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
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
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
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|
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
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
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
No luck for moral luck
بدون شانس برای شانس اخلاقی-2019
Moral philosophers and psychologists often assume that people judge morally lucky and morally unlucky agents differently, an assumption that stands at the heart of the Puzzle of Moral Luck. We examine whether the asymmetry is found for reflective intuitions regarding wrongness, blame, permissibility, and punishment judg- ments, whether people’s concrete, case-based judgments align with their explicit, abstract principles regarding moral luck, and what psychological mechanisms might drive the effect. Our experiments produce three findings: First, in within-subjects experiments favorable to reflective deliberation, the vast majority of people judge a lucky and an unlucky agent as equally blameworthy, and their actions as equally wrong and permissible. The philosophical Puzzle of Moral Luck, and the challenge to the very possibility of systematic ethics it is frequently taken to engender, thus simply do not arise. Second, punishment judgments are significantly more outcome- dependent than wrongness, blame, and permissibility judgments. While this constitutes evidence in favor of current Dual Process Theories of moral judgment, the latter need to be qualified: punishment and blame judgments do not seem to be driven by the same process, as is commonly argued in the literature. Third, in between-subjects experiments, outcome has an effect on all four types of moral judgments. This effect is mediated by negligence ascriptions and can ultimately be explained as due to differing probability ascriptions across cases.
Keywords: Moral luck | Moral judgment | Outcome eﬀect | Dual process theory of moral judgment | Hindsight bias
Energy, uncertainty, and entrepreneurship: John D Rockefeller’s sequential approach to transaction costs management in the early oil industry
انرژی ، عدم اطمینان و کارآفرینی: رویکرد پی در پی جان دی راکفلر در مدیریت هزینه های معامله در صنعت اولیه نفت-2019
This article delves into the challenge of successful entrepreneurship in the energy industry under conditions of uncertainty by examining the case of John D Rockefeller’s Standard Oil Company, which rapidly seized control of an initially-uncertain industry. It finds that Rockefeller cemented control through a willingness to internalise contextual uncertainty (related to the nature of the energy business) as a stepping stone to managing contractual uncertainty (related to transactions with other parties). This finding suggests that thinking sequentially about the management of contextual and contractual uncertainty aids entrepreneurial success in the field of energy. This suggestion accords with standing calls in the transaction costs literature, which means that findings may generalise to some extent. However, the exploratory nature of the analysis implies the need for further research about the argument’s compatibility with modern energy practices and its generalisability.
Keywords: Uncertainty | Rockefeller | Standard Oil Company | Entrepreneurship | Transaction costs
First-principles and Machine Learning Predictions of Elasticity in Severely Lattice-distorted High-Entropy Alloys with Experimental Validation
اصول اول و پیش بینی یادگیری ماشین از الاستیسیته در آلیاژهای آنتروپی با تحریف شدید شبکه با استفاده از اعتبار سنجی تجربی-2019
Stiffness usually increases with the lattice-distortion-induced strain, as observed in many nanostructures. Partly due to the size differences in the component elements, severe lattice distortion naturally exists in high entropy alloys (HEAs). The single-phase face-centered-cubic (FCC) Al0.3CoCrFeNi HEA, which has large size differences among its constituent elements, is an ideal system to study the relationship between the elastic properties and lattice distortion using a combined experimental and computational approach based on in-situ neutron-diffraction (ND) characterizations, and first-principles calculations. Analysis of the interatomic distance distributions from calculations of optimized special quasi random structure (SQS) found that the HEA has a high degree of lattice distortion. When the lattice distortion is explicitly considered, elastic properties calculated using SQS are in excellent agreement with experimental measurements for the HEA. The calculated elastic constant values are within 5% of the ND measurements. A comparison of calculations from the optimized SQS and the SQS with ideal lattice sites indicate that the lattice distortion results in the reduced stiffness. The optimized SQS has a bulk modulus of 177 GPa compared to the ideal lattice SQS with a bulk modulus of 194 GPa. Machine learning (ML) modeling is also implemented to explore the use of fast, and computationally efficient models for predicting the elastic moduli of HEAs. ML models trained on a large dataset of inorganic structures are shown to make accurate predictions of elastic properties for the HEA. The ML models also demonstrate the dependence of bulk and shear moduli on several material features which can act as guides for tuning elastic properties in HEAs.
Keywords: First-principles calculation | Elastic constants | in situ tension test | Neutron diffraction | Machine learning