Narrow Results

This paper concerns the modeling of smart health sensors for data monitoring, where the data are eventually noisy, with correlated noise. In this work, we applied Clifford-based wavelets/multiwavelets for correlated noise in multi-sensors health data monitoring by estimating the set of sensor nodes minimizing an eventual error computed by the signal values at the selected nodes mostly caused by the correlated noise. Instead of directly minimizing the estimation error, we focused on evaluating a multi-level scheme based on multiwavelets for the estimation of the error between the parameter vector and its sub-vector of those nodes. Numerical simulations are provided with a comparison to some recent existing works. This model showed a performance and a fast time execution compared to those existing works. This model exceeds these models by the non-necessity to assume a priory structure of the data. Wavelets are capable to detect, localize, and eliminate the noise, even correlated, efficiently via the independent uncorrelated multiwavelets’ components.

This study complements the emerging literature on the COVID-19 pandemic and provides direction, in the case of Nigeria, for targeting monetary policy response to mitigate the pandemic’s economic consequences. We simulate three scenarios: (i) do-nothing; (ii) reduce MPR gradually and (iii) reduce MPR drastically; amidst falling oil prices. The do-nothing scenario, although inflationary, would produce a marginal appreciation of the Naira/USD exchange rate. Gradual or drastic reduction of MPR would deliver relative price stability, but will undermine exchange rate stability and deplete external reserves. MPR should optimally not be reduced below 12% in response to the economic effect of the pandemic.

Multicellular models of homogeneous and isotropic human atria have been developed by incorporating cellular models of membrane electrical activity of single human atrial myocyte into a parabolic partial differential equation. These models are used to study the rate dependent conduction velocity of excitation wave, vulnerability of tissue to reentry and dynamical behaviors of reentry. Bidomain models were also developed to study the actions of a large and brief external electrical stimulus on wave propagation in human atria. These studies provide basic insights to understand the onset and termination of atrial arrhythmias in the human heart.

During the past decades the components involved in cellular signal transduction from membrane receptors to gene activation in the nucleus have been studied in detail. Based on the qualitative biochemical knowledge, signalling pathways are drawn as static graphical schemes. However, the dynamics and control of information processing through signalling cascades is not understood. Here we show that based on time resolved measurements it is possible to quantitatively model the nonlinear dynamics of signal transduction. To select an appropriate model we performed parameter estimation by maximum likelihood and statistical testing. We apply this approach to the JAK-STAT signalling pathway that was believed to represent a feed-forward cascade. We show by comparison of different models that this hypothesis is insufficient to explain the experimental data and suggest a new model including a delayed feedback.

On analyzing data of biochemical reaction dynamics monitored by time-resolved spectroscopy, one faces the problem that the concentration time courses of the involved components are not directly observed, but the superposition of their absorption spectra. Furthermore the single spectra are often unknown, because the corresponding reagents cannot be isolated. We propose a method based on Bock's multiple shooting algorithm to estimate the rate constants and individual spectra simultaneously. Applying this procedure to a biochemical reaction we identify the specific rate constants characterizing the reaction dynamics as well as the nonobservable absorption spectra. The results lead to a better understanding of the kinetics of a novel modification reaction which was used as trapping reaction in disulfide bond mediated protein folding reactions.

In modeling nonlinear dynamical systems, error growth can arise both because of dynamical error in the model, and observational error in measurements of the underlying system. Errors thus introduced can be further amplified by the system dynamics. This paper develops a method to approximate error growth, based on the model drift, for the general case when the model is given by a (possibly large) set of ordinary differential equations. The ability of the model to shadow (stay close to) the true system is analyzed, and a criterion for model quality based on the error dynamics is proposed. Examples are given with a range of models, which are chosen to be representative of different types of model error.

In much of the previous study of switched dynamical systems, it has been assumed that switching occurs at a common border between two regions in the same space as the system trajectory crosses the border. However, models arising from this consideration cannot cover systems whose trajectories do not actually "cross" the border. A typical example is the current-mode controlled boost converter whose trajectory is "reflected" at the border. In this paper, we propose a general method to model switched dynamical systems. Also, we suggest an analytical procedure to determine periodic solutions and their stability. The method is developed in terms of solution flows, and no solution has to be explicitly written. Most practical switched dynamical systems can be modeled and analyzed by this method.

Nonlinear dynamics of a real plane and periodically forced triple pendulum is investigated experimentally and numerically. Mathematical modeling includes details, taking into account some characteristic features (for example, real characteristics of joints built by the use of roller bearings) as well as some imperfections (asymmetry of the forcing) of the real system. Parameters of the model are obtained by a combination of the estimation from experimental data and direct measurements of the system's geometric and physical parameters. A few versions of the model of resistance in the joints are tested in the identification process. Good agreement between both numerical simulation results and experimental measurements have been obtained and presented. Some novel features of our real system chaotic dynamics have also been reported, and a novel approach of the rolling bearings friction modeling is proposed, among other.

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modifications in their intrinsic organization. For instance, the network of transportation routes between cities and towns (hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways (paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation (in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

A study of a DC–DC boost converter fed by a photovoltaic (PV) generator and supplying a constant voltage load is presented. The input port of the converter is controlled using fixed frequency pulse width modulation (PWM) based on the loss-free resistor (LFR) concept whose parameter is selected with the aim to force the PV generator to work at its maximum power point. Under this control strategy, it is shown that the system can exhibit complex nonlinear behaviors for certain ranges of parameter values. First, using the nonlinear models of the converter and the PV source, the dynamics of the system are explored in terms of some of its parameters such as the proportional gain of the controller and the output DC bus voltage. To present a comprehensive approach to the overall system behavior under parameter changes, a series of bifurcation diagrams are computed from the circuit-level switched model and from a simplified model both implemented in PSIM^© software showing a remarkable agreement. These diagrams show that the first instability that takes place in the system period-1 orbit when a primary parameter is varied is a smooth period-doubling bifurcation and that the nonlinearity of the PV generator is irrelevant for predicting this phenomenon. Different bifurcation scenarios can take place for the resulting period-2 subharmonic regime depending on a secondary bifurcation parameter. The boundary between the desired period-1 orbit and subharmonic oscillation resulting from period-doubling in the parameter space is obtained by calculating the eigenvalues of the monodromy matrix of the simplified model. The results from this model have been validated with time-domain numerical simulation using the circuit-level switched model and also experimentally from a laboratory prototype. This study can help in selecting the parameter values of the circuit in order to delimit the region of period-1 operation of the converter which is of practical interest in PV systems.

We have investigated the scenarios of transition to chaos in the mathematical model of a genetic system constituted by a single transcription factor-encoding gene, the expression of which is self-regulated by a feedback loop that involves protein isoforms. Alternative splicing results in the synthesis of protein isoforms providing opposite regulatory outcomes — activation or repression. The model is represented by a differential equation with two delayed arguments. The possibility of transition to chaos dynamics via all classical scenarios: a cascade of period-doubling bifurcations, quasiperiodicity and type-I, type-II and type-III intermittencies, has been numerically demonstrated. The parametric features of each type of transition to chaos have been described.

Differential equations that switch between different modes of behavior across a surface of discontinuity are used to model, for example, electronic switches, mechanical contact, predator–prey preference changes, and genetic or cellular regulation. Switching in such systems is unlikely to occur precisely at the ideal discontinuity surface, but instead can involve various spatiotemporal delays or noise. If a system switches between more than two modes, across a boundary formed by the intersection of discontinuity surfaces, then its motion along that intersection becomes highly sensitive to such nonidealities. If switching across the surfaces is affected by hysteresis, time delay, or discretization, then motion along the intersection can be affected by erratic variations that we characterize as “jitter”. Introducing noise, or smoothing out the discontinuity, instead leads to steady motion along the intersection well described by the so-called canopy extension of Filippov’s sliding concept (which applies when the discontinuity surface is a simple hypersurface). We illustrate the results with numerical experiments and an example from power electronics, providing explanations for the phenomenon as far as they are known.

Over the past few decades, interval arithmetic has been attracting widespread interest from the scientific community. With the expansion of computing power, scientific computing is encountering a noteworthy shift from floating-point arithmetic toward increased use of interval arithmetic. Notwithstanding the significant reliability of interval arithmetic, this paper presents a theoretical inconsistency in a simulation of dynamical systems using a well-known implementation of arithmetic interval. We have observed that two natural interval extensions present an empty intersection during a finite time range, which is contrary to the fundamental theorem of interval analysis. We have proposed a procedure to at least partially overcome this problem, based on the union of the two generated pseudo-orbits. This paper also shows a successful case of interval arithmetic application in the reduction of interval width size on the simulation of discrete map. The implications of our findings on the reliability of scientific computing using interval arithmetic have been properly addressed using two numerical examples.

In this paper, constant-frequency peak-current control is analyzed focusing on the operation above the subharmonic threshold limit. The analysis is performed by mixing analytical and numerical approaches. Two levels of normalization are introduced: on the converter level and on the switching cell level, resulting in unified analysis regardless of the converter type. A function that maps the inductor current value at the beginning of a switching period to its value at the end of the switching period is derived. The analysis is performed by iterating this mapping, leading to information of the inductor current periodicity and the switching cell averaged output current. It is shown that before reaching chaotic state a converter passes through a sequence of bifurcations involving discontinuous conduction modes characterized by higher order periodicity. Boundaries of the region where the higher order discontinuous conduction modes occur are derived. Obtained dependence of the switching cell output current average on the operating parameters is used to derive a small signal model. The model parameters expose huge variations in the areas of deep subharmonic operation. The results are experimentally verified.

Permeability is an important hydraulic parameter for characterizing heat and mass transfer properties of fibrous porous media. However, it is difficult to be quantitatively predicted due to the complex and irregular pore structure of fibrous porous media. Fractal geometry has been verified to be an effective method for determining the permeability of fibrous porous media. In this study, recent works on the permeability of fibrous porous media by means of fractal geometry are reviewed, the advances for each presented fractal model are analyzed and summarized, parameter equations used in available fractal permeability models are also briefly compared and reviewed. Future work for more generalized permeability model of fibrous porous media need to conducted by considering the special characters of fibrous materials, uniform pore structure parameter model and the influence factor of capillary pressure, electrokinetic phenomena, etc.

In recent years, unconventional reservoirs have drawn tremendous attention worldwide. This special issue collects a series of recent works on various fractal-based approaches in unconventional reservoirs. The topics covered in this introduction include fractal characterization of pore (throat) structure and its influences on the physical properties of unconventional rocks, fractal characteristics of crack propagation in coal and fluid flow in rock fracture network under shearing, porous flow phenomena and gas adsorption mechanism, fractal geophysical method in reservoirs.

A series of stock prices typically shows a large trend and smaller fluctuations. These two parts are often studied together, as if parts of a single process; but they appear to be separately caused. In this paper, the two parts are analyzed separately, so that one does not distort the other, and some spurious interaction terms are avoided. This contributes a model, in which a wide range of features of stock price behavior are identified. With logarithms of stock prices, the two parts become of more comparable size. This is found to lead to a simpler additive model. On a logarithmic scale, the stock prices show the trend as a straight line (which can be extrapolated), with added fluctuations filling a narrow band. The trend and fluctuations are thus separated. The trend appears to be largely generated by a positive feedback process, describing investor behavior. The width of the fluctuation band does not grow with time, so positive feedback is not its cause. The movement of stock prices can be understood by analyzing the trend and fluctuations as separate processes; the latter considered as a stationary stochastic process with a scale factor. This analysis is applied to a historical dataset $(S\&P500$ index of daily prices from February 1928). Here, the fluctuations are autocorrelated over short time intervals; there is little structure, except for market crash periods, when variability increases. The slope of the trend showed some jumps, not predictable from price history. This approach to modeling describes many aspects of stock price behavior, which are usually discussed in behavioral finance.

This paper summarizes the approaches to and the implications of bottom–up infrastructure modeling in the framework of the EMF28 model comparison "Europe 2050: The Effects of Technology Choices on EU Climate Policy". It includes models covering all the sectors currently under scrutiny by the European Infrastructure Priorities: Electricity, natural gas, and CO₂. Results suggest that some infrastructure enhancement is required to achieve the decarbonization, and that the network development needs can be attained in a reasonable timeframe. In the electricity sector, additional cross-border interconnection is required, but generation and the development of low-cost renewables is a more challenging task. For natural gas, the falling total consumption could be satisfied by the current infrastructure in place, and even in a high-gas scenario the infrastructure implications remain manageable. Model results on the future role of Carbon Capture, Transport, and Sequestration (CCTS) vary, and suggest that most of the transportation infrastructure might be required in and around the North Sea.

There is broad consensus among scientists that climate change is altering weather patterns around the world. However, economists are only beginning to develop comprehensive tools that allow for the quantification of such weather changes on countries' economies and people. This paper presents a modeling suite that links the downscaling of global climate models, crop modeling, global economic modeling, and sub-national-level dynamic computable equilibrium modeling. Important to note is that this approach allows for decomposing the potential global and local economic effects on countries, including various economic sectors and different household groups. We apply this modeling suite to Syria, a relevant case study given the country's location in a region that is consistently projected to be among those hit hardest by climate change. We find that, despite a certain degree of endogenous adaptation, local impacts of climate change (through declining yields) are likely to affect Syria beyond the agricultural sector and farmers and also reduce economy-wide growth and incomes of urban households in the long term. The overall effects of global climate change (through higher food prices) are also negative, but some farmers may reap the benefit of higher prices. Combining local and global climate change scenarios shows welfare losses across all rural and urban household groups, whereas the poorest household groups are the hardest hit.

This paper describes and analyzes a proposal for a pay-for-cuts carbon treaty and illustrates how it would work with numerical simulations for 186 countries. A treaty board would pay each country’s government an amount P for each CO₂ ton the country cuts by any method. Each government would decide how to get its firms and households to cut CO₂ emissions; the board would encourage a carbon tax or cap-and-trade but not require it. $R_i$ is the number of tons that country i reduces its CO₂ emissions. $R_i$ equals country i’s base emissions minus its actual emissions which are publicly available data. Country i’s government would receive $P{R}_i$ from the board. The board’s $P{R}_i$ payments would be financed by country government contributions to the board. A formula would give each country’s government the amount it must contribute each year. The formula would aim to make government contributions equal to the board’s $P{R}_i$ payments and equitably distribute the burdens from cutting world emissions. Representatives of the countries would have to approve the board’s most important decisions.