Publications by authors named "Jaroslaw Goclawski"

Clay vessels have a wide variety of functions in social activities in the Hallstatt period. In addition to food storage and processing, they were used for ritual purposes and as funerary vessels. The paper presents the results of archaeological and chromatographic studies of 31 vases from two different Hallstatt culture sites in lower Silesia (Poland).

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Mechanical stress induces distinct anatomical, molecular, and morphological changes in Urtica dioica, affecting trichome development, gene expression, and leaf morphology under controlled conditions The experiments were performed on common nettle, a widely known plant characterized by high variability of leaf morphology and responsiveness to mechanical touch. A specially constructed experimental device was used to study the impact of mechanical stress on Urtica dioica plants under strictly controlled parameters of the mechanical stimulus (touching) and environment in the growth chamber. The general anatomical structure of the plants that were touched was similar to that of control plants, but the shape of the internodes' cross section was different.

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This paper describes a new method of textile pilling prediction, based on multivariate analysis of the spatial layer above the surface. The original idea of the method is the acquisition of 3D fabric image using optical coherence tomography (OCT) with infrared light, which allows for the fabric fuzz visualization with high sensitivity. The pilling layer, reconstructed with the resolution of [Formula: see text], includes reliable textural information related to the amount of loose fibers and bunches appearing as a result of abrasion.

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Textiles require finishing to improve their usability and functionality but in the first place, to reduce of pilling tendency, which affects all kinds of synthetic and natural fabrics. Several laser ablation tests have been applied to the selected fabrics with different chemical composition to reveal the impact of this process on the pilling behavior. To reflect the pilling changes, two textural descriptors have been proposed to textile images obtained with optical coherence tomography (OCT).

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Pilling is caused by friction pulling and fuzzing the fibers of a material. Pilling is normally evaluated by visually counting the pills on a flat fabric surface. Here, we propose an objective method of pilling assessment, based on the textural characteristics of the fabric shown in optical coherence tomography (OCT) images.

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This paper compares methods for measuring selected morphological features on the surface of thin metallic layers applied to flexible textile substrates. The methods were tested on a silver layer with a thickness of several hundred nanometers, which was applied to a textile composite with the trade name Cordura. Measurements were carried out at the micro scale using both optical coherent tomography (OCT) and the traditional contact method of using a profilometer.

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This article presents a new method for the extraction and measurement of the polyurethane layer of Cordura textile composites using optical coherence tomography. The knowledge of coating layer properties in these composites is very important, as it affects mechanical parameters such as stiffness and bending rigidity. Unlike microscopic measurements, which require cross-section samples of the material, the proposed approach is non-invasive.

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Background: Chlorophyll fluorescence analysis is one of the non-invasive techniques widely used to detect and quantify the stress-induced changes in the photosynthetic apparatus. Quantitative information is obtained as a series of images and the specific fluorescence parameters are evaluated inside the regions of interest outlined separately on each leaf image. As the performance of photosynthesis is highly heterogeneous over a leaf surface, the areas of interest selected for generating numeric data are crucial for a reliable analysis.

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The paper presents an image processing method for the quantitative assessment of ROS accumulation areas in leaves stained with DAB or NBT for HO and O detection, respectively. Three types of images determined by the combination of staining method and background color are considered. The method is based on the principle of supervised machine learning with manually labeled image patterns used for training.

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This paper considers the problem of image segmentation using the random walker algorithm. In the case of 3D images, the method uses an extreme amount of memory and time resources. These are required in order to represent the corresponding enormous image graph and to solve the resulting sparse linear system.

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