The work highlights the analysis of the influence of technological and structural parameters of the screw press, namely the step of the turns of the screw shaft of its speed and the area of openings for the removal of cake on the energy parameters of the process of oil expression. Based on the results of the multifactorial experiment, regression dependence allows us to estimate the influence of the parameters and operating modes of the screw press on the power consumption of the screw press and can be used in the process of synthesis and modeling of machines intended for pressing of vegetable oils from oil-containing raw materials. The surfaces of the response of the dependence of the power consumption of the screw press from the area of the openings for the removal of the cake as well as the step of the turns and the speed of the screw shaft have been constructed. Providing the following parameters of the pressing process: turn pitch of the auger shaft X10 = 21.8 mm; area of openings for oil cake withdrawal X20 = 107.99 mm2; rotation frequency of the auger shaft X30 = 5.12 min -1, the power consumption of the screw press will be 170 W, ensuring the maximum yield of oil q = 36%.

The paper presents selected test results of the PERKINS 1104D-E44TA engine, adjusted to being dualfuelled with compressed natural gas and Diesel fuel. The tests were carried out with maximum possible dosing of natural gas and then with lowering its dosing by approximately half. The obtained test results were compared with the results of tests carried out when the engine was powered with Diesel fuel only.

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Nowadays, many cities all over the world suffer from noise pollution. Noise is an invisible danger that can cause health problems for both people and wildlife. Therefore, it is essential to estimate the environmental noise level and implement corrective measures. There are a number of noise identification techniques, and the choice of the most appropriate technique depends upon the information required and its application. Analyzing audio data requires three key aspects to be considered such as time period, amplitude, and frequency. Based on the above parameters, the source of noise can be identified. This research paper suggests the utilization of artificial intelligence and machine learning algorithms for the traffic noise detection process. Computational methods are the fastest and most innovative way to analyze raw data sets and predict results. Identifying patterns in these methods requires a large amount of data and computing power. Machine learning models can be trained using three types of data: experimental sound libraries, audio datasets purchased from data providers, and data collected by domain experts. In the scope of the study, an experimental dataset was used to train a model that predicts the correct outcomes based on the inputs, using supervised learning. Developing an accurate model requires high-quality data input. However, incorrect data collection can cause noise in feature sets, as can human error or instrument error. Traffic sound events in the real environment do not usually occur in isolation but tend to have a significant overlap with other sound events. A part of this paper is dedicated to the problems that may arise during traffic noise detection, like incorrect data processing and data collection. It also discusses the ways to improve the quality of the input data. The study also states that the field of transport noise detection would greatly benefit from the development of a centralized railway database based on constructive railroad data, and from a centralized database with railway-specific datasets. Based on preliminary results of traffic noise analysis, modernization of the tram lines was proposed to reduce the environmental noise.

The article considers the possibility and priority of using the Internet of Things, especially its implementation in the surface water monitoring system. The feasibility of developing a complex system of interactive monitoring of surface water using IoT technologies has been substantiated, such a system will significantly improve water monitoring in real-time and ensure the gradual implementation of new sensor capabilities, such as collecting data on the deviation of parameters from the specified normative indicators of water quality in natural reservoirs. An interactive system for intelligent monitoring of water quality in natural reservoirs using Internet of Things technologies and tools has been developed, among others, the Node MCU 1.0 Wi-Fi microcontroller based on the ESP8266 microcontroller was used, as well as PH4502s analog sensor, the DHT-11 water and environmental temperature sensor, the DFRobot water turbidity and signal conversion board V2. The results were displayed on a 2.2- inch QVGA TFT LCD. The microcontroller unit (MCU) is connected to the sensors and further processing is performed on the server unit. The choice of a cloud server was justified, and the transfer of received data was transferred to the cloud using IoT-based ThingSpeak open-source software for water quality monitoring. The computer design environment Autodesk was used to increase the efficiency of design, in particular, the arrangement of elements, ensuring functionality, and ergonomics. The software and hardware of the device were designed with open-source software Fritzing and Arduino (IDE). Based on the obtained statistical data about the quality of water in natural reservoirs, a modern network of smart devices was implemented, such a network is a monitoring and notification system, which considers the linking of data to the time and place of positioning. Features of obtaining data on the results of water quality monitoring in natural reservoirs in real time for consumers were presented, with such monitoring, it is possible to predict and take the necessary measures to prevent possible negative impacts.