Short-term load forecast in electric energy system in Bulgaria

As the accuracy of the electricity load forecast is crucial in providing better cost effective risk management plans, this paper proposes a Short Term Electricity Load Forecast (STLF) model with high forecasting accuracy. Two kind of neural networks, Multilayer Perceptron network model and Radial Basis Function network model, are presented and compared using the mean absolute percentage error. The data used in the models are electricity load historical data. Even though the very good performance of the used model for the load data, weather parameters, especially the temperature, take important part for the energy predicting which is taken into account in this paper. A comparative evaluation between a traditional statistical method and artificial neural networks is presented

Retrofitting measure of an ageing multi-purpose ship exposed to short-sea LNG operation

The objective of the present study is to analyse the retrofitting measure needed for an ageing multi-purpose ship exposed to a short sea LNG operation in the Black Sea region. Different technical aspects of the retrofitting related to using LNG as fuel for short-sea shipping, including the required volume of the liquefied natural gas, the appropriate type of tanks and the location of tanks on the ship, changes in the main engine and needed additional equipment, are discussed. The cost-benefit feasibility analysis is performed considering the historical and current price of LNG fuel and different taxes related to the generated CO2, examining the Varna-Poti-Varna and Varna-Istanbul-Varna routes

Appropriate Conversion of Machine Learning Data

Data is an important part of computer technology and, as such, explains the strong dependence of machine learning algorithms on it. The operation of any corresponding algorithm is directly dependent on the type of data and the proper data representation increases the productivity of these algorithms. Advanced in the present article is an algorithm for data pre-processing in a form that is most suitable for machine learning algorithms, with cryptographic secret keys being used as input data. The experimental results were satisfactory, and with the utilization of secret keys with significant differences, the recognition obtained is about 100%

Cell adhesion molecules in pleural effusions with different etiology

The pleura, including the mesothelial and underlying mesenchymal cells and extracellular matrix, is often involved in pathological processes of not completely defined mechanisms. Pleural cells are specialized in performing barrier and secretory functions and require careful study to gather a meaningful clinical information.Biomedical Reviews 1996; 6: 121-123

Experimental investigation of a variable geometry vertical axis wind turbine

An experimental study is presented on the performance of a vertical axis wind turbine with variable blade geometry of the design developed by Austin Farrah. This is experimentally compared with the performance of a correspondingly sized Bach-type Savonius turbine using the same electrical generator and measurement instrumentation in a wind tunnel. Experiments were performed for Reynolds numbers, based on blade chord, in the range 5 × 103 to 1 × 105, and for blade settings between −40° and +40o. The study shows that for the tip speed ratios that have been investigated, the Farrah vertical axis wind turbine design can only marginally outperform a corresponding two-bladed Bach-type Savonius turbine and then only when its blades are set to 40° pitch angle. The presence of a small inner cylinder, which rotates with the turbine, does not enhance its performance due to the fact that it is immersed in an extensive column of relatively static air

Accelerating Molecular Graph Neural Networks via Knowledge Distillation

Recent advances in graph neural networks (GNNs) have enabled more comprehensive modeling of molecules and molecular systems, thereby enhancing the precision of molecular property prediction and molecular simulations. Nonetheless, as the field has been progressing to bigger and more complex architectures, state-of-the-art GNNs have become largely prohibitive for many large-scale applications. In this paper, we explore the utility of knowledge distillation (KD) for accelerating molecular GNNs. To this end, we devise KD strategies that facilitate the distillation of hidden representations in directional and equivariant GNNs, and evaluate their performance on the regression task of energy and force prediction. We validate our protocols across different teacher-student configurations and datasets, and demonstrate that they can consistently boost the predictive accuracy of student models without any modifications to their architecture. Moreover, we conduct comprehensive optimization of various components of our framework, and investigate the potential of data augmentation to further enhance performance. All in all, we manage to close the gap in predictive accuracy between teacher and student models by as much as 96.7% and 62.5% for energy and force prediction respectively, while fully preserving the inference throughput of the more lightweight models.Comment: Accepted as a conference paper at NeurIPS 202

A Setup for Preparation of Glass-Carbon Coatings on TiO2-Nb2O5 Intended for Hip Joint Prostheses

A setup for preparation of glass-carbon coatings on TiO2-Nb2O5 ceramic materials, intended for implants for surgery of hip joint prostheses, is described. The setup described consists of vacuum tight ceramic chamber; programmable high temperature furnace and a system for controlled introduction of inert gas into the chamber. The setup allows working with temperatures up to 1350 oC, controlled heating rates from 1 to 15 oC/min and chamber pressures down to 10-2 mmHg