Non-stochastic Model Research Articles

FIST GEOINFORMATION SYSTEM MODEL USING FOG COMPUTING IN DESTABILIZATION

Objective . The objective of the study is to develop a model of a geoinformation system functioning in destabilization, as well as indicators that assess the ability of a geoinformation system to perform its functions in destabilization. Methods . Geographic information systems are becom ing an integral part of almost all information and decision-making systems. The research methods are based on a deterministic or stochastic model of destabilization, but the geographic information system is characterized by a non-stochastic model. Results . It is shown that any task exists in the geoinformation system in the form of consumption of four types of performance: computers, storage devices, communication channels, input/output devices. Based on this, a model of the geoinformation system (FIST – Full Infrastructure of Sources Toolkit) was developed, which allows evaluating the margin of safety of the geoinformation system, expressed in the available of all four types. The model considers elements of different degrees of mobility and and is suitable for describing classical, cloud, and fog geoinformation systems. An example of the model operation in destabilization is given. A theorem on the independence of events of solving problems for any directed graph is formulated and proved. Based on the model, integral and differential efficiency indicators are proposed. The integrated indicator characterizes the share of tasks solved over a time interval; the differential indicator characterizes the performance margin. Conclusion . The developed model and indicators can be used to design new and evaluate existing geographic information systems.

Wave Propagation Dynamics in a Fractional Zener Model with Stochastic Excitation

Abstract Equations of motion for a Zener model describing a viscoelastic rod are investigated and conditions ensuring the existence, uniqueness and regularity properties of solutions are obtained. Restrictions on the coefficients in the constitutive equation are determined by a weak form of the dissipation inequality. Various stochastic processes related to the Karhunen-Loéve expansion theorem are presented as a model for random perturbances. Results show that displacement disturbances propagate with an infinite speed. Some corrections of already published results for a non-stochastic model are also provided.

Decline in Temperature Variability on Svalbard

AbstractThe variability in the temperature on Svalbard, Norway, has been decreasing over the last four decades. This may be due to the reduction in sea ice, transitioning the regional climate to a more stable, coastal one. We quantify this transition in terms of decreasing volatility in a daily average temperature time series at Svalbard Airport from 1976 to 2019. We use two different approaches: a nonstochastic model and a time-dependent generalized autoregressive conditional heteroskedasticity (GARCH) model. These parametric approaches include a time-dependent trend, where the slope depends on the day of the year. For Svalbard, the slope has a minimum in late August and the steepest slope during winter is estimated to be −0.1°C2 yr−1. The nonstochastic model, for which the conditional and unconditional variances are the same, only depends on the marginal distribution and is perhaps the easiest to interpret. The GARCH model extends the nonstochastic model by including short-range temporal dependence in the volatility and is thus more locally adapted. Volatility modeling is important for a complete statistical description of the temperature dynamics on Svalbard as an Arctic representative. In combination with increasing temperatures, the volatility reduction makes the extremely cold days during winter occur less frequently. Although we focus exclusively on the Svalbard Airport series, the models should be suitable for other temperature or climatic time series.

Folding Determinants of Transmembrane β-Barrels Using Engineered OMP Chimeras.

Transmembrane β-barrel proteins (OMPs) are highly robust structures for engineering and development of nanopore channels, surface biosensors, and display libraries. Expanding the applications of designed OMPs requires the identification of elements essential for β-barrel scaffold formation and stability. Here, we have designed chimeric 8-stranded OMPs composed of strand hybrids of Escherichia coli OmpX and Yersinia pestis Ail, and identified molecular motifs essential for β-barrel scaffold formation. For the OmpX/Ail chimeras, we find that the central hairpin strands β4-β5 in tandem are vital for β-barrel folding. We also show that the central hairpin can facilitate OMP assembly even when present as the N- or C-terminal strands. Further, the C-terminal β-signal and strand length are important but neither sufficient nor mutually exclusive for β-barrel assembly. Our results point to a nonstochastic model for assembly of chimeric β-barrels in lipidic micelles. The assembly likely follows a predefined nucleation at the central hairpin only when presented in tandem, with some influence from its absolute position in the barrel. Our findings can lead to the design of engineered barrels that retain the OMP assembly elements necessary to attain well-folded, stable, yet malleable scaffolds, for bionanotechnology applications.

Prediction of plug loads in office buildings: Simplified and probabilistic methods

To predict buildings’ energy use, multiple systems and processes must be considered. Next to factors such as building fabric and construction, indoor environmental control systems, and weather conditions, the energy demand attributable to buildings’ internal heat gains resulting from inhabitants, lighting, and equipment usage also needs to be addressed. Given this background, the present contribution focuses on plug loads in office buildings associated mainly with computers and peripherals. Using long-term observational data obtained from a continuously monitored office building in Vienna, we specifically explore the relationship between inhabitants’ presence, installed power for equipment, and the resulting electrical energy use. The findings facilitate the formulation of both simplified and probabilistic office plug loads predictions methods. Thereby, the model evaluation results suggest that the non-stochastic model provides fairly reasonable predictions of annual energy use associated with plug loads. However, the stochastic plug load model – together with a stochastic occupancy model – outperforms the simplified model in predicting the plug loads peak and distribution.

A Performance Review of Sexual and Reproductive Health Centers in Sierra Leone: A Data Envelopment Analysis Approach

How is success measured in the field of health care, particularly for nonprofit or nongovernmental organizations dedicated to provide help to underprivileged patients? Who determines if extra dollars of preventive care are worth more or less than an invented pharmaceutical drug? How can the health care industry compare hospital recovery and rehabilitation rates? Such questions are difficult to answer, because healthcare institutions lack a uniform definition of success, which usually encompasses tangible factors, such as number of patients served, number of medical patents obtained, number of surgeries performed, and patient-to-cost ratios. Certain health care organizations look at less tangible factors, such as an ability to provide outstanding treatment, overall medical research reputation, and the impact of their inventions/procedures in the market. Given such a wide diversity of success indicators and given the fact those health care providers often pursue particular aspects of “health” outcomes; a valid and reliable performance comparison of provider success can be difficult. This research looks at and maps out the nature and characteristics of a nongovernmental institution that provides sexual and reproductive healthcare services in the West African nation of Sierra Leone. The problems associated with measuring the performance of such an institution mirror the issues mentioned above. This researcher decided to use the non-stochastic frontier model of Data Envelopment Analysis (DEA) to solve some of these issues. DEA, commonly used in Operations Research literature to evaluate the efficiency of multiple producers, can, under certain assumptions, define a locus of technically-efficient points that could be estimated where multiple inputs and outputs and market prices are absent.

Optimizing agricultural land-use portfolios with scarce data—A non-stochastic model

The theory of portfolio selection has often been applied to help improve economic decisions about the environment. Applying this theory requires information on the covariance of uncertain returns between all combinations of the economic options and also assumes that returns are normally distributed. As it is usually difficult to fulfill all data requirements and assumptions, this paper proposes a variant of robust portfolio optimization as an alternative that needs less pre-information. The approach considers future uncertainties in a non-stochastic fashion through possible deviations from the nominal return of land-use alternatives. Maximizing the economic return of the land-use portfolio is conditional on meeting an inclusive set of constraints. These demand that a pre-defined return threshold is achieved by the robust solution for each uncertainty scenario considered. Based on data for eight agricultural crops common in the Ecuadorian lowlands, a comparison with portfolios generated by classical stochastic mean-variance optimization shows greater land-use diversification (through increased Shannon indices), but only moderate expected economic loss of non-stochastic robust land-use portfolios. We conclude that non-stochastic derivation of land-use portfolios is a good alternative to the classical stochastic model, in situations where information on economic input parameters is scarce.

A Dynamic Model of Sovereign Debt and Defaults: Understanding the Incentives of the Borrower

This paper develops a non-stochastic sovereign-debt dynamic model, with increasing cost of capital. It finds that there are parameters for which there is a stable fixed borrowing level, and there are parameters for which the model depicts the case of serial defaulters. Levels of debt that tend towards the fixed point eventually achieve higher levels of consumption than would be attainable under financial autarky. In the cases of unstable paths (those that eventually end in default), consumption decreases as debt increases, because an increasing amount of domestic production needs to be added to current borrowing in order to pay debt obligations. The model shows how, as the cost of capital decreases, the equilibrium level of borrowing increases, and more levels of debt become sustainable in the long run. On the other hand, higher desired returns from creditors have the effect of lowering the stable borrowing level and the threshold for sustainable levels of debt. Very high levels of desired returns, R, may lead countries to become serial defaulters. This occurs because the stable equilibrium point disappears and every initial borrowing amount ends in default. A high level of R is not the only thing that can take a country to a state of chronic default; short punishments for defaulting are far more important. In the benchmark model, at least 21 periods of punishment are necessary to avoid chronic defaults. High levels of impatience can also make a country a serial defaulter.

Different regions of HIV-1 subtype C env are associated with placental localization and in utero mother-to-child transmission.

HIV infections are initiated by a limited number of variants that diverge into a diverse quasispecies swarm. During in utero mother-to-child transmission (IU MTCT), transmitted viral variants must pass through multiple unique environments, and our previously published data suggest a nonstochastic model of transmission. As an alternative to a stochastic model of viral transmission, we hypothesize that viral selection in the placental environment influences the character of the viral quasispecies when HIV-1 is transmitted in utero. To test this hypothesis, we used single-template amplification to isolate HIV-1 envelope gene (env) sequences from both peripheral plasma and the placentas of eight nontransmitting (NT) and nine IU-transmitting participants. Statistically significant compartmentalization between peripheral and placental HIV-1 env was detected in one of the eight NT cases and six of the nine IU MTCT cases. In addition, viral sequences isolated from IU MTCT placental tissue showed variation in env V1 loop lengths compared to matched maternal sequences, while NT placental env sequences did not. Finally, comparison of env sequences from NT and IU MTCT participants indicated statistically significant differences in Kyte-Doolittle hydropathy in the signal peptide, C2, V3, and C3 regions. Our working hypothesis is that the hydropathy differences in Env associated with IU MTCT alter viral cellular tropism or affinity, allowing HIV-1 to efficiently infect placentally localized cells.

Computational discovery of novel trypanosomicidal drug-like chemicals by using bond-based non-stochastic and stochastic quadratic maps and linear discriminant analysis

Herein we present results of a quantitative structure–activity relationship (QSAR) studies to classify and design, in a rational way, new antitrypanosomal compounds by using non-stochastic and stochastic bond-based quadratic indices. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop QSAR models based on linear discriminant analysis (LDA). Non-stochastic model correctly classifies more than 93% and 95% of chemicals in both training and external prediction groups, respectively. On the other hand, the stochastic model shows an accuracy of about the 87% for both series. As an experiment of virtual lead generation, the present approach is finally satisfactorily applied to the virtual evaluation of 9 already synthesized in house compounds. The in vitro antitrypanosomal activity of this series against epimastigote forms of Trypanosoma cruzi is assayed. The model is able to predict correctly the behaviour for the majority of these compounds. Four compounds (FER16, FER32, FER33 and FER 132) showed more than 70% of epimastigote inhibition at a concentration of 100 μg/mL (86.74%, 78.12%, 88.85% and 72.10%, respectively) and two of these chemicals, FER16 (78.22% of AE) and FER33 (81.31% of AE), also showed good activity at a concentration of 10 μg/mL. At the same concentration, compound FER16 showed lower value of cytotoxicity (15.44%), and compound FER33 showed very low value of 1.37%. Taking into account all these results, we can say that these three compounds can be optimized in forthcoming works, but we consider that compound FER33 is the best candidate. Even though none of them resulted more active than Nifurtimox, the current results constitute a step forward in the search for efficient ways to discover new lead antitrypanosomals.

Cosmogenic nuclide concentrations in episodically eroding surfaces: Theoretical results

The buildup of cosmogenic nuclides in an eroding surface can be used to infer erosion rates and exposure ages. This situation is often modelled by assuming that the erosion rate is constant in time. In many cases, however, the erosion is episodic: surface denudation occurs by the spalling off of slabs of rock at discrete times. We consider a stochastic model of such exfoliation processes, and compute the expected behavior of the cosmogenic nuclide concentration. We also consider a nonstochastic model, in which the exfoliation events occur at regularly spaced intervals. These two models represent extreme end members of the episodic spallation scenarios: in the first, the spallation events are uncorrelated in time, while in the second they are tightly correlated. Understanding how the nuclide concentration is related to the timing and the magnitude of these events is important in making geologic inferences.

Foundations of the functional approach for signal analysis

In this paper, the mathematical foundation of the functional (or nonstochastic) approach for signal analysis is established. The considered approach is alternative to the classical one that models signals as realizations of stochastic processes. The work follows the fraction-of-time probability approach introduced by Gardner. By applying the concept of relative measure used by Bochner, Bohr, Haviland, Jessen, Wiener, and Wintner and by Kac and Steinhaus, a probabilistic—but nonstochastic—model is built starting from a single function of time (the signal at hand). Therefore, signals are modeled without resorting to an underlying ensemble of realizations, i.e., the stochastic process model. Several existing results are put in a common, rigorous, measure-theory based setup. It is shown that by using the relative measure concept, a distribution function, the expectation operator, and all the familiar probabilistic parameters can be constructed starting from a single function of time. The new concept of joint relative measurability of two or more functions is introduced in this paper which is shown to be necessary for the joint characterization of signals. Moreover, by using such a concept, the independence of signals is defined. The joint relative measurability property is then used to prove the nonstochastic counterparts of several useful theorems for signal analysis. It is shown that the convergence of parameter estimators requires (analytical) assumptions on the single function of time that are much easier to verify than the classical ergodicity assumptions on stochastic processes. As an example of application, nonrelatively measurable functions are shown to be useful in the design of secure information transmission systems.

MONETARY POLICY RULES UNDER UNCERTAINTY: EMPIRICAL EVIDENCE, ADAPTIVE LEARNING, AND ROBUST CONTROL

We first explore empirical evidence of parameter and shock uncertainties in a state-space model with Markov switching. The evidence indicates that uncertainties in the U.S. economy have been too great to accurately define monetary policy rules. We then explore monetary policy rules under uncertainty with two approaches: the RLS learning algorithm and robust control. The former allows the parameters to be learned for a given model. Yet, as our results of the RLS learning in a framework of optimal control indicate, the state variables do not necessarily converge even in a nonstochastic model. The latter, by permitting uncertainty with respect to model misspecification, allows for a broader framework. Our study on robust control shows that robust optimal monetary policy rules reveal a stronger response to fluctuations in inflation and output than when no uncertainty exists, implying that uncertainty does not necessarily require caution.

Effectiveness of Stochastic Neural Network for Prediction of Fall or Rise of TOPIX

Stochastic neural network is a hierarchical network of stochastic neurons which emit 0 or 1 with the probability determined by the values of inputs. We have developed an efficient training algorithm so as to maximize the likelihood of such a neural network. This algorithm enables us to apply the stochastic neural network to a practical problem like prediction of fall or rise of Tokyo Stock Price Index (TOPIX). We trained it with the data from 1994 to 1996 and predicted the fall or rise of 1 day ahead of TOPIX for the period from 1997 to 2000. The result is quite promising. The accuracy of the prediction of the stochastic network is the 60.28%, although those of non-stochastic neural network, autoregressive model and GARCH model are 50.02, 51.38 and 57.21%, respectively. However, the stochastic neural network is not so advantageous over other networks or models for prediction of the TOPIX used for training. This means that the stochastic neural network is less over fitting to the training data than others, and results in the best prediction. We will demonstrate how the stochastic neural network learns well non-linear structure behind of the data in comparison to other models or networks, including Generalized Linear model (GLM).

Stable Noisy K-state Markov Sunspots

We consider a linear stochastic univariate rational expectations model, with a predetermined variable, and consider solutions driven by an extraneous finite state Markov process as well as by the fundamental noise. We obtain conditions for existence of noisy k-state sunspot equilibria (noisy k-SSEs) and, for the case k=2, of noisy k-state dependent sunspot equilibria (noisy k-*SDSs). k-*SDSs are driven by a finite state sunspot but have an infinite range of values even in the nonstochastic model. Stability under econometric learning is analyzed using representations that nest both types of solution. For the case k=2, we find that noisy 2-SSEs and noisy 2-*SDSs are learnable for parameters in proper subsets of the regions of their existence.

On the Synthesis Problem for Optimal Control Systems

A new approach to the synthesis problem of optimal controls of feedback type is considered. Algorithms of operating optimal controllers which are able to calculate values of optimal feedbacks during each particular control process in real time are described. The algorithm of operating the controller of the first type (continuous controller) is based on solving special (defining) equations in real time using the Newton method. For operating the optimal controller of the second type (discrete controller) the dual method of linear programming is used. In control problems for systems under uncertainty, a nonstochastic model is used, around which guaranteed results are obtained. Under incomplete and inexact information, problems of optimal observation and identification are introduced. Algorithms of operating the optimal estimator and identifier which are able to construct estimates of available information required for operating the optimal output controller in real time are described. Results are illustrated by optimization problems of the fourth order control system.

Realization of optimal output feedbacks

Abstract Optimal synthesis problem for dynamical systems under uncertainty is under consideration. A nonstochastic model of uncertainty is used around which guaranteed results are obtained. Under incomplete and inexact information, a problem of optimal observation is introduced. Algorithms of operating optimal estimators which are able to construct estimates of available information required for operating the optimal output controller in real time are described.

Risk, the spirit of capitalism and growth: The implications of a preference for capital

This paper incorporates the “spirit of capitalism,” or a “preference for capital” into a stochastic growth model. In a non-stochastic model, such as Zou (1994) , this “preference for capital” accelerates growth by reducing the rate of time preference. In a stochastic setting, however, the desire for accumulation affects growth in more complicated ways. An increase in the capitalist spirit always reduces the rate of time preference, but it may increase or decrease risk aversion. In turn, growth may accelerate or decelerate, depending upon consumers' willingness to substitute over time, and upon the range of investments in the economy.

Stationary equilibrium transition rules for an overlapping generations model with uncertainty

An overlapping generations model incorporating random production shocks is studied. Households have finite life spans. Futures markets are incomplete. Agents have full information in one case, and receive only a limited signal in another. In both instances the existence of a time-autonomous transition rule is proved such that if all agents forecast using it, the economy's actual growth will bear the predictions out. The rule corresponds to a steady state for a non-stochastic model. With limited information, it is seen to depend on all past signals. Several approximation theorems which may facilitate future applications are presented.

Violation of the optical bloch equations in power broadening of an impurity-ion solid, ruby

Power broadening (PB) of the optical holes for the transition (-1/2) 4A 2↔(-1/2)Ē( 2E) in ruby R 1 line was studied in the magnetic field (0.5 k0e) along the c-axis. It was found that the PB was much narrower than that expected from the optical Bloch equations. A qualitative interpretation based on a nonstochastic model is given.