Text stream to temporal network - A dynamic heartbeat graph to detect emerging events on twitter

© 2018, Springer International Publishing AG, part of Springer Nature. Huge mounds of data are generated every second on the Internet. People around the globe publish and share information related to real-world events they experience every day. This provides a valuable opportunity to analyze the content of this information to detect real-world happenings, however, it is quite challenging task. In this work, we propose a novel graph-based approach named the Dynamic Heartbeat Graph (DHG) that not only detects the events at an early stage, but also suppresses them in the upcoming adjacent data stream in order to highlight new emerging events. This characteristic makes the proposed method interesting and efficient in finding emerging events and related topics. The experiment results on real-world datasets (i.e. FA Cup Final and Super Tuesday 2012) show a considerable improvement in most cases, while time complexity remains very attractive

Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks

© 2019 Elsevier Ltd With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real-time. Detecting events from Twitter stream requires complex techniques. Event-related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co-occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real-world datasets (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state-of-the-art techniques

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth - A four-year prospective study

Background: Stunting affects up to one-third of the children in low-to-middle income countries (LMICs) and has been correlated with decline in cognitive capacity and vaccine immunogenicity. Early identification of infants at risk is critical for early intervention and prevention of morbidity. The aim of this study was to investigate patterns of growth in infants up through 48 months of age to assess whether the growth of infants with stunting eventually improved as well as the potential predictors of growth.Methods: Height-for-age z-scores (HAZ) of children from Matiari (rural site, Pakistan) at birth, 18 months, and 48 months were obtained. Results of serum-based biomarkers collected at 6 and 9 months were recorded. A descriptive analysis of the population was followed by assessment of growth predictors via traditional machine learning random forest models.Results: Of the 107 children who were followed up till 48 months of age, 51% were stunted (HAZ \u3c - 2) at birth which increased to 54% by 48 months of age. Stunting status for the majority of children at 48 months was found to be the same as at 18 months. Most children with large gains started off stunted or severely stunted, while all of those with notably large losses were not stunted at birth. Random forest models identified HAZ at birth as the most important feature in predicting HAZ at 18 months. Of the biomarkers, AGP (Alpha- 1-acid Glycoprotein), CRP (C-Reactive Protein), and IL1 (interleukin-1) were identified as strong subsequent growth predictors across both the classification and regressor models.Conclusion: We demonstrated that children most children with stunting at birth remained stunted at 48 months of age. Value was added for predicting growth outcomes with the use of traditional machine learning random forest models. HAZ at birth was found to be a strong predictor of subsequent growth in infants up through 48 months of age. Biomarkers of systemic inflammation, AGP, CRP, IL1, were also strong predictors of growth outcomes. These findings provide support for continued focus on interventions prenatally, at birth, and early infancy in children at risk for stunting who live in resource-constrained regions of the world

Plant growth promoting rhizobacteria (PGPR) as green bioinoculants: Recent developments, constraints, and prospects

The quest for enhancing agricultural yields due to increased pressure on food production has inevitably led to the indiscriminate use of chemical fertilizers and other agrochemicals. Biofertilizers are emerging as a suitable alternative to counteract the adverse environmental impacts exerted by synthetic agrochemicals. Biofertilizers facilitate the overall growth and yield of crops in an eco-friendly manner. They contain living or dormant microbes, which are applied to the soil or used for treating crop seeds. One of the foremost candidates in this respect is rhizobacteria. Plant growth promoting rhizobacteria (PGPR) are an important cluster of beneficial, root-colonizing bacteria thriving in the plant rhizosphere and bulk soil. They exhibit synergistic and antagonistic interactions with the soil microbiota and engage in an array of activities of ecological significance. They promote plant growth by facilitating biotic and abiotic stress tolerance and support the nutrition of host plants. Due to their active growth endorsing activities, PGPRs are considered an eco-friendly alternative to hazardous chemical fertilizers. The use of PGPRs as biofertilizers is a biological approach toward the sustainable intensification of agriculture. However, their application for increasing agricultural yields has several pros and cons. Application of potential biofertilizers that perform well in the laboratory and greenhouse conditions often fails to deliver the expected effects on plant development in field settings. Here we review the different types of PGPR-based biofertilizers, discuss the challenges faced in the widespread adoption of biofertilizers, and deliberate the prospects of using biofertilizers to promote sustainable agriculture

White Electroluminescence Using ZnO Nanotubes/GaN Heterostructure Light-Emitting Diode

We report the fabrication of heterostructure white light–emitting diode (LED) comprised of n-ZnO nanotubes (NTs) aqueous chemically synthesized on p-GaN substrate. Room temperature electroluminescence (EL) of the LED demonstrates strong broadband white emission spectrum consisting of predominating peak centred at 560 nm and relatively weak violet–blue emission peak at 450 nm under forward bias. The broadband EL emission covering the whole visible spectrum has been attributed to the large surface area and high surface states of ZnO NTs produced during the etching process. In addition, comparison of the EL emission colour quality shows that ZnO nanotubes have much better quality than that of the ZnO nanorods. The colour-rendering index of the white light obtained from the nanotubes was 87, while the nanorods-based LED emit yellowish colour

Improvements in wheat productivity and soil quality can accomplish by co-application of biochars and chemical fertilizers

The beneficial role of biochar is evident in most of infertile soils, however this is argued that increment in crop yield owing to biochar application does not always achieve in cultivated/fertile soils. The nutrient biochar believed to enhance crop yield and soil fertility than structural biochar that may offset the positive effect of chemical fertilizer on crop performance but improves soil structural properties. Therefore, we investigated the effect of biochars [produced from nutrient rich feedstocks like poultry manure (PMB) and farmyard manure (FMB) and structural feedstocks such as wood chips (WCB) and kitchen waste (KWB)], and chemical fertilizers (CF) when applied alone or in combination on soil chemical properties, wheat growth, yield and nitrogen uptake in a cultivated clay loam soil. Sole biochar treatments increased the total carbon and mineral nitrogen content that were 21 and 106% higher, respectively compared to control after 128 days (P 0.05) except PMB, the nutrient biochar (P < 0.05). Compared to control, grain yield was 6 and 12% lower in WCB and FMB, respectively but not differed from KWB, PMB or WCB-CF. Conversely, co-application of biochars and CF treatments increased crop biological yield but the increment was the highest in nutrient biochars FMB or PMB (29 or 26%), than structural biochars WCB and KWB (15 and 13%), respectively (P < 0.05). For N uptake, this increment varies between 16 and 27% and again nutrient biochar has significantly higher N uptake than structural biochars. Hence, nutrient biochars (i.e. PMB) benefited the soil fertility and crop productivity more than structural biochars. Therefore, for immediate crop benefits, it is recommended to use nutrient biochar alone or in combination with chemical fertilizer. Such practice will improve crop performance and the quality of cultivated soil

Future and potential spending on health 2015-40: Development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background: The amount of resources, particularly prepaid resources, available for health can affect access to health care and health outcomes. Although health spending tends to increase with economic development, tremendous variation exists among health financing systems. Estimates of future spending can be beneficial for policy makers and planners, and can identify financing gaps. In this study, we estimate future gross domestic product (GDP), all-sector government spending, and health spending disaggregated by source, and we compare expected future spending to potential future spending. Methods: We extracted GDP, government spending in 184 countries from 1980-2015, and health spend data from 1995-2014. We used a series of ensemble models to estimate future GDP, all-sector government spending, development assistance for health, and government, out-of-pocket, and prepaid private health spending through 2040. We used frontier analyses to identify patterns exhibited by the countries that dedicate the most funding to health, and used these frontiers to estimate potential health spending for each low-income or middle-income country. All estimates are inflation and purchasing power adjusted. Findings: We estimated that global spending on health will increase from US$9.21 trillion in 2014 to$24.24 trillion (uncertainty interval [UI] 20.47-29.72) in 2040. We expect per capita health spending to increase fastest in upper-middle-income countries, at 5.3% (UI 4.1-6.8) per year. This growth is driven by continued growth in GDP, government spending, and government health spending. Lower-middle income countries are expected to grow at 4.2% (3.8-4.9). High-income countries are expected to grow at 2.1% (UI 1.8-2.4) and low-income countries are expected to grow at 1.8% (1.0-2.8). Despite this growth, health spending per capita in low-income countries is expected to remain low, at $154 (UI 133-181) per capita in 2030 and$195 (157-258) per capita in 2040. Increases in national health spending to reach the level of the countries who spend the most on health, relative to their level of economic development, would mean $321 (157-258) per capita was available for health in 2040 in low-income countries. Interpretation: Health spending is associated with economic development but past trends and relationships suggest that spending will remain variable, and low in some low-resource settings. Policy change could lead to increased health spending, although for the poorest countries external support might remain essential

Burden of musculoskeletal disorders in the Eastern Mediterranean Region, 1990–2013: findings from the Global Burden of Disease Study 2013

Moradi-Lakeh M, Forouzanfar MH, Vollset SE, et al. Burden of musculoskeletal disorders in the Eastern Mediterranean Region, 1990–2013: findings from the Global Burden of Disease Study 2013. Annals of the Rheumatic Diseases. 2017;76(8):annrheumdis-2016-210146