روش ردیابی خودرو بهبود یافته برای IEEE 802:11p
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 10
توسعه روش های موقعیت یابی با قابلیت تحرک- بالا با استفاده از استاندارد IEEE 802.11p در شبکه های ادهاک وسایل نقلیه (VANETs) به دلیل نقاط ضعف در ناحیه های GNSS-dark مانند جنگل ها، تونل و غیره، و اشتباهات ناشی از GNSS-dark در نتایج ، ضروری است. برآورد زمان دقیق رسیدن(TOA) مبتنی بر مدل مسافت یابی ، به عنوان یکی از چالش های سیستم پیشگیری از برخورد اتومبیل ها، توجه زیادی را به خود جلب است. در این مقاله، روش پیشنهادی TOA یا روش تخمین مسافت با راهنمای کوتاه IEEE 802.11p پیشنهاد شد تا اثربخشی اندازه گیری های وسایل نقلیه چندکاره و نسبت نویز سیگنال کم (SNR) را کاهش دهد. ابتدا، TOA با استفاده از همبستگی خودکار و همبستگی-متقاطع برآورد شد. سپس، رویکرد sum برای یافتن مبدا دقیق زمان ارائه شد. نتایج شبیه سازی در کانال اتحادیه بین المللی مخابرات خودرو (ITUA) و کانال نویز گاوسی سفید افزایشی (AWGN)، برتری الگوریتم پیشنهادی را در شرایط SNR کم و محیط چندکاره ثابت می کند.
کليدواژه: برآورد TOA | IEEE 802.11p | VANETS | دامنه | همبستگی خودکار | همبستگی- متقابل
|مقاله ترجمه شده|
A course on big data analytics
دوره ای در تجزیه و تحلیل داده های بزرگ-2018
This report details a course on big data analytics designed for undergraduate junior and senior computer science students. The course is heavily focused on projects and writing code for big data processing. It is designed to help students learn parallel and distributed computing frameworks and techniques commonly used in industry. The curriculum includes a progression of projects requiring increasingly sophisticated big data processing ranging from data preprocessing with Linux tools, distributed pro cessing with Hadoop MapReduce and Spark, and database queries with Hive and Google’s BigQuery. We discuss hardware infrastructure and experimentally evaluate the cost/benefit of an on-premise server versus Amazon’s Elastic MapReduce. Finally, we showcase outcomes of our course in terms of student engagement and anonymous student feedback.
Keywords: Curriculum ، Undergraduate education ، Big data ،Cloud computing
The Rise of Big Data in Oncology
ظهور داده های بزرگ در انکولوژی-2018
OBJECTIVES: To describe big data and data science in the context of oncology nursing care. DATA SOURCES: Peer-reviewed and lay publications. CONCLUSION: The rapid expansion of real-world evidence from sources such as the electronic health record, genomic sequencing, administrative claims and other data sources has outstripped the ability of clinicians and researchers to manually review and analyze it. To promote high-quality, high-value cancer care, big data platforms must be constructed from standardized data sources to support extraction of meaningful, comparable insights. IMPLICATIONS FOR NURSING PRACTICE: Nurses must advocate for the use of stan dardized vocabularies and common data elements that represent terms and concepts that are meaningful to patient care. he term “big data” first appeared in the literature in 1997 by researchers at NASA as they described the challenges to store the volume of information generated as a result of a new, data-intensive type of computational work.1 In 2008, a white paper entitled “Big-Data Computing: Creating revolutionary breakthroughs in commerce, science and society,” highlighted the rapid integration of data-driven strategies across settings ranging from Wal-Mart’s (then) 4 petabyte (4000 trillion bytes) data warehouse to the 15 petabytes of data projected to be generated annually by the Large Hadron Collider particle accelerator project,2 and is credited with widespread adoption of the term.3
KEY WORDS: electronic health records, meaningful use, artificial intelligence, neoplasms
Great Basin land managers provide detailed feedback about usefulness of two climate information web applications
مدیران زمین های بزرگ بازخوردهای جزئیاتی درباره قابل مفید بودن برنامه کاربردی اینترنتی اطلاعات آب و هوایی فراهم می کنند-2018
Land managers in the Great Basin are working to maintain or restore sagebrush ecosystems as climate change exacerbates existing threats. Web applications delivering climate change and climate impacts information have the potential to assist their efforts. Although many web applications containing climate information currently exist, few have been co-produced with land managers or have incorporated information specifically focused on land managers’ needs. Through surveys and interviews, we gathered detailed feedback from federal, state, and tribal sagebrush land managers in the Great Basin on climate information web applications targeting land management. We found that a) managers are searching for weather and climate information they can incorporate into their current management strategies and plans; b) they are willing to be educated on how to find and understand climate related web applications; c) both field and administrative-type managers want data for timescales ranging from seasonal to decadal; d) managers want multiple levels of climate information, from simple summaries, to detailed descriptions accessible through the application; and e) managers are interested in applications that evaluate uncertainty and provide projected climate impacts.
keywords: Great Basin |Sagebrush |Land management |Climate change |Web application |Co-production
Bloom filter based optimization scheme for massive data handling in IoT environment
طرح بهینه سازی فیلتر مبتنی بر بلوم برای جاجایی داده های گسترده در محیط اینترنت اشیا-2018
With the widespread popularity of big data usage across various applications, need for efficient storage, processing, and retrieval of massive datasets generated from different applications has become inevitable. Further, handling of these datasets has become one of the biggest challenges for the research community due to the involved heterogeneity in their formats. This can be attributed to their diverse sources of generation ranging from sensors to on-line transactions data and social media access. In this direction, probabilistic data structures (PDS) are suitable for large-scale data processing, approximate predictions, fast retrieval and unstructured data storage. In conventional databases, entire data needs to be stored in memory for efficient processing, but applications involving real time in-stream data demand time-bound query output in a single pass. Hence, this paper proposes Accommodative Bloom filter (ABF), a variant of scalable bloom filter, where insertion of bulk data is done using the addition of new filters vertically. Array of m bits is divided into b buckets of l bits each and new filters of size ‘m/k′ are added to each bucket to accommodate the incoming data. Data generated from various sensors has been considered for experimental purposes where query processing is done at two levels to improve the accuracy and reduce the search time. It has been found that insertion and search time complexity of ABF does not increase with increase in number of elements. Further, results indicate that ABF outperforms the existing variants of Bloom filters in terms of false positive rates and query complexity, especially when dealing with instream data
Keywords: Internet of Things ، Big data analytics ، Probabilistic data structures ، Bloom filter ، In-stream data processing
Determinants of CSER practices for reducing greenhouse gas emissions: From the perspectives of administrative managers in tour operators
عوامل تعیین کننده اقدامات SCER برای کاهش انتشار گاز گلخانه ای: از نقطه نظر مدیران اجرایی در عملگران گردشگری-2018
Responsible corporate action has long been recognized as a vital step toward sustainability. Recently, this notion has also been introduced in tourism practices. Consequently, researchers have gradually become involved in exploring how tourism CSER is practiced, what might motivate it, and the relationship between financial performance and accredited actions. However, studies have primarily focused on the hospitality, airline, and cruise industries, and been geographically limited to Europe and North America. In order to fill this research gap, this study measures Taiwanese tour operators CSER activeness in reducing GHG emission according to a comprehensive set of items ranging from firm operation to destination management. Particularly, an extended TPB model has been employed to examine significant predictors of CSER performance, from the perspective of administrative managers. The findings indicate that managers’ attitudes regarding the benefits to the society and company interests are the most important predictors of business operations, supply chain, and destination management in CSER practices, respectively. The age of tourism business also plays an important role. This study contributes to the theoretical enhancement of CSER and TPB. Also, several practical suggestions are proposed in this study that will enhance the CSER profiles of tour operators.
keywords: Corporate social environmental responsibility (CSER) |Corporate social responsibility (CSR) |Green gas house (GHG) emissions |Climate change |Theory of planned behavior (TPB) |Tour operators
Harnessing social media for health information management
تحت کنترل درآوردن رسانه های اجتماعی برای مدیریت اطلاعات سلامت-2018
The remarkable upsurge of social media has dramatic impacts on health care research and practice. Social media are reshaping health information management in a variety of ways, ranging from providing cost-effective ways to improve clinician-patient communication and exchange health-related information and experience, to enabling the discovery of new medical knowledge and information. Despite some demonstrated initial success, social media use and analytics for improving health as a research field is still at its infancy. Information systems researchers can potentially play a key role in advancing the field. This study proposes a conceptual framework for social media-based health information management by drawing on multi-disciplinary research. With the guidance of the framework, this paper presents related research challenges, identifies important yet under-explored research issues, and discusses promising directions for future research.
keywords: Conceptual framework |Health information management |Data analytics |Social media
A review of short-term event studies in operations and supply chain management
بررسی مطالعات رویدادهای کوتاه مدت در عملیات و مدیریت زنجیره تامین-2018
The short-term event study method, grounded in the Efficient Market Hypothesis, is one of the most widely used tools for quantifying the impact of a specific event on a firms shareholder value. As the short-term event study method has been increasingly employed by researchers to investigate various operations and supply chain management (OSCM) events, it is timely to conduct a systematic review of the method to examine how it has been implemented in the OSCM literature and what could be improved to deploy it for future OSCM research. Analyzing 29 short-term event studies published in renowned OSCM journals between 1995 and 2017, we find that OSCM researchers generally follow the standard procedures in conducting event studies, but pay less attention to some methodological issues ranging from addressing the confounding events to expanding the event windows. Based on our analysis, we provide several recommendations for future event studies in OSCM, such as the opportunity for studying external events in the non-U.S. context, the caution of expanding the event windows, and the need to deal with the self-selection bias.
Keywords: Short-term event study ، Shareholder value ، Abnormal return ، Operations management ، Supply chain management ، Literature review
Urban pluvial flooding and stormwater management: A contemporary review of China’s challenges and “sponge cities” strategy
مدیریت سیلاب و مدیریت فاضلاب شهری: مرور همزمان چالش های چینی و استراتژی "شهرهای اسپانیایی"-2018
In recent years, urban pluvial flooding caused by extreme rainfall has increasingly occurred across China. This paper reviews the challenges faced by China in addressing urban pluvial flooding and managing urban storm water, with a particular focus on a policy initiative termed sponge cities. The paper first synthetically presents pluvial flood disasters in urbanized areas, and analyses their causes and formation mechanisms. It then in troduces China’s sponge cities initiative and discusses policy implementation in relation to contemporary un derstanding of sustainable urban stormwater management and international experience with innovative prac tices. The initiative, while theoretically well grounded and appropriate by its design principles, is shown subject to diverse implementation challenges, ranging from technological complexity to limited or lack of governance capacity as reflected in management ideology, knowledge and capacity of learning, participatory and integrated governance, investment financing, implementation pathway, planning and organization, and project evaluation. The paper offers some strategies for addressing those challenges, which include: 1) continuous experiment-based deep learning through pilot and institutionalization of knowledge and information management with city-to-city peering learning mechanisms, 2) establishment of institutional mechanisms dedicated to participatory, co ordinated and integrated governance of the policy initiative, 3) increased government role in creating favorable conditions for investments, and 4) appropriate planning and an adaptive approach to policy implementation. The paper concludes that the sponge cities initiative can be an effective approach only if China commits to appropriate technical, governance, financial, and organizational measures to effectively address the challenges for policy implementation.
Keywords: Urban pluvial flooding ، China ، Stormwater management ، Urban planning ، Governance ، Low impact development
Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis
پیش بینی ویژگی های شخصیتی پنج بزرگ از رد پای دیجیتال در رسانه های اجتماعی: متا آنالیز-2018
The growing use of social media among Internet users produces a vast and new source of user generated eco logical data, such as textual posts and images, which can be collected for research purposes. The increasing convergence between social and computer sciences has led researchers to develop automated methods to extract and analyze these digital footprints to predict personality traits. These social media-based predictions can then be used for a variety of purposes, including tailoring online services to improve user experience, enhance re commender systems, and as a possible screening and implementation tool for public health. In this paper, we conduct a series of meta-analyses to determine the predictive power of digital footprints collected from social media over Big 5 personality traits. Further, we investigate the impact of different types of digital footprints on prediction accuracy. Results of analyses show that the predictive power of digital footprints over personality traits is in line with the standard “correlational upper-limit” for behavior to predict personality, with correlations ranging from 0.29 (Agreeableness) to 0.40 (Extraversion). Overall, our findings indicate that accuracy of pre dictions is consistent across Big 5 traits, and that accuracy improves when analyses include demographics and multiple types of digital footprints.
Keywords: Social media ، Digital footprint ، Big 5 traits ، Personality ، Data mining ، Predictive modeling