Number of found documents: 1529
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Mathematics and Optimal control theory meet Pharmacy: Towards application of special techniques in modeling, control and optimization of biochemical networks
Papáček, Štěpán; Matonoha, Ctirad; Duintjer Tebbens, Jurjen
2021 - English
Similarly to other scienti c domains, the expenses related to in silico modeling in pharmacology need not be extensively apologized. Vis a vis both in vitro and in vivo experiments, physiologically-based pharmacokinetic (PBPK) and pharmacodynamic models represent an important tool for the assessment of drug safety before its approval, as well as a viable option in designing dosing regimens. In this contribution, some special techniques related to the mathematical modeling, control and optimization of biochemical networks are presented on a paradigmatic example of enzyme kinetics. Keywords: Dynamical system; Systems pharmacology; Biochemical network; Input-output regulation; Optimization Fulltext is available at external website.
Mathematics and Optimal control theory meet Pharmacy: Towards application of special techniques in modeling, control and optimization of biochemical networks

Similarly to other scienti c domains, the expenses related to in silico modeling in pharmacology need not be extensively apologized. Vis a vis both in vitro and in vivo experiments, ...

Papáček, Štěpán; Matonoha, Ctirad; Duintjer Tebbens, Jurjen
Ústav teorie informace a automatizace, 2021

REGULATORY NETWORK OF DRUG-INDUCED ENZYME PRODUCTION: PARAMETER ESTIMATION BASED ON THE PERIODIC DOSING RESPONSE MEASUREMENT
Papáček, Štěpán; Lynnyk, Volodymyr; Rehák, Branislav
2021 - English
The common goal of systems pharmacology, i.e. systems biology applied to the eld of pharmacology, is to rely less on trial and error in designing an input-output systems, e.g. therapeutic schedules. In this paper we present, on the paradigmatic example of a regulatory network of drug-induced enzyme production, the further development of the study published by Duintjer Tebbens et al. (2019) in the Applications of Mathematics. Here, the key feature is that the nonlinear model in form of an ODE system is controlled (or periodically forced) by an input signal being a drug intake. Our aim is to test the model features under both periodic and nonrecurring dosing, and eventually to provide an innovative method for a parameter estimation based on the periodic dosing response measurement. Keywords: Dynamical system; Regulatory network; Input-output; Regulation; Parameter estimation; FFT Fulltext is available at external website.
REGULATORY NETWORK OF DRUG-INDUCED ENZYME PRODUCTION: PARAMETER ESTIMATION BASED ON THE PERIODIC DOSING RESPONSE MEASUREMENT

The common goal of systems pharmacology, i.e. systems biology applied to the eld of pharmacology, is to rely less on trial and error in designing an input-output systems, e.g. therapeutic schedules. ...

Papáček, Štěpán; Lynnyk, Volodymyr; Rehák, Branislav
Ústav teorie informace a automatizace, 2021

A NUMERICAL METHOD FOR THE SOLUTION OF THE NONLINEAR OBSERVER PROBLEM
Rehák, Branislav
2021 - English
The central part in the process of solving the observer problem for nonlinear systems is to nd a solution of a partial differential equation of first order. The original method proposed to solve this equation used expansions into Taylor polynomials, however, it suffers from rather restrictive assumptions while the approach proposed here allows to generalize these requirements. Its characteristic feature is that it is based on the application of the Finite Element\nMethod. An illustrating example is provided. Keywords: Finite element method; Observer; Partial differential equation Fulltext is available at external website.
A NUMERICAL METHOD FOR THE SOLUTION OF THE NONLINEAR OBSERVER PROBLEM

The central part in the process of solving the observer problem for nonlinear systems is to nd a solution of a partial differential equation of first order. The original method proposed to solve ...

Rehák, Branislav
Ústav teorie informace a automatizace, 2021

Unsupervised Verification of Fake News by Public Opinion
Grim, Jiří
2021 - English
In this paper we discuss a simple way to evaluate the messages in social networks automatically, without any special content analysis or external intervention. We presume, that a large number of social network participants is capable of a relatively reliable evaluation of materials presented in the network. Considering a simple binary evaluation scheme (like/dislike), we propose a transparent algorithm with the aim to increase the voting power of reliable network members by means of weights. The algorithm supports the votes which correlate with the more reliable weighted majority and, in turn, the modified weights improve the quality of the weighted majority voting. In this sense the weighting is controlled only by a general coincidence of voting members while the specific content of messages is unimportant. The iterative optimization procedure is unsupervised and does not require any external intervention with only one exception, as discussed in Sec. 5.2 .\n\nIn simulation experiments the algorithm nearly exactly identifies the reliable members by means of weights. Using the reinforced weights we can compute for a new message the weighted sum of votes as a quantitative measure of its positive or negative nature. In this way any fake news can be recognized as negative and indicated as controversial. The accuracy of the resulting weighted decision making was essentially higher than a simple majority voting and has been considerably robust with respect to possible external manipulations.\n\nThe main motivation of the proposed algorithm is its application in a large social network. The content of evaluated messages is unimportant, only the related decision making of participants is registered and compared with the weighted vote with the aim to identify the most reliable voters. A large number of participants and communicated messages should enable to design a reliable and robust weighted voting scheme. Ideally the resulting weighted vote should provide a generally acceptable emotional feedback for network participants and could be used to indicate positive or controversial news in a suitably chosen quantitative way. The optimization algorithm has to be simple, transparent and intuitive to make the weighted vote well acceptable as a general evaluation tool.\n Keywords: weighted voting; unsupervised optimization Fulltext is available at external website.
Unsupervised Verification of Fake News by Public Opinion

In this paper we discuss a simple way to evaluate the messages in social networks automatically, without any special content analysis or external intervention. We presume, that a large number of ...

Grim, Jiří
Ústav teorie informace a automatizace, 2021

Subjective well-being and the individual material situation in Central Europe: A Bayesian network approach
Švorc, Jan; Vomlel, Jiří
2020 - English
The objective of this paper is to explore the associations between the subjective well-being (SWB) and the subjective and objective measures of the individual material situation in the four post-communist countries of Central Europe (the Czech Republic, Hungary, Poland, and Slovakia). The material situation is measured by income, relative income compared to others, relative income compared to one’s own past, perceived economic strain, financial problems, material deprivation, and housing problems. Our analysis is based on empirical data from the third wave of European Quality of Life Study conducted in 2011. Bayesian networks as a graphical representation of the relations between SWB and the material situation have been constructed in five versions. The models have been assessed using the Bayesian Information Criterion (BIC) and SWB prediction accuracy, and compared\nwith Ordinal Logistic Regression (OLR). Expert knowledge, as well as three different algorithms (greedy, Gobnilp, and Tree-augmented Naive Bayes) were used for learning the network structures. Network parameters were learned using the EM algorithm. Parameters based on OLR were learned for a version of the expert model. The Gobnilp model, the Markov equivalent to the greedy model, is BIC optimal. The OLR predicts SWB slightly better than the other models. We conclude that the objective material conditions' influence on SWB is rather indirect, through the subjective situational assessment of various aspects related to the individual material conditions. Keywords: Subjective Well-Being; Income; Economic Strain; Material Deprivation; Bayesian Networks; Central Europe Fulltext is available at external website.
Subjective well-being and the individual material situation in Central Europe: A Bayesian network approach

The objective of this paper is to explore the associations between the subjective well-being (SWB) and the subjective and objective measures of the individual material situation in the four ...

Švorc, Jan; Vomlel, Jiří
Ústav teorie informace a automatizace, 2020

Institutions, Financial Development, and Small Business Survival: Evidence from European Emerging Economies
Iwasaki, I.; Kočenda, Evžen; Shida, Y.
2020 - English
In this paper, we traced the survival status of 94,401 small businesses in 17 European emerging markets from 2007–2017 and empirically examined the determinants of their survival, focusing on institutional quality and financial development. We found that institutional quality and level of financial development exhibit statistically significant and economically meaningful impacts on the survival probability of the SMEs being researched. The evidence holds even when we control for a set of firm-level characteristics such as ownership structure, financial performance, firm size, and age. The findings are also uniform across industries and country groups and robust beyond the difference in assumption of hazard distribution. Keywords: small business; survival analysis; European emerging markets Fulltext is available at external website.
Institutions, Financial Development, and Small Business Survival: Evidence from European Emerging Economies

In this paper, we traced the survival status of 94,401 small businesses in 17 European emerging markets from 2007–2017 and empirically examined the determinants of their survival, focusing on ...

Iwasaki, I.; Kočenda, Evžen; Shida, Y.
Ústav teorie informace a automatizace, 2020

Financial Crime and Punishment: A Meta-Analysis
de Batz, L.; Kočenda, Evžen
2020 - English
We examine how the publication of intentional financial crimes committed by listed firms is interpreted by financial markets, using a systematic and quantitative review of existing empirical studies. Specifically, we conduct a meta-regression analysis and investigate the extent and nature of the impact that the publication of financial misconducts exerts on stock returns. We survey 111 studies, published between 1978 and 2020, with a total of 439 estimates from event studies. Our key finding is that the average abnormal returns calculated from this empirical literature are affected by a negative publication selection bias. Still, after controlling for this bias, our meta-analysis indicates that publications of financial crimes are followed by statistically significant negative abnormal returns, which suggests the existence of an informational effect. Finally, the MRA results demonstrate that crimes committed in common law countries, alleged crimes, and accounting crimes carry particularly weighty information for market participants. The results call for more transparency on side of enforcers along enforcement procedures, to foster timely and proportionate market reactions and support efficient markets. Keywords: Meta-Analysis; Event study; Financial Misconduct; Fraud; Financial Markets; Returns; Listed Companies; Information and Market Efficiency Fulltext is available at external website.
Financial Crime and Punishment: A Meta-Analysis

We examine how the publication of intentional financial crimes committed by listed firms is interpreted by financial markets, using a systematic and quantitative review of existing empirical studies. ...

de Batz, L.; Kočenda, Evžen
Ústav teorie informace a automatizace, 2020

Selective Attention in Exchange Rate Forecasting
Kapounek, S.; Kučerová, Z.; Kočenda, Evžen
2020 - English
We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market participants process only a limited amount of information. Our analysis includes more than 100,000 news articles relevant to the six most-traded foreign exchange currency pairs for the period of 1979–2016. We employ a dynamic model averaging approach to reduce model selection uncertainty and to identify time-varying probability to include regressors in our models. Our results show that smaller sizes models accounting for the presence of selective attention offer improved fitting and forecasting results. Specifically, we document a growing impact of foreign trade and monetary policy news on the euro/dollar exchange rate following the global financial crisis. Overall, our results point to the existence of selective attention in the case of most currency pairs. Keywords: exchange rate; selective attention; news; forecasting; dynamic model averaging Fulltext is available at external website.
Selective Attention in Exchange Rate Forecasting

We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market ...

Kapounek, S.; Kučerová, Z.; Kočenda, Evžen
Ústav teorie informace a automatizace, 2020

ECB monetary policy and commodity prices
Aliyev, S.; Kočenda, Evžen
2020 - English
We analyze the impact of the ECB monetary policies on global aggregate and sectoral commodity prices using monthly data from January 2001 till August 2019. We employ a SVAR model and assess separately period of conventional monetary policy before global financial crisis (GFC) and unconventional monetary policy during post-crisis period. Our key results indicate that contractionary monetary policy shocks have positive effects on the aggregate and sectoral commodity prices during both conventional and unconvetional monetary policy periods. The effect is statistically significant for aggregate commodity prices during post-crisis period. In terms of sectoral impact, the effect is statistically significant for food prices in both periods and for fuel prices during post-crisis period; other commodities display positive but statistically insignificant responses. Further, we demonstrate that the impact of the ECB monetary policy on commodity prices increased remarkably after the GFC. Our results also suggest that the effect of the ECB monetary policy on commodity prices does not transmit directly through market demand and supply expectations channel, but rather through the exchange rate channel that influences the European market demand directly. Keywords: European Central Bank; commodity prices; monetary policy Fulltext is available at external website.
ECB monetary policy and commodity prices

We analyze the impact of the ECB monetary policies on global aggregate and sectoral commodity prices using monthly data from January 2001 till August 2019. We employ a SVAR model and assess separately ...

Aliyev, S.; Kočenda, Evžen
Ústav teorie informace a automatizace, 2020

Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy
Murray, Sean Ernest; Quinn, Anthony
2020 - English
This research report outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid carcinoma. The work seeks to address some limitations of previous approaches [4] which involve generic, non-selective transfer of archival data. It is proposed that improvements in patient-specific inferences may be achieved via transferring external population knowledge selectively. This involves matching the patient to a similar sub-population based on available metadata, generating a Gaussian Mixture Model within the partitioned data, and optimally transferring a data predictive distribution from the sub-population to the specific patient. Additionally, a performance evaluation method is proposed and early-stage results presented. Keywords: Bayesian estimation; thyroid carcinoma; patient-specific inferences Fulltext is available at external website.
Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy

This research report outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid ...

Murray, Sean Ernest; Quinn, Anthony
Ústav teorie informace a automatizace, 2020

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