Single-point Model Research Articles

Fevering Interstellar Ices Have More CH3OD

Monodeuterated methanol is thought to form during the prestellar core stage of star formation. Observed variations in the CH2DOH/CH3OD ratio suggest that its formation is strongly dependent on the surrounding cloud conditions. Thus, it is a potential tracer of the physical conditions before the onset of star formation. A single-point physical model representative of a typical prestellar core is coupled to chemical models to investigate potential formation pathways toward deuterated methanol at the prestellar stage. Simple addition reactions of H and D are not able to reproduce observed abundances. The implementation of an experimentally verified abstraction scheme leads to the efficient formation of methyl-deuterated methanol, but lacks sufficient formation of hydroxy-deuterated methanol. CH3OD is most likely formed at a later evolutionary stage, potentially from H–D exchange reactions in warm ices between HDO (and D2O) and CH3OH. The CH2DOH/CH3OD ratio is not an appropriate tracer of the physical conditions during the prestellar stage, but might be better suited as a tracer of ice heating.

Development and analysis of the two-regime transient tyre model for combined slip

This paper refines the two-regime transient theory developed by Romano et al. [Romano L, Bruzelius F, Jacobson B. Unsteady-state brush theory. Vehicle Syst Dyn. 2020;59:11–29. DOI: 10.1080/00423114.2020.1774625.] to include the effect of combined slip. A nonlinear system is derived that describes the non-steady generation of tyre forces and considers the coupling between the longitudinal and lateral characteristics. The proposed formulation accounts for both the carcass and the bristle dynamics, and represents a generalisation of the single contact point models. A formal analysis is conducted to investigate the effect of the tyre carcass anisotropy on the properties of the system. It is concluded that a fundamental role is played by the ratio between the longitudinal and lateral relaxation lengths. In particular, it is demonstrated that the maximum slip that guarantees (partial) adhesion conditions does not coincide with the stationary value and decreases considerably for highly anisotropic tyres. The dissipative nature of the model is also analysed using elementary tools borrowed from the classic theory for nonlinear systems. A comparison is performed against the single contact point models, showing a good agreement especially towards the full-nonlinear one. Furthermore, compared to the single contact point models, the two-regime appears to be able to better replicate the exact dynamics of the tyre forces predicted by the complete brush theory. Finally, the transient model is partially validated against experimental results.

A multiple-point monitoring model for concrete dam displacements based on correlated multiple-output support vector regression

Displacements reflect the overall behavior of a concrete dam; thus, it is of vital importance to evaluate the overall structural health status by displacement-based mathematical monitoring models. However, most of the existing monitoring models focus on point-by-point displacement modeling, ignoring the correlations among displacements at different measurement points. This study therefore proposes a model for dam multiple-point displacement monitoring based on the support vector regression (SVR) algorithm. The improved SVR-based model with multiple-output formulation is a new development based on the statistical learning theory, which can simultaneously analyze and predict displacements at multiple-measurement points. Furthermore, by introducing the weight vectors that separate the common and individual information, the potential correlations among multiple-point displacements can be fully exploited by the multiple-output SVR. Combining the above two improvements, a multiple-point monitoring model for dam displacements considering spatiotemporal correlations, referred to as correlated multiple-output SVR (CMOSVR), is constructed. The proposed model is verified using in-situ monitoring from a full-scale concrete gravity dam. The accuracy, robustness, and efficiency of the CMOSVR-based model are compared with those of conventional single-point monitoring models, such as classical hydrostatic-seasonal-time model and standard SVR-based model. Empirical results show that in both real and simulated noisy scenarios, the CMOSVR-based multiple-point model can achieve a better monitoring performance with less modeling time cost. Moreover, the superior performance of CMOSVR-based model does not require a very strong correlation among multiple-point displacements, which considerably improves the adaptability of the monitoring model to various possible scenarios. The novel multiple-point model will provide an effective technical support tool for ensuring the safe operation of dams.

Wind power time series simulation model based on typical daily output processes and Markov algorithm

The simulation of wind power time series is a key process in renewable power allocation planning, operation mode calculation, and safety assessment. Traditional single-point modeling methods discretely generate wind power at each moment; however, they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency. To resolve this problem, a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed. First, a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented. Second, considering the typical daily output processes as status variables, a wind power time series simulation model based on Markov algorithm is constructed. Finally, a case is analyzed based on the measured data of a wind farm in China. The proposed model is then compared with traditional methods to verify its effectiveness and applicability. The comparison results indicate that the statistical characteristics, probability distributions, and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods. Moreover, modeling efficiency considerably improves.

Research on Trajectory Tracking of Sliding Mode Control Based on Adaptive Preview Time

The preview model is one of the common methods used in trajectory tracking. The traditional fixed preview time is not adaptable to most speeds and road conditions, which not only reduces the tracking accuracy but also reduces the vehicle stability. Therefore, a controller can be designed to determine the adaptive preview time based on an optimization function of the lateral deviation, the road boundary, and the road boundary of the whole vehicle motion response characteristics. Traditional optimal preview control theory predicts the next state of the vehicle by the assumption of constant transverse pendulum angular velocity. In this paper, an expectation-based approach is used to find the ideal steering wheel turning angle based on the adaptive preview time, and a single-point preview model is established. Based on the two-degree-of-freedom dynamics model, a sliding mode controller is designed for control, and the low-pass filters are designed to suppress jitter in the sliding mode controller. Simulation results with different preview times, different speeds and different road adhesion coefficients prove that the controller has a good control effect and has good effectiveness and adaptability to speed and adhesion coefficient.

Wind speed prediction using measurements from neighboring locations and combining the extreme learning machine and the AdaBoost algorithm

Wind speed prediction plays an essential role in wind energy utilization. However, most existing studies of wind speed forecasting used data from one location to build models and forecasts, which limited the accuracy of wind speed forecasting. Therefore, to improve the prediction accuracy at a target location, this study proposes a multiple-point model based on data from multiple locations for short-term wind speed prediction. The model, which utilizes wind speed measurements from neighboring locations and combines the extreme learning machine (ELM) with the AdaBoost algorithm, is named the multiple-point-AdaBoost-ELM model. Data from seventeen automatic meteorological stations in the Heihe River Basin are used, four stations at different positions are taken as target stations for multi-time-scale wind speed prediction, and six models and several metrics are involved for comparative analysis and comprehensive evaluation. The results show that: (1) the prediction performance of the proposed multiple-point-AdaBoost-ELM model is significantly superior to that of the compared single-point models; (2) the prediction accuracy of the multiple-point-AdaBoost-ELM model is relatively less affected by the prediction time-scale than that of the corresponding single-point model; and (3) the stations located at the center of multiple stations can obtain more accurate prediction results than those located near the edges of the region. Therefore, the proposed multiple-point-AdaBoost-ELM model is a more promising method than traditional single-point modeling methods. The proposed method fully uses historical wind speed at surrounding locations to enhance the wind speed predictions at target locations, makes up for the deficiency of the wind speed forecasting using data from one location, and expands a new way for wind speed prediction.

Understanding Hygroscopicity of Theophylline via a Novel Cocrystal Polymorph: A Charge Density Study.

The charge density distribution in a novel cocrystal (1) complex of 1,3-dimethylxanthine (theophylline) and propanedioic acid (malonic acid) has been determined. The molecules crystallize in the triclinic, centrosymmetric space group P1̅, with four independent molecules (Z = 4) in the asymmetric unit (two molecules each of theophylline and malonic acid). Theophylline has a notably high hygroscopic nature, and numerous cocrystals have shown a significant improvement in stability to humidity. A charge density study of the novel polymorph has identified interesting theoretical results correlating the stability enhancement of theophylline via cocrystallization. Topological analysis of the electron density highlighted key differences (up to 17.8) in Laplacian (∇2ρ) between the experimental (EXP) and single-point (SP) models, mainly around intermolecular-bonded carbonyls. Further investigation via molecular electrostatic potential maps reaffirmed that the charge redistribution enhanced intramolecular hydrogen bonding, predominantly for N(2') and N(2) (61.2 and 61.8 kJ mol-1, respectively). An overall weaker lattice energy of the triclinic form (-126.1 kJ mol-1) compared to that of the monoclinic form (-133.8 kJ mol-1) suggests a lower energy threshold to overcome to initiate dissociation. Future work via physical testing of the novel cocrystal in both dissolution and solubility will further solidify the correlation between theoretical and experimental results.

Physical properties of mesoporous scoria and pumice volcanic rocks

In this study, the chemical composition, crystal structure, texture properties, and thermal properties of five powdered samples of scoria and pumice volcanic rock from different Harrats were investigated. It was observed that volcanic rocks show variations in chemical compositions, crystal structure, texture, and thermal properties. All samples comprised SiO2, Al2O3, CaO, and Fe2O3 as the major elements and contained both amorphous and crystalline phases. Textural parameters such as surface area and porosity were determined using various calculation models. The surface area of scoria samples was between 0.85 and 1.71 m2 g−1 (Brunauer–Emmett–Teller and Single point model), 0.293–1.028 m2 g−1 (Barrett–Joyner–Halenda model), and 1.02–2.35 m2 g−1 (Langmuir model). While for pumice, the calculated values of the surface area were 1.67 m2 g−1 (Brunauer–Emmett–Teller and Single point model), 0.763 m2 g−1 (Barrett–Joyner–Halenda model), and 2.24 m2 g−1 (Langmuir model). The adsorption-desorption isotherm curves reveal that the scoria and pumice particles under study have mesoporous sizes between 7.89 and 9.81 nm, respectively. The differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) results of scoria and pumice samples illustrate a thermally stable material at high temperatures. TGA results show a weight gain by about 1.0% has been observed in the scoria samples in the region beyond 600 °C that may indicate a probable oxidation phenomenon with a change in color. While the DSC results of the red scoria and pumice did not show any recrystallization or oxidation, but only showed a small loss of weight in the TGA result. The diversity in molecular composition, texture, and structure of scoria and pumice volcanic rock samples provide for promising natural stable mesoporous materials that can be used in various mesoporous technologies or applications such as solar cells.

Trajectories of high-sensitivity cardiac troponin I in the two decades before cardiovascular death in Whitehall II

Abstract Background High-sensitivity cardiac troponin may be a promising biomarker that could be used for personalised cardiovascular risk prediction and monitoring in the general population. Temporal changes in high-sensitivity cardiac troponin before cardiovascular death are largely unexplored. Purpose Using the longitudinal Whitehall II cohort, we evaluated whether three serial high-sensitivity cardiac troponin I measurements over 15 years improved prediction of cardiovascular death when compared to a single time point at baseline. Methods Whitehall II is an ongoing longitudinal observation cohort study of 10,308 civil servants, and we included participants who had at least one cardiac troponin measurement and outcome data available. We constructed time trajectories to evaluate the temporal pattern of cardiac troponin I in those who died from cardiovascular disease as compared to those who did not. Cox regression and joint models were used to investigate the association of cardiac troponin I in relation to cardiovascular death using single time point (at baseline) and repeated measurements (at baseline, 10 and 15 years), respectively. The discriminative ability was assessed by the concordance index. Results In total, we included 7,293 individuals (mean age of 58 years [SD 7] at baseline, 29.4% women). Of these, 5,818 (79.8%) and 4,045 (55.5%) individuals had a second and third cardiac troponin I concentration measured, respectively. Cardiovascular death occurred in 281 (3.9%) individuals during a median follow-up of 21.4 [IQR, 15.8 to 21.8] years. In the 21-year trajectories of cardiac troponin I, we observed higher baseline concentrations in those who died due to cardiovascular disease as compared to those who did not (median 5 [IQR, 2 to 9] ng/L versus 3 [IQR, 2 to 5] ng/L respectively, Figure). Cardiac troponin I was an independent predictor of cardiovascular death, and the hazard ratio (HR) derived from the joint model that included serial cardiac troponin measurements was higher than the HR derived from the single time point model (single time point model: adjusted HR 1.53, 95% Confidence Interval [CI] 1.37 to 1.70 per naturally log transformed unit of cardiac troponin I, versus repeated measurements model: adjusted HR 1.79, 95% CI 1.58 to 2.02). The discriminative ability of the cardiac troponin model improved when using repeated measurements (concordance index of unadjusted cardiac troponin models, single time point: 0.668 versus repeated measurements: 0.724). Conclusions Our study shows that cardiac troponin I trajectories were persistently higher among individuals who died from cardiovascular disease. Cardiac troponin I is a strong independent predictor of cardiovascular death, and incorporating repeated measurements of cardiac troponin improves cardiovascular risk prediction in the general population. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Cardiac troponin I measurements and analysis were supported by were supported by Siemens Healthineers. The study was supported by Health Data Research UK which receives its funding from HDR UK Ltd (HDR-5012) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and the Wellcome Trust. NLM is supported by the British Heart Foundation through a Senior Clinical Research Fellowship (FS/16/14/32023), Programme Grant (RG/20/10/34966) and a Research Excellence Award (RE/18/5/34216). The funders had no role in the study and the decision to submit this work to be considered for publication.

Resilient [formula omitted] control for uncertain turbofan linear switched systems with hybrid switching mechanism and disturbance observer

In this paper, a composite resilient anti-disturbance H∞ control scheme is presented for the turbofan linear switched systems with uncertainties and external disturbances via the disturbance observer-based control (DOBC) approach. Since the single-point linearization model cannot be used to accurately describe the engine characteristics of the whole envelope, the multi-model switching method is adopted to preferably describe the working characteristics of the turbofan. A hybrid switching scheme based on state-dependent switching and minimum dwell time constraint is developed to ensure the stability of closed-loop systems and satisfy the dwell-time requirement of turbofan systems. Then, a robust resilient controller is built by combining the DOBC approach and resilient control technique. Based on the multiple Lyapunov functions method, some easy-to-test results are given to guarantee that the closed-loop switched systems are asymptotically stable with H∞ property. Finally, the effectiveness of the proposed method is verified through some simulations and comparisons.

Aerostructural wing shape optimization assisted by algorithmic differentiation

With more efficient structures, last trends in aeronautics have witnessed an increased flexibility of wings, calling for adequate design and optimization approaches. To correctly model the coupled physics, aerostructural optimization has progressively become more important, being nowadays performed also considering higher-fidelity discipline methods, i.e., CFD for aerodynamics and FEM for structures. In this paper a methodology for high-fidelity gradient-based aerostructural optimization of wings, including aerodynamic and structural nonlinearities, is presented. The main key feature of the method is its modularity: each discipline solver, independently employing algorithmic differentiation for the evaluation of adjoint-based sensitivities, is interfaced at high-level by means of a wrapper to both solve the aerostructural primal problem and evaluate exact discrete gradients of the coupled problem. The implemented capability, ad-hoc created to demonstrate the methodology, and freely available within the open-source SU2 multiphysics suite, is applied to perform aerostructural optimization of aeroelastic test cases based on the ONERA M6 and NASA CRM wings. Single-point optimizations, employing Euler or RANS flow models, are carried out to find wing optimal outer mold line in terms of aerodynamic efficiency. Results remark the importance of taking into account the aerostructural coupling when performing wing shape optimization.

Mental Actions and Explainability in Kripkean Semantics: What Else do I Know? (Student Abstract)

The ability of an agent to distinguish the ramification effects of an action from its direct effects adds value to the explainability of its decisions. In this work, we propose to encode the ramification effects of ontic and epistemic actions as single-point update models in an epistemic planning domain modeled with Kripkean semantics of Knowledge and Belief. We call them “mental actions”. We discuss a preliminary approach to realize our idea, and we conclude by pointing out some optimizations as our ongoing pursuit.

A new model and method of terrain-aided positioning confidence interval estimation

There are typically unknown deviation tides and stochastic measurement errors in digital elevation map and real-time measurement terrain map, which result in the failure of the matching algorithm. To address the problem of the robust terrain-aided position under the condition of unknown tidal and measurement errors, a new terrain-aided position (TAP) model and a tide level estimation method based on a single-point observation model are proposed in this study. Accordingly, a jump model of the TAP points that considers the nonlinear mapping relationship between the position point and local terrain is proposed. Finally, the effectiveness of the TAP and confidence interval estimation algorithms are verified based on shipborne measurement data with a difference in terrain variability. Future research will focus on revealing the relation between terrain characteristics and directionality of the TAP confidence interval.

Test Procedures for Change Point in a General Class of Distributions

This paper is concerned with the change point analysis in a general class of distributions. The quasi-Bayes and likelihood ratio test procedures are considered to test the null hypothesis of no change point. Exact and asymptotic behaviors of the two test statistics are derived. To compare the performances of two test procedures, numerical significance levels and powers of tests are tabulated for certain selected values of the parameters. Estimation of the change point based on these two test procedures are also considered. Moreover, the epidemic change point problem is studied as an alternative model for the single change point model. A real data set with epidemic change model is analyzed by two test procedures.

Spatiotemporal hybrid model for concrete arch dam deformation monitoring considering chaotic effect of residual series

Single-measuring point deformation monitoring model is the most popular method in dam health monitoring. Considering that single-point monitoring model cannot comprehensively reflect the overall deformation properties of dams, a spatiotemporal hybrid model of multi-point deformation monitoring for concrete arch dams is proposed. Meanwhile, considering the chaotic effect of residual series, the support vector machine optimized by particle swarm optimization algorithm (PSO-SVM) is adopted to analyze and forecast the residual series. Hence, a spatiotemporal hybrid model for concrete arch dam deformation monitoring considering the chaotic effect of residual series is proposed in the study. Based on the theory of single-measuring point deformation monitoring, a spatiotemporal hybrid model is established by introducing space coordinate and calculating hydraulic component with finite element method. Then, with the good nonlinear processing ability of PSO-SVM, the chaotic effect of residual series is analyzed and predicted by PSO-SVM. Subsequently, a spatiotemporal hybrid model for concrete arch dam deformation monitoring considering chaotic effect of residual series is established by superimposing the residual prediction term with the predicted value of the spatiotemporal hybrid model. Engineering example show that the proposed model has better fitting and predicting precisions compared with the conventional single-point monitoring models, and it can analyze and predict the deformations of multi-point simultaneously. In addition, the proposed model reduces the workload of modelling point by point in single-point monitoring model, which considerably improves the practicality and computational efficiency of deformation-based health monitoring of concrete arch dams.

Product information diffusion model and reasoning process in consumer behavior

Information diffusion on social media has become a major approach in people's daily communication, and the value contained therein holds great interest for both academic and industrial communities. However, the process of information diffusion is affected by many factors, and the complexity of that process has not been fully explored. Most previous studies have concentrated on the strategies and driving forces in social media operations, as well as the identification of influential seed nodes, yet analyses of consumer behavior choice in the process of information diffusion are rare. Thus, This study proposes a multipoint cross-diffusion model based on MapReduce, which improves the single-point model and can better describe the product information diffusion process. On that basis, a Bayesian network model of product information diffusion was constructed to analyze the associations between factors and consumer behaviors. Moreover, the posterior probability of consumer behavior choice affected by a series of factors in the information forwarding process was considered and analyzed. This study's findings can be used to estimate the posterior probability that users will purchase, forward, or stay silent, thereby predicting the effect of product diffusion and obtaining the quantitative relationships between factors and consumer behavior.

Improvement of the weighted multi-point (WMP) radiation model for diffusive flames by the application of a set of stochastic optimisation algorithms

The weighted multi-point source model (WMP) has been proposed to model flame radiation from far to near distances, encompassing regions where the single point (SP) model cannot predict it adequately. To develop its formulation, the WMP has been studied as an inverse problem, being optimised to minimise the error between experimental and numerical data. Efficient optimisation methods are thus necessary, and a performance study applied to the WMP model is essential for its development. This study aims to evaluate which types of stochastic algorithms are the best for this problem and to improve the current radiation model comparing the results to those by previous studies. Five algorithms with different characteristics are chosen and tuned for the WMP problem with a design of experiments (DoE) methodology, and applied to each configuration of the model. The best performance was shown by the grey wolf optimiser (GWO), allying stability to fast convergence. The best solution improved previous results by 23.7%, and was also 82.72% better than the solution calculated with the SP model, and 76.24% better than the one calculated with the canonical WMP. A trend in radiation emission distribution is observed with results by previous studies, guiding better formulations in weight distribution.

Braking process identification of high-speed trains for automatic train stop control

Automatic train stop control (ATSC) is a key function of the automatic train operation (ATO) system. An accurate braking process model can help to improve the control strategy. In this paper, the braking process for stop control of high-speed trains is formulated as a single-point time delay model, based on the principle of practical braking processes. Furthermore, a Picard iteration based identification method is first applied to the time delay system, and a train braking process identification method is proposed. The method is straightforward, and the parameters can be identified based on the principle of ordinary differential equations. The effectiveness of the braking process model and the identification method is illustrated by real-life experimental data.

Random field model of sequential ground motions

This paper proposes a random field model of sequential ground motions, which considers the spatial correlation between the mainshock and aftershock from a stochastic view. First, the correlation between the mainshock and aftershock under the physical “source–path–local site” mechanism is explained. Based on the mechanism, the point-source model, and uniform-isotropic-medium model, a random single-point model for sequential ground motion with eight basic parameters is presented. Second, the random field model is developed from the random single-point model. More than 1000 pairs of sequential ground motions are used to identify and analyze statistically the basic parameters in the model. Moreover, the probability distribution of each parameter is presented based on copula functions. The results show that the spatial correlations of sequential ground motions can be effectively simulated based on the proposed random field model. Furthermore, it is possible to realistically reproduce the attenuation and time lag of ground motions at a local site.

NMR pseudocontact shifts in a symmetric protein homotrimer

NMR pseudocontact shifts are a valuable tool for structural and functional studies of proteins. Protein multimers mediate key functional roles in biology, but methods for their study by pseudocontact shifts are so far not available. Paramagnetic tags attached to identical subunits in multimeric proteins cause a combined pseudocontact shift that cannot be described by the standard single-point model. Here, we report pseudocontact shifts generated simultaneously by three paramagnetic Tm-M7PyThiazole-DOTA tags to the trimeric molecular chaperone Skp and provide an approach for the analysis of this and related symmetric systems. The pseudocontact shifts were described by a “three-point” model, in which positions and parameters of the three paramagnetic tags were fitted. A good correlation between experimental data and predicted values was found, validating the approach. The study establishes that pseudocontact shifts can readily be applied to multimeric proteins, offering new perspectives for studies of large protein complexes by paramagnetic NMR spectroscopy.