Signal transduction in biological cells is effected by signaling pathways that typically include multiple feedback loops. Here we analyze information transfer through a prototypical signaling module with biochemical feedback. The module switches stochastically between an inactive and active state; the input to the module governs the activation rate while the output (i.e., the product concentration) perturbs the inactivation rate. Using a novel perturbative approach, we compute the rate with which information about the input is gained from observation of the output. We obtain an explicit analytical result valid to first order in feedback strength and to second order in the strength of input. The total information gained during an extended time interval is found to depend on the feedback strength only through the total number of activation/inactivation events.

• MeSH
• biologické modely * MeSH
• gating iontového kanálu fyziologie   MeSH
• lidé MeSH
• počítačová simulace MeSH
• signální transdukce fyziologie   MeSH
• ukládání a vyhledávání informací metody   MeSH
• vápník metabolismus   MeSH
• vápníkové kanály metabolismus   MeSH
• zpětná vazba fyziologická fyziologie   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.

• MeSH
• Accipitridae * MeSH
• algoritmy MeSH
• neuronové sítě MeSH
• oxid zinečnatý * MeSH
• propylaminy MeSH
• sulfidy MeSH
• textilie MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

• MeSH
• organizační inovace * MeSH
• rozhodování MeSH
• společenská třída MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Evropa MeSH

OBJECTIVE: The article contains results of longitudinal research. The aim of the research was to find out how psychomotor therapy with the use of elements of ergotherapy (with the support of cognitive functions; with the support of robotic assisted therapy) on the support and development skills of people with sclerosis multiplex (SM; sclerosis multiplex) in facilities providing social services members of the probands. PROBAND SAMPLE: The research was carried out in 46 probands, with 43.5% of men and 56.5% of women aged 65 - 67 years. The main relevant feature for the selection of probands was the established diagnosis Multiple Sclerosis (according to ICD-10; G35). Another relevant feature for the selection of probands was the length of stay in the facility, which was at least 1 year from the actual start of the facility. The assembled research sample was divided according to other criteria by deliberate selection into the experimental group and the control group. The experimental group participated actively in our intervention and consisted of 23 probands of which 10 were men and 13 were women. The control group only participated in the therapies performed at the facility and did not participate in our intervention. The control group also included 10 men and 13 women. The intervention itself lasted 5 months, three times a week for 40 to 55 minutes. We provided input and output data using a standardized test (FIM test; FIM test; Functional Independence Measure). Subsequent comparison of obtained data between input and output testing was performed by Tuckey HSD test at significance level α = 0.05. RESULTS: The achieved results (at the significance level α = 0.05) show that the experimental group underwent better results in comparison with the initial testing and in comparison with the control group, which rather stagnated in the results, respectively. slightly worsened compared to initial testing.

• MeSH
• kognice fyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• rodina MeSH
• roztroušená skleróza terapie   MeSH
• senioři MeSH
• sociální dovednosti * MeSH
• stárnutí * MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

• Klíčová slova
• systém R, R software,
• MeSH
• automatizované zpracování dat MeSH
• software MeSH
• statistika jako téma MeSH
• Publikační typ
• monografie MeSH
• příručky MeSH

• Konspekt
• Kombinatorika. Teorie grafů. Matematická statistika. Operační výzkum. Matematické modelování
• NLK Obory
• statistika, zdravotnická statistika

• MeSH
• biologie buňky MeSH
• matematika MeSH
• molekulární biologie MeSH
• molekulární modely MeSH
• teoretické modely MeSH
• Publikační typ
• monografie MeSH

• Konspekt
• Matematika
• NLK Obory
• přírodní vědy
• cytologie, klinická cytologie
• biologie

• Konspekt
• Statistika
• NLK Obory
• statistika, zdravotnická statistika

BACKGROUND: Policymakers, who are constantly discussing growing health expenditures, should know whether the health system is efficient. We can provide them with such information through international health system efficiency evaluations. The main objectives of this study are: (a) to evaluate the efficiency of health systems in 28 developed countries by multiple-criteria decision analysis (MCDA) and data envelopment analysis (DEA) and (b) to identify reasonable benchmark countries for the Czech Republic, for which we collect information on the relative importance of health system inputs and outputs. METHODS: We used MCDA and DEA to evaluate the efficiency of the health systems of 28 developed countries. The models included four health system inputs (health expenditure as a relative share of GDP, the number of physicians, nurses, and hospital beds) and three health system outputs (life expectancy at birth, healthy life expectancy, and infant mortality rate). The sample covers 27 OECD countries and Russia, which is also included in the OECD database. To determine the input and output weights, we used a questionnaire sent to health policy experts in the Czech Republic. RESULTS: We obtained subjective information on the relative importance of the health system inputs and outputs from 27 Czech health policy experts. We evaluated health system efficiency using four MCDA and two DEA models. According to the MCDA models, Turkey, Poland, and Israel were found to have efficient health systems. The Czech Republic ranked 16th, 19th, 15th, and 17th. The benchmark countries for the Czech Republic's health system were Israel, Estonia, Luxembourg, Italy, the UK, Spain, Slovenia, and Canada. The DEA model with the constant returns to scale identified four technically efficient health systems: Turkey, the UK, Canada, and Sweden. The Czech Republic was found to be one of the worst-performing health systems. The DEA model with the variable returns to scale identified 15 technically efficient health systems. We found that efficiency results are quite robust. With two exceptions, the Spearman rank correlations between each pair of models were statistically significant at the 0.05 level. CONCLUSIONS: During the model formulation, we investigated the pitfalls of efficiency measurement in health care and used several practical solutions. We consider MCDA and DEA, above all, as exploratory methods, not methods providing definitive answers.

• Publikační typ
• časopisecké články MeSH

• Konspekt
• Biologické vědy
• NLK Obory
• biologie
• fyziologie

The aim of the research is to determine the technical efficiency of legendary hockey players in the National Hockey League (NHL), to create a ranking of these players and to reveal the best NHL players of all time. The research uses statistical data on 379 players from the 1944/45 season to the 2023/24 season. The methodology is based on multi-criteria analysis, specifically the concept of data envelopment analysis (DEA). The DEA provides an objective measure of the overall playing profile of hockey players and can help supplement the information in rankings provided by sports journalists. Andersen and Petersen's model is used to evaluate the data collected, providing super efficiency scores by aggregating NHL statistics related to various aspects of the game to produce a final ranking of hockey legends. The concept of data envelopment analysis works with multiple variables and allows for greater objectivity to be incorporated into the rankings. The number of games played is chosen as one of the model's input variables. The output variables include: number of goals scored, number of assists, plus/minus, inverse of penalty minutes, points per game, number of shots, number of individual awards, and number of Stanley Cups won. The research named Wayne Gretzky, Butch Goring and Serge Savard as the best players in the NHL historically in terms of technical efficiency. Among other things, the ranking of legendary hockey players revealed that players with a high number of games played or points scored are not necessarily technically efficient.