Stabilization of Solar-Wind Hybrid Power System by Using SMES

The depleting fossil fuel reserves and increasing concern towards global warming created the need to surge for the renewable energy sources. Wind and solar power generation are two of the most promising renewable power generation technologies. This paper deals with the simulation of a Solar-Photovoltaic and Wind hybrid power generation system equipped with Superconducting Magnetic Energy Storage (SMES) in MATLAB/SIMULINK environment. The Solar-Photovoltaic Module and Permanent Magnet Synchronous Generator (PMSG) based wind turbine is simulated separately. Then they are connected to a dc bus. Since the intermittent nature of Solar and Wind makes the system unreliable, so an energy storage system SMES is introduced to reduce output fluctuations. Varying wind speed and solar irradiance value are taken as the input parameters. The simulation results show that a system with SMES is more reliable than a system without SMES.DOI:http://dx.doi.org/10.11591/ijece.v4i3.603

An Enhanced Estimator to Multi-objective OSPF Weight Setting Problem

Open shortest path first (OSPF) is a routing protocol which is widely used in the industry. Its functionality mainly depends on the weights assigned to the links. Given the traffic demands on a network, setting weights such that congestion can be avoided is an NP-hard problem. Optimizing these link weights leads to efficient network utilization which is the main goal of traffic engineering. In this paper, simulated annealing iterative heuristic is applied to this problem. This will provide close-to-optimal solutions that can be used for network provisioning. For this problem, the cost function that has been used in the literature depends solely on the links utilization and therefore optimizes only the network utilization. In this paper, our goal is to optimize the number of congested links in the network in addition to the utilization. Therefore, we propose a new cost function that depends on the utilization and the extra load caused by congested links in the network. This provides the network designer with more flexibility to optimize desired parameters. Our results show less number of congested links and comparable extra load in the network when compared to results of using the existing cost functio

The burden of unintentional drowning: Global, regional and national estimates of mortality from the Global Burden of Disease 2017 Study

__Background:__ Drowning is a leading cause of injury-related mortality globally. Unintentional drowning (International Classification of Diseases (ICD) 10 codes W65-74 and ICD9 E910) is one of the 30 mutually exclusive and collectively exhaustive causes of injury-related mortality in the Global Burden of Disease (GBD) study. This study's objective is to describe unintentional drowning using GBD estimates from 1990 to 2017. __Methods:__ Unintentional drowning from GBD 2017 was estimated for cause-specific mortality and years of life lost (YLLs), age, sex, country, region, Socio-demographic Index (SDI) quintile, and trends from 1990 to 2017. GBD 2017 used standard GBD methods for estimating mortality from drowning. __Results:__ Globally, unintentional drowning mortality decreased by 44.5% between 1990 and 2017, from 531 956 (uncertainty interval (UI): 484 107 to 572 854) to 295 210 (284 493 to 306 187) deaths. Global age-standardised mortality rates decreased 57.4%, from 9.3 (8.5 to 10.0) in 1990 to 4.0 (3.8 to 4.1) per 100 000 per annum in 2017. Unintentional drowning-associated mortality was generally higher in children, males and in low-SDI to middle-SDI countries. China, India, Pakistan and Bangladesh accounted for 51.2% of all drowning deaths in 2017. Oceania was the region with the highest rate of age-standardised YLLs in 2017, with 45 434 (40 850 to 50 539) YLLs per 100 000 across both sexes. __Conclusions:__ There has been a decline in global drowning rates. This study shows that the decline was not consistent across countries. The results reinforce the need for continued and improved policy, prevention and research efforts, with a focus on low-and middle-income countries

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Integrating Data Mining and Microsimulation Modelling to Reduce Traffic Congestion: A Case Study of Signalized Intersections in Dhaka, Bangladesh

A growing body of research has applied intelligent transportation technologies to reduce traffic congestion at signalized intersections. However, most of these studies have not considered the systematic integration of traffic data collection methods when simulating optimum signal timing. The present study developed a three-part system to create optimized variable signal timing profiles for a congested intersection in Dhaka, regulated by fixed-time traffic signals. Video footage of traffic from the studied intersection was analyzed using a computer vision tool that extracted traffic flow data. The data underwent a further data-mining process, resulting in greater than 90% data accuracy. The final data set was then analyzed by a local traffic expert. Two hybrid scenarios based on the data and the expert’s input were created and simulated at the micro level. The resultant, custom, variable timing profiles for the traffic signals yielded a 40% reduction in vehicle queue length, increases in average travel speed, and a significant overall reduction in traffic congestion

Understanding High-Dose, Ultra-High Dose Rate, and Spatially Fractionated Radiation Therapy.

The National Cancer Institute's Radiation Research Program, in collaboration with the Radiosurgery Society, hosted a workshop called Understanding High-Dose, Ultra-High Dose Rate and Spatially Fractionated Radiotherapy on August 20 and 21, 2018 to bring together experts in experimental and clinical experience in these and related fields. Critically, the overall aims were to understand the biological underpinning of these emerging techniques and the technical/physical parameters that must be further defined to drive clinical practice through innovative biologically based clinical trials