Experimental Study of a Closed Loop Pulsating Heat Pipe Aiming at Assessing the Optimized Operational Conditions

In recent times pulsating heat pipes turn into a pleasing field of study for researchers as useful devices to enhance and intensify heat transfer. Experimental data under various operating conditions for a better theoretical understanding are needed. This paper presents an experimental study on a closed loop pulsating heat pipe made of copper with 14 U-turns. The experiments were carried out for different working fluids (acetone, distilled water, ethanol and methanol) and their mixing, heat input, filling ratio and evacuation levels to optimize operational conditions. Thermal resistance at steady state specified for performance evaluation and showed better results for acetone. The mentioned working fluids in filling ratio of 60 % have better operation and acetone has the best. As well as experiments in different evacuation levels showed, more evacuation lead to more performance

A Wised Routing Protocols for Leo Satellite Networks

This Study proposes a routing strategy of combining a packet scheduling with congestion control policy that applied for LEO satellite network with high speed and multiple traffic. It not only ensures the QoS of different traffic, but also can avoid low priority traffic to be "starve" due to their weak resource competitiveness, thus it guarantees the throughput and performance of the network. In the end, we set up a LEO satellite network simulation platform in OPNET to verify the effectiveness of the proposed algorithm.Comment: The 10th Asian Control Conference (ASCC), Universiti Teknologi Malaysia, Malaysi

Deep learning stereo matching algorithm using Siamese network

Autonomous vehicle has become a very hot topic for researchers in recent years. One of the important sensors used in these vehicles is Stereo Cameras/Vision. Stereo vision systems are used to estimate the depth from the two cameras installed on robots or vehicles. This method can deliver the 3D position of all objects captured in the scene at a lower cost and higher density compared to LIDAR. Recently, neural net-works are vastly investigated and used in image processing problems and deep learning networks which has surpassed traditional computer vision methods specially in object recognition. In this paper, we propose to use a GPU with a new Siamese deep learning method to speed up the stereo matching algorithm. In this work, we use a high end Nvidia DGX workstation to train and test our algorithm and compare the results with normal GPUs and CPUs. Based on numerical evaluation, the Nvidia DGX can train a neural network with higher input image resolution approximately 8 times faster than a normal GPU and 40 times faster than a Core i7 8 Cores CPU. Since it has the ability to train on a higher resolution the network can be trained in more iteration and results in higher accuracy

Fast and robust stereo matching algorithm for obstacle detection in robotic vision systems

In this paper, we propose a new area-based stereo matching method by improving the classical Census transform. It is a difficult task to match the corresponding points in two images taken by stereo cameras, mostly under variant illumination and non-ideal conditions. The classic Census nonparametric transform offers some improvements in the accuracy of disparity map in these conditions but it also has some disadvantages. Because of the complexity of the algorithm, the performance is not suitable for real-time robotic systems. In order to solve this problem, this paper presents the differential transform using Maximum intensity differences of the pixel placed in the center of a defined window and the pixel in the neighborhood to reduce complexity and obtain better performance compared to the Census transform. Experimental results show that the proposed method, achieves better efficiency in terms of speed and memory consumption. Moreover, we have added a new feature to widen the depth detection range. With the help of the proposed method, robots can detect obstacles between 25cm to 400cm from robot cameras. The result shows that the method has the ability to work in a wide variety of lighting conditions, while the stereo matching performs the depth detection computation with speed of 30FPS

Anti-Cancer Drugs Effective in Retinoblastoma: Based on a Protein-Protein Interaction Network

Background: This paper investigates the effects of potential drugs on differentially expressed genes (DEGs) associated with substantial alterations in retinoblastoma malignancy.Material and Methods: The GSE125903 dataset consisting of ten samples was used in this study (seven cancer patients and three control samples). The genes were ordered according to their adjusted p value, and 2000 top differential expressed genes with adj p values less than 0.01 were chosen as statistically significant. The STRING database version 11.0 was used to display the interaction among genes. The Cytoscape3.8.2 and the Clusterviz plugin software were used to construct the modules for the PPI network, and five clusters of genes were formed. The DGIdb v4.2.0 database was used to study drug-gene interactions and identify potentially beneficial medicines for retinoblastoma malignancy. The DAVID v.6.8 database was used to study gene ontology (GO) and important biological pathways.Results: CISPLATIN, TAMOXIFEN, and CYCLOPHOSPHAMIDE are the medicines that have been shown to be successful in treating retinoblastoma in our study. Additionally, we conducted a research on three other drugs: GEMCITABINE, OLAPARIB, and MITOXANTRONE. Although it is used to treat other diseases, it seems to have no apparent effects on retinoblastoma cancer treatment.Conclusion: CISPLATIN, a drug that causes apoptosis in tumors, has been proven to be the most effective therapy for retinoblastoma and should be included in treatment regimens for this illness. Of course, we obtained this information based on bioinformatics techniques, and more clinical trials are needed for more reliable results.Keywords: Protein-Protein Interaction Network; Retinoblastoma; Anti-Cancer

Drug Repurposing for Age-Related Macular Degeneration (AMD) Based on Gene Co-Expression Network Analysis

Background: Age-related macular degeneration (AMD) is a lesser-known eye disease in the world that gradually destroys a person’s vision by creating dark spots in the center of vision. Material and Methods: Samples of AMD-related genes were extracted from the NCBI, then the gene expression network (GCN) was extracted. In addition, pathway enrichment analysis was performed to investigate the role of co-expressed genes in AMD. Finally, the drug-gene interaction network was plotted.Results: The results of this work based on bioinformatics showed that many genes are involved in AMD disease, the most important of which are the genes of TYROBP, LILRB2, LCP2, PTPRC, CFH, SPARC, HTR5A.Overexpression of these genes can be considered as basic biomarkers for this disease, we separated some of which we had from the gene co-expression network and some from the results of genes ontology (genes that have a P value ≤ 0.05). The most important drugs were isolated from the drug-gene network based on degree, which included 5 drugs including ocriplasmin, collagenase clostridium histolyticum, topiramate, primidone, butalbital.Conclusion: Among the genes we found, three genes of CFH, TYROBP, SPARC seem to be more important than the others. Among drugs, ocriplasmin, topiramate, primidone can play a more important role based on the degree in the drug-gene network, because all steps are performed with different bioinformatics methods, clinical trials must confirm or reject the results.Keywords: Age-Related Macular Degeneration; AMD; Co-Expression Network; Drug Repurposing

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019 : A systematic analysis for the Global Burden of Disease Study 2019

Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC