Oxidation Kinetics of some Lower Oxyacids of Phosphorus by Picolinium Chlorochromate: Determination of Reactive Reducing Species

Picolinium chlorochromate (PICC) in dimethylsuloxide (DMSO) oxidizes lower oxyacids of phosphorus, forming matching oxyacids with phosphorus in a higher oxidation state. The reaction shows a stoichiometry of 1:1. In relation to PICC, the response is first order. Regarding the reductants, a kinetics of the Michaelis-Menten type was noticed. Acrylonitrile does not undergo polymerization as a result of the reaction. Hydrogen ions function as catalysts for reactions. The form of the hydrogen-ion dependency is: kobs = a + b[H+]. Deuterated phosphinic and phenylphosphinic acids showed a significant primary kinetic isotope impact during oxidation. Nineteen different organic solvents were used to study the oxidation. The multiparametric equations of Taft and Swain were used to analyze the solvent effects. The influence of the solvent shows that the polarity of the solvent is crucial to the process. The penta-coordinated tautomer of the phosphorus oxyacid has been shown to be the reactive reductant, and it has been determined that the tricoordinated forms of phosphorus oxyacids do not take part in the oxidation process. It has been hypothesized that the rate-determining phase involves the transfer of a hydride ion

Heuristic-Based Automatic Pruning of Deep Neural Networks

The performance of a deep neural network (deep NN) is dependent upon a significant number of weight parameters that need to be trained which is a computational bottleneck. The growing trend of deeper architectures poses a restriction on the training and inference scheme on resource-constrained devices. Pruning is an important method for removing the deep NN\u27s unimportant parameters and making their deployment easier on resource-constrained devices for practical applications. In this paper, we proposed a heuristics-based novel filter pruning method to automatically identify and prune the unimportant filters and make the inference process faster on devices with limited resource availability. The selection of the unimportant filters is made by a novel pruning estimator (γ). The proposed method is tested on various convolutional architectures AlexNet, VGG16, ResNet34, and datasets CIFAR10, CIFAR100, and ImageNet. The experimental results on a large-scale ImageNet dataset show that the FLOPs of the VGG16 can be reduced up to 77.47%, achieving ≈5x inference speedup. The FLOPs of a more popular ResNet34 model are reduced by 41.94% while retaining competitive performance compared to other state-of-the-art methods

RELATIONSHIP BETWEEN THE ANTHROPOMETRIC VARIABLES AND THROWING SKILL IN MALE SOFTBALL PLAYERS

The aim of the present study was to assess the relationship between the anthropometric variables and throwing skill in among male softball players. Total 150 male university and national level softball players of different universities and states of India were selected thorough purposive sampling technique. The age of subjects ranged between 18 to 30 years. Body weight of the subjects was measured with a portable weighing machine. Height measurements were taken using the standard anthropometric rod. Body mass index was then calculated using the formula weight (kg)/height2 (m). The circumferences of body parts were measured with the help of flexible steel tape. The diameters of the body parts were assessed with sliding caliper. The skinfolds thicknesses of body parts of the subjects were taken with Harpenden skinfold caliper. Percentage body fat as estimated from the sum of skinfolds was calculated using standardized equations. Throwing skill of the players was assessed as given in the AAHPERD softball skill test battery. Karl Pearson’s product moment co-efficient of correlation was computed to assess the relationship between anthropometric characteristics and throwing skill test of softball among the softball players. The result of the study shows that height (p=0.026), weight (p=0.008), total arm length (p=0.001), the upper arm length (p=0.018) and lower arm length (p=0.007) had significant relationship with the throwing skill in softball. The throwing skill was significantly associated with the upper arm circumference (r=0.265, p=0.001), biacromial (p=0.007) and bicondylar humerus (p=0.009) diameters. Lean body mass (p=0.000) was also found to be significantly associated with the throwing skill test in the male softball players.  Article visualizations

Descriptive study of functional outcome and complication of fracture calcaneum treated with locking calcaneum plate

Background: Fracture of calcaneum accounts for about 2% of all fractures and 75% of all calcaneum fractures are intra-articular. Numerous controversies existed regarding optimal treatment of displaced intraarticular calcaneum fractures. In this study evaluate post-operative functional outcome and complication of fracture calcaneum treated with locking calcaneum plate.Methods: This hospital based prospective descriptive study was conducted on 108 patients (120 calcaneum fracture) operated between July 2016 to December 2018 by open reduction and internal fixation with locking calcaneum plate through extensive lateral approach at Department of Orthopaedics, SMS Medical college and hospital, Jaipur. All close displaced intraarticular calcaneal fracture was included in the study.Results: Average duration between injury and surgery was 8.3±2.97 days. Out of 120 calcaneum fracture 52 fractures (43.33%) were Sander’s type II, 52 fractures (43.33%) were Sander’s type III, and 16 fractures (13.33%) were Sander’s type IV. Pain on weight bearing was noted in 20 patients (16.66%) implant prominence was noted in 8 patients and delayed wound healing was seen in 4 patients. Maryland foot score was excellent in 44 fracture (36.67%), good in 56 fractures (46.67%), fair in 8 fractures (6.67%), and poor in 12 fractures (10%).Conclusions: Open reduction and internal fixation (ORIF) with locking calcaneum plate in an indicated case, with respect to soft tissue envelope and early rehabilitation, leads to better therapeutic results as compared to other operative technique

Investigation of a cluster of acute-onset seizures and deaths among children, Sirohi District, Rajasthan, April 2022

Background: Following reports of seven deaths among children with seizure and vomiting in the village of Sirohi, Rajasthan in April 2022, an epidemiological investigation was conducted. Methods: We established a hospital-based surveillance and did active case finding. A case was defined as vomiting and acute onset seizure or altered sensorium in 8000 IU/L). Lab results for food and CSF samples were inconclusive for bacterial and viral markers. Toxicology screen of one death was negative for organophosphates. In 25% (2/8), tick pools tested positive for rickettsia. All cases had houses in vicinity or within fennel crop area. Conclusions: This acute health event cluster presenting as acute seizures with rapid progression among children in a rural setting was likely due to environmental toxin consumption; high fatality may result from uncorrected metabolic derangement. Aflatoxin is commonly known to infect fennel crops. We recommend early identification and case management to prioritize metabolic derangement correction; continued surveillance and a systematic epidemiological investigation to evaluate the role of environmental toxins particularly aflatoxin as the underlying etiology for similar events in future

Transition metal induced- magnetization and spin-polarisation in black arsenic phosphorous

The electronic and magnetic properties of two-dimensional black Arsenic Phosphorus (b-AsP) on adsorption of Transition Metals (TM) on its surface are investigated using density functional theory (DFT) based on first principles calculations. Spin-density of states (S-DOS) and the bandstructure of all the transition metals adsorbed structures have been plotted which reveals their change from non-magnetic to magnetic behaviour. The results suggest that pristine b-AsP which is a non-magnetic semiconductor turns into a half metal ferromagnet (HMF) on adsorption of Co, Fe and Ti, and it turns into a ferromagnet (FM) on adsorption of Cr and Zr. The total magnetic moments were also calculated to further support our results and findings. Strong magnetic moments were observed for Cr, Fe and Ti adsorbed b-AsP structures. Ag, Cu and Mo adsorption over b-AsP results into non-magnetic metallic characteristics with very weak magnetic moments. This transformation from a non-magnetic semiconductor to a magnetic HMF or FM material demonstrates the potential use of b-AsP in designing spin magnetic devices for various spin-based applications

Oxidative regeneration of carbonyl compounds from oximes by morpholinium chlorochromate: A kinetic and mechanistic study

362-366The oxidative deoximination of several aldo- and keto-oximes by morpholinium chlorochromate in dimethyl sulphoxide, exhibits a first order dependence on both the oxime and MCC. The oxidation of ketoximes is slower than that of aldoximes. The rate of oxidation of aldoximes correlate well in terms of Pavelich- Taft dual substituent-parameter equation. The low positive value of polar reaction constant indicates a nucleophilic attack by a chromate oxygen on the carbon. The reaction is subject to steric hindrance by the alkyl groups. The reaction of acetaldoxime has been studied in nineteen different organic solvents. The solvent effect has been analysed by multiparametric equations. A mechanism involving the formation of a cyclic intermediate in the rate-determining step has been proposed

Heuristic-Based Automatic Pruning of Deep Neural Networks

The performance of a deep neural network (deep NN) is dependent upon a significant number of weight parameters that need to be trained which is a computational bottleneck. The growing trend of deeper architectures poses a restriction on the training and inference scheme on resource-constrained devices. Pruning is an important method for removing the deep NN\u27s unimportant parameters and making their deployment easier on resource-constrained devices for practical applications. In this paper, we proposed a heuristics-based novel filter pruning method to automatically identify and prune the unimportant filters and make the inference process faster on devices with limited resource availability. The selection of the unimportant filters is made by a novel pruning estimator (γ). The proposed method is tested on various convolutional architectures AlexNet, VGG16, ResNet34, and datasets CIFAR10, CIFAR100, and ImageNet. The experimental results on a large-scale ImageNet dataset show that the FLOPs of the VGG16 can be reduced up to 77.47%, achieving ≈5x inference speedup. The FLOPs of a more popular ResNet34 model are reduced by 41.94% while retaining competitive performance compared to other state-of-the-art methods

A Comprehensive Survey on Model Compression and Acceleration

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable improvement in computer vision, natural language processing, stock prediction, forecasting, and audio processing to name a few. The size of the trained DL model is large for these complex tasks, which makes it difficult to deploy on resource-constrained devices. For instance, size of the pre-trained VGG16 model trained on the ImageNet dataset is more than 500 MB. Resource-constrained devices such as mobile phones and internet of things devices have limited memory and less computation power. For real-time applications, the trained models should be deployed on resource-constrained devices. Popular convolutional neural network models have millions of parameters that leads to increase in the size of the trained model. Hence, it becomes essential to compress and accelerate these models before deploying on resource-constrained devices while making the least compromise with the model accuracy. It is a challenging task to retain the same accuracy after compressing the model. To address this challenge, in the last couple of years many researchers have suggested different techniques for model compression and acceleration. In this paper, we have presented a survey of various techniques suggested for compressing and accelerating the ML and DL models. We have also discussed the challenges of the existing techniques and have provided future research directions in the field

A Transfer Learning with Structured Filter Pruning Approach for Improved Breast Cancer Classification on Point-Of-Care Devices

Background and objective: A significant progress has been made in automated medical diagnosis with the advent of deep learning methods in recent years. However, deploying a deep learning model for mobile and small-scale, low-cost devices is a major bottleneck. Further, breast cancer is more prevalent currently, and ductal carcinoma being its most common type. Although many machine/deep learning methods have already been investigated, still, there is a need for further improvement. Method: This paper proposes a novel deep convolutional neural network (CNN) based transfer learning approach complemented with structured filter pruning for histopathological image classification, and to bring down the run-time resource requirement of the trained deep learning models. In the proposed method, first, the less important filters are pruned from the convolutional layers and then the pruned models are trained on the histopathological image dataset. Results: We performed extensive experiments using three popular pre-trained CNNs, VGG19, ResNet34, and ResNet50. With VGG19 pruned model, we achieved an accuracy of 91.25% outperforming earlier methods on the same dataset and architecture while reducing 63.46% FLOPs. Whereas, with the ResNet34 pruned model, the accuracy increases to 91.80% with 40.63% fewer FLOPs. Moreover, with the ResNet50 model, we achieved an accuracy of 92.07% with 30.97% less FLOPs. Conclusion: The experimental results reveal that the pre-trained model\u27s performance complemented with filter pruning exceeds original pre-trained models. Another important outcome of the research is that the pruned model with reduced resource requirements can be deployed in point-of-care devices for automated diagnosis applications with ease