Exponential Approaches Research Articles

Relaxing the assumption of constant transition rates in a multi-state model in hospital epidemiology

BackgroundMulti-state models are being increasingly used to capture complex disease pathways. The convenient formula of the exponential multi-state model can facilitate a quick and accessible understanding of the data. However, assuming time constant transition rates is not always plausible. On the other hand, obtaining predictions from a fitted model with time-dependent transitions can be challenging. One proposed solution is to utilise a general simulation algorithm to calculate predictions from a fitted multi-state model.MethodsPredictions obtained from an exponential multi-state model were compared to those obtained from two different parametric models and to non-parametric Aalen-Johansen estimates. The first comparative approach fitted a multi-state model with transition-specific distributions, chosen separately based on the Akaike Information Criterion. The second approach was a Royston-Parmar multi-state model with 4 degrees of freedom, which was chosen as a reference model flexible enough to capture complex hazard shapes. All quantities were obtained analytically for the exponential and Aalen-Johansen approaches. The transition rates for the two comparative approaches were also obtained analytically, while all other quantities were obtained from the fitted models via a general simulation algorithm. Metrics investigated were: transition probabilities, attributable mortality (AM), population attributable fraction (PAF) and expected length of stay. This work was performed on previously analysed hospital acquired infection (HAI) data. By definition, a HAI takes three days to develop and therefore selected metrics were also predicted from time 3 (delayed entry).ResultsDespite clear deviations from the constant transition rates assumption, the empirical estimates of the transition probabilities were approximated reasonably well by the exponential model. However, functions of the transition probabilities, e.g. AM and PAF, were not well approximated and the comparative models offered considerable improvements for these metrics. They also provided consistent predictions with the empirical estimates in the case of delayed entry time, unlike the exponential model.ConclusionWe conclude that methods and software are readily available for obtaining predictions from multi-state models that do not assume constant transition rates. The multistate package in Stata facilitates a range of predictions with confidence intervals, which can provide a more comprehensive understanding of the data. User-friendly code is provided.

Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model

The economic and health impacts resulting from the greenhouse effect is a major concern in many countries. The transportation sector is one of the major contributors to greenhouse gas (GHG) emissions worldwide. Almost 15 percent of the global GHG and over 20 percent of energy-related CO2 emissions are produced by the transportation sector. Quantifying GHG emissions from the road transport sector assists in assessing the existing vehicles’ energy consumptions and in proposing technological interventions for enhancing vehicle efficiency and reducing energy-supply greenhouse gas intensity. This paper aims to develop a model for the projection of GHG emissions from the road transport sector. We consider the Vehicle-Kilometre by Mode (VKM) to Number of Transportation Vehicles (NTV) ratio for the six different modes of transportation. These modes include motorcycles, passenger cars, tractors, single-unit trucks, buses and light trucks data from the North American Transportation Statistics (NATS) online database over a period of 22 years. We use multivariate regression and double exponential approaches to model the projection of GHG emissions. The results indicate that the VKM to NTV ratio for the different transportation modes has a significant effect on GHG emissions, with the coefficient of determination adjusted R2 and R2 values of 89.46% and 91.8%, respectively. This shows that VKM and NTV are the main factors influencing GHG emission growth. The developed model is used to examine various scenarios for introducing plug-in hybrid electric vehicles and battery electric vehicles in the future. If there will be a switch to battery electric vehicles, a 62.2 % reduction in CO2 emissions would occur. The results of this paper will be useful in developing appropriate planning, policies, and strategies to reduce GHG emissions from the road transport sector.

A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece

The aim of this paper is the generation of a time-series based statistical data-driven procedure in order to track an outbreak. At first are used univariate time series models in order to predict the evolution of the reported cases. Moreover, are considered combinations of the models in order to provide more accurate and robust results. Additionally, statistical probability distributions are considered in order to generate future scenarios. Final step is the build and use of an epidemiological model (tSIR) and the calculation of an epidemiological ratio (R0) for estimating the termination of the outbreak. The time series models include Exponential Smoothing and ARIMA approaches from the classical models, also Feed-Forward Artificial Neural Networks and Multivariate Adaptive Regression Splines from the machine learning toolbox. Combinations include simple mean, Newbolt-Granger and Bates-Granger approaches. Finally, the tSIR model and the R0 ratio are used for estimating the spread and the reversion of the pandemic. The suggested procedure is used to track the COVID-19 epidemic in Greece. This epidemic has appeared in China in December 2019 and has been widespread since then to all over the world. Greece is the center of this empirical study as is considered an early successful paradigm of resistance against the virus.

The running curvaton * * LH is funded by Hunan Natural Science Foundation (2020JJ5452) and Hunan Provincial Department of Education (19B464). WL is funded by NSFC (1175012)

In this paper, we propose a homogeneous curvaton mechanism that operates during the preheating process and in which the effective mass is running (i.e., its potential consists of a coupling term and an exponential term whose contribution is subdominant thereto). This mechanism can be classified into either narrow resonance or broad resonance cases, with the spectral index of the curvaton consituting the deciding criteria. The inflationary potential is that of chaotic inflation (i.e., a quadratic potential), which could result in a smooth transition into the preheating process. The entropy perturbations are converted into curvature perturbations, which we validate using the formalism. By neglecting the exponential term's contribution to the curvaton potential, we calculate the power spectrum and the nonlinear non-Gaussian parameter . Our calculations analytically show that these two observables are independent of the inflaton potential. Finally, when the curvaton decays (and the inflaton field vanishes), the exponential potential approaches a constant value similar to that of the cosmological constant, which may play the role of dark energy.

Reactive mixing in rotating packed beds: On the packing’s role and mixing modeling

Rotating packed beds (RPBs) are some of the most promising modular and intensified apparatuses for continuous mixing of large liquid streams. In this study, the influence of packing depth, liquid distribution, and volumetric ratio on mixing processes are for the first time investigated in detail. It is shown that the packing seems to be more important at low volumetric ratios and an overlapping liquid distribution, but less important – or even interfering with fast mixing – at higher volumetric ratios and an opposite liquid distribution. Based on these findings, a promising novel type of HiGee equipment is suggested, that is particularly suitable for fast mixing at high volumetric ratios. Additionally, the linear and the exponential approaches of the incorporation model, as well as of the Interaction-by-Exchange-with-the-Mean (IEM) model, are investigated for their suitability to accurately describe the mixing process. On the basis of revised kinetics for the Dushman reaction, micromixing time parameters in the order of 10−1 ms to 101 ms are obtained for the investigated operating conditions.

Quantum Zeno effect in a circuit-QED system

We study the quantum Zeno effect of the circuit-QED system at finite temperature for both selective measurement and nonselective measurement proposals. For the selective measurement proposal, we explicitly show that the counter-rotating terms (CRTs) become as significant as rotating wave terms (RWTs) with increase of temperature. We also derive an analytic expression for the survival probability which correctly captures the leading contribution for both RWTs and CRTs in the frequent measurement limit. Beyond this limit, we observe a nonexponential decay of the survival probability by further considering the effect of the variation of initial states during repeated measurements. For the nonselective measurement proposal, we find that the survival probability demonstrates the same behavior as in the selective measurement case in the short-time regime while it deviates from an exponential form and approaches a nonvanishing value in the long-time scale due to the repopulation effect.

Simulation of Growth and Leaf Area Index of Quality Protein Maize Varieties in the Southwestern Savannah Region of the DR-Congo

Logistic and exponential approaches have been used to simulate plant growth and leaf area index (LAI) in different growing conditions. The objective of the present study was to develop and evaluate an approach to simulate maize LAI that expresses key physiological and phonological processes using a minimum entry requirement for Quality Protein maize (QPM) varieties grown in the southwestern region of the DR-Congo. Data for the development and testing of the model were collected manually in experimental plots using a non-destructive method. Simulation results revealed measurable variations between crop seasons (long season A and short season B) and between the two varieties (Mudishi-1 and Mudishi-3) for height, number of visible leaves, and LAI. For both seasons, Mudishi-3, a short stature variety was associated with expected stable yield based on simulation data. In general, the model simulated reliably all the parameters including the LAI. The LAI value for mudishi-1 was higher than that of Mudishi-3. There were significant differences among the model parameters (K, Ti, a, b, Tf) and between the two varieties. In all crop conditions studied and for the two varieties, the senescence rate (a) was higher, while the growth rate (b) was lower compared to the estimates based on the STICS model.

Performance Evaluation of Medium-Term Load Forecasting Approaches: A Case Study of Ogun State, Nigeria

The place of electrical energy in enhancement of this computer age cannot be over-emphasised. Its forecast plays a significant functions in energy industry, helps the government and private sectors in making the precise decision regarding energy management practices. This paper presents performance evaluation of medium-term load forecasting techniques: a case study of Ogun State, Nigeria. Two different approaches were used using the previous load consumption in 2017 for the forecast. Least square approach compared with regression exponential approaches gave the least value of Mean Average Percentage Error (MAPE) and Root Mean Square Error (RMSE), which are 1.8212% and 0.004472 respectively. The anticipated percentage load growth for the months of July-December, 2018 forecasted with least square approach were 34.06%, 33.54%, 36.10%, 31.10%, 32.23% and 30.15% respectively, acute gas supply caused by pipeline vandalisation and theft of distribution/sub-station materials could be held responsible for low load growth in the month of December. The results of this analysis will assist the Regional Headquarters, Ibadan Electricity Distribution Company (IBEDC), Abeokuta, Ogun State in making effective planning, operation and management of energy across the state.

Combining Orthogonal Chain‐End Deprotections and Thiol–Maleimide Michael Coupling: Engineering Discrete Oligomers by an Iterative Growth Strategy

AbstractOrthogonal maleimide and thiol deprotections were combined with thiol–maleimide coupling to synthesize discrete oligomers/macromolecules on a gram scale with molecular weights up to 27.4 kDa (128mer, 7.9 g) using an iterative exponential growth strategy with a degree of polymerization (DP) of 2n−1. Using the same chemistry, a “readable” sequence‐defined oligomer and a discrete cyclic topology were also created. Furthermore, uniform dendrons were fabricated using sequential growth (DP=2n−1) or double exponential dendrimer growth approaches (DP=2 −1) with significantly accelerated growth rates. A versatile, efficient, and metal‐free method for construction of discrete oligomers with tailored structures and a high growth rate would greatly facilitate research into the structure–property relationships of sophisticated polymeric materials.

Combining Orthogonal Chain-End Deprotections and Thiol-Maleimide Michael Coupling: Engineering Discrete Oligomers by an Iterative Growth Strategy.

Orthogonal maleimide and thiol deprotections were combined with thiol-maleimide coupling to synthesize discrete oligomers/macromolecules on a gram scale with molecular weights up to 27.4 kDa (128mer, 7.9 g) using an iterative exponential growth strategy with a degree of polymerization (DP) of 2n -1. Using the same chemistry, a "readable" sequence-defined oligomer and a discrete cyclic topology were also created. Furthermore, uniform dendrons were fabricated using sequential growth (DP=2n -1) or double exponential dendrimer growth approaches (DP=22n -1) with significantly accelerated growth rates. A versatile, efficient, and metal-free method for construction of discrete oligomers with tailored structures and a high growth rate would greatly facilitate research into the structure-property relationships of sophisticated polymeric materials.

A stiffly accurate integrator for elastodynamic problems

We present a new integration algorithm for the accurate and efficient solution of stiff elastodynamic problems governed by the second-order ordinary differential equations of structural mechanics. Current methods have the shortcoming that their performance is highly dependent on the numerical stiffness of the underlying system that often leads to unrealistic behavior or a significant loss of efficiency. To overcome these limitations, we present a new integration method which is based on a mathematical reformulation of the underlying differential equations, an exponential treatment of the full nonlinear forcing operator as opposed to more standard partially implicit or exponential approaches, and the utilization of the concept of stiff accuracy which ensures that the efficiency of the simulations is significantly less sensitive to increased stiffness. As a consequence, we are able to tremendously accelerate the simulation of stiff systems compared to established integrators and significantly increase the overall accuracy. The advantageous behavior of this approach is demonstrated on a broad spectrum of complex examples like deformable bodies, textiles, bristles, and human hair. Our easily parallelizable integrator enables more complex and realistic models to be explored in visual computing without compromising efficiency.

Degenerations of rational Bézier surface with weights in the form of exponential function

Rational Bezier surface is a widely used surface fitting tool in CAD. When all the weights of a rational Bezier surface go to infinity in the form of power function, the limit of surface is the regular control surface induced by some lifting function, which is called toric degenerations of rational Bezier surfaces. In this paper, we study on the degenerations of the rational Bezier surface with weights in the exponential function and indicate the difference of our result and the work of Garc´ia-Puente et al. Through the transformation of weights in the form of exponential function and power function, the regular control surface of rational Bezier surface with weights in the exponential function is defined, which is just the limit of the surface. Compared with the power function, the exponential function approaches infinity faster, which leads to surface with the weights in the form of exponential function degenerates faster.

DATA MINING WORKSPACE AS AN OPTIMIZATION PREDICTION TECHNIQUE FOR SOLVING TRANSPORT PROBLEMS

Summary. This article addresses the study related to forecasting with an actual highspeed decision making under careful modelling of time series data. The study uses datamining modelling for algorithmic optimization of transport goals. Our finding brings to the future adequate techniques for the fitting of a prediction model. This model is going to be used for analyses of the future transaction costs in the frontiers of the Czech Republic. Time series prediction methods for the performance of prediction models in the package of Statistics are Exponential, ARIMA and Neural Network approaches. The primary target for a predictive scenario in the data mining workspace is to provide modelling data faster and with more versatility than the other management techniques.

Weighted proportional fairness and pricing based resource allocation for uplink offloading using IP flow mobility

Mobile data offloading has been proposed as a solution for the network congestion problem that is continuously aggravating due to the increase in mobile data demand. However, the majority of the state-of-the-art is focused on the downlink offloading, while the change of mobile user habits, like mobile content creation and uploading, makes uplink offloading a rising issue. In this work we focus on the uplink offloading using IP Flow Mobility (IFOM). IFOM allows a LTE mobile User Equipment (UE) to maintain two concurrent data streams, one through LTE and the other through WiFi access technology, that presents uplink limitations due to the inherent fairness design of IEEE 802.11 DCF by employing the CSMA/CA scheme with a binary exponential backoff algorithm. In this paper, we propose a weighted proportionally fair bandwidth allocation algorithm for the data volume that is being offloaded through WiFi, in conjunction with a pricing-based rate allocation for the rest of the data volume needs of the UEs that are transmitted through the LTE uplink. We aim to improve the energy efficiency of the UEs and to increase the offloaded data volume under the concurrent use of access technologies that IFOM allows. In the weighted proportionally fair WiFi bandwidth allocation, we consider both the different upload data needs of the UEs, along with their LTE spectrum efficiency and propose an access mechanism that improves the use of WiFi access in uplink offloading. In the LTE part, we propose a two-stage pricing-based rate allocation under both linear and exponential pricing approaches, aiming to satisfy all offloading UEs regarding their LTE uplink access. We theoretically analyse the proposed algorithms and evaluate their performance through simulations. We compare their performance with the 802.11 DCF access scheme and with a state-of-the-art access algorithm under different number of offloading UEs and for both linear and exponential pricing-based rate allocation for the LTE uplink. Through the evaluation of energy efficiency, offloading capabilities and throughput performance, we provide an improved uplink access scheme for UEs that operate with IFOM for uplink offloading.

Observational Study of Wind Shear in Northeastern Brazil

The wind energy share is growing fast in Brazil and a better understanding of wind speed vertical profiles is essential for accurate power density estimates. The wind shear is highly variable in space and time, being influenced by surface layer stability. Studies concerning wind vertical profiles may have a significant contribution to the Brazilian energy sector. However, wind observational data are very scarce and most of them were acquired in automated weather stations. This work investigates the wind vertical profile data acquired at a wind mast in a semi-arid region of Brazilian Northeastern region, correlating it to stability conditions of the surface boundary layer, surface roughness and friction velocity. The results indicated that strong winds (>7 m sec-1) overcome the stability effects, allowing a better estimate of roughness length, friction velocity and exponential coefficient. In a second step, the wind speed at 50 m agl. Were estimated from wind measurements at 25 m agl. The wind estimates were compared to observations, showing that exponential and logarithmic approaches were able to simulate wind profile. The exponential approach presented the lowest BIAS for rainy season, but overestimated high wind velocities. Finally, a sensitivity analysis demonstrated how uncertainties on roughness length and exponential coefficient impacts on BIAS deviation and reduce the confidence on wind power density estimates. The results demonstrated the importance in understanding how wind vertical profile is related to atmospheric stability condition in order to get reliable values for the shear parameters from mast observations. It is still more critical if wind database is short-term and when dealing with lower wind speeds.

F0 contour approximation model for a one-stream tonal word recognition system

The performance of a non-tonal speech recognition system degrades when confronted with the task of recognizing tonal words. Several speech recognition applications require tonal word recognition. Therefore, this paper considers how to create a suitable tone model for a tonal syllable recognition system serving application devices based on a one-stream scheme. The fundamental frequency contour (F0 contour) approximation model is proposed here to estimate F0 continuity contours for all of a tonal word. The processes of approximation include voice detection, F0 smoothing, F0 forecasting, and F0 normalization. To model the F0 contours of unvoiced regions belonging to F0 forecasting, a linear regression function is used to create an approximate F0 contour. Experimental results indicate that the proposed model improves the accuracy of tonal word recognition by 8.6% and 12.2%, respectively, compared with conventional random and exponential approaches.

From Circuit Theory, Simulation to SPICE<sup>Diego<\/sup>: A Matrix Exponential Approach for Time-Domain Analysis of Large-Scale Circuits

SPICE (Simulation Program with Integrated Circuit Emphasis) is a widely used circuit simulation framework for integrated circuit designs. The basic skeleton of SPICE time domain simulation was derived from the versions developed in UC Berkeley during the 1970s. In this paper, we report most recent numerical integration methods to improve traditional SPICE time integration schemes, which are based on linear multi-step and low order approximation for the circuit differential equation system. Recently, matrix exponential based time domain simulation algorithms are being developed to address long-term issues in the standard numerical integration methods. We review the related techniques in matrix exponential based approaches and state several distinguished features in challenging simulation problems, such as linear power network analysis and nonlinear circuit system simulation (SPICEDiego). We believe that the matrix exponential approaches can shed new light on the research and development of future circuit simulation algorithmic systems.

Empirical Modeling of Annual Fishery Landings

Forecasting plays an essential role in policy formulation and implementation especially in the management of fisheries resources. In this paper, various techniques of forecasting using time series analysis were evaluated on annual fishery production data. In addition to the Box-Jenkins approach, other methods such as the feed forward neural network and exponential smoothing approaches were also examined. A parsimonious model for each forecasting approach was then selected using penalized likelihoods. The chosen models were then evaluated based on their ability to produce accurate forecasts. Implications of the findings as discussed revealed that no particular method was ideal for modeling all landings. Hence when forecasting fishery landings, it is recommended that different structural approaches be compared before selecting an appropriate one for use.

Risk model validation for BRICS countries: a value-at-risk, expected shortfall and extreme value theory approach

In this paper, we employ value-at-risk (VaR) and expected shortfall (ES) as risk measures to assess the competency of several volatility models, based on the stock indexes of the BRICS countries (Brazil, Russia, India, China and South Africa) after the financial crisis. The aim is to determine the most appropriate model for each country and identify a set of common models for the BRICS countries. A family of generalized autoregressive conditional heteroscedasticity (GARCH) models, extreme value theory (EVT) and a dynamic bivariate technique are considered to demonstrate different volatility dynamics. Some models are ruled out under the presented backtesting operations, while the nonrejected models are ranked using two loss functions. In spite of returns displaying large kurtosis, the models with normal innovation generally prove to have better estimates ofVaR and ES. GARCH and fractionally integrated GARCH are preferred by Brazil and Russia, while India favors dual models and the Student t distribution. The Glosten, Jagannathan and Runkle GARCH-N is the best model for South Africa, which is the only country that rejects the stationary EVT approach. Despite these contrasting results, we find that it is possible to obtain more than one model that can be used to model all of the BRICS countries: these are the integrated GARCH-N and exponential GARCH-EVT approaches.

A Study of a Non-Standard Eigenvalue Problem and its Application to Three-Layer Immiscible Porous Media and Hele-Shaw Flows with Exponential Viscous Profile

We consider a non-standard eigenvalue problem arising in stability studies of 3-layer immiscible porous media and Hele-Shaw flows which contain the viscous profile of the middle layer as a coefficient in the eigenvalue problem. We characterize the eigenvalues and eigenfunctions of this eigenvalue problem. We then apply this characterization to an exponential viscous profile and numerically compute the associated eigenvalues and eigenfunctions. We provide an explicit sequence of numbers that give upper and lower bounds on the eigenvalues. We also discuss the limiting cases when either the length of the middle layer approaches zero or the exponential viscous profile approaches a constant viscosity profile.