Global warming and human-induced eutrophication drive the occurrence of various cyanotoxins in aquatic environments. These metabolites reveal diversified mechanisms of action, encompassing cyto-, neuro-, hepato-, nephro-, and neurotoxicity, and pose a threat to aquatic biota and human health. In the present paper, we review data on the occurrence of the most studied cyanotoxins, microcystins, nodularins, cylindrospermopsin, anatoxins, and saxitoxins, in the aquatic environment, as well as their potential bioaccumulation and toxicity in fish. Microcystins are the most studied among all known cyanotoxins, although other toxic cyanobacterial metabolites are also commonly identified in aquatic environments and can reveal high toxicity in fish. Except for primary toxicity signs, cyanotoxins adversely affect the antioxidant system and anti-/pro-oxidant balance. Cyanotoxins also negatively impact the mitochondrial and endoplasmic reticulum by increasing intracellular reactive oxygen species. Furthermore, fish exposed to microcystins and cylindrospermopsin exhibit various immunomodulatory, inflammatory, and endocrine responses. Even though cyanotoxins exert a complex pressure on fish, numerous aspects are yet to be the subject of in-depth investigation. Metabolites other than microcystins should be studied more thoroughly to understand the long-term effects in fish and provide a robust background for monitoring and management actions.

Modern object recognition algorithms have very high precision. At the same time, they require high computational power. Thus, widely used low-power IoT devices, which gather a substantial amount of data, cannot directly apply the corresponding machine learning algorithms to process it due to the lack of local computational resources. A method for fast detection and classification of moving objects for low-power single-board computers is shown in this paper. The developed algorithm uses geometric parameters of an object as well as scene-related parameters as features for classification. The extraction and classification of these features is a relatively simple process which can be executed by low-power IoT devices. The algorithm aims to recognize the most common objects in the street environment, e.g., pedestrians, cyclists, and cars. The algorithm can be applied in the dark environment by processing images from a near-infrared camera. The method has been tested on both synthetic virtual scenes and real-world data. The research showed that a low-performance computing system, such as a Raspberry Pi 3, is able to classify objects with acceptable frame rate and accuracy.

The increasing frequency of wind turbine failures due to extreme weather conditions necessitates the implementation of new solutions to enhance their operational reliability. This paper presents an automatic rotor drop system specifically designed for wind turbines equipped with the Onipko rotor. The system aims to protect turbines from damage caused by critical wind speeds, reducing maintenance costs and extending the equipment’s lifespan. The unique design of the Onipko rotor allows it to operate at wind speeds as low as 0.1 m/s. However, its high drag coefficient and lack of aerodynamic optimization make it susceptible to mechanical stress and structural instability under strong gusts, requiring additional protective measures. The paper presents a calculation of the critical wind speed at which protective measures must be initiated. Through mathematical modeling, this study demonstrates the effectiveness of the rotor drop system in ensuring safe operation at wind speeds reaching 23.5 m/s. The optimization of the PI controller parameters provides a rapid response and stability, significantly enhancing the resilience of wind turbines to adverse weather conditions.