{"id":31,"date":"2024-07-04T14:23:51","date_gmt":"2024-07-04T12:23:51","guid":{"rendered":"https:\/\/odas.ujep.cz\/?page_id=31"},"modified":"2026-01-07T14:44:35","modified_gmt":"2026-01-07T13:44:35","slug":"projekty","status":"publish","type":"page","link":"https:\/\/odas.ujep.cz\/en\/projekty\/","title":{"rendered":"Projects"},"content":{"rendered":"

On this page, you will find a list of our completed projects, which demonstrate the breadth of our expertise and practical impact in these areas.<\/p>\n\n\n\n

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Our Projects<\/h2>\n\n\n\n
DIGITECH<\/strong><\/summary>\n
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DIGITECH is a research project<\/strong> focused on connecting computer science and healthcare<\/strong>. It utilizes artificial intelligence, machine learning, and virtual reality methods to digitalize diagnostic and therapeutic processes. The goal is to streamline healthcare, personalize treatment, and transfer research findings into practice<\/strong>. zde<\/a><\/strong>.<\/p>\n<\/div>\n<\/details>\n\n\n\n

CreaThon<\/strong><\/summary>\n
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CreaThon is part of a broader RUR<\/a><\/strong> project focused on digitalization<\/strong> and innovation in healthcare<\/strong>. A key element is the collaboration with healthcare facilities. These facilities define specific problems and challenges, which then serve as assignments for an intensive, 24-hour hackathon-style event<\/strong>.<\/p>\n\n\n\n

During the event, students work alongside industry experts <\/strong>to design technological solutions aimed at modernizing healthcare processes, particularly in hospitals <\/strong>within the \u00dast\u00ed nad Labem Region. The outcomes include functional software prototypes and data models. In this way, CreaThon bridges the gap between education and practice, contributing to the technological transformation of the regional healthcare system.<\/p>\n\n\n\n

At the same time, the project enhances students' practical skills and connects them with the real needs of the region<\/strong>. The goal of the RUR project is to support the structural transformation of the \u00dast\u00ed nad Labem Region and strengthen its position both within the Czech Republic and on an international level.<\/p>\n<\/div>\n<\/details>\n\n\n\n

PhysicoConnect.cz<\/strong><\/summary>\n
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This platform is focused on creating a multicenter database of de-identified intensive care patient data, with a strong emphasis on high-frequency records from multimodal monitoring. The objective is to support multidisciplinary research across collaborating research centers.<\/p>\n\n\n\n

More information can be found here<\/strong><\/a>.<\/p>\n<\/div>\n<\/details>\n<\/div>\n\n\n\n

Prediction of the phase behavior of block copolymers using particle modeling and machine learning<\/strong><\/summary>\n

As part of the Student Grant Competition, the project aims to develop a model that utilizes molecular simulations and machine learning to rapidly predict the equilibrium microscopic structure of block copolymers. This significantly reduces the time required to construct phase diagrams of complex polymeric systems.<\/p>\n<\/details>\n\n\n\n

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Completed Projects<\/h2>\n\n\n\n
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Computer Analysis of Medical Data<\/h3>\n\n\n\n
Artificial Intelligence in Automatic Segmentation and Evaluation of Radiological Images<\/strong><\/summary>\n
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Artificial Neural Network (ANN) models <\/strong>represent a mathematical depiction of the human nervous system and are widely used to solve non-linear problems in biomedicine and human medicine. In the field of spine disease management<\/strong>, artificial intelligence is utilized, for example, for diagnosing and assessing disease progression, extracting clinically relevant information from radiographic images, predicting outcomes, performing biomechanical assessments of the spine, and for research purposes. An ANN is a machine learning technique<\/strong> that learns the relationships between defined input and output variables. By analyzing datasets processed according to current evidence-based standards, these systems can serve as decision-support tools that help achieve optimal clinical and radiological outcomes and minimize the risk of complications.<\/p>\n<\/div>\n<\/details>\n\n\n\n

Predictive Modelling<\/h3>\n\n\n\n
Data Analysis at ADLER Czech, a.s. (nyn\u00ed MALFINI a.s.)<\/strong><\/summary>\n
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The aim of the project was to design and test a model based on recurrent neural networks<\/strong> for predicting sales of traded commodities. Another objective was to compare the accuracy of commodity sales estimates between autoregression-based<\/strong> models<\/strong>. The solution included the selection and analysis of input data, including data cleaning, and the design of a functional network architecture relative to the provided data. A series of competing models were sequentially tested, differing in input data length, regressors used, network architecture and topology, and their parameters. The models were compared in terms of their sales prediction performance for specified time periods. For each commodity sold in a given market, it was determined which model was the most suitable and whether it was competitive with the model currently in use.<\/p>\n\n\n\n

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Sample behavior of modeled time series for various products<\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div>\n<\/details>\n\n\n\n
Analysis of the use of machine learning methods for monitoring and predicting groundwater status development in a mining area<\/strong><\/summary>\n
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The aim of the project was to design a linear regression model for modeling water table depth in boreholes.<\/strong> The project is based on provided meteorological and geophysical data. An integral part of the work is data consolidation and verification of its suitability for modeling. The model design includes the selection and construction of additional appropriate variables, such as aggregated precipitation over a specific period, average temperature, or the length of the time frame, for each borehole. These variables are subsequently used as regressors in the model. The expected outputs are not aimed at predicting future behavior, but at quantitatively assessing the influence of individual regressors on the water table depth.<\/p>\n\n\n\n

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Partial project outputs: model behavior (left) and model error behavior (right).<\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div>\n<\/details>\n<\/div>\n\n\n\n
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Computational Simulation of Complex Systems<\/h3>\n\n\n\n
Computational Simulation of the Phase Behavior of Polymer Brushes<\/strong><\/summary>\n
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Nanogels are polymeric nanoparticles<\/strong> utilized in targeted therapy due to their high absorption capacity, controlled size during synthesis, and ability to alter their properties based on their surrounding environment. The capability to anchor functional groups onto the nanogel surface allows researchers to control its surface and mechanical properties. Mesoscale simulations<\/strong> were used to model the surface of a nanogel composed of polyethylene glycol and polyethylene imine (shown in gray in the figure), which was modified with alkyl-amine chains. The simulation results demonstrated that the grafting density and the alkyl-amine length play a crucial role in making the surface accessible to or shielded from the environment, thereby allowing control over the absorption capacity of the nanogel and the subsequent release of the encapsulated substance. The description and results are adapted from Posel et al., Nanomaterials 2019, 9, 1514.<\/p>\n\n\n\n

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Computational Simulation of Racemic Mixture Separation<\/strong><\/summary>\n
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During drug manufacturing in the pharmaceutical industry, chiral molecules <\/strong>enter into reactions, and the resulting product is very often a racemic mixture<\/strong>. While one component of the mixture is pharmaceutically active, the other is usually harmful or reduces the desired effect. For this reason, it is essential to separate these two chemically identical components. Currently, several methods for the separation of racemic mixtures exist, but their disadvantage is often low efficiency or high cost. Today, membrane separation methods <\/strong>appear to be the most promising. However, the selection of a suitable membrane is mostly based on intuition alone, without a full understanding of the separation process. Computer simulations represent a suitable tool for thoroughly investigating interactions between individual atoms, thereby providing complete insight into the studied system. In this topic, we focus on modeling the interactions of different enantiomers of pharmaceutically active substances with selected chiral selectors to describe the mechanism of chiral recognition.<\/p>\n\n\n\n

Po\u010d\u00edta\u010dov\u00e1 simulace chir\u00e1ln\u00ed selekce ibuprofenu<\/strong><\/h5>\n\n\n\n

Ibuprofen je typick\u00fdm p\u0159\u00edkladem racemick\u00e9 sm\u011bsi<\/strong>. Jeho dva enantiomery, ozna\u010dovan\u00e9 jako (R)- a (S)-Ibuprofen, maj\u00ed stejn\u00e9 termodynamick\u00e9 vlastnosti, av\u0161ak v organismu vykazuj\u00ed odli\u0161n\u00e9 \u00fa\u010dinky. Zat\u00edmco (S)-Ibuprofen je v t\u011ble aktivn\u00ed a ulevuje od bolesti, (R)-Ibuprofen nem\u00e1 pozorovateln\u00fd \u00fa\u010dinek a sni\u017euje v\u00fdsledn\u00fd efekt l\u00e9ku. P\u0159i pokusu o zachycen\u00ed (R)-Ibuprofenu z racemick\u00e9 sm\u011bsi byla jako chir\u00e1ln\u00ed selektor pou\u017eita molekula \u03b2-cyclodextrinu<\/strong>. Molekul\u00e1rn\u011b dynamick\u00e9 simulace uk\u00e1zaly, \u017ee (R)-Ibuprofen je schopen s chir\u00e1ln\u00edm selektorem vytvo\u0159it v\u00edce vazeb ne\u017e jeho (S)-forma, a je tedy mo\u017en\u00e9 ho t\u00edmto zp\u016fsobem odd\u011blit z racemick\u00e9 sm\u011bsi. Efektivitu selekce ov\u0161em siln\u011b ovliv\u0148uje rozpou\u0161t\u011bdlo, kter\u00e9 m\u016f\u017ee zabrat dostupn\u00e9 vod\u00edkov\u00e9 vazby a t\u00edm sn\u00ed\u017eit \u00fa\u010dinnost d\u011blen\u00ed. V\u00fdb\u011br vhodn\u00e9ho rozpou\u0161t\u011bdla je p\u0159edm\u011btem dal\u0161\u00edho v\u00fdzkumu.<\/p>\n\n\n\n

V\u00fdsledky a popis p\u0159evzaty z \u0160kv\u00e1ra et al., J. Mol. Liq.<\/em> 2020, 302, 112575.<\/p>\n\n\n\n

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Computer Simulation of Pedestrian Motion Using CFD<\/strong><\/summary>\n
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Pedestrian movement modeling is a complex, non-linear problem <\/strong>that involves crowd behavior and the self-organizing capability of the system. Pedestrian movement <\/strong>is influenced by a wide range of factors, including both social and psychological ones, as well as physical factors, such as environmental influences. One of the key applications is the simulation of crowd movement during panic situations, such as fires or floods. The results of such simulations can be utilized in the design of evacuation plans, for instance, for the optimal placement of exits.<\/p>\n\n\n\n

In principle, we distinguish between two types of models: macroscopic<\/strong> and microscopic<\/strong>.<\/p>\n\n\n\n

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  • From a macroscopic perspective, pedestrians can be perceived as an intelligent fluid, with their movement described by equations similar to those for compressible flow. However, it is also necessary to take into account non-physical factors, which can be modeled using the so-called social force<\/strong>. A pedestrian's movement is thus influenced by the proximity of other pedestrians and exits.<\/li>\n\n\n\n
  • Microscopic models focus on the individual pedestrian as a particle, with the crowd modeled as a system composed of these particles. Various modeling<\/strong> approaches can be utilized, such as description using ordinary differential equations, agent-based modeling<\/strong>, or cellular automata<\/strong>.<\/li>\n<\/ul>\n\n\n\n

    More detailed information can be found, for example, in Kubera et al., Mathematics<\/em> 2021, 9, 1525, nebo v Felcman et al., Mathematical Modelling of Natural Phenomena<\/em> 2021, 16, 11.<\/p>\n<\/div>\n\n\n\n

    \"\"<\/figure>\n<\/details>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

    Na t\u00e9to str\u00e1nce naleznete seznam na\u0161ich realizovan\u00fdch projekt\u016f, kter\u00e9 demonstruj\u00ed \u0161\u00ed\u0159i na\u0161\u00ed odbornosti a praktick\u00e9ho p\u0159\u00ednosu v t\u011bchto oblastech. Aktu\u00e1ln\u011b \u0159e\u0161en\u00e9 projekty Realizovan\u00e9 projekty Po\u010d\u00edta\u010dov\u00e1 anal\u00fdza medic\u00ednsk\u00fdch dat Prediktivn\u00ed modelov\u00e1n\u00ed Po\u010d\u00edta\u010dov\u00e1 simulace komplexn\u00edch syst\u00e9m\u016f<\/p>","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"folder":[],"class_list":["post-31","page","type-page","status-publish","hentry"],"featured_image_src":null,"_links":{"self":[{"href":"https:\/\/odas.ujep.cz\/en\/wp-json\/wp\/v2\/pages\/31","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/odas.ujep.cz\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/odas.ujep.cz\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/odas.ujep.cz\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/odas.ujep.cz\/en\/wp-json\/wp\/v2\/comments?post=31"}],"version-history":[{"count":53,"href":"https:\/\/odas.ujep.cz\/en\/wp-json\/wp\/v2\/pages\/31\/revisions"}],"predecessor-version":[{"id":799,"href":"https:\/\/odas.ujep.cz\/en\/wp-json\/wp\/v2\/pages\/31\/revisions\/799"}],"wp:attachment":[{"href":"https:\/\/odas.ujep.cz\/en\/wp-json\/wp\/v2\/media?parent=31"}],"wp:term":[{"taxonomy":"folder","embeddable":true,"href":"https:\/\/odas.ujep.cz\/en\/wp-json\/wp\/v2\/folder?post=31"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}