Numerical Modeling of Solitons in a Low Birefringent Optical Fiber

A numerical investigation, based on the split-step Fourier transform algorithm of all optical switching of solitons in a low birefringent optical fiber is presented. The numerical algorithm is described in detail. This revised version was published online in August 2006 with corrections to the Cover Date.     

Titanium(IV) tungstosilicate and titanium(IV) tungstophosphate: two new inorganic ion exchangers

Crystalline phases of Ti(IV) tungstosilicate and Ti(IV) tungstophosphate have been synthesised. The ion-exchange capacities of Ti(IV) tungstosilicate and Ti(IV) tungstophosphate have been reported as 0.44 and 0.80 mequiv./g, respectively. Both materials show monofunctional ion-exchange characteristic and are stable in 0.1 M solutions of HNO3, HCl, H2SO4 and acetone and benzene. Ti(IV) tungstosilicate is found to be more stable thermally than Ti(IV) tungstophosphate (loss in ion-exchange capacity was found as 58 and 80%, respectively for samples heated at 200 degrees C). The Kd values for heavy metals such as Pb, Hg, Cd, Sb, Co, Zn, Ni, Fe, Cr etc. have been reported in demineralised water and two surfactant media by batch processes. Cr3+, Fe3+ and Sn4+ are totally adsorbed on both the materials in demineralised water while a decrease in Kd value with increase in concentration of two surfactants is reported. On the basis of Kd values for metal ions, thirteen binary separations and five ternary separations on Ti(IV) tungstosilicate and thirteen different binary separations and four different ternary separations on Ti(IV) tungstophosphate have been achieved. Separation of methylamine from ethylamine has been done by GC on a column packed with Ti(IV) tungstophosphate.     

High temperature reactions of titanium(IV) oxide with some alkali persulfates and their decomposition products

The solid state reactions between TiO₂ and Na₂S₂O₈ or K₂S₂O₈ have been investigated using TG, DTG, DTA, IR, and X-ray diffraction studies in the range of 20 to 1000°C.It has been shown that TiO₂ reacts stoichiometrically (1 : 1) with Na₂S₂O₈ in the range of 160 and 220°C forming the complex sodium monoperoxodisulfato—titanium(IV) as characterized by IR and X-ray analysis. The new complex then decomposes into the reactants above 190°C.An exothermic reaction has been observed between TiO₂ and molten K₂S₂O₇ at mole ratio 1:2 respectively and higher, in the range of 280 and 350°C. The IR and X-ray analyses have shown the formation of a complex namely, potassium tetrasulfato titanium(IV) for which the formula and structure have been proposed. This complex decomposes at higher temperatures into K₂SO₄ and a mixed sulfate of potassium and titanium. The mixed sulfate melts at 620°C and decomposes into K₂SO₄, TiO₂, and the gaseous SO₃.On the other hand, Na₂S₂O₈ decomposes in a special mode producing a polymeric product of Na₁₀S₉O₃₂. Decomposition of this species occurs after melting at 560°C into Na₂SO₄ and sulfur oxides. The decomposition reaction has been proved to be catalysed by TiO₂ itself.     

Copper-catalyzed N-arylation of diazoles with aryl bromides using KF/Al2O3: an improved protocol

Copper-catalyzed N-arylation of diazoles can be accomplished using air-stable CuI as a copper source and 1,10-phenanthroline in the presence of KF/Al₂O₃ as a base. This is a simple and efficient method for the coupling of aryl bromide with diazoles. Different functionalized aryl bromides were coupled with diazoles using this system.     

Synthesis of (R)-alpha-benzylmethionine: a novel rearrangement during alkylation of the Seebach (R)-methionine oxazolidinone

Alkylation of the enolate of the Seebach (R)-methionine oxazolidinone with benzyl bromide gave the expected benzylated product in low yield. The major product was a novel amine arising from oxazolidinone cleavage, decarboxylation, alkylation and finally hydrolysis. The rearrangement could be suppressed by using a more reactive electrophile or by using the N-Cbz instead of the N-benzoyl protecting group, and the required (R)-alpha-benzyl-methionine was obtained in 78% yield and in an enantiomeric ratio of 90:10.     

Trident Nano-Indexing the Proteomics Table: Next-Version Clustering of Iron Carbide NPs and Protein Corona

Protein corona composition and precise physiological understanding of differentially expressed proteins are key for identifying disease biomarkers. In this report, we presented a distinctive quantitative proteomics table of molecular cell signaling differentially expressed proteins of corona that formed on iron carbide nanoparticles (NPs). High-performance liquid chromatography/electrospray ionization coupled with ion trap mass analyzer (HPLC/ESI-Orbitrap) and MASCOT helped quantify 142 differentially expressed proteins. Among these proteins, 104 proteins showed upregulated behavior and 38 proteins were downregulated with respect to the control, whereas 48, 32 and 24 proteins were upregulated and 8, 9 and 21 were downregulated CW (control with unmodified NPs), CY (control with modified NPs) and WY (modified and unmodified NPs), respectively. These proteins were further categorized on behalf of their regularity, locality, molecular functionality and molecular masses using gene ontology (GO). A STRING analysis was used to target the specific range of proteins involved in metabolic pathways and molecular processing in different kinds of binding functionalities, such as RNA, DNA, ATP, ADP, GTP, GDP and calcium ion bindings. Thus, this study will help develop efficient protocols for the identification of latent biomarkers in early disease detection using protein fingerprints.     

DAD-Net: Classification of Alzheimer’s Disease Using ADASYN Oversampling Technique and Optimized Neural Network

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.