The world population is projected to reach 9.8 billion in 2050, and 11.2 billion in 2100 (United Nations) and people will need food, and decrease the farming land. Thus, the importance of Internet of Things (IoT) and Computer Science (CS) in plant disease management are increasing now-a-days. Mobile apps, remote sensing, spectral signature analysis, artificial neural networks (ANN), and deep learning monitors are commonly used in plant disease and pest management. IoT improves crop yield by fostering new farming methods along with the improvement of monitoring and management through cloud computing. In the quest for effective plant disease control, the future lies in cutting-edge technologies. The integration of IoT, artificial intelligence, and data analytics revolutionizes monitoring and diagnosis, enabling timely and precise interventions. Cloud computing facilitates real-time data sharing and analysis empower farmers to combat diseases with unprecedented efficiency. By harnessing these innovations, agriculture can embrace sustainable practices and safeguard crop health, ensuring a bountiful and secure future for the global food supply.

The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG heartbeat in order to achieve better statistics. Experiments were performed using a self-collected Lviv Biometric Dataset. This database contains over 1400 records for 95 unique persons. The baseline identification accuracy without any correction is around 86%. After applying the outlier correction the results were improved up to 98% for autoencoder based algorithms and up to 97.1% for sliding Euclidean window. Adding outlier correction stage in the biometric identification process results in increased processing time (up to 20%), however, it is not critical in the most use-cases.

The aim of the article is to reproduce and compare the peculiarities of the ethnocultural image of a vain person, as verbalized in Ukrainian and Polish phraseology. The subject of analysis is the structural‐semantic and functional peculiarities of Ukrainian and Polish phraseological units, in which vanity is conceptualized as an emotional state of superiority, arrogance, pride, and which have a pronounced negative connotation. The study found that in the common Ukrainian‐Polish perception, a vain person is a person who considers himself/herself superior to others, and, accordingly, others negatively evaluate this position. Most often, vanity in Polish and Ukrainian phraseology is conceptualized through the image of a person with their head raised high, puffed up, with protruding lips, whose appearance and habits resemble the behaviour of a beautiful pompous bird: a peacock or a rooster (in Ukrainian and Polish ethnoculture), a crane or a turkey (only in Polish), goldeneye or a screech‐owl (only in Ukrainian). Also common is the idea of a vain person who thinks he/she is the smartest, while others think that something is wrong with him/her. Comparing the analyzed phraseological units in the selected languages allows us to better understand the peculiarities of the image, which became the impetus for the creation of the phraseological nomination, to establish the regularities and mechanisms of the verbal explication of vanity in Ukrainian and Polish linguistic cultures.