, and those for vessel position predictions through time have an accuracy of 81.043%.
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Zhu, Jieshun, Arun Kumar, Hui Wang, and Bohua Huang. "Sea Surface Temperature Predictions in NCEP CFSv2 Using a Simple Ocean Initialization Scheme." Monthly Weather Review 143, no. 8 (August 1, 2015): 3176–91. http://dx.doi.org/10.1175/mwr-d-14-00297.1.
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Abstract In contrast to operational climate predictions based on sophisticated ocean data assimilation schemes at the National Centers for Environmental Predictions (NCEP), this study applied a simple ocean initialization scheme to the NCEP latest seasonal prediction model, the Climate Forecast System, version 2 (CFSv2). In the scheme, sea surface temperature (SST) was the only observed information applied to derive ocean initial states. The physical basis for the method is that, through air–sea coupling, SST is capable of reproducing some observed features of ocean evolutions by forcing the atmospheric winds. SST predictions based on the scheme are compared against hindcasts from the National (lately North American) Multimodel Ensemble (NMME) project. It was found that due to substantial biases in the tropical eastern Pacific in the ocean initial conditions produced by SST assimilation, ENSO SST predictions were not as good as those with sophisticated initialization schemes (e.g., hindcasts in the NMME project). However, in other basins, SST predictions based on a simple ocean initialization procedure were not worse (sometimes even better) than those with sophisticated initialization schemes. These comparisons indicate that it was helpful that subsurface ocean information be assimilated to improve the tropical Pacific SST predictions, while SST-based ocean assimilation was an effective way to enhance SST prediction capability in other ocean basins. By examining multimodel ensembles with the simple scheme-based hindcasts either included or excluded in NMME, it is also suggested that including the hindcast would generally benefit multimodel ensemble forecasts. In addition, possible ways to further improve ENSO SST predictions with the simple initialization scheme are also discussed.
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Tielker, Nicolas, Stefan Güssregen, and Stefan M. Kast. "SAMPL7 physical property prediction from EC-RISM theory." Journal of Computer-Aided Molecular Design 35, no. 8 (July 19, 2021): 933–41. http://dx.doi.org/10.1007/s10822-021-00410-9.
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AbstractInspired by the successful application of the embedded cluster reference interaction site model (EC-RISM), a combination of quantum–mechanical calculations with three-dimensional RISM theory to predict Gibbs energies of species in solution within the SAMPL6.1 (acidity constants, pKa) and SAMPL6.2 (octanol–water partition coefficients, log P) the methodology was applied to the recent SAMPL7 physical property challenge on aqueous pKa and octanol–water log P values. Not part of the challenge but provided by the organizers, we also computed distribution coefficients log D7.4 from predicted pKa and log P data. While macroscopic pKa predictions compared very favorably with experimental data (root mean square error, RMSE 0.72 pK units), the performance of the log P model (RMSE 1.84) fell behind expectations from the SAMPL6.2 challenge, leading to reasonable log D7.4 predictions (RMSE 1.69) from combining the independent calculations. In the post-submission phase, conformations generated by different methodology yielded results that did not significantly improve the original predictions. While overall satisfactory compared to previous log D challenges, the predicted data suggest that further effort is needed for optimizing the robustness of the partition coefficient model within EC-RISM calculations and for shaping the agreement between experimental conditions and the corresponding model description.
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Paasche, Hendrik. "Translating tomographic ambiguity into the probabilistic inference of hydrologic and engineering target parameters." GEOPHYSICS 82, no. 4 (July 1, 2017): EN67—EN79. http://dx.doi.org/10.1190/geo2015-0618.1.
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Geophysical tomography allows for spatially continuous imaging of physical parameters. In many hydrological or engineering exploration tasks, other parameters than those imaged by geophysical tomography are of higher interest, but they cannot be measured continuously in space. We have developed a methodology linking multiple tomograms imaging different physical parameters with a sparsely measured target parameter striving to achieve probabilistic, spatially continuous predictions of the target parameter distribution. Building on a fully nonlinear tomographic model reconstruction searching the solution space globally, we translate the tomographic model reconstruction ambiguity into the prediction of the target parameter. In doing so, we structurally integrate physically different tomograms achieved by individual inversion by transforming them into fuzzy sets. In a postinversion analysis, systems of linear equations are then set up and solved linking the fuzzy sets and sparse information about the target parameter, e.g., measured in boreholes. The method is fully data driven and does not require knowledge or assumptions about the expected relations between the tomographically imaged physical parameters and the target parameter. It is applicable to 2D and 3D tomographic data. Practically, the parameter interrelations can be of any complexity, including nonuniqueness. We evaluate the methodology using a synthetic database allowing for maximal control of the achieved predictions. We exemplarily predict 2D probabilistic models of porosity based on sparse porosity logging data and sets of equivalently plausible radar and seismic-velocity tomograms.
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Sun, Yongjiao, Yaning Song, Baiyou Qiao, and Boyang Li. "Distributed Typhoon Track Prediction Based on Complex Features and Multitask Learning." Complexity 2021 (July 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/5661292.
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Typhoons are common natural phenomena that often have disastrous aftermaths, particularly in coastal areas. Consequently, typhoon track prediction has always been an important research topic. It chiefly involves predicting the movement of a typhoon according to its history. However, the formation and movement of typhoons is a complex process, which in turn makes accurate prediction more complicated; the potential location of typhoons is related to both historical and future factors. Existing works do not fully consider these factors; thus, there is significant room for improving the accuracy of predictions. To this end, we presented a novel typhoon track prediction framework comprising complex historical features—climatic, geographical, and physical features—as well as a deep-learning network based on multitask learning. We implemented the framework in a distributed system, thereby improving the training efficiency of the network. We verified the efficiency of the proposed framework on real datasets.
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Bai, Ruijie, Jinping Li, Fanzhi Zeng, and Chao Yan. "Mechanism and Performance Differences between the SSG/LRR-ω and SST Turbulence Models in Separated Flows." Aerospace 9, no. 1 (December 30, 2021): 20. http://dx.doi.org/10.3390/aerospace9010020.
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Accurate predictions of flow separation are important for aerospace design, flight accident avoidance, and the development of fluid mechanics. However, the complexity of the separation process makes accurate predictions challenging for all known Reynolds-averaged Navier–Stokes (RANS) methods, and the underlying mechanism of action remains unclear. This paper analyzes the specific reasons for the defective predictions of the turbulence models applied to separated flows, explores the physical properties that impact the predictions, and investigates their specific mechanisms. Taking the Menter SST and the Speziale-Sarkar–Gatski/Launder–Reece–Rodi (SSG/LRR)-ω models as representatives, three typical separated flow cases are calculated. The performance differences between the two turbulence models applied to the different separated flow calculations are then compared. Refine the vital physical properties and analyze their calculation from the basic assumptions, modeling ideas, and construction of the turbulence models. The numerical results show that the underestimation of Reynolds stress is a significant factor in the unsatisfactory prediction of separation. In the SST model, Bradshaw’s assumption imposes the turbulent energy equilibrium condition in all regions and the eddy–viscosity coefficient is underestimated, which leads to advanced separation and lagging reattachment. In the SSG/LRR-ω model, the fidelity with which the pressure–strain term is modeled is a profound factor affecting the calculation accuracy.
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Carreiro, Adam P., Cheryl A. Howe, Sarah L. Kozey, and Patty S. Freedson. "Physical Activity Energy Expenditure Predictions Using Accelerometry And Heart Rate." Medicine & Science in Sports & Exercise 41 (May 2009): 208. http://dx.doi.org/10.1249/01.mss.0000355190.73734.08.
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Rao, K. P., S. M. Doraivelu, and K. Sivaram. "Physical modelling studies using spike forging to verify analytical predictions." Journal of Materials Processing Technology 28, no. 3 (December 1991): 295–306. http://dx.doi.org/10.1016/0924-0136(91)90139-6.
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Morel, Maxime, Doug J. Booker, Frédéric Gob, and Nicolas Lamouroux. "Intercontinental predictions of river hydraulic geometry from catchment physical characteristics." Journal of Hydrology 582 (March 2020): 124292. http://dx.doi.org/10.1016/j.jhydrol.2019.124292.
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Bhat, Ajaz Ahmad, Vishwanathan Mohan, Giulio Sandini, and Pietro Morasso. "Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences." Journal of The Royal Society Interface 13, no. 120 (July 2016): 20160310. http://dx.doi.org/10.1098/rsif.2016.0310.
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Emerging studies indicate that several species such as corvids, apes and children solve ‘The Crow and the Pitcher’ task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause–effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended ‘learning–prediction–abstraction’ loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration.
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Chen, Han YH, Karel Klinka, and Richard D. Kabzems. "Site index, site quality, and foliar nutrients of trembling aspen: relationships and predictions." Canadian Journal of Forest Research 28, no. 12 (December 1, 1998): 1743–55. http://dx.doi.org/10.1139/x98-154.
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To examine the relationships between trembling aspen (Populus tremuloides Michx.) productivity, environmental attributes, and foliar nutrients and to make accurate predictions of trembling aspen productivity, we sampled 60 naturally established, fire-originated, and even-aged trembling aspen stands in northern British Columbia. Trembling aspen site index significantly varied with latitude, elevation, aspect, slope position, edatopes, some forest floor and mineral soil physical and chemical properties, and concentrations of some foliar nutrients. To predict site index, we developed multiple linear regression models using climatic variables, topographic properties, edatopes, soil physical and chemical properties, or foliar nutrients as predictors. Model accountability for variation of site index differed in decreasing order from soil model, climatic model, forest floor model, foliar nutrient model, edatope model, topographic model, to mineral soil model. Examined by the test data set, all models were unbiased, but they had different levels of precision in prediction in decreasing order from edatope model, soil model, forest floor model, mineral soil model, foliar nutrient model, climatic model, to topographic model. The soil and foliar nutrients models may provide insight into ecosystem processes, but the models using climatic variables and topographic properties or edatopes as predictors are recommended for predicting trembling aspen site index.
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Usowicz, B., J. B. Usowicz, and L. B. Usowicz. "Physical-Statistical Model of Thermal Conductivity of Nanofluids." Journal of Nanomaterials 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/756765.
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A physical-statistical model for predicting the effective thermal conductivity of nanofluids is proposed. The volumetric unit of nanofluids in the model consists of solid, liquid, and gas particles and is treated as a system made up of regular geometric figures, spheres, filling the volumetric unit by layers. The model assumes that connections between layers of the spheres and between neighbouring spheres in the layer are represented by serial and parallel connections of thermal resistors, respectively. This model is expressed in terms of thermal resistance of nanoparticles and fluids and the multinomial distribution of particles in the nanofluids. The results for predicted and measured effective thermal conductivity of several nanofluids (Al2O3/ethylene glycol-based and Al2O3/water-based; CuO/ethylene glycol-based and CuO/water-based; and TiO2/ethylene glycol-based) are presented. The physical-statistical model shows a reasonably good agreement with the experimental results and gives more accurate predictions for the effective thermal conductivity of nanofluids compared to existing classical models.
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Yang, Yang, and Ting Fong May Chui. "Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods." Hydrology and Earth System Sciences 25, no. 11 (November 11, 2021): 5839–58. http://dx.doi.org/10.5194/hess-25-5839-2021.
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Abstract. Sustainable urban drainage systems (SuDS) are decentralized stormwater management practices that mimic natural drainage processes. The hydrological processes of SuDS are often modeled using process-based models. However, it can require considerable effort to set up these models. This study thus proposes a machine learning (ML) method to directly learn the statistical correlations between the hydrological responses of SuDS and the forcing variables at sub-hourly timescales from observation data. The proposed methods are applied to two SuDS catchments with different sizes, SuDS practice types, and data availabilities in the USA for discharge prediction. The resulting models have high prediction accuracies (Nash–Sutcliffe efficiency, NSE, >0.70). ML explanation methods are then employed to derive the basis of each ML prediction, based on which the hydrological processes being modeled are then inferred. The physical realism of the inferred hydrological processes is then compared to that would be expected based on the domain-specific knowledge of the system being modeled. The inferred processes of some models, however, are found to be physically implausible. For instance, negative contributions of rainfall to runoff have been identified in some models. This study further empirically shows that an ML model's ability to provide accurate predictions can be uncorrelated with its ability to offer plausible explanations to the physical processes being modeled. Finally, this study provides a high-level overview of the practices of inferring physical processes from the ML modeling results and shows both conceptually and empirically that large uncertainty exists in every step of the inference processes. In summary, this study shows that ML methods are a useful tool for predicting the hydrological responses of SuDS catchments, and the hydrological processes inferred from modeling results should be interpreted cautiously due to the existence of large uncertainty in the inference processes.
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Goudar, Ananya Divakar, Hema K S, Inchara T R, and Meghana Kalmat. "PREDICTING STOCK MARKET TRENDS USING MACHINE LEARNING AND DEEP LEARNING ALGORITHM." International Research Journal of Computer Science 9, no. 8 (August 13, 2022): 281–85. http://dx.doi.org/10.26562/irjcs.2022.v0908.25.
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Intention of StockMarket Prediction is to forecast future worth of a company's monetary stocks. utilising machine learning produces forecasts based on the values of current stock market indices Using their prior values as training data is a new development in stock market prediction technology. Predicting the performance of the stock market is one of the most challenging tasks.Prediction involves a huge number of variables, such as the distinction between physical and psychological factors, rational and irrational conduct, and more. Share prices are unpredictable and challenging to forecast accurately as a result of a combination of all these variables. Many researchers have conducted studies on the upcoming market developments movement. Data is a key source of efficiency because stock is made up of dynamic data. The prediction's efficiency has an effect on the same chances. To make accurate predictions, machine learning employs a variety of models. Machine learning techniques have the potential to uncover previously unknown patterns and insights using features like an organization's most recent announcements, quarterly sales statistics, and so forth, which can then be utilised to produce impeccably correct forecasts. The project's goal is to predict stock values using machine learning that is based on long short-term memory (LSTM) and regression. All of the variables—open, close, low, high, and volume are taken into account.
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Lambert, R. K., S. L. Codd, M. R. Alley, and R. J. Pack. "Physical determinants of bronchial mucosal folding." Journal of Applied Physiology 77, no. 3 (September 1, 1994): 1206–16. http://dx.doi.org/10.1152/jappl.1994.77.3.1206.
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It has recently been proposed, on the basis of a theoretical analysis, that the folding of the mucosa provides a significant component of airway stiffness. The model predicted that the stiffness of an airway was directly related to the number of epithelial folds that developed. In this study we examine the possibility that the folding pattern is determined by the physical requirements that the folding membrane must stay within the boundary of the smooth muscle wall, that the submucosal mass is constant, and that the strain energy of the folding membrane is the minimum possible within the geometric constraints. Model predictions are compared with morphometric data from the noncartilaginous airways of 17 sheep lungs. The data are in agreement with our predictions, which are based on the assumption that the folding membrane thickness is proportional to the submucosal thickness (in a fully dilated airway). The outcome of this analysis is that the increase in intrinsic stiffness of the folding membrane resulting from the increased thickness outweighs the decrease in stiffness conferred by the fewer folds required by the thicker submucosa. It is suggested that the increase in folding membrane thickness observed in asthma could be viewed as a protective mechanism that tends to reduce hyperresponsiveness.