Glycinium hydrogen fumarate glycine solvate monohydrate

In the title compound, C2H6NO2 +·C4H3O4 −·C2H5NO2·H2O, the asymmetric unit contains two glycine residues, one protonated and one in the zwitterionic form, a hydrogen fumarate anion and a water mol­ecule. Through N—H⋯O and O—H⋯O hydrogen bonds, mol­ecules assemble in layers parallel to the (10) plane, one layer of hydrogen fumarate anions alternating with two layers of glycine mol­ecules. In each glycine layer, hydrogen bonds generate an R 4 4(19) graph-set motif. Further hydrogen bonds involving the water mol­ecule and the hydrogen fumarate anions result in the formation of a three-dimensional network

Hexaaqua­zinc(II) dipicrate

In the title compound, [Zn(H2O)6](C6H2N3O7)2, the ZnII ion is located on an inversion center and is coordinated by six water mol­ecules in an octa­hedral geometry. The picrate anions have no coordination inter­actions with the ZnII atom. The three nitro groups are twisted away from the attached benzene ring by19.8 (3), 6.5 (4) and 28.6 (3)°. There are numerous O—H⋯O hydrogen bonds in the crystal structure

Hexaaqua­cadmium(II) dipicrate monohydrate

In the structure of the title compound, [Cd(H2O)6](C6H2N3O7)2·H2O, the CdII ion is located on an inversion center and is coordinated by six water mol­ecules in an octa­hedral geometry. The picrate anions have no coordination inter­actions with the CdII ion. The three nitro groups are twisted away from the attached benzene ring, making dihedral angles of 17.89 (3), 27.94 (4) and 13.65 (3)°. There are numerous O—H⋯O hydrogen bonds in the crystal structure, involving coordinated and uncoordinated water molecules

Deep Learning-Based Automated Landmark Localization for Evan’s Index Computation

Hydrocephalus is a neurological disorder characterized by the accumulation of cerebrospinal fluid in the brain, leading to an enlargement of the ventricular system. Among its subtypes is idiopathic normal pressure hydrocephalus (iNPH), characterized by normal cerebrospinal fluid pressure. Accurately diagnosing iNPH presents considerable difficulties due to its non-specific clinical manifestations. This thesis presents an innovative approach for calculating Evan’s Index by accurately estimating the Anterior Commisure (AC), Posterior Commisure (PC), and Vertex of the Superior Pontine Sulcus (VSPS) landmarks thereby aiding the iNPH diagnosis process. The primary emphasis of this study lies in harnessing dedicated frameworks tailored for medical image segmentation. The project constructs a streamlined pipeline that precisely segments the AC, PC, and VSPS regions, and also performs MRI scan alignment to calculate the Evan’s Index. The study investigates the effectiveness of this approach in providing an automated and efficient estimation of the landmark points. The methodology includes network training, and evaluation, followed by the analysis of the results. The outcomes of this study highlight the potential of deep learning techniques in assisting clinicians with iNPH diagnosis.

Deep Learning-Based Automated Landmark Localization for Evan’s Index Computation

Hydrocephalus is a neurological disorder characterized by the accumulation of cerebrospinal fluid in the brain, leading to an enlargement of the ventricular system. Among its subtypes is idiopathic normal pressure hydrocephalus (iNPH), characterized by normal cerebrospinal fluid pressure. Accurately diagnosing iNPH presents considerable difficulties due to its non-specific clinical manifestations. This thesis presents an innovative approach for calculating Evan’s Index by accurately estimating the Anterior Commisure (AC), Posterior Commisure (PC), and Vertex of the Superior Pontine Sulcus (VSPS) landmarks thereby aiding the iNPH diagnosis process. The primary emphasis of this study lies in harnessing dedicated frameworks tailored for medical image segmentation. The project constructs a streamlined pipeline that precisely segments the AC, PC, and VSPS regions, and also performs MRI scan alignment to calculate the Evan’s Index. The study investigates the effectiveness of this approach in providing an automated and efficient estimation of the landmark points. The methodology includes network training, and evaluation, followed by the analysis of the results. The outcomes of this study highlight the potential of deep learning techniques in assisting clinicians with iNPH diagnosis.

Linear and nonlinear optical studies on 3,6-bis (2 pyridyl) pyridazine

3,6-Bis (2 pyridyl) pyridazine has been synthesized and characterized by NMR, XRD and elemental analyses. The vibrational studies were carried out by using FTIR and Raman spectroscopy and the modes of vibrations were analysed and compared with the theoretically calculated values. The nonlinear optical property of the title compound was examined by Kurtz-Perry method and Hyper Raleigh scattering with the fundamental wavelength of 1064nm. This compound possesses less SHG efficiency but large first hyperpolarizability. (C) 2013 Elsevier GmbH. All rights reserved

Growth and characterization of a new potential NLO material from the amino acid family—L-prolinium picrate

L-prolinium picrate (C5H10NO2)+. (C6H2N3O7)�, an organic nonlinear optical (NLO) material possessing a large second harmonic generation (SHG) efficiency (74 times higher than that of the standard KDP) was grown by slow evaporation method. The identity of the crystals was confirmed by using single-crystal X-ray diffraction. The crystalline perfection was studied by multicrystal X-ray diffractometer. Fourier transform infrared (FTIR) spectroscopic studies, optical behavior such as UV–visible–NIR absorption and SHG conversion efficiency were investigated to explore the NLO characteristics of the above material. The structural features of the material leading to the large SHG efficiency are discussed. Microhardness measurements and dielectric studies of the compound were also carried out