The growing global population and increasing food demand highlight the need for sustainable agricultural practices that balance productivity with environmental protection. Traditional blanket pesticide spraying leads to overuse of chemicals, environmental pollution, and biodiversity loss. This study aims to develop an innovative approach to precision pest management using mobile computing, computer vision, and deep learning techniques. A mobile measurement platform equipped with cameras and an onboard computer was designed to collect real-time field data and detect pest infestations. The system uses an advanced object detection algorithm based on the YOLOv4 architecture, trained on a custom dataset of rapeseed pest images. Modifications were made to enhance detection accuracy, especially for small objects. Field tests demonstrated the system’s ability to identify and count pests, such as the pollen beetle (Brassicogethes aeneus), in rapeseed crops. The collected data, combined with GPS information, generated pest density maps, which can guide site-specific pesticide applications. The results show that the proposed method achieved a mean average precision (mAP) of 83.7% on the test dataset. Field measurements conducted during the traversal of rapeseed fields enabled the creation of density maps illustrating the distribution of pollen beetles. Based on these maps, the potential for pesticide savings was demonstrated, and the migration dynamics of pollen beetle were discussed.

This article presents a prototype of a new, non-invasive, cuffless, self-calibrating blood pressure measuring device equipped with a pneumatic pressure sensor. The developed sensor has a double function: it measures the waveform of blood pressure and calibrates the device. The device was used to conduct proof-of-concept measurements on 10 volunteers. The main novelty of the device is the pneumatic pressure sensor, which works on the principle of a pneumatic nozzle flapper amplifier with negative feedback. The developed device does not require a cuff and can be used on arteries where cuff placement would be impossible (e.g., on the carotid artery). The obtained results showed that the systolic and diastolic pressure measurement errors of the proposed device did not exceed ±6.6% and ±8.1%, respectively.

Efficient heat recuperation is one of the crucial aspects affecting the performance of mixed-refrigerant Joule-Thomson (MR JT) cryocoolers. This becomes even more important in the case of precooled systems which improves the performance, but on the other hand, emphasizes the challenges related to the effectiveness of the recuperation process. Modelling of recuperation process is challenging itself as recuperation concerns both gaseous, liquid phases and two-phase flow. Moreover, application of mixed-refrigerant brings a lot complexity to the modelling of MR thermo-hydraulic properties. At last, experimental validation of modelling results requires very sophisticated measurement setup especially in terms of mixed-refrigerant composition which can significantly differ from charged proportions. This paper present modelling approach of complete two-phase recuperation heat transfer process. In total, 96 correlations combinations were examined. The results are compared with the experimental results achieved using novel validated method of noninvasive, continuous method of determining composition. Carried modelling validated with experimental results provides useful insight concerning heat transfer phenomena in recuperation section of MR JT cryocoolers or other mixed-refrigerant based systems.

Efficient heat recuperation is one of the most critical factors influencing the performance of mixed-refrigerant Joule–Thomson (MR JT) cryocoolers. This becomes even more significant in precooled systems, which enhance overall performance but simultaneously intensify the challenges associated with recuperation effectiveness. Modeling the recuperation process is inherently complex because it involves gaseous, liquid, and two-phase flow regimes. Furthermore, the use of mixed refrigerants introduces additional complexity in predicting thermo-hydraulic properties. Experimental validation of modeling results is also highly demanding, particularly with respect to determining the mixed-refrigerant composition, which may differ substantially from the initially charged proportions during operation. This paper presents a comprehensive modeling approach for the complete two-phase recuperative heat transfer process. In total, 96 combinations of heat transfer correlations were evaluated. The modeling results were compared with experimental data obtained using a novel, validated, noninvasive, continuous method for determining refrigerant composition. Such an approach, including analysis of the impact of MR composition uncertainty on the results, has not been performed in previous, similar studies. The optimal set of correlations was identified, yielding a mean average error of 20.16K without composition correction (when applying a composition correction of 13.67K). The validated modeling approach provides valuable insight into heat transfer phenomena occurring in the recuperation section of MR JT cryocoolers and other mixed-refrigerant-based systems.