123 research outputs found
Origins and Development of Digital Journalism: Influence of Culture and Practices
No full textNotably, a Google search with the statement “articles on digital journalism” provides readers with more than 700,000 results. That number truly vouches for increasing research in this field. However, it has been over three decades since digital technologies were introduced into journalism. It is noted that not many agree with the term digital journalism and the processes attached to it. For others, it is more like a routine process; they consider it as mere “journalism” rather than digital journalism. In this chapter, while the author explores the definitions of the expression digital journalism, the researcher also comes up with a narrative review of the origins and development of this journalism stream. This chapter discusses the history of digital journalism, where the earliest reference to using technology in journalism was recorded in the 1950s. It also tries to define it through an extensive study of available literature. In this research, the author investigated and evaluated the influence of convergence culture in shaping digital journalism. This led to the current scenario where everything is interconnected, in the cloud, readily available and on-demand for consumption. While concluding this chapter, the author highlights how different streams of journalism have adjusted to the new wave of technology, giving rise to new skill-based job opportunities. Ultimately, the author elucidates how digital journalism is an ever-evolving process by discussing new and ongoing changes on various digital platforms that are changing the whole game in today’s times and will be even more relevant for future journalism practices.
Keywords: Digital Journalism, Convergence Culture, Journalism and Technology, History of Journalism, Digital Media, Evolution of Journalism
Correction to: Modular Communication as a Structure for Sustainability Within Social Enterprises
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Assessing Performance of a Disinfection Process at a Partially Nitrifying Water Reclamation Plant using quantitative Polymerase Chain Reaction and Microbial Culture Base Technique.
Get PDFWater shortages in the western United States have made alternative sources of water for landscape irrigation increasingly important. Tertiary treated wastewater is used to irrigate greenbelt areas, school grounds in certain communities and many parks in Southern California. However, the disinfection (or the microbial removal efficiencies) are directed toward removal of state standards that require the removal of coliform bacteria. Other bacterial removal that can have a negative effect on the quality of the water are not routinely monitored for and necessarily controlled. As a result, inefficient disinfection, caused by inadequancies or inconsistencies in filtration and disinfection processes has become a routine phenomenon.Occurrence of bacteria in the recycled distribution system can relate to biofouling of pipes and blocking of emitters in the irrigation system, increasing maintenance costs and performance failures that can require landscaping replacements. Furthermore, if ammonia oxidizing bacteria enter the distribution system and become established in recycled water reservoirs, disinfection residuals (chloramines) can be degraded and disinfection residual lost. For these reasons this study applied quantitative polymerase chain reaction to quantify removal efficiencies of bacteria and compare the numbers of total bacterial numbers as well as the ammonia oxidizing bacteria (AOB) from four different stages of a tertiary wastewater treatment plant secondary flow (equalization basin, before, after filtration and from the P11flow equalization basin, after chlorination respectively) located in Southern California. The results showed sampling machine/pump used to extract the water at any stage must be cleansed regularly. This finding is based on the first 5 weeks of our samples that showed unexpectedly high CFU values, while the sampling was being carried out with a sampling machine, as opposed to 9 later samplings when we used a sampling stick. Moreover, the plate counts and qPCR analysis across 18 weeks of samples indicated that the bacteria numbers as well as the log removal through the four stages varied largely from week to week, with the tertiary treatment log removal ranging from 1-5 logs. Also, the log removals across consecutive treatment stages, such as EQ-before filtration, before-after filtration, after filtration-after disinfection and before filtration-after disinfection were in the range of 0-3 logs, 1-2 logs, 1-7 logs and 1-5 logs respectively
A Novel Approach to Transform Relational Database into Graph Database using Neo4j
No full textME, CSEDNowadays, many companies rely on cloud services to meet their data storage
requirements. Cloud does not support traditional relational databases because of
scalability. Therefore, data needs to be migrated from relational to cloud databases.
Graph Databases are one of the cloud databases which have been invented for fast
traversal of millions of nodes that are interconnected via number of relations.
They are becoming popular and efficient choice for storing and querying highly
interconnected data. Traversal queries in SQL which require many joins to query
relational database can be easily and quickly traversed using graph databases. Graph
Databases have applications in many domains such as social network, organization
management, banking, insurance, fraud detection, etc. Data migration is becoming a
topic of interest these days because of increase in need of various data exchange
formats. In this paper an approach has been suggested to convert relational database to
graph database. An example database of auto-insurance has been used for the
experiment. Experimental results have been presented to show feasibility of the
proposed methodology. Query translation is done from SQL to Cypher query
language. Query execution efficiency comparison is done on source and target
database
Deep learning predictive modelling for electronic health records
No full textBeheshti, RahmatollahWith the digitization of health records over the last two decades there is a large amount of health records data collected electronically. This data provides unprecedented research opportunities to build clinical prediction models to estimate future health risks. Working with EHRs is known to be challenging due to their volume, different data types, and quality issues. The complex characteristics and quality issues in EHR can be listed as: 1) large feature space, 2) unequal lengths of medical histories, 3) a different number of observations (per patient), 4) irregular intervals between visits, and 5) missing values. Tackling these issues is the fundamental task to efficiently utilize the massive EHR data to build clinical prediction models. ☐ The non-linear complexity and temporal relationships in electronic health records (EHRs) limit the capability of traditional machine learning methods to perform clinical predictive tasks with high accuracy. In this work, we focus on using deep learning techniques to capture complex patterns in EHR data and address its data quality issues to build deep learning clinical prediction models with improved accuracy. We present a hybrid sequential deep learning model to capture static and longitudinal patterns in EHR data. To address missingness and irregular time intervals in EHR time-series data, we propose a model to interpolate and extrapolate values to perform concurrent imputation and prediction. We also propose a prediction model design to learn from different lengths of medical histories and provide the varying lengths of future time-series prediction. Lastly, we provide open source software with the collection of various cleaning, pre-processing, modeling, and evaluation techniques to build clinical prediction models using open-source EHR data. Our proposed models and techniques can be utilized for different disease prediction tasks using EHR data although we primarily focus on their application in childhood obesity prediction.University of Delaware, Department of Computer and Information SciencesPh.D
Optical image encryption using fresnel zone plate mask based on fast walsh hadamard transform
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A Meta-analysis of Age-related effect on Loss Aversion
No full textProspect Theoretic Loss aversion (λ) suggests we overvalue losses compared to gains during decision making but the influence of age on loss aversion is unclear. We conducted a random-effects meta-analysis on 7 studies that measured loss aversion and the age of participants. The pooled estimate (β=0.0886, 95%CI [0.0087 0.1686] revealed an effect of age on loss aversion. Considering age as continuous (n=1103, 5 studies; β = -0.005233, t= -2.511, p = 0.0122) or categorical (n=1856, 8 studies; β=0.00856, t= 0.111, p = 0.912) reflected substantial differences. Dividing participants broadly (< 46 and > 45 years) to explore differences in estimates of loss aversion (λ>1, λ =1, λ<1), also found significant differences, X2 (2, 1856)=7.5362, p = 0.0231. These findings underscore the veracity of meta-analysis for advancing and explicating previously underexplored aspects of decision theory
Exploring the Role of Epigenetics in Cereal and Leguminous Crops Exposed to Abiotic Stress
No full textOral Health Beliefs, Attitudes, and Practices of South Asian Migrants: A Systematic Review
No full textOral health is a burden among all populations and is linked with major chronic diseases such as cardiovascular diseases. Migrants, in particular South Asians, have poor oral health which requires further understanding to better inform oral health interventions by targeting specific aspects of this heterogenous South Asian population. This review is undertaken to systematically synthesize the evidence of oral health understandings, knowledge, attitudes, beliefs, practices, and behaviors of South Asian migrants residing in high-income countries. A comprehensive systematic search of seven electronic databases and hand-searching for peer-reviewed studies was conducted. All study designs were included, and quality assessment conducted. Of the 1614 records identified, 17 were included for synthesis and 12 were quantitative in design. These studies were primarily conducted in the UK, USA, Canada, and Europe. South Asian migrants had inadequate oral health knowledge, attitudes, and practices—influenced by culture, social norms, and religiosity. In the absence of symptoms, preventive oral hygiene practices were limited. Barriers to access varied with country of origin; from lack of trust in dentists and treatment cost in studies with India as the country of origin, to religiosity, among poorer nations such as Bangladesh. Fewer studies focused on recent arrivals from Bhutan or the Maldives. Culturally and socially appropriate strategies must be developed to target oral health issues and a “one-size” fits all approach will be ineffective in addressing the needs of South Asian migrants
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