Study of permeability characteristics of membranes Quarterly report, 9 May - 9 Aug. 1969

Demineralizing gear pump system with mixed bed ion exchange columns for salt and volume transport experimen

Study of permeability characteristics of membranes Quarterly report, 9 Feb. - 9 May 1969

Permeability characteristics of membrane

Spin-density-wave transition of (TMTSF)2_2PF6_6 at high magnetic fields

The transverse magnetoresistance of the Bechgaard salt (TMTSF)$_2$PF$_6$ has been measured for various pressures, with the field up to 24 T parallel to the lowest conductivity direction c$^{\ast}$. A quadratic behavior is observed in the magnetic field dependence of the spin-density-wave (SDW) transition temperature $T_{\rm {SDW}}$. With increasing pressure, $T_{\rm {SDW}}$ decreases and the coefficient of the quadratic term increases. These results are consistent with the prediction of the mean-field theory based on the nesting of the quasi one-dimensional Fermi surface. Using a mean field theory, $T_{\rm {SDW}}$ for the perfect nesting case is estimated as about 16 K. This means that even at ambient pressure where $T_{\rm {SDW}}$ is 12 K, the SDW phase of (TMTSF)$_2$PF$_6$ is substantially suppressed by the two-dimensionality of the system.Comment: 11pages,6figures(EPS), accepted for publication in PR

Thin-thick surface phase coexistence and boundary tension of the square-well fluid on a weak attractive surface

Prewetting transition is studied for the square-well fluid of attractive-well diameter ??ff ff =1.5 in the presence of a homogeneous surface modeled by the square-well potential of attractive well from 0.8 ff to 1.8 ff. We investigate surface phase coexistence of thin-thick film transition using grand-canonical transition matrix Monte Carlo (GC-TMMC) and histogram reweighting techniques. Molecular dynamics (MD) and GC-TMMC are utilized to predict the properties of the fluid for various surface fluid affinities. Occurrences of prewetting transition with the variation of surface affinity are observed for a domain of reduced temperature from T* =0.62 to 0.75. We have used MD and GC-TMMC+finite size scaling (FSS) simulations to calculate the boundary tension as a function of temperature as well as surface affinity. Boundary tensions via MD and GC-TMMC+FSS methods are in good agreement. The boundary tension increases with the decrease of wall-fluid affinity. Prewetting critical properties are calculated using rectilinear diameter approach and scaling analysis. We found that critical temperature and density increase with the decrease of wall-fluid affinity.open101

The technology transfer of non-ferrous alloys casting during the middle age

The article reports on the findings from the metallographic analysis of 13th c. archaeological objects from Chełm (eastern Poland). The group submitted for analysis includes jeweller’s dies used in the production of women’s ceremonial ornaments, crucibles and bronze ornaments. The Mongol invasion of 13th c. had caused craftsmen from central areas of East Europe to flee and seek shelter in the western parts of Rus. It may be safe to interpret the finds from Chełm as the remains of a jeweller’s workshop, the site of casting and working copper alloys and silver. The analysis of the technology used in casting copper alloys and silver in the jeweller’s workshop were made using optical microscopy, X-ray spectroscopy and X-ray radiography

Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques

A new approach for the segmentation of gland units in histological images is proposed with the aim of contributing to the improvement of the prostate cancer diagnosis. Clustering methods on several colour spaces are applied to each sample in order to generate a binary mask of the different tissue components. From the mask of lumen candidates, the Locally Constrained Watershed Transform (LCWT) is applied as a novel gland segmentation technique never before used in this type of images. 500 random gland candidates, both benign and pathological, are selected to evaluate the LCWT technique providing results of Dice coefficient of 0.85. Several shape and textural descriptors in combination with contextual features and a fractal analysis are applied, in a novel way, on different colour spaces achieving a total of 297 features to discern between artefacts and true glands. The most relevant features are then selected by an exhaustive statistical analysis in terms of independence between variables and dependence with the class. 3.200 artefacts, 3.195 benign glands and 3.000 pathological glands are obtained, from a data set of 1468 images at 10x magnification. A careful strategy of data partition is implemented to robustly address the classification problem between artefacts and glands. Both linear and non-linear approaches are considered using machine learning techniques based on Support Vector Machines (SVM) and feedforward neural networks achieving values of sensitivity, specificity and accuracy of 0.92, 0.97 and 0.95, respectivelyThis work has been funded by the Ministry of Economy, Industry and Competitiveness under the SICAP project (DPI2016-77869-C2-1-R). The work of Adri´an Colomer has been supported by the Spanish FPI Grant BES-2014-067889. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this researchGarcía-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V.; Peñaranda, F.; Sales, MÁ. (2018). Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 642-650. https://doi.org/10.1007/978-3-030-03493-1_67S642650Gleason, D.F.: Histologic grading and clinical staging of prostatic carcinoma. In: Urologic Pathology (1977)Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: MIAAB Workshop, pp. 1–8 (2007)Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: gland segmentation and structural features. Pattern Recogn. Lett. 33(7), 951–961 (2012)Kwak, J.T., Hewitt, S.M.: Multiview boosting digital pathology analysis of prostate cancer. Comput. Methods Programs Biomed. 142, 91–99 (2017)Ren, J., Sadimin, E., Foran, D.J., Qi, X.: Computer aided analysis of prostate histopathology images to support a refined gleason grading system. In: SPIE Medical Imaging, International Society for Optics and Photonics, p. 101331V (2017)Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (2013)Nguyen, K., Sarkar, A., Jain, A.K.: Structure and context in prostatic gland segmentation and classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 115–123. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_15Beare, R.: A locally constrained watershed transform. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1063–1074 (2006)Gertych, A., et al.: Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Med. Imaging Graph. 46, 197–208 (2015)Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)Huang, P., Lee, C.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Med. Imaging 28(7), 1037–1050 (2009)Ruifrok, A.C., Johnston, D.A., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001