Swaineliasen1703

Deployment of the deep neural networks (DNNs) on resource-constrained devices is a challenging task due to their limited memory and computational power. In most cases, the pruning techniques do not prune the DNNs to full extent and redundancy still exists in these models. Considering this, a mixed filter pruning approach based on principal component analysis (PCA) and geometric median is presented. First, a pre-trained model is analyzed by using PCA to identify the important filters for every layer. These important filters are then used to reconstruct the network with a fewer number of layers and a fewer number of filters per layer. A new network with optimized structure is constructed and trained on the data. Secondly, the trained model is then analyzed using geometric median as a base. The redundant filters are identified and removed which results in further compression of the network. Finally, the pruned model is fine tuned to regain the accuracy. Experiments on CIFAR-10, CIFAR-100 and ILSVRC 2017 datasets show that the proposed mixed pruning approach is feasible and can compress the network to greater extent without any significant loss to accuracy. With VGG-16 on CIFAR-10, the number of operations and parameters are reduced to 18.56× and 3.33×, respectively, with almost 1% loss in the accuracy. The compression rate for AlexNet on CIFAR-10 dataset is 2.61× and 4.85× in terms of number of operations and number of parameters, respectively, with a gain of 1.2% in the accuracy. On CIFAR-100, VGG-19 is compressed by 16.02 X in terms of number of operations and 36× in terms of number of parameters with a 2.6% loss of accuracy. Similarly, the compression rate for VGG-19 network on ILSVRC 2017 dataset is 1.87× and 2.4× for operations and parameters with 0.5% loss in accuracy.In the process of intelligent system operation fault diagnosis and decision making, the multi-source, heterogeneous, complex, and fuzzy characteristics of information make the conflict, uncertainty, and validity problems appear in the process of information fusion, which has not been solved. In this study, we analyze the credibility and variation of conflict among evidence from the perspective of conflict credibility weight and propose an improved model of multi-source information fusion based on Dempster-Shafer theory (DST). From the perspectives of the weighting strategy and Euclidean distance strategy, we process the basic probability assignment (BPA) of evidence and assign the credible weight of conflict between evidence to achieve the extraction of credible conflicts and the adoption of credible conflicts in the process of evidence fusion. The improved algorithm weakens the problem of uncertainty and ambiguity caused by conflicts in the information fusion process, and reduces the impact of information complexity on analysis results. And it carries a practical application out with the fault diagnosis of wind turbine system to analyze the operation status of wind turbines in a wind farm to verify the effectiveness of the proposed algorithm. The result shows that under the conditions of improved distance metric evidence discrepancy and credible conflict quantification, the algorithm better shows the conflict and correlation among the evidence. It improves the accuracy of system operation reliability analysis, improves the utilization rate of wind energy resources, and has practical implication value.The accurate segmentation of retinal vessels images can not only be used to evaluate and monitor various ophthalmic diseases, but also timely reflect systemic diseases such as diabetes and blood diseases. Therefore, the study on segmentation of retinal vessels images is of great significance for the diagnosis of visually threatening diseases. In recent years, especially the convolutional neural networks (CNN) based on UNet and its variant have been widely used in various medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global and long-distance semantic information interaction well due to the local computing characteristics of convolution operation, which limits the development of medical image segmentation tasks. Transformer, currently popular in computer vision, has global computing features, but due to the lack of low-level details, local feature information extraction is insufficient. In this paper, we propose Patches Convolution Attention based Transformer UNe 0.9622, 0.9796 and 0.9812, Sensitivity reached 0.8576, 0.8703 and 0.8493, respectively. In addition, PCAT-UNET also achieved good results in two other F1-Score and Specificity indicators.The loaded mechanical function of transtibial prostheses that result from the clinical assembly, tuning, and alignment of modular prosthetic components can directly influence an end user's biomechanics and overall mobility. Footwear is known to affect prosthesis mechanical properties, and while the options of footwear are limited for most commercial feet due to their fixed geometry, there exists a selection of commercial prosthetic feet that can accommodate a moderate rise in heel height. These feet are particularly relevant to women prosthesis users who often desire to don footwear spanning a range of heel heights. The aim of this study was to assess the effects of adding women's footwear (flat, trainer, 5.08 cm heel) on the mechanical properties (deformation and energy efficiency) of four models of heel-height accommodating prosthetic feet. Properties were measured through loading-unloading at simulated initial contact, midstance and terminal stance orientations with a universal materials test system, and statistically compared to a barefoot condition. Results suggest that the addition of footwear can alter the level of foot deformation under load, which may be a function of the shoe and alignment. Moreover, while each foot displayed different amounts of energy storage and return, the addition of footwear yielded similar levels of energy efficiency across foot models. Overall, prosthesis users who don shoes of varying heel heights onto adjustable prosthetic feet and their treating clinicians should be aware of the potential changes in mechanical function that could affect the user experience.Flagellin is a key bacterial virulence factor that can stimulate molecular immune signaling in both animals and plants. The detailed mechanisms of recognizing flagellin and mounting an efficient immune response have been uncovered in vertebrates; however, whether invertebrates can discriminate flagellin remains largely unknown. In the present study, the homolog of human SHOC2 leucine rich repeat scaffold protein in kuruma shrimp (Marsupenaeus japonicus), designated MjShoc2, was found to interact with Vibrio anguillarum flagellin A (FlaA) using yeast two-hybrid and pull-down assays. MjShoc2 plays a role in antibacterial response by mediating the FlaA-induced expression of certain antibacterial effectors, including lectin and antimicrobial peptide. FlaA challenge, via MjShoc2, led to phosphorylation of extracellular regulated kinase (Erk), and the subsequent activation of signal transducer and activator of transcription (Stat), ultimately inducing the expression of effectors. Therefore, by establishing the FlaA/MjShoc2/Erk/Stat signaling axis, this study revealed a new antibacterial strategy in shrimp, and provides insights into the flagellin sensing mechanism in invertebrates.

Out-of-pocket (OOP) payment is the major payment strategy for healthcare in Bangladesh, and the share of OOP expenditure has increased alarmingly. Dhaka is recognised as one of the fastest-growing megacities in the world. The objective of this study is to capture the self-reported illnesses among urban citizens and to identify whether and to what extent socioeconomic, demographic and behavioural factors of the population influence OOP healthcare expenditures.

This study utilises cross-sectional survey data collected from May to August 2019 in urban Dhaka, Bangladesh. learn more A total of 3,100 households were randomly selected. Simple descriptive statistics including frequencies, percentage, mean (95% CI), median and inter-quartile range were presented. Bivariate analysis and multivariate regression models were employed.

We observed that acute illnesses (e.g., fever, flu/cough) were dominant among participants. Among the chronic illnesses, approximately 9.6% of people had diabetes, while 5.3% had high/low blood pealthier households tended to choose better healthcare facilities and spend more. A pro-poor policy initiative and even an urban health protection scheme may be necessary to ensure that healthcare services are accessible and affordable, in line with the Bangladesh National Urban Health Strategy.In the last four decades, the problem of income inequality has gradually become one of the most serious social problems in China at both the regional and individual levels. Recently, the central government announced that the main social contradiction is that between people's growing need for a better life and unbalanced and insufficient economic development. In this study, we analyse the effects of income distribution on individuals' health using a series of indicators of income distribution and different measures of individuals' health status. By utilizing data from the China Health and Nutrition Survey (CHNS) from 1989 to 2015, our empirical findings show that self-reported health (SRH), activities of daily living (ADLs), and diabetes mellitus appear to be negatively related to the income share of rich people when average income is equalized among counties, which indicates that individuals' health will deteriorate as the income share of rich people increases. In addition, our results show that there is an inverted U-shaped relationship between income inequality, as measured by the county-level Gini coefficient, and individuals' health status. We also find that income inequality affects health through the accessibility of healthcare facilities and public infrastructures and through hazardous health behaviours such as smoking and alcohol use. These findings suggest that reducing income inequality could be an important means of improving the overall health of China's population.

To determine the Effect of Hybrid functional electrically stimulated (FES) Exercise on Body Composition during the Sub-acute Phase of Spinal Cord Injury (SCI).

Randomized Clinical Trial.

Rehabilitation Hospital.

Patients within sub-acute phase (3-24 months) of SCI.

We investigated if high-intensity exercise training via the addition of functional electrically stimulated (FES) leg muscles, provides sufficient stimulus to mitigate against body composition changes in the sub-acute phase after SCI.

We explored potential effects of FES row training (FESRT) on body fat gain, lean mass loss, and cardiometabolic parameters and compared the effects of 6-month of FESRT (n = 18) to standard of care (SOC, n = 13). Those in SOC were crossed over to FESRT.

FESRT resulted in greater exercise capacity and a tendency for lesser total body fat accumulation with a significant increase in total and leg lean mass (p<0.05). In addition pelvis and total bone mineral density declines were significantly less (p<0.