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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 Exploring criminal responsibility of PTSD patients; findings from a survey in Chinese Mainland courts
بررسی مسئولیت کیفری بیماران مبتلا به PTSD؛ یافته های یک نظرسنجی در دادگاه های سرزمین اصلی چین-2019
Background. – The Wenchuan Earthquake in Sichuan Province is China’s deadliest natural disaster in a generation; after such disturbance, a kind of mental illness named post-traumatic stress disorder (PTSD, also called delayed psychogenic reaction) raises concern in Mainland China, but probably not rapidly sufficient. Different from that in the USA, earthquake is both the reason and focus of PTSD research in China. Methods. – In order to find out the relationship between the PTSD defense and criminal responsibility in Mainland China, the authors decided to use certain academic tools and analysis judicial decisions (816 cases). The authors identified key information from government official websites. Results. – Data demonstrated that research regarding PTSD increases considerably after the Wenchuan earthquake in 2008. However, data also showed that Chinese courts are hesitant in accepting PTSD as a mental defense for criminals, despite relevant existing rules. Some legal ambiguities, such as lack of procedures or instructions for the connection between diagnosis and judgment, can be observed when courts encounter criminals with PTSD. Conclusions. – PTSD patients occur in all races, classes, religions, and nationalities and some would unfortunately be criminals. This pattern reveals concern for the boundary between the reasonable use and abuse of PTSD in view of medico-legal expertise practice. Expert testimony or opinion cannot replace the judges’ decision. Chinese courts should learn from the American Bar Association and accept the three-part analysis for forensic consideration of PTSD. Further details regarding the regulations for resolving the criminal responsibility of PTSD patients should be obtained.
Keywords: Criminal Responsibility | Legal Identification | Mainland China | Post-traumatic Stress Disorder
مقاله انگلیسی
4 Estimating monthly wet sulfur (S) deposition flux over China using an ensemble model of improved machine learning and geostatistical approach
برآورد شار رسوب ماهانه گوگرد مرطوب (S) بر روی چین با استفاده از مدل گروهی از یادگیری ماشین پیشرفته و روش زمین آماری-2019
The wet S deposition was treated as a key issue because it played the negative on the soil acidification, biodiversity loss, and global climate change. However, the limited ground-level monitoring sites make it difficult to fully clarify the spatiotemporal variations of wet S deposition over China. Therefore, an ensemble model of improved machine learning and geostatistical method named fruit fly optimization algorithm-random forestspatiotemporal Kriging (FOA-RF-STK) model was developed to estimate the nationwide S deposition based on the emission inventory, meteorological factors, and other geographical covariates. The ensemble model can capture the relationship between predictors and S deposition flux with the better performance (R2=0.68, root mean square error (RMSE)=7.51 kg ha−1 yr−1) compared with the original RF model (R2=0.52, RMSE=8.99 kg ha−1 yr−1). Based on the improved model, it predicted that the highest and lowest S deposition flux were mainly concentrated on the Southeast China (69.57 kg S ha−1 yr−1) and Inner Mongolia (42.37 kg S ha−1 yr−1), respectively. The estimated wet S deposition flux displayed the remarkably seasonal variation with the highest value in summer (22.22 kg S ha−1 sea−1), follwed by ones in autumn (18.30 kg S ha−1 sea−1), spring (16.27 kg S ha−1 sea−1), and the lowest one in winter (14.71 kg S ha−1 sea−1), which was closely associated with the rainfall amounts. The study provides a novel approach for the S deposition estimation at a national scale.
Keywords: Wet S deposition | Machine learning | Geostatistical approach | China
مقاله انگلیسی
5 Deep Learning-Driven Particle Swarm Optimisation for Additive Manufacturing Energy Optimisation
بهینه سازی ازدحام ذرات با محوریت یادگیری عمیق برای بهینه سازی انرژی تولید افزودنی-2019
The additive manufacturing (AM) process is characterised as a high energy-consuming process, which has a significant impact on the environment and sustainability. The topic of AM energy consumption modelling, prediction, and optimisation has then become a research focus in both industry and academia. This issue involves many relevant features, such as material condition, process operation, part and process design, working environment, and so on. While existing studies reveal that AM energy consumption modelling largely depends on the design-relevant features in practice, it has not been given sufficient attention. Therefore, in this study, design-relevant features are firstly examined with respect to energy modelling. These features are typically determined by part designers and process operators before production. The AM energy consumption knowledge, hidden in the design-relevant features, is exploited for prediction modelling through a design-relevant data analytics approach. Based on the new modelling approach, a novel deep learning-driven particle swarm optimisation (DLD-PSO) method is proposed to optimise the energy utility. Deep learning is introduced to address several issues, in terms of increasing the search speed and enhancing the global best of PSO. Finally, using the design-relevant data collected from a real-world AM system in production, a case study is presented to validate the proposed modelling approach, and the results reveal its merits. Meanwhile, optimisation has also been carried out to guide part designers and process operators to revise their designs and decisions in order to reduce the energy consumption of the designated AM system under study.
Keywords: Additive Manufacturing | Energy Consumption Modelling | Prediction and Optimisation | Deep Learning | Particle Swarm Optimisation
مقاله انگلیسی
6 A knowledge-based expert system to assess power plant project cost overrun risks
یک سیستم خبره مبتنی بر دانش برای ارزیابی هزینه ریسک بیش ازحد پروژه نیروگاهی-2019
Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated na- ture of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and un- certainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model ( i.e. a modified Bayesian belief network model) . The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) sig- nificantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved ̶ in this case, the complex- ity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor’s poor plan- ning and scheduling.
Keywords: Cost overruns | Risk assessment | Power plant projects | Fuzzy logic | Canonical model
مقاله انگلیسی
7 تاثیر قیمت روی تبلیغ شفاهی: بازدید کننده های دفعه اولی دربرابر بازدید کننده های خیلی تکراری
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 27
بسیاری از مقصدهای گردشگری شدیدا" روی بازدید کننده های تکراری تمرکز کرده و به آنها وابسته هستند. بنابراین یک فرض اساسی این است که بازدید کننده های تکراری سودآورتر هستند (مثلا" ازطریق هزینه های بازاریابی پایین تر) و تبلیغ شفاهی مثبت آنها برای جذب مهمانان جدید ضروری می باشد. در این مقاله ما یک مطالعه تجربی مقیاس – بزرگ را برای بررسی تاثیر قیمت برای بازدید کننده های دفعه اولی و تکراری اقامتگاههای اسکی ارائه می دهیم. ما با به کارگیری یک دیدگاه مدلسازی سلسله مراتبی خطی نشان می دهیم که قیمت رابطه ای منفی با تبلیغ شفاهی برای بازدید کننده های دفعه اولی دارد و قیمت هیچ تاثیری روی تبلیغ شفاهی برای بازدید کننده های تکراری ندارد. بنابراین ما نشان می دهیم که تاثیر قیمت روی تبلیغ شفاهی برای بازدید کننده های تکراری کاهش می یابد.
مقاله ترجمه شده
8 Conservation of data deficient species under multiple threats: Lessons from an iconic tropical butterfly (Teinopalpus aureus)
حفاظت از گونه های کمبود داده در معرض تهدیدات متعدد: درسهایی از یک پروانه گرمسیری نمادین (Teinopalpus aureus)-2019
With increasing pressure from wildlife trade, conservation efforts must balance deficiencies in distribution data for species (the Wallacean shortfall) with the risk of increasing accessibility of locality for collectors. The Golden Kaiser-I-Hind (Teinopalpus aureus Mell) is an iconic butterfly restricted to Southeast Asia, popular in trade markets but lacking in ecological and conservation information. We compiled occurrence records and used them to assess multiple threats of T. aureus distribution-wide and at the national level. Results of species distribution models suggest that suitable habitats of T. aureus are montane forests in mid to high elevations in Southern China, Laos and Vietnam. However, habitat networks for the species are poorly connected, with some portions of its distribution experiencing intensive deforestation and threatened by climate change. The trade assessment results showed specimens of T. aureus were available for sale with high prices, indicating potential pressure from trade markets. We also found different conservation statuses and efforts to protect T. aureus across countries; the species is under strict protection in China, moderate protection in Vietnam and has no protection in Laos. Both recorded locations and projected distribution in the three countries were poorly covered by protected areas. These results together demonstrate the importance of distribution data in conservation management of threa- tened species while highlighting trade-offs inherent in not making location information widely available when trade pressure is present. Finally, we strongly encourage cross-border cooperation in sharing ecological in- formation for consistent conservation management of species under multiple threats from habitat loss, climate change and illegal wildlife trade.
Keywords: Climate change | Cross-border conservation | Habitat loss | Insect conservation | Southeast Asia | Wildlife trade
مقاله انگلیسی
9 Institutional entrepreneurship in the platform economy: How Uber tried (and failed) to change the Dutch taxi law
رآفرینی نهادی در اقتصاد پلتفرم: چگونه Uber تلاش کرد (و نتوانست) قانون تاکسی هلند را تغییر دهد-2019
Platform innovations like Uber and Airbnb allow peers to transact outside established market institutions. From an institutional perspective, platform companies follow a reverse innovation process compared to innovation within traditional regulatory systems: they first launch their online platform and ask for government permission only later. We analyze the emergence of Uber as an institutional entrepreneur in The Netherlands and the strategies it employed in a failed attempt to get its UberPop service legalized through changes in Dutch taxi law. We conclude that Uber’s failure to change the Dutch taxi law stemmed from the difficulty to leverage pragmatic legitimacy among users into favorable regulatory changes in a highly institutionalized regime, because Uber’s institutional work strategies were not aligned.
Keywords: Platform economy | Uber | Ridesourcing | Institutional change | Legitimacy | Regulation
مقاله انگلیسی
10 Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel
یادگیری ماشین با هدایت متالورژی فیزیکی و طراحی هوشمند مصنوعی از فولاد ضد زنگ قوی-2019
With the development of the materials genome philosophy and data mining methodologies, machine learning (ML) has been widely applied for discovering new materials in various systems including highend steels with improved performance. Although recently, some attempts have been made to incorporate physical features in the ML process, its effects have not been demonstrated and systematically analysed nor experimentally validated with prototype alloys. To address this issue, a physical metallurgy (PM) -guided ML model was developed, wherein intermediate parameters were generated based on original inputs and PM principles, e.g., equilibrium volume fraction (Vf) and driving force (Df) for precipitation, and these were added to the original dataset vectors as extra dimensions to participate in and guide the ML process. As a result, the ML process becomes more robust when dealing with small datasets by improving the data quality and enriching data information. Therefore, a new material design method is proposed combining PM-guided ML regression, ML classifier and a genetic algorithm (GA). The model was successfully applied to the design of advanced ultrahigh-strength stainless steels using only a small database extracted from the literature. The proposed prototype alloy with a leaner chemistry but better mechanical properties has been produced experimentally and an excellent agreement was obtained for the predicted optimal parameter settings and the final properties. In addition, the present work also clearly demonstrated that implementation of PM parameters can improve the design accuracy and efficiency by eliminating intermediate solutions not obeying PM principles in the ML process. Furthermore, various important factors influencing the generalizability of the ML model are discussed in detail.
Keywords: Alloy design | Machine learning | Physical metallurgy | Small sample problem | Stainless steel
مقاله انگلیسی
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