Determining the level of blood pressure (BP) in a non-invasive way and without a sphygmomanometer cuff is of great relevance when conducting continuous monitoring or screening studies. In this regard, a method for predicting BP parameters based on the signals of the photoplethysmogram (PPG) and electrocardiogram (ECG) signals has been developed. It is proposed to use, as informative features, the time of pulse wave propagation (PTT) and a set of calculated pulse parameters of PPG. PTT is defined as the time intervals between the R-wave of the ECG and the corresponding characteristic points on the PPG acquired optically from the finger. As parameters of the PPG pulse, the known characteristics of this signal described in the literature are used, as well as additional informative features selected during the study. In accordance with the above, the tools of machine learning theory were used to construct a classifier model and regression models. The approach described in this paper to determine BP makes it possible to use 10-second ECG signals in any of the 12 common leads and PPG signals from any optical type of sensor. The built model of the classifier detects three levels of BP: low, normal, and high, at the accuracy metric=0.8494. The regression models predict systolic, diastolic, and mean BP parameters in accordance with the requirements of the British Hypertension Society (BHS) standard by the magnitude of the absolute error. The proposed method for assessing the level of BP involves real-time measurements and can be used in the design of measuring equipment for screening studies, as well as in continuous monitoring tasks within the framework of BHS requirements.

The research focuses on developing a novel method for the automatic recognition of human psychoemotional states (PES) using deep learning technology. This method is centered on analyzing speech signals to classify distinct emotional states. The primary challenge addressed by this research is to accurately perform multiclass classification of seven human psychoemotional states, namely joy, fear, anger, sadness, disgust, surprise, and a neutral state. Traditional methods have struggled to accurately distinguish these complex emotional nuances in speech. The study successfully developed a model capable of extracting informative features from audio recordings, specifically mel spectrograms and mel-frequency cepstral coefficients. These features were then used to train two deep convolutional neural networks, resulting in a classifier model. The uniqueness of this research lies in its use of a dual-feature approach and the employment of deep convolutional neural networks for classification. This approach has demonstrated high accuracy in emotion recognition, with an accuracy rate of 0.93 in the validation subset. The high accuracy and effectiveness of the model can be attributed to the comprehensive and synergistic use of mel spectrograms and mel-frequency cepstral coefficients, which provide a more nuanced analysis of emotional expressions in speech. The method presented in this research has broad applicability in various domains, including enhancing human-machine interface interactions, implementation in the aviation industry, healthcare, marketing, and other fields where understanding human emotions through speech is crucial

The study aim was to develop a remote diagnostic monitoring system for pumping equipment with an open architecture to improve the reliability and efficiency of pump operation in various industrial sectors. The system is designed for the periodic collection and analysis of vibration and temperature signals, which allows for the prompt identification of potential equipment malfunctions and avoidance of emergency shutdowns during the production process. The aim of this study was to develop an effective open architecture for a diagnostic monitoring system for pumping equipment based on IoT technologies. The primary focus is on creating a system architecture that simplifies the installation and operation of equipment, ensures scalability and ease of integration with existing enterprise information systems, and reduces material implementation costs. To achieve this goal, the following objectives were addressed within the study: 1) selection of informative features from vibration signals that allow for the diagnosis of the most common faults in pumping equipment during periodic monitoring; 2) selection of hardware specifications that ensure the diagnostic monitoring system meets the stated requirements; and 3) development of a software and network architecture for the diagnostic monitoring system based on open hardware and software standards. The results of the experiments demonstrated that the developed system enables effective monitoring of the condition of pumping equipment and reduces the risk of emergency shutdowns, thereby optimizing operating costs. The incorporation of wireless technologies, open software products, and standards makes systems flexible and cost-effective, which is especially important for small and medium-sized industrial enterprises. Conclusion. The use of the proposed monitoring system improves the reliability of pumping equipment and maintenance management based on the current state data.

This article discusses the principle of operation of a potentiometric level meter for measuring the level of an electrically conductive liquid in a vessel. An electrically conductive liquid is measured by an indirect method in a grounded tank. The device consists of a sensor with low electrical resistance, an alternator, a metal vessel wall and a small signal amplifier.The nonlinearity of the transfer function does not allow the measured values of the sensor to be used without preliminary linearization. Therefore, it is necessary to develop a sensor model in an electrically conductive liquid and determine factors affecting the accuracy of level measurement.To assess the accuracy of measurements, the article presents a model of the electric field inside the electrolyte created by a sensor of a potentiometric level meter in a vessel with a cylindrical wall. Numerical methods based on the finite element method are used to calculate potentials and currents inside the electrolyte. The finite element model and the finite element grid allows to consider the transfer of potentials between finite elements.It is shown that the level measurement error in a potentiometric level meter has an unacceptable value and depends on the liquid level and on the location of the level meter.Based on the obtained model, the factors influencing the measured value of the liquid level were determined and the absolute and relative measurement errors were calculated. Further steps have also been identified to improve the accuracy of measuring the level gauge.

This paper considers the process of developing a method to recognize the causes of plant growth deviations from normal using the advancements in artificial intelligence. The medicinal plant Aloe arborescens L. was chosen as the object of this research given that this plant had been for decades one of the best-selling new products in the world. Aloe arborescens L. is famous for its medicinal properties used in medicine, cosmetology, and even the food industry. Diagnosing the abnormalities in the plant development in a timely and accurate manner plays an important role in preventing the loss of crop production yields. The current study has built a method for recognizing the causes of abnormalities in the development of Aloe arborescens L. caused by a lack of watering or lighting, based on the use of transfer training of the VGG-16 convolutional neural network (United Kingdom). A given architecture is aimed at recognizing objects in images, which is the main reason for using it to achieve the goal set. The analysis of the quality metrics of the proposed image classification process by specified classes has revealed high recognition reliability (for a normally developing plant, 91 %; for a plant without proper watering, 89 %; and for a plant without proper lighting, 83 %). The analysis of the validity of test sample recognition has demonstrated a similar validity of the plant's classification to one of three classes: 92.6 %; 87.5 %; and 85.5 %, respectively. The results reported here make it possible to supplement the automated systems that control the mode parameters of hydroponic installations by the world's major producers with the main feedback on the deviation of the plant's development from the specified values