Single-state Model Research Articles

Millimeter-Wave and High-Resolution Infrared Spectroscopy of the Ground and Seven Lowest Fundamental States of 1H-1,2,4-Triazole.

A combined analysis of millimeter-wave (70-700 GHz) and rotationally resolved infrared (400-1200 cm-1) spectra of the ground state and seven fundamental vibrational modes of 1H-1,2,4-triazole is reported. While the lowest-energy vibrationally excited state (ν18) is well-treated using a single-state distorted-rotor Hamiltonian, the second (ν17) and third (ν16) vibrationally excited states are involved in strong c-type Coriolis coupling and require an appropriate two-state Hamiltonian. The oblate nature of 1H-1,2,4-triazole is sufficiently close to the oblate symmetric-top limit that the analysis requires the use of A-reduced, sextic centrifugally distorted-rotor Hamiltonian models in the Ir representation in order to achieve low σfit values. The coupling between ν17 (A″) and ν16 (A″) resulted in many transitions with slightly perturbed frequencies, many highly displaced resonant intrastate transitions, and 165 nominal interstate transitions. Modeling the spectra of ν17 and ν16 required three c-axis Coriolis-coupling terms (Fab, FabJ, and FabK) to treat the interaction. Many of the nominal interstate transitions form clearly discernible Q-branch bands, comprising degenerate sets of a- and b-type transitions. The rotational spectra of four higher-energy vibrationally excited states (ν15, ν14, ν13, and ν12), which form a complex polyad involving Coriolis and anharmonic coupling interactions, were analyzed by single-state models, thus producing only effective spectroscopic constants. Inclusion of rotationally resolved infrared transitions enabled the accurate and precise determination of vibrational band origins for the four lowest-energy fundamental states: ν18 = 542.601 824 3 (28) cm-1, ν17 = 665.183 128 5 (43) cm-1, ν16 = 682.256 910 5 (43) cm-1, and ν15 = 847.557 400 (11) cm-1.

A four-state Markov model for modelling bursty traffic and benchmarking of random early detection

Active Queue Management (AQM) techniques are crucial for managing packet transmission efficiently, maintaining network performance, and preventing congestion in routers. However, achieving these objectives demands precise traffic modeling and simulations in extreme and unstable conditions. The internet traffic has distinct characteristics, such as aggregation, burstiness, and correlation. This paper presents an innovative approach for modeling internet traffic, addressing the limitations of conventional modeling and conventional AQM methods' development, which are primarily designed to stabilize the network traffic. The proposed model leverages the power of multiple Markov Modulated Bernoulli Processes (MMBPs) to tackle the challenges of traffic modeling and AQM development. Multiple states with varying probabilities are used to model packet arrivals, thus capturing the burstiness inherent in internet traffic. Yet, the overall probability is maintained identical, irrespective of the number of states (one, two, or four), by solving linear equations with multiple variables. Random Early Detection (RED) was used as a case study method with different packet arrival probabilities based on MMBPs with one, two, and four states. The results showed that the proposed model influences the outcomes of AQM methods. Furthermore, it was found that RED might not effectively address network burstiness due to its relatively slow reaction time. As a result, it can be concluded that RED performs optimally only with a single-state model.

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

PREDICTING CLIENT RETURN TO ADULT PROTECTIVE SERVICES: A SECONDARY ANALYSIS OF RECURRENCE USING NATIONAL APS DATA

Abstract APS programs are provided by state and local governments across the U.S. to serve older adults and adults with disabilities facing abuse, neglect, or exploitation (“maltreatment”). The purpose of this analysis was to identify predictors of recurrence, which occurs when clients return to APS for investigation/services after their cases have been closed. The analysis combined several data sources, including the National Adult Maltreatment Reporting System (NAMRS), the first comprehensive, national data reporting system for APS programs. This analysis represents the first study of this kind to use NAMRS data. The main analyses used Bayesian logistic regression with client, case, and state-level APS system characteristics to predict 12-month recurrence in a multi-state model and in separate single-state models. The sample included data on 1,211,360 APS episodes that closed from 2015-2019 in 19 states. These episodes contained information on 946,477 unique APS clients. About one in five clients in this analysis experienced at least one episode of recurrence. Multivariate results revealed several significant predictors of recurrence (e.g., gender, maltreatment type, maltreatment disposition, episode length, case closure reason), with the largest being age (85+ vs. ≤59 AOR=0.876) and self-neglect (AOR=1.132). The findings may be helpful for APS programs to better recognize and serve clients at high risk of returning to APS after their cases close. The findings also highlight the ambiguity surrounding the concept of recurrence in the APS field and the need for further research to determine the circumstances in which recurrence may be considered a negative or a positive outcome.

Calibration of the Ångström–Prescott Model for Accurately Estimating Solar Radiation Spatial Distribution in Areas with Few Global Solar Radiation Stations: A Case Study of the China Tropical Zone

The Ångström–Prescott formula is commonly used in climatological calculation methods of solar radiation simulation. Aiming at the characteristics of a vast area, few meteorological stations, and uneven distribution in the tropical regions of China, in order to obtain the optimal parameters of the global solar radiation calculation model, this study proposes a suitable monthly global solar radiation model based on the single-station approach and the between-groups linkage of the A–P model, which utilizes monthly measured meteorological data from 80 meteorological stations spanning the period from 1996 to 2016 in the tropical zone of China, considering the similarity in changes of monthly sunshine percentage between stations. The applicability and accuracy of the correction parameters (a and b coefficients) were tested and evaluated, and then the modified parameters were extended to conventional meteorological stations through Thiessen polygons. Finally, the spatial distribution of solar radiation in the tropical region of China was simulated by kriging, IDW, and spline interpolation techniques. The results show the following: (1) The single-station model exhibited the highest accuracy in simulating the average annual global solar radiation, followed by the model based on the between-groups linkage. After optimizing the a and b coefficients, the simulation accuracy of the average annual global solar radiation increased by 5.3%, 8.1%, and 4.4% for the whole year, dry season, and wet season, respectively. (2) Through cross-validation, the most suitable spatial interpolation methods for the whole year, dry season, and wet season in the tropical zone of China were IDW, Kriging, and Spline, respectively. This research has positive implications for improving the accuracy of solar radiation prediction and guiding regional agricultural production.

Current and Future Role of Natural Gas Supply Chains in the Transition to a Low-Carbon Hydrogen Economy: A Comprehensive Review on Integrated Natural Gas Supply Chain Optimisation Models

Natural gas is the most growing fossil fuel due to its environmental advantages. For the economical transportation of natural gas to distant markets, physical (i.e., liquefaction and compression) or chemical (i.e., direct and indirect) monetisation options must be considered to reduce volume and meet the demand of different markets. Planning natural gas supply chains is a complex problem in today’s turbulent markets, especially considering the uncertainties associated with final market demand and competition with emerging renewable and hydrogen energies. This review study evaluates the latest research on mathematical programming (i.e., MILP and MINLP) as a decision-making tool for designing and planning natural gas supply chains under different planning horizons. The first part of this study assesses the status of existing natural gas infrastructures by addressing readily available natural monetisation options, quantitative tools for selecting monetisation options, and single-state and multistate natural gas supply chain optimisation models. The second part investigates hydrogen as a potential energy carrier for integration with natural gas supply chains, carbon capture utilisation, and storage technologies. This integration is foreseen to decarbonise systems, diversify the product portfolio, and fill the gap between current supply chains and the future market need of cleaner energy commodities. Since natural gas markets are turbulent and hydrogen energy has the potential to replace fossil fuels in the future, addressing stochastic conditions and demand uncertainty is vital to hedge against risks through designing a responsive supply chain in the project’s early design stages. Hence, hydrogen supply chain optimisation studies and the latest works on hydrogen–natural gas supply chain optimisation were reviewed under deterministic and stochastic conditions. Only quantitative mathematical models for supply chain optimisation, including linear and nonlinear programming models, were considered in this study to evaluate the effectiveness of each proposed approach.

Learning the Deep and the Shallow: Deep-Learning-Based Depth Phase Picking and Earthquake Depth Estimation

Abstract Automated teleseismic earthquake monitoring is an essential part of global seismicity analysis. Although constraining epicenters in an automated fashion is an established technique, constraining event depths is substantially more difficult. One solution to this challenge is teleseismic depth phases, but these can currently not be identified precisely by automatic detection methods. Here, we propose two deep-learning models, DepthPhaseTEAM and DepthPhaseNet, to detect and pick depth phases. For training the models, we create a dataset based on the ISC-EHB bulletin—a high-quality catalog with detailed phase annotations. We show how backprojecting the predicted phase arrival probability curves onto the depth axis yields accurate estimates of earthquake depth. Furthermore, we show how a multistation model, DepthPhaseTEAM, leads to better and more consistent predictions than the single-station model, DepthPhaseNet. To allow direct application of our models, we integrate them within the SeisBench library.

Transformer Graph Convolutional Network for Relative Travel-Time Shift Prediction

Abstract Predicting surface-wave travel-time shifts is valuable for analyzing potential effects caused by changes in medium properties, station clock errors, instrument response errors, and other factors. Many current neural networks used in seismology are single-station models trained using single-station (pair) data. However, most seismic methods require knowledge of the spatial positions between multiple stations. Multiple stations contain rich interrelationships and spatial information that cannot be exploited by single-station models. We proposed a multistation neural network structure Transformer Graph Convolutional Network (TGCN) that utilizes temporal attention and spatial attention to capture spatiotemporal information for predicting relative travel-time shifts. Before that, we introduced a method that treats station pairs as nodes and constructs a graph with multiple station pairs. We collected original ambient noise waveforms from 2017 to 2019 in the Alaska region and 2010 to 2014 in the southern California region to obtain relative travel-time shift sequences of station pairs for model training and testing. To showcase the improvement of spatial information to the model, we compared TGCN with two other baseline single-station models—temporal convolutional network and long short-term memory. Our proposed method predicted travel-time values more accurately than the two baseline models, and it also exhibited slower decay in performance when predicting over larger intervals. We also found that the number of station pairs has an impact on the model. When there are a sufficient number of station pairs, the model can effectively utilize the rich spatial information and achieve higher accuracy. Our approach, which incorporates spatiotemporal information, provides outputs that are more efficient and accurate compared with the traditional single-station (pair) method that only considers temporal information, suggesting that spatial information does enhance the performance of the model.

Spline-slip model for friction hysteresis identification

The paper concerns the study of the quadratic b-spline curve application for the regime of pre-sliding displacements in the GMS friction hysteresis model approximation. The impact of the modification on the effectiveness of modelling the friction properties and computational efficiency is analysed. The curve approximation technique with smooth quadratic b-spline, instead of piecewise linear curve generated by Maxwell elements, transforms the multi-state GMS model into a single-state model (the spline-slip model). The solution makes the precision of friction modelling in the spline-slip model independent on the number of parameters, thus eliminating the main problem of the GMS model. The simulations show that the new model incorporates all the friction modelling capabilities provided in the GMS model.

Estimating streamflow of the Kızılırmak River, Turkey with single- and multi-station datasets using Random Forests.

Predicting missing historical or forecasting streamflows for future periods is a challenging task. This paper presents open-source data-driven machine learning models for streamflow prediction. The Random Forests algorithm is employed and the results are compared with other machine learning algorithms. The developed models are applied to the Kızılırmak River, Turkey. First model is built with streamflow of a single station (SS), and the second model is built with streamflows of multiple stations (MS). The SS model uses input parameters derived from one streamflow station. The MS model uses streamflow observations of nearby stations. Both models are tested to estimate missing historical and predict future streamflows. Model prediction performances are measured by root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), and percent bias (PBIAS). The SS model has an RMSE of 8.54, NSE and R2 of 0.98, and PBIAS of 0.7% for the historical period. The MS model has an RMSE of 17.65, NSE of 0.91, R2 of 0.93, and PBIAS of -13.64% for the future period. The SS model is useful to estimate missing historical streamflows, while the MS model provides better predictions for future periods, with its ability to better catch flow trends.

Statistical kinetic modeling procedure for cationic polymerization and its application to polyisobutylene

AbstractA statistical modeling procedure is established for carbocation polymerization with the characteristics of living with chain transfer. A single state model is suggested for cases of narrow molecular weight distribution, whereas a multi‐state model is claimed for cases of broad distribution. In the multi‐state case, it is demonstrated how the overall kinetics can be derived using the expectations and variances of the kinetic parameters rather than those of individual states, thereby simplifying the modeling step. Although the expectation and variance do not follow the relationship of the Arrhenius equation, a procedure is given for how to account for temperature dependence. The proposed statistical method minimizes the number of parameters to identify and therefore is expected to help establish a model with fewer experiments. The whole procedure is demonstrated through an experimental study of isobutylene polymerization.

Distribution model transferability for a wide-ranging species, the Gray Wolf

Using existing data can be a reliable and cost-effective way to predict species distributions, and particularly useful for recovering or expanding species. We developed a current gray wolf (Canis lupus) distribution model for the western Great Lakes region, USA, and evaluated the spatial transferability of single-state models to the region. This study is the first assessment of transferability in a wide-ranging carnivore, as well as one of few developed for large spatial extents. We collected 3500 wolf locations from winter surveys in Minnesota (2017–2019), Wisconsin (2019–2020), and Michigan (2017–2020). We included 10 variables: proportion of natural cover, pastures, and crops; distance to natural cover, agriculture, developed land, and water; major and minor road density; and snowfall (1-km res.). We created a regional ensemble distribution by weight-averaging eight models based on their performance. We also developed single-state models, and estimated spatial transferability using two approaches: state cross-validation and extrapolation. We assessed performance by quantifying correlations, receiver operating characteristic curves (ROC), sensitivities, and two niche similarity indices. The regional area estimated to be most suitable for wolves during winter (threshold = maximum sensitivity/specificity) was 106,465 km2 (MN = 48,083 km2, WI = 27,757 km2, MI = 30,625 km2) and correctly predicted 88% of wolf locations analyzed. Increasing natural cover and distance to crops were consistently important for determining regional and single-state wolf distribution. Extrapolation (vs. cross-validation) produced results with the greatest performance metrics, and were most similar to the regional model, yet good internal performance was unrelated to greater extrapolation performance. Factors influencing species distributions are scale-dependent and can vary across areas due to behavioral plasticity. When extending inferences beyond the current occurrence of individuals, assessing variation in ecology such as habitat selection, as well as methodological factors including model performance, will be critical to avoid poor scientific interpretations and develop effective conservation applications. In particular, accurate distribution models for recovering or recovered carnivores can be used to develop plans for habitat management, quantify potential of unoccupied habitat, assess connectivity modeling, and mitigate conflict, facilitating long-term species persistence.

MA'AT: A Web-Based Application to Determine Rotamer Population Distributions in Solution from Nuclear Magnetic Resonance Spin-Coupling Constants.

A hybrid experimental-computational method to determine conformational equilibria of molecules in solution has been developed based on the use of redundant nuclear magnetic resonance (NMR) spin-spin coupling constants (spin-couplings; J-couplings), density functional theory (DFT) calculations, and circular statistics. The mathematics that underpins the method, known as MA'AT analysis, is presented, and key components of a computer program that applies this algorithm are discussed. The method was tested using single-state and multi-state models to identify the factors required to obtain reliable results, to establish the limitations of the method, and to highlight techniques to evaluate the uniqueness of solution.

Effects of economic policy uncertainty: A regime switching connectedness approach

This paper investigates the impact of economic policy uncertainty (EPU) in the US and China on international financial markets across different states. Although earlier studies have examined the interactions between EPUs and financial markets, few considered the issue from the state-dependence perspective. To fill this gap, we extend the Diebold and Yilmaz connectedness to the two-state Markov Switching Vector Autoregressive model to conduct the empirical analysis. Empirical results indicate connectedness is larger (smaller) in the high (low) volatility state than in the single-state model. Moreover, the two EPUs play distinct roles at different time scales. On the weekly time scale, EPUs are influenced by the financial markets only in the low volatility state while the China EPU becomes a net information sender in the high volatility state. Our findings imply that more attention should be devoted to the possible negative impact of China EPU.

An Analysis of a Two-State Markovian Retrial Queueing Model with Priority Customers

Objective: This study considered a system of retrial queues with two types of customers: high-priority and low-priority. This study deals to find the time dependent probabilities of exact number of arrivals and departures from the system when server is free or busy. Numerical solution and graphical representation will also be presented. Method: For this model, we solved difference differential equations recursively and used Laplace transformation to obtain the transient state probabilities of exact number of arrivals and departures from the system when server is free or busy. Findings: Timedependent probabilities of exact number of arrivals (primary arrivals, arrivals in high priority queue, arrivals in low priority queue) in the system and exact number of departures (primary departures, departures from high priority queue, departures from low priority queue) from the system by a given time for when the server is idle and when the server is busy are obtained. Various interesting performance measures along with some special cases are also obtained. Conversion of two state model into single state model was discussed. Numerical illustrations are also presented using MATLAB programming along with the busy period probabilities of the system and server. Novelty: In past research, models considered arrivals and departures from the orbit whereas in present model arrivals and departures from the system are studied along with the concept of retrial and priority customers. Applications: Priority retrial queues are used in many applications like real time systems, operating systems, manufacturing system, simulation and medical service systems. Keywords: Arrivals; Departures; Probability; Priority; Retrial

Investigation of the relationship between f0f2 and the TEC using single station neural network models

The relationship between the ionospheric critical plasma frequencies (f0F2) and GNSS-TEC (Global Navigation Satellite System –Total Electron Content) measurements was investigated using an artificial neural network method. About 20 pairs of ionosonde-GNSS receiver stations from 2000 to 2016 were used. Results from this work indicate that the relationship between f0F2 and TEC is mostly affected by the seasons, followed by the level of solar activity, and then the local time. Geomagnetic activity was the least significant of the factors investigated. The relationship between f0F2 and TEC was also shown to exhibit spatial variation; the variation is less conspicuous for closely located stations. Single station models predicted the f0F2 more accurately at their particular localities and clearly overestimated values of the f0F2 ionosonde observations when used at different localities. This finding indicates that model predictions are better (in terms of reduced prediction errors) for the stations for which they are developed than for a different station. Our result visibly point out that models developed for a particular station cannot be effectively applied in another station located farther apart in space. The new approach described in this study represents an important contribution in space weather prediction.

Strong anomalous diffusive behaviors of the two-state random walk process.

The phenomenon of the two-state process is observed in various systems and is increasingly attracting attention, such that there is a need for a theoretical model of the process. In this paper, we present a prototypal two-state random walk (TSRW) model of a renewal process alternating between the continuous-time random walk (CTRW) state and Lévy walk (LW) state. The jump length distribution of the CTRW state is assumed to be Gaussian whereas the time distributions of the two states are both considered to follow a power law. The diffusive behavior is analyzed and discussed by calculating the mean squared displacement (MSD) analytically and numerically. The results reveal that it displays strong anomalous diffusive behaviors caused by random motions of both states, i.e., two anomalous diffusion terms coexist in the expression of the MSD, and the time distribution which has the heavier tail determines their forms. Moreover, because the two diffusion terms originate from different mechanisms, we find that the diffusion can be characterized by either the term with the largest diffusion exponent or the term with the largest diffusion coefficient at long timescales, which shows very different properties from the single-state process. In addition, the two-state nature of the process of the particle moving in a velocity field makes the TSRW model applicable to describe it. Results obtained from the two-state model reveal that the diffusion can even exhibit subdiffusive behavior, which is significantly different from known results obtained using the single-state model.

Enhanced Battery Power Constraint Handling in MPC-Based HEV Energy Management: A Two-Phase Dual-Model Approach

This article presents a novel two-phase dual-model approach to more appropriately handle battery power constraint in model predictive control (MPC)-based hybrid electric vehicle (HEV) energy management. The approach fuses two typical battery models with different fidelities in two consecutive phases of MPC's prediction horizon, where more accurate first-order resistor-capacitor (RC) model is adopted in the near phase, while a purely ohmic impedance model is used in the more distant phase. More importantly, it is mathematically rigorously proved that the first-order RC model can be reformulated as an equivalent single-state model while preserving accurate voltage calculation, provided that the length of phase one is properly determined. Finally, the formulated optimal energy management problem is solved using a forward dynamic programming (FDP) algorithm. Both numerical examination and model in the loop (MiL) test using an experimentally validated forward-facing simulator demonstrate that compared with traditional approach, the proposed approach can achieve better optimality in the condition of very sparse state discretization, which is imperative for real-time implementation.

NGA-subduction global ground motion models with regional adjustment factors

We develop semi-empirical ground motion models (GMMs) for peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral accelerations for periods from 0.01 to 10 s, for the median orientation-independent horizontal component of subduction earthquake ground motion. The GMMs are applicable to interface and intraslab subduction earthquakes in Japan, Taiwan, Mexico, Central America, South America, Alaska, the Aleutian Islands, and Cascadia. The GMMs are developed using a combination of data inspection, data regression with respect to physics-informed functions, ground-motion simulations, and geometrical constraints for certain model components. The GMMs capture observed differences in source and path effects for interface and intraslab events, conditioned on moment magnitude, rupture distance, and hypocentral depth. Site effect and aleatory variability models are shared between event types. Regionalized GMM components include the model constant (that controls ground motion amplitude), anelastic attenuation, magnitude-scaling break point, linear site response, and sediment depth terms. We develop models for the aleatory between-event variability [Formula: see text], within-event variability [Formula: see text], single-station within-event variability [Formula: see text], and site-to-site variability [Formula: see text]. Ergodic analyses should use the median GMM and aleatory variability computed using the between-event and within-event variability models. An analysis incorporating non-ergodic site response should use the median GMM at the reference shear-wave velocity condition, a site-specific site response model, and aleatory variability computed using the between-event and single-station within-event variability models. Epistemic uncertainty in the median model is represented by standard deviations on the regional model constants, which facilitates scaled-backbone representations of model uncertainty in hazard analyses.

A two-dimensional Multiserver Queuing system with repeated attempts and impatience

Objective: This study discusses a two-state multiserver retrial queueing system, where the customer may leave the system due to impatience. In this paper, we deal with the time dependent probabilities when all, some or none servers are busy. Method: For this model, we solved difference differential equations recursively and obtained the time dependent probabilities when all, some or none servers are busy. Findings: Time dependent probabilities of exact number of arrivals and exact number of departures at when all, some or none servers are busy are obtained. In this paper, some kind of verification and converting two state model into single state model are discussed. Some special cases of interest are also discussed. Novelty: In communication networks, multiple servers are used to reduce traffic congestion and improve system performance. The operation mode of a call center with repeated attempts provides an initial motivation for our study. Keywords: Impatience; Multiserver; Probability; Queueing; Retrial