Michal Tkáč

Purpose: This study aimed to analyse the experience of two participating workplaces in introducing innovative robotically assisted rehabilitation in clinical practice and influencing the quality of life of patients. Methodology/Approach: Online questionnaires were distributed to the rehabilitation staff of the two workplaces, and the Functional Independence Measurement (FIM) and Barthel index were measured before the start of rehabilitation and after the completion of rehabilitation. Results showed that the robots were highly effective, easy to use, and significant in achieving the therapeutic goal. Rehabilitation specialists who already have experience in robotic rehabilitation perceive the expected impacts with a higher efficiency of 10% compared to those who do not have experience with robot-assisted therapy. In a selected group of patients, a significant positive effect on the functional status of patients and their quality of life was demonstrated. Research Limitation/Implication: Robotically assisted rehabilitation has a positive effect on functional status and quality of life, motivating rehabilitation workers even when treating patients with severe disabilities. Originality/Value of paper: Data collected are beneficial for health, educational, and social fields.

Purpose: The aim of this paper is to propose nonlinear autoregressive neural network which can improve quality of bond price forecasting. Methodology/Approach: Due to the complex nature of market information that influence bonds, artificial intelligence could be accurate, robust and fast choice of bond price prediction method. Findings: Our results have reached a coefficient of determination higher than 95% in the training, validation and testing sets. Moreover, we proposed the nonlinear autoregressive network with external inputs using 50 year interest-rate swaps denominated in EUR and volatility index VIX as two external variables. Research Limitation/Implication: Our sample of daily prices between 4th January 2016 and 13th January 2021 (totally 1,270 trading days) suggest that both Levenberg-Marquardt and Scaled conjugate gradient learning algorithms achieved excellent results. Originality/Value of paper: Despite the fact that both learning algorithms achieved satisfying outcomes, implementation of an independent variable into the autoregressive neural network environment had no significant impact on prediction ability of the model.

The paper deals with problematic nature of measuring of process performance. It includes a designed procedure of process performance monitoring, evaluation of logistic processes quality, and also measuring of the impact of marketing activities on the profitability of process output, i.e. a product, by means of appropriate indicator.There are several performance indicators that companies use to monitor the performance of their processes and business strategies with respect to their objectives. To monitor these indicators, enterprises rely on dashboards that present one or more indicators along with contextual information to help decision makers identify deviations and their root causes. Associated benefits related to the process performance measurement system can be seen, for example, in better decision-making, flexible human resource management and process management structures. By using rolled steel sheets in a large metallurgical plant as an example, there will be shown how the performance of the rolling process can be improved by monitoring the tangible financial indicator. Subsequently, the experience was from case management companies presented to further incorporate a practical view of implementation and related issues. Finally, the reasons why the organization prefers the observed indicator during implementation of the process performance of measurement system is explored in order to understand the causes and consequences.