Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA model for short-term forecasts, data on the spread of the COVID-19 virus in Lithuania is used, the forecasts of epidemic dynamics were examined, and the results were presented in the study. Nevertheless, the approach presented might be applied to any country and other pandemic situations. The COVID-19 outbreak started at different times in different countries, hence some countries have a longer history of the disease with more historical data than others. The paper proposes a novel approach to data registration and machine learning-based analysis using data from attention-based countries for forecast validation to predict trends of the spread of COVID-19 and assess risks.     

Application of cleaner technologies in milk processing industry to improve the environmental efficiency

Changes in production processes and products that result in improvement of environmental, economic and social performance of enterprises are an important element of the overall process towards more sustainable production. The aim of this article is to demonstrate the application of cleaner production and eco-design as sustainable production tools to improve the environmental efficiency of milk processing industry. Milk processing industry is one of the largest and dynamically developing branches of industry in the world. The main impact of milk processing industry on the environment is related to energy and water consumption, and waste and wastewater generation. A number of potential solutions to improve the environmental performance of milk processing industry, to reduce energy and resources consumption are analysed: substitution of cleaning agent in the milk receiving bar for washing of milk tankers with the specialised acidic detergent, integration of the automated CIP washing system in the butter bar, implementation of water recycling system to collect warm (35?°C) water, integration of the membrane technologies for the evaporation process and the use of filtrate received during the condensation for steam generation in the boiler house. Finally, an eco-design solution for cans of milk products is presented. All these proposals have been implemented in the milk processing company.