VLIT NODE Sensor Technology and Prefarm

Precision farming systems are based on a detailed monitoring of information and data that are necessary for successful decision-making in crop production. The system is designed for data collection from several resources. In past years an extensive research and development work has been done in the field of wireless sensor networks (WSN) in the world. When a wireless sensor network (WSN) is used for agricultural purposes, it has to provide first of all a long-reach signal. The present paper describes new long distance RFID based technology implementation - VLIT NODE.Wireless Sensor Network, Precision Agriculture, RFID., Research and Development/Tech Change/Emerging Technologies, Research Methods/ Statistical Methods, GA, IN,

Application personalized Ventilation in Air Conditioning

Předmětem diplomové práce je aplikace osobního větrání ve vzduchotechnice. Hlavní cíl práce je sestrojení fyzických umělých modelů vyústek pro osobní větrání a jejich experimentální ověření v laboratoři. Získané informace z experimentálního měření se aplikují v návrhu nuceného osobního větrání na zadané budově. Výsledkem práce je zhodnocení použití osobního větrání pro praxi s uvedenými výhodami a nevýhodami.The subject of this thesis is the application of personalized ventilation in air. The main aim of the work is to construct physical models of artificial ventilation outlets for personal and their experimental verification in the laboratory. The information obtained from experimental measurements are applied in the design of forced personal ventilation to the specified building. Result of this work is to evaluate the use of personalized ventilation for practice with these advantages and disadvantages.

Offline Mode Support in Mobile Applications

Jednim z cilu teto prace je pruzkum ruznych pristupu k vyvoji mobilnich aplikaci se za- merenim na vyuziti webovych technologii. Nasledne jsou zkoumany a porovnany existujici reseni pro podporu offline rezimu s opozdenou synchronizaci dat u mobilnich aplikaci. Zaver teto prace tvori navrh a implementace mobilni aplikace, ktera vyuzije zkoumane technologie.The goal of this thesis is to research different approaches to mobile application development with a focus on the use of web technologies. Next, research and comparison of technologies that can be used to achieve offline support in mobile applications is done. As a later stage of the work, a showcase mobile application is designed and implemented with the use of researched technologies.

Design of MR clutch water cooling

Tato práce se zabývá návrhem a výrobou vodního chlazení pro magnetoreologickou spojku. Problém chlazení MR spojky byl řešen návrhem několika variant, ze kterých byla nakonec vybrána ta nejlepší. Byl vytvořen model a následně vyrobeno vodního chlazení, které se upevnilo na stator MR spojky. Chlazení kompletně obklopuje tělo spojky a tím dosahuje vysokého přenosu tepla mezi chladícím a chlazeným systémem. Návrh byl vytvářen s ohledem na dobrou složitelnost, takže jeho použití není složité.This paper describes the design and manufacture of water refrigeration for magneto clutch. MR clutch cooling problem was solved by designing of several variants of which was finally chosen the best. Model of water cooling was developed and subsequently made. The cooling is consolidated on a stator of MR clutch. Cooling completely surrounds the body of an MR clutch and thus achieves a high heat transfer between the cooling system and cooled. The proposal was developed with a view to good foldability, so its use is not complicated.

Post-collection acidification of spot urine sample is not needed before measurement of electrolytes

Kidney stone formers can have higher oxalate and phosphate salt amounts in their urine than healthy people and we hypothesized that its acidification may be useful. The study aims to compare results of urine concentrations of calcium, magnesium, and inorganic phosphorus in the midstream portion of first voided morning urine samples without (FMU) and with post-collection acidification (FMUa) in kidney stone patients. This is a prospective single center study. A total of 138 kidney stone patients with spot urine samples were included in the study. Urine concentrations of calcium, magnesium and inorganic phosphorus were measured with and without post-collection acidification. Acidification was performed by adding 5 µL of 6 mol/L HCl to 1 mL of urine. The median age (range) of all participants was 56 (18-87) years. The median paired differences between FMU and FMUa concentrations of calcium, magnesium, and inorganic phosphorus were: - 0.040 mmol/L, 0.035 mmol/L, and 0.060 mmol/L, respectively. They were statistically different: P < 0.001, P < 0.001, P = 0.004, respectively. These differences are not clinically significant because biological variations of these markers are much higher. No clinically significant differences in urinary calcium, magnesium, and inorganic phosphorus concentrations between FMU and FMUa in patients with kidney stones were found

Pendulum Energy Harvester with Amplifier

This paper presents a new principle of inductive vibration power harvester. Harvester is a pendulum that uses energy capacitor which is the mass. The mass is connected to the pendulum via a gearbox to achieve greater movement of the pendulum that generates an electromagnetic voltage. The harvester is developed at a very low frequency (1-10 Hz) which uses the rectified magnetic fluxes. Magnets are statically placed in the harvester case, and relative motion is carried out by the coil. Magnets are static, and the coil moves due to the weight ratio of magnets which the steel leads of the magnetic flux and the coil itself. This paper is focused on a harvester with a mechanical amplifier with the proposed technique is brings the plow harvester access with an auxiliary force. The experimental results indicate that the optimal results of the harvester with an accumulator for the resonant zone are 3.75 Hz, 7 Hz, and 10 Hz

Lidová píseň v hudební výchově na základních a středních školách v České republice

Title in English: Folk Song in Music Education at Primary and Secondary Schools in the Czech Republic The monograph presents and reflects the results of a specific research project of the same name (MUNI/A/1423/2018) carried out by a team of academic staff and students of the doctoral program of the Department of Music at the Faculty of Education, Masaryk University. The folklore song scope represents an important didactic basis, permeates music education at elementary, partly also secondary schools and specialized instrumental or singing education at elementary art schools. Due to the influence of multilayered cultural expressions and the influence of multimedia means, however, the question of how to present the musical and folklore heritage to school youth is more prominent. The object of investigation was the application of folk music to school education

Strategies and software tools for engineering protein tunnels and dynamical gates

Improvements in the catalytic activity, substrate specificity or enantioselectivity of enzymes are traditionally achieved by modification of enzymes’ active sites. We have recently proposed that the enzyme engineering endeavors should target both the active sites and the access tunnels/channels [1,2]. Using the model enzymes haloalkane dehalogenases, we have demonstrated that engineering of access tunnels provides enzymes with significantly improved catalytic properties [3] and stability [4]. User-friendly software tools Caver [5], Caver Analyst [6], CaverDock [7] and Caver Web [8], have been developed for the computational design of protein tunnels/channels; FireProt [9] and HotSpot Wizard [10] for automated design of stabilizing mutations and smart libraries. Using these tools we were able to introduce a new tunnel to a protein structure and tweak its conformational dynamics. This engineering strategy has led to improved catalytic efficiency [2], enhanced promiscuity or even a functional switch (unpublished). Our concepts and software tools are widely applicable to various enzymes with known structures and buried active sites. 1. Damborsky, J., et al., 2009: Computational Tools for Designing and Engineering Biocatalysts. Current Opinion in Chemical Biology 13: 26-34. 2. Prokop, Z., et al., 2012: Engineering of Protein Tunnels: Keyhole-lock-key Model for Catalysis by the Enzymes with Buried Active Sites. Protein Engineering Handbook, Wiley-VCH, Weinheim, pp. 421-464. 3. Brezovsky, J., et al., 2016: Engineering a de Novo Transport Tunnel. ACS Catalysis 6: 7597-7610. 4. Koudelakova, T., et al., 2013: Engineering Enzyme Stability and Resistance to an Organic Cosolvent by Modification of Residues in the Access Tunnel. Angewandte Chemie 52: 1959-1963. 5. Chovancova, E., et al., 2012: CAVER 3.0: A Tool for Analysis of Transport Pathways in Dynamic Protein Structures. PLOS Computational Biology 8: e1002708. 6. Jurcik, A., et al., 2018: CAVER Analyst 2.0: Analysis and Visualization of Channels and Tunnels in Protein Structures and Molecular Dynamics Trajectories. Bioinformatics 34: 3586-3588. 7. Vavra, O., et al., 2019: CaverDock 1.0: A New Tool for Analysis of Ligand Binding and Unbinding Based on Molecular Docking. Bioinformatics (under review). 8. Stourac, J., et al. 2019: Caver Web 1.0: Identification of Tunnels and Channels in Proteins and Analysis of Ligand Transport. Nucleic Acids Research (under review). 9. Musil, M., et al., 2017: FireProt: Web Server for Automated Design of Thermostable Proteins. Nucleic Acids Research 45: W393-W399. 10. Sumbalova, L. et al., 2018: HotSpot Wizard 3.0: Automated Design of Site-Specific Mutations and Smart Libraries in Protein Engineering. Nucleic Acids Research 46: W356-W362

Initial coin offering prediction comparison using Ridge regression, artificial neural network, random forest regression, and hybrid ANN-Ridge

Can machine learning take a prediction to win an investment in ICO (Initial Coin Offering)? In this research work, our objective is to answer this question. Four popular and lower computational demanding approaches including Ridge regression (RR), Artificial neural network (ANN), Random forest regression (RFR), and a hybrid ANN-Ridge regression are compared in terms of accuracy metrics to predict ICO value after six months. We use a dataset collected from 109 ICOs that were obtained from the cryptocurrency websites after data preprocessing. The dataset consists of 12 fields covering the main factors that affect the value of an ICO. One-hot encoding technique is applied to convert the alphanumeric form into a binary format to perform better predictions; thus, the dataset has been expanded to 128 columns and 109 rows. Input data (variables) and ICO value are non-linear dependent. The Artificial neural network algorithm offers a bio-inspired mathematical model to solve the complex non-linear relationship between input variables and ICO value. The linear regression model has problems with overfitting and multicollinearity that make the ICO prediction inaccurate. On the contrary, the Ridge regression algorithm overcomes the correlation problem that independent variables are highly correlated to the output value when dealing with ICO data. Random forest regression does avoid overfitting by growing a large decision tree to minimize the prediction error. Hybrid ANN-Ridge regression leverages the strengths of both algorithms to improve prediction accuracy. By combining ANN’s ability to capture complex non-linear relationships with the regularization capabilities of Ridge regression, the hybrid can potentially provide better predictive performance compared to using either algorithm individually. After the training process with the cross-validation technique and the parameter fitting process, we obtained several models but selected three of the best in each algorithm based on metrics of RMSE (Root Mean Square Error), R2 (R-squared), and MAE (Mean Absolute Error). The validation results show that the presented Ridge regression approach has an accuracy of at most 99% of the actual value. The Artificial neural network predicts the ICO value with an accuracy of up to 98% of the actual value after six months. Additionally, the Random forest regression and the hybrid ANN-Ridge regression improve the predictive accuracy to 98% actual value

Exploring hybrid models for short-term local weather forecasting in IoT environment

This paper explores using and hybridizing simple prediction models to maximize the accuracy of local weather prediction while maintaining low computational effort and the need to process and acquire large volumes of data. A hybrid RF-LSTM model is proposed and evaluated in this research paper for the task of short-term local weather forecasting. The local weather stations are built within an acceptable radius of the measured area and are designed to provide a short period of forecasting-usually within one hour. The lack of local weather data might be problematic for an accurate short-term valuable prediction in sustainable applications like agriculture, transportation, energy management, and daily life. Weather forecasting is not trivial because of the non-linear nature of time series. Thus, traditional forecasting methods cannot predict the weather accurately. The advantage of the ARIMA model lies in forecasting the linear part, while the SVR model indicates the non-linear characteristic of the weather data. Both non-linear and linear approaches can represent the combined model. The hybrid ARIMA-SVR model strengthens the matched points of the ARIMA model and the SVR model in weather forecasting. The LSTM and random forest are both popular algorithms used for regression problems. LSTM is more suitable for tasks involving sequential data with long-term dependencies. Random Forest leverages the wisdom of crowds by combining multiple decision trees, providing robust predictions, and reducing overfitting. Hybrid Random forest-LSTM potentially leverages the robustness and feature importance of Random Forest along with the ability of LSTM to capture sequential dependencies. The comparison results show that the hybrid RF-LSTM model reduces the forecasting errors in metrics of MAE, R-squared, and RMSE. The proposed hybrid model can also capture the actual temperature trend in its prediction performance, which makes it even more relevant for many other possible decision-making steps in sustainable applications. Furthermore, this paper also proposes the design of a weather station based on a real-time edge IoT system. The RF-LSTM leverages the parallelized characteristics of each decision tree in the forest to accelerate the training process and faster inferences. Thus, the hybrid RF-LSTM model offers advantages in terms of faster execution speed and computational efficiency in both PC and Raspberry Pi boards. However, the RF-LSTM consumes the highest peak memory usage due to being a combination of two different models