Optimized and validated spectrophotometric methods for the determination of hydroxyzine hydrochloride in pharmaceuticals and urine using iodine and picric acid

Two simple, rapid, cost-effective and sensitive spectrophotometric procedures are proposed for the determination of hydroxyzine dihydrochloride (HDH) in pharmaceuticals and in spiked human urine. The methods are based on the charge transfer complexation reaction of the drug with either iodine (I2) as a σ-acceptor (method A) in dichloromethane or picric acid (PA) as a π-acceptor (method B) in chloroform. The coloured products exhibit absorption maxima at 380 and 400 nm for I2 and PA, respectively. The Beer Law was obeyed over the concentration ranges of 1.25-15 and 3.75-45 μg mL-1 for method A and method B, respectively. The molar absorptivity values, Sandell sensitivities, limits of detection (LOD) and quantification (LOQ) are reported. The accuracy and precision of the methods were evaluated on intra-day and inter-day basis. The proposed methods were successfully applied for the determination of HDH in tablets and spiked human urine

Dual Role of Electron-Accepting Metal-Carboxylate Ligands: Reversible Expansion of Exciton Delocalization and Passivation of Nonradiative Trap-States in Molecule-like CdSe Nanocrystals

This paper reports large bathochromic shifts of up to 260 meV in both the excitonic absorption and emission peaks of oleylamine (OLA)-passivated molecule-like (CdSe)34 nanocrystals caused by postsynthetic treatment with the electron accepting Cd(O2CPh)2 complex at room temperature. These shifts are found to be reversible upon removal of Cd(O2CPh)2 by N,N,N′,N′-tetramethylethylene-1,2-diamine. 1H NMR and FTIR characterizations of the nanocrystals demonstrate that the OLA remained attached to the surface of the nanocrystals during the reversible removal of Cd(O2CPh)2. On the basis of surface ligand characterization, X-ray powder diffraction measurements, and additional control experiments, we propose that these peak red shifts are a consequence of the delocalization of confined exciton wave functions into the interfacial electronic states that are formed from interaction of the LUMO of the nanocrystals and the LUMO of Cd(O2CPh)2, as opposed to originating from a change in size or reorganization of the inorganic core. Furthermore, attachment of Cd(O2CPh)2 to the OLA-passivated (CdSe)34 nanocrystal surface increases the photoluminescence quantum yield from 5% to an unprecedentedly high 70% and causes a 3-fold increase of the photoluminescence lifetime, which are attributed to a combination of passivation of nonradiative surface trap states and relaxation of exciton confinement. Taken together, our work demonstrates the unique aspects of surface ligand chemistry in controlling the excitonic absorption and emission properties of ultrasmall (CdSe)34 nanocrystals, which could expedite their potential applications in solid-state device fabrication

Experiences with Mycobacterium leprae soluble antigens in a leprosy endemic population

Rees and Convit antigens prepared from armadillo-derived Mycobacterium leprae were used for skin testing in two leprosy endemic villages to understand their use in the epidemiology of leprosy. In all, 2602 individuals comprising 202 patients with leprosy detected in a prevalence survey, 476 household contacts and 1924 persons residing in non-case households were tested with two antigens. There was a strong and positive correlation ( r = 0.85) between reactions to the Rees and Convit antigens. The distribution of reactions was bimodal and considering reactions of 12 mm or more as ‘positive’, the positivity rate steeply increased with the increase in age. However. the distributions of reactions to these antigens in patients with leprosy. their household contacts and persons living in non-case households were very similar. These results indicate that Rees and Convit antigens are not useful in the identification of M. leprae infection or in the confirmation of leprosy diagnosis in a leprosy endemic population with a high prevalence of nonspecific sensitivity

Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images

The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a “proof of concept” about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis

Efficient Hardware Implementation of Probabilistic Gradient Descent Bit Flipping

This paper presents a new Bit Flipping (BF) decoder, called Probabilistic Parallel Bit Flipping (PPBF) for Low-Density Parity-Check (LDPC) codes on the Binary Symmetric Channel. In PPBF, the flipping operation is preceded with a probabilistic behavior which is shown to improve significantly the error correction performance. The advantage of PPBF comes from the fact that, no global computation is required during the decoding process and from that, all the computations can be executed in the local computing units and in-parallel. PPBF provides a considerable improvement of the decoding frequency and complexity, compared to other known BF decoders, while obtaining a significant gain in error correction. One improved version of PPBF, called non-syndrome PPBF (NS-PPBF) is also introduced, in which the global syndrome check is moved out of the critical path and a new terminating mechanism is proposed. In order to show the superiority of the new decoders in terms of hardware efficiency and decoding throughput, the corresponding hardware architectures are presented in the second part of the paper. The ASIC synthesis results confirm that, the decoding frequency of the proposed decoders is significantly improved, much higher than the BF decoders of literature while requiring lower complexity to be efficiently implemented

Design and Development of Energy Meter using GSM

Power theft is the biggest problem in recent days which causes lot of loss to electricity boards. Incountries like India, these situations are more often, if we can prevent these thefts, we can save lot ofpower. Electrical power theft detection system is used to detect an unauthorized tapping ondistribution lines. Implementation part of this system is a distribution network of electrical powersupply system.This project aims at developing a system which helps in monitoring the readings froman energy meter and controlling the switching of energy meter. This system also has tamper switch,which helps in illegal removing of energy meter cabinet and alerts the authorities in the form of textmessage. This also sends data to householder in real-time with tamper alert status too

ACUTE TOXICITY OF CHLORANTRANILIPROLE TO FRESH WATER FISH CTENOPHARINGODON IDELLA (VALENCIENNES, 1844)

Pesticidal pollution constitutes the most dangerous health hazard apart from creating adverse effects on fish production .The aim of the present study was to determine the acute toxicity of chlorantraniliprole to the fresh water fish Grass carp (Ctenopharingodon idella), experimental fish were exposed to different concentrations of chlorantraniliprole. The 96h LC50 value of chlorantraniliprole on the fish was found to be 11.008mg/l. The variation in the LC values is due to its dependence upon various factors viz., sensitivity to the toxicant, its concentration and duration of exposure. Increase in opercular movement was initially observed but later decreased with increase of exposure period. They slowly became lethargic, restless, and secreted excess mucus all over the body. Intermittently some of the fish were hyper excited resulting in erratic movements. An excess secretion of mucous in fish forms a non-specific response against toxicants, thereby probably reducing toxicant contact. Further study needs the processes by which these chemicals affect physiology and pathological changes and of fish and their bio-concentration and bio- accumulation in fish tissues.   Key words: Health hazard, concentration, LC values, bioconcentration and physiolog

TOMATO LEAF DISEASE DIAGNOSIS USING CNN

Tomato is a horticultural crop that is cultivated globally, but it faces increasing threats from various diseases, including bacterial spot, tomato mosaic, and yellow leaf curl, which are the most prevalent and devastating, leading to significant crop losses. Early and accurate detection is crucial for effective management of these diseases. A convolutional neural network (CNN) approach has been introduced in the present study to precisely classify these specific tomato leaf diseases. A dataset comprising a total of 9,448 leaf images, representing three diseases and healthy samples, were used to train the CNN model. The evaluation metrics, including precision, recall, and F1-score values, ranged from 0.96 to 0.99, 0.97 to 1.00, and 0.98 to 1.00, respectively. The model achieved a promising accuracy of 98.99% in detecting these diseases

Optimizing CNN Performance for Tomato Disease Classification with Advanced Data Augmentation Techniques

Tomato (Solanum lycopersicum), a vital horticultural crop, faces increasing challenges from diseases such as bacterial spot, tomato mosaic, and yellow leaf curl, causing substantial global crop losses. Timely and accurate disease detection is crucial for effective management strategies. This research introduces a sophisticated method for detecting tomato leaf diseases by enhancing a model with diverse data augmentation techniques. Evaluation metrics including precision, recall, and F1-score consistently demonstrate high performance, ranging from 1.00 to 0.99 to 1.00, respectively. By incorporating Mixup, CutMix, Adversarial examples, Style Transfer, and Image Blending during training, the model achieves remarkable validation accuracies: 99.47%, 99.26%, 99.42%, 99.68%, and 99.63%, respectively. Notably, the highest accuracy of 99.68% is achieved using Style Transfer augmentation. In contrast, a Convolutional Neural Network (CNN) employing conventional augmentation techniques achieves a prediction accuracy of 98.99% for the same tomato diseases. These results underscore the significant improvement in disease prediction accuracy through the integration of advanced augmentation techniques with CNNs. The study highlights CNNs with advanced augmentation as the optimal choice for accurately predicting tomato diseases