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1 Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105
The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data
مقاله انگلیسی
2 الگوریتم تکاملی چند هدفی مبتنی بر شبکه عصبی برای زمانبندی گردش کار پویا در محاسبات ابری
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 45
زمانبندی گردشکار یک موضوع پژوهشی است که به طور گسترده در محاسبات ابری مورد مطالعه قرار گرفته است و از منابع ابری برای کارهای گردش کار استفاده می¬شود و برای این منظور اهداف مشخص شده در QoS را لحاظ می¬کند. در این مقاله، مسئله زمانبندی گردش کار پویا را به عنوان یک مسئله بهینه سازی چند هدفه پویا (DMOP) مدل می¬کنیم که در آن منبع پویایی سازی بر اساس خرابی منابع و تعداد اهداف است که ممکن است با گذر زمان تغییر کنند. خطاهای نرم افزاری و یا نقص سخت افزاری ممکن است باعث ایجاد پویایی نوع اول شوند. از سوی دیگر مواجهه با سناریوهای زندگی واقعی در محاسبات ابری ممکن است تعداد اهداف را در طی اجرای گردش کار تغییر دهد. در این مطالعه یک الگوریتم تکاملی چند هدفه پویا مبتنی بر پیش بینی را به نام الگوریتم NN-DNSGA-II ارائه می¬دهیم و برای این منظور شبکه عصبی مصنوعی را با الگوریتم NGSA-II ترکیب می¬کنیم. علاوه بر این پنج الگوریتم پویای مبتنی بر غیرپیش بینی از ادبیات موضوعی برای مسئله زمانبندی گردش کار پویا ارائه می¬شوند. راه¬حل¬های زمانبندی با در نظر گرفتن شش هدف یافت می¬شوند: حداقل سازی هزینه ساخت، انرژی و درجه عدم تعادل و حداکثر سازی قابلیت اطمینان و کاربرد. مطالعات تجربی مبتنی بر کاربردهای دنیای واقعی از سیستم مدیریت گردش کار Pegasus نشان می¬دهد که الگوریتم NN-DNSGA-II ما به طور قابل توجهی از الگوریتم¬های جایگزین خود در بیشتر موارد بهتر کار می¬کند با توجه به معیارهایی که برای DMOP با مورد واقعی پارتو بهینه در نظر گرفته می¬شود از جمله تعداد راه¬حل¬های غیرغالب، فاصله¬گذاری Schott و شاخص Hypervolume.
مقاله ترجمه شده
3 به سوی تقسیم بندی شبکه 5G برای شبکه های ادهاک خودرویی: یک رویکرد انتها به انتها
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 16
شبکه های 5G نه تنها از افزایش نرخ داده ها پشتیبانی می کنند، بکه همچنین می بایست زیرساخت مشترکی را فراهم کنند که براساس آن سرویس های جدید همراه با نیازمندی های بسیار متفاوت کیفیت سرویس (QoS) شبکه با تاخیر کمتر ارائه شود. به طور دقیق تر، کاربردهای شبکه های خودرویی چند منظوره (VANET) که اساساً گرایش آن ها به مسائل ایمنی و سرگرمی است (مانند پخش ویدیویی و مرورگر وب) در حال افزایش است. بیشتر این کاربردها دارای محدودیت های جدی از نظر تاخیر در حد چند میلی ثانیه هستند و نیاز به اطمینان پذیری بالایی دارند. پلتفورم نسل پنجم برای بررسی چنین نیازهایی نیازمند ایجاد شبکه های مجازی برنامه پذیر و راهکارهای مختلف ترافیکی همانند تقسیم بندی (برش) شبکه است. به این منظور در این مقاله یک مکانیزم تقسیم بندی پویا و برنامه پذیر انتها به انتها در شبکه LTE مبتنی بر M-CORD پیشنهاد می دهیم. یکی از ویژگی های کلیدی M-CORD که مکانیزم پیشنهاد تقسیم بندی شبکه از آن استفاده می کند، EPC مجازی است که سفارشی سازی و اصلاح را امکان پذیر می سازد. M-CORD کارکرد ضروری را برای برنامه ریزی تعاریف تقسیم بندی فراهم می کند که در آن مکانیزم پیشنهادی به طور کامل از رویکرد تعریف شده نرم افزاری خود پیروی می کند. علاوه بر این، ما نشان می دهیم که چگونه دستگاه ها انتهایی قرار گرفته در بخش های مختلف براساس QoS های متفاوت براساس نوع کاربر انتهایی تخصیص داده می شوند. این نتایج نشان می دهند که مکانیزم پیشنهادی تقسیم بندی شبکه بخش های مناسب را انتخاب می کند و منابع را به کاربران براساس نیازها و نوع سرویس آن ها اختصاص می دهد.
کلمات کلیدی: تقسیم بندی شبکه | نسل پنجم (5G) | M-CORD | LTE | NSSF | VANET
مقاله ترجمه شده
4 Highway crash detection and risk estimation using deep learning
تشخیص تصادف بزرگراه و تخمین ریسک با استفاده از یادگیری عمیق-2020
Crash Detection is essential in providing timely information to traffic management centers and the public to reduce its adverse effects. Prediction of crash risk is vital for avoiding secondary crashes and safeguarding highway traffic. For many years, researchers have explored several techniques for early and precise detection of crashes to aid in traffic incident management. With recent advancements in data collection techniques, abundant real-time traffic data is available for use. Big data infrastructure and machine learning algorithms can utilize this data to provide suitable solutions for the highway traffic safety system. This paper explores the feasibility of using deep learning models to detect crash occurrence and predict crash risk. Volume, Speed and Sensor Occupancy data collected from roadside radar sensors along Interstate 235 in Des Moines, IA is used for this study. This real-world traffic data is used to design feature set for the deep learning models for crash detection and crash risk prediction. The results show that a deep model has better crash detection performance and similar crash prediction performance than state of the art shallow models. Additionally, a sensitivity analysis was conducted for crash risk prediction using data 1-minute, 5-minutes and 10-minutes prior to crash occurrence. It was observed that is hard to predict the crash risk of a traffic condition, 10 min prior to a crash.
Keywords: Crash detection | Crash prediction | Deep learning
مقاله انگلیسی
5 Common pattern of distribution for Mesoamerican Triatoma dimidiata suggest geological and ecological association
الگوی متداول توزیع برای آمریکای مرکزی Triatoma dimidiata پیشنهاد انجمن زمین شناسی و زیست محیطی-2020
The phylogeny of the Triatoma dimidiata complex has been widely assessed with different genetic and morphological data, which has allowed to reach the consensus that the complex consists of at least three taxonomic units. However, these taxonomic units seem to have a distribution related to geography throughout Mesoamerica, with different groupings depending on the source of information used. In the present study, we aimed to determine if there is a common biogeographical, genetic and phenetic distribution pattern among the T. dimidiata species in Mesoamerica and if this pattern is related to ecological and geological variability of the region. We found that panbiogeographical analysis showed three generalized tracks that coincide with genetic/ phenetic data which showed a general pattern of distribution in two big clusters to the north and south of Mesoamerica. We also found that these clusters were significantly related to geological tectonic plates and ecotypes. We conclude that the geological history may be a plausible explanation for the greater differentiation observed in the T. dimidiata complex, but that the current ecological characteristics of the morphotectonic units or ecotypes may be responsible for the additional variation observed and therefore differential control strategies for each cluster considering geological history and ecotype should be used. Further, more detailed biogeographical and landscape genetic analyses are necessary with the goal to elucidate T. dimidiata differentiation related with ecological and geological variables in the region and the possible epidemiological and evolutionary consequences.
Keywords: Sylvatic and domestic ecotypes | Genetic and phenetic differentiation | Morphotectonic units | Mayan and Chortí geological tectonic plat
مقاله انگلیسی
6 Scientific Authors in a Changing World of Scholarly Communication: What Does the Future Hold?
نویسندگان علمی در دنیای متغیر ارتباطات علمی: آینده چیست؟-2020
Scholarly communication in science, technology, and medicine has been organized around journal-based scientific publishing for the past 350 years. Scientific publishing has unique business models and includes stakeholders with conflicting interests—publishers, funders, libraries, and scholars who create, curate, and consume the literature. Massive growth and change in scholarly communication, coinciding with digitalization, have amplified stresses inherent in traditional scientific publishing, as evidenced by overwhelmed editors and reviewers, increased retraction rates, emergence of pseudo-journals, strained library budgets, and debates about the metrics of academic recognition for scholarly achievements. Simultaneously, several open access models are gaining traction and online technologies offer opportunities to augment traditional tasks of scientific publishing, develop integrated discovery services, and establish global and equitable scholarly communication through crowdsourcing, software development, big data management, and machine learning. These rapidly evolving developments raise financial, legal, and ethical dilemmas that require solutions, while successful strategies are difficult to predict. Key challenges and trends are reviewed from the authors’ perspective about how to engage the scholarly community in this multifaceted process.
KEYWORDS: Open access | Peer review | Predatory publishing | Preprint repository | Self-archiving
مقاله انگلیسی
7 Associations of hospital discharge services with potentially avoidable readmissions within 30 days among older adults after rehabilitation in acute care hospitals in Tokyo, Japan
انجمن خدمات ترخیص بیمارستان با بستری مجدد بالقوه قابل اجتناب در عرض 30 روز در میان سالمندان بعد از توانبخشی در بیمارستانهای مراقبت حاد در توکیو ، ژاپن-2020
OBJECTIVE: To examine the associations of three major hospital discharge services covered under health insurance (discharge planning, rehabilitation discharge instruction, and coordination with community care) with potentially avoidable readmissions within 30 days (30-day PAR) in older adults after rehabilitation in acute care hospitals in Tokyo, Japan.
DESIGN: Retrospective cohort study using a large-scale medical claims database of all Tokyo residents aged ≥75 years. SETTING: Acute care hospitals PARTICIPANTS: Patients who underwent rehabilitation and were discharged to home (n=31,247; mean age: 84.1 years, standard deviation: 5.7 years) between October 2013 and July 2014.
INTERVENTIONS: None.
MAIN OUTCOME MEASURE: 30-day PAR.
RESULTS: Among the patients, 883 (2.9%) experienced 30-day PAR. A multivariable logistic generalized estimating equation model (with a logit link function and binominal sampling distribution) that adjusted for patient characteristics and clustering within hospitals showed that the discharge services were not significantly associated with 30-day PAR. The odds ratios were 0.962 (95% confidence interval [CI]: 0.805-1.151) for discharge planning, 1.060 (95% CI: 0.916-1.227) for rehabilitation discharge instruction, and 1.118 (95% CI: 0.817-1.529) for coordination with community care. In contrast, the odds of 30-day PAR among patients with home medical care services were 1.431 times higher than those of patients without these services (P<0.001), and the odds of 30-day PAR among patients with a higher number (median or higher) of rehabilitation units were 2.031 times higher than those of patients with a lower number (below median) (P<0.001). Also, the odds of 30-day PAR among patients with a higher hospital frailty risk score (median or higher) were 1.252 times higher than those of patients with a lower score (below median) (P=0.001).
CONCLUSIONS: The insurance-covered discharge services were not associated with 30-day PAR, and the development of comprehensive transitional care programs through the integration of existing discharge services may help to reduce such readmissions.
Copyright © 2020. Published by Elsevier Inc.
KEYWORDS: Big data; health services for the aged; patient readmission; rehabilitation; transitional care
مقاله انگلیسی
8 Rigor and reproducibility for data analysis and design in the behavioral sciences
دقت و تکرارپذیری برای تجزیه و تحلیل داده ها و طراحی در علوم رفتاری-2020
The rigor and reproducibility of science methods depends heavily on the appropriate use of statistical methods to answer research questions and make meaningful and accurate inferences based on data. The increasing analytic complexity and valuation of novel statistical and methodological approaches to data place greater emphasis on statistical review. We will outline the controversies within statistical sciences that threaten rigor and reproducibility of research published in the behavioral sciences and discuss ongoing approaches to generate reliable and valid inferences from data. We outline nine major areas to consider for generally evaluating the rigor and reproducibility of published articles and apply this framework to the 116 Behaviour Research and Therapy (BRAT) articles published in 2018. The results of our analysis highlight a pattern of missing rigor and reproducibility elements, especially pre-registration of study hypotheses, links to statistical code/output, and explicit archiving or sharing data used in analyses. We recommend reviewers consider these elements in their peer review and that journals consider publishing results of these rigor and reproducibility ratings with manuscripts to incentivize authors to publish these elements with their manuscript.
KEYWORDS: statistics | big data | reproducibility | reliability | p-hacking
مقاله انگلیسی
9 Identification of animal individuals using deep learning: A case study of giant panda
شناسایی فردی حیوانی با استفاده از یادگیری عمیق: یک مطالعه موردی از پاندا غول پیکر-2020
Giant panda (Ailuropoda melanoleuca) is an iconic species of conservation. However, long-term monitoring of wild giant pandas has been a challenge, largely due to the lack of appropriate method for the identification of target panda individuals. Although there are some traditional methods, such as distance-bamboo stem fragments methods, molecular biological method, and manual visual identification, they all have some limitations that can restrict their application. Therefore, it is urgent to explore a reliable and efficient approach to identify giant panda individuals. Here, we applied the deep learning technology and developed a novel face-identification model based on convolutional neural network to identify giant panda individuals. The model was able to identify 95% of giant panda individuals in the validation dataset. In all simulated field situations where the quality of photo data was degraded, the model still accurately identified more than 90% of panda individuals. The identification accuracy of our model is robust to brightness, small rotation, and cleanness of photos, although large rotation angle (> 20°) of photos has significant influence on the identification accuracy of the model (P < 0.01). Our model can be applied in future studies of giant panda such as long-term monitoring, big data analysis for behavior and be adapted for individual identification of other wildlife species.
Keywords: Deep learning | convolutional neural network | Individual identification | Giant panda
مقاله انگلیسی
10 The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources
سؤالاتی که می پرسیم: فرصت ها و چالش های استفاده از تجزیه و تحلیل داده های بزرگ برای مدیریت استراتژیک منابع سرمایه انسانی-2020
Big data analytics have transformed research in many fields, including the business areas of marketing, accounting and finance, and supply chain management. Yet, the discussion surrounding big data analytics in human resource management has primarily focused on job candidate screenings. In this article, we consider how significant strategic human capital questions can be addressed with big data analytics, enabling HR to enhance overall firm performance. We also examine how new data sources that help assess workforce performance in real time can assist in the identification and development of the knowledge stars that contribute to firm performance disproportionately as well as help reinforce firm capabilities. But in order for big data analytics to be successful in the HR field, regulatory and ethical challenges must also be addressed; these include privacy concerns and, in Europe, the General Data Protection Regulation (GDPR). We conclude by discussing how big data analytics can facilitate strategic change within HR and the organization as a whole.
KEYWORDS: Big data analytics | Workforce analytics | Stakeholder | management | Strategic human | capital | Knowledge stars | Human resource | management
مقاله انگلیسی
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