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The influence of atmospheric plasma discharge (APD) of the Gliding Arc type and low-pressure microwave plasma discharge (LPMD) on the class of various seeds was investigated. Pea, wheat, and sunflower seeds, representing legumes, cereals, and oilseeds, respectively, were selected for the treatment. Our study aimed to verify the effect of plasma treatment on the water contact angle and the wettability of the seeds. Treatment time, working gas flow and microwave power were varied to determine their impact. All treated and untreated variants were used to conduct the water contact angle measurements to compare their wettability. APD treatment seemed to be utterly ineffective for improving the wettability for most process parameters. On the contrary, LPMD manifested a much more efficient impact. The maximum effect was found for the 800 W microwave power applied to the pea seed for 30 s. The contact angle achieved decreased by approximately 50% compared to the untreated sample. These results indicate that LPMD may be an effective alternative to traditional pre-sowing seed treatments used in agriculture for water intake enhancement. Still, it is strongly dependent on the seed’s type and the used process parameters.

Infrared thermography (IRT) is a noninvasive and safe method of displaying the temperature map of objects that can be used to detect hoof diseases and lameness to reduce significant financial costs and physically stress animals. A qualitative bibliometric method based on the analysis of publications by the authors themselves using sophisticated tools of scientific databases was applied in this work. This review presents the fundamentals of IRT as well as recent developments in IRT detection in dairy science, including preprocessing, segmentation, and classification of objects in IRT images. In addition, recent studies dealing with the detection of hoof diseases and lameness using IRT are reviewed. As a result of this study, select previous studies are confronted in terms of technical aspects of IRT measurements such as emissivity, distance, temperature range, and reflected air temperature. Subsequently, recommendations for future IRT measurements are discussed.

This paper focuses on the statistical analysis of mimetic muscle rehabilitation after head and neck surgery causing facial paresis in patients after head and neck surgery. Our work deals with an evaluation problem of mimetic muscle rehabilitation that is observed by a Kinect stereo-vision camera. After a specific brain surgery, patients are often affected by face palsy, and rehabilitation to renew mimetic muscle innervation takes several months. It is important to be able to observe the rehabilitation process in an objective way. The most commonly used House–Brackmann (HB) scale is based on the clinician’s subjective opinion. This paper compares different methods of supervised learning classification that should be independent of the clinician’s opinion. We compare a parametric model (based on logistic regression), non-parametric model (based on random forests), and neural networks. The classification problem that we have studied combines a limited dataset (it contains only 122 measurements of 93 patients) of complex observations (each measurement consists of a collection of time curves) with an ordinal response variable. To balance the frequencies of the considered classes in our data set, we reclassified the samples from HB4 to HB3 and HB5 to HB6—it means that only four HB grades are used for classification algorithm. The parametric statistical model was found to be the most suitable thanks to its stability, tractability, and reasonable performance in terms of both accuracy and precision.