Incorporating Betweenness Centrality in Compressive Sensing for Congestion Detection

This paper presents a new Compressive Sensing (CS) scheme for detecting network congested links. We focus on decreasing the required number of measurements to detect all congested links in the context of network tomography. We have expanded the LASSO objective function by adding a new term corresponding to the prior knowledge based on the relationship between the congested links and the corresponding link Betweenness Centrality (BC). The accuracy of the proposed model is verified by simulations on two real datasets. The results demonstrate that our model outperformed the state-of-the-art CS based method with significant improvements in terms of F-Score

ON A GENERALIZATION OF NC-MCCOY RINGS

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Treatment of chronic hepatitis D patients with pegylated interferon: a real-world experience

Background: Published experience of treating chronic hepatitis D patients with pegylated interferon (PEG-IFN)-alpha is limited. The aim of this study was to determine the efficacy of 48 weeks of treatment with PEG-IFN in naive patients outside the clinical trial setting, in the real world. Methods: Patients with chronic hepatitis D were treated with PEG-IFN. The primary end points were sustained clearance of HDV RNA and normal alanine aminotransferase (ALT) at 24 weeks post-treatment. Results: The total number of patients treated with PEG-IFN was 104; 91 males, mean age ±SD 30.1 ±10.0 years (range 15-55). Cirrhosis was present in 41 patients. With an intention-to-treat analysis, end of treatment virological response (ETR) was achieved in 44 (42.3%), normalization of ALT in 38 (35%) and a combined response in 23 (22.1%) patients. Sustained virological response (SVR) at 24 weeks post-treatment was seen in 24 (23.1%) patients each for the virological and biochemical responses and in 13 (12.5%) as combined response. Both ETR and SVR were associated with a negative HDV RNA at 24 weeks of treatment (P=0.001 and P=0.000, respectively). Detectable HDV RNA at this point had a positive predictive value of 0.95 (range 0.85-0.99) for detectable RNA at 6 months post-treatment. End of treatment biological response, that is, normal ALT at the end of treatment was also a predictor of ETR and SVR (P=0.004 and P=0.041, respectively). Conclusion:: Treatment with PEG-IFN for hepatitis D is of limited efficacy. Detectable HDV RNA at 24 weeks of treatment is a predictor for a failed SVR

Solar still efficiency enhancement by using graphene oxide/ paraffin composite

Solar-driven water desalination technologies are rapidly developing with various links to other renewable sources. However, the efficiency of such systems severely depends on the design parameters. The present study focused on using graphene oxide (GO) with the ɸ = 0.2, 0.4 and 0.6 wt. % dispersed in paraffin, as phase change materials (PCMs), to improve the productivity of a solar still for desalination applications. The outcomes showed that by adding more graphene oxide to paraffin, the melting temperature reduces. Solar still with GO/ paraffin showed 25% productivity improvement in comparison with the solar still with only PCM. The obtained Nusselt number during the melting time also represented that free convection heat transfer into the melted region of the solar still has been enhanced by adding dispersed GO to the PCM, compared to the base paraffin. Also, increasing the hot wall temperature augments the Nusselt number. Finally, an empirical equation was derived to correlate the average Nusselt number as a function of Rayleigh number (Ra), the Stefan number (Ste), the subcooling factor (Sb) and the Fourier number (Fo). The obtained correlation depicted that Nusselt number enhancement has a reverse relation with Fourier number

Ultrasound and Perforated Viscus; Dirty Fluid, Dirty Shadows, and Peritoneal Enhancement.

Early detection of free air in the peritoneal cavity is vital in diagnosis of life-threatening emergencies, and can play a significant role in expediting treatment. We present a series of cases in which bedside ultrasound (US) in the emergency department accurately identified evidence of free intra-peritoneal air and echogenic (dirty) free fluid consistent with a surgical final diagnosis of a perforated hollow viscus. In all patients with suspected perforated viscus, clinicians were able to accurately identify the signs of pneumoperitoneum including enhanced peritoneal stripe sign (EPSS), peritoneal stripe reverberations, and focal air collections associated with dirty shadowing or distal multiple reflections as ring down artifacts. In all cases, hollow viscus perforation was confirmed surgically. It seems that, performing US in patients with suspected perforated viscus can accurately identify presence of intra-peritoneal echogenic or dirty free fluid as well as evidence of free air, and may expedite patient management

Toward Real-Time Image Annotation Using Marginalized Coupled Dictionary Learning

In most image retrieval systems, images include various high-level semantics, called tags or annotations. Virtually all the state-of-the-art image annotation methods that handle imbalanced labeling are search-based techniques which are time-consuming. In this paper, a novel coupled dictionary learning approach is proposed to learn a limited number of visual prototypes and their corresponding semantics simultaneously. This approach leads to a real-time image annotation procedure. Another contribution of this paper is that utilizes a marginalized loss function instead of the squared loss function that is inappropriate for image annotation with imbalanced labels. We have employed a marginalized loss function in our method to leverage a simple and effective method of prototype updating. Meanwhile, we have introduced ${\ell}_1$ regularization on semantic prototypes to preserve the sparse and imbalanced nature of labels in learned semantic prototypes. Finally, comprehensive experimental results on various datasets demonstrate the efficiency of the proposed method for image annotation tasks in terms of accuracy and time. The reference implementation is publicly available on https://github.com/hamid-amiri/MCDL-Image-Annotation.Comment: @article{roostaiyan2022toward, title={Toward real-time image annotation using marginalized coupled dictionary learning}, author={Roostaiyan, Seyed Mahdi and Hosseini, Mohammad Mehdi and Kashani, Mahya Mohammadi and Amiri, S Hamid}, journal={Journal of Real-Time Image Processing}, volume={19}, number={3}, pages={623--638}, year={2022}, publisher={Springer}

Modelling of the Selected Physical Properties of the Fava Bean with Various Moisture Contents UsingFuzzy Logic Design

The current paper indicates the systematic determination of the optimal conditions for the selected physical properties of the fava bean. The effects of varying moisture content of the Barkat fava bean grown in Golestan, Iran, in the range of 9.3-31.3% (Input) on the 15 selected physical properties of the crop, including geometric values as such length; width; thickness; arithmetic and geometric mean diameter; sphericity index surface and the area of the image; gravity and frictional parameters like the weight of 1000 seeds; true density; bulk density; volume and porosity as well as friction (filling and vacating angle stability) as the outputs were predicted. Afterwards, a model relying on fuzzy logic for the prediction of the 15 outputs had been presented. To build the model, training and testing using experimental results from the Barkat fava bean were conducted. The data used as the input of the fuzzy logic model are arranged in a format of one input parameter that covers the percentage of the moisture contents of the beans. In relation to the varying moisture content (input), the outcomes (15 physical parameters) were predicted. The correlation coefficients obtained between the experimental and predicted outputs as well as the Mean Standard Deviation indicated the competence of fuzzy logic design in predicting the selected physical properties of fava bean seeds. Practical ApplicationToday, because of the high demand for crops to be used extensively in the human diet, enhancements in the efficiency of the processing are getting more attention. In this way, finding and/or the determination of the optimal conditions for processing with minimum waste looks very substantial. Therefore, the use of prediction methods in food processing is considered to be a tool for improving the efficiency and the quality of the produced products. In this regard, the fuzzy logic design as a novel prediction tool, along with response surface methodology (RSM) and Artificial Neural Network (ANN), are applied extensively. Therefore Fuzzy Logic Design is optimized to predict the some of the selected physical properties of fava bean, as a function of seed's moisture content. Therefore predicting the behavior of this crop against different moisture contents can improve the quality and performance of the products with the minimum wastes during very short time.(c) 2016 Wiley Periodicals, Incinfo:eu-repo/semantics/publishedVersio