با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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91 |
How does liability affect prices? Railroad sparks and timber
بدهی چگونه بر قیمت ها تأثیر می گذارد؟ جرقه و چوب راه آهن-2020 This paper analyzes how judicially-determined liability assignments affect valuations and prices. On two
occasions in 2007, a railway company caused a fire to break out in the State of Washington. The two fires
burned down some of the neighboring properties’ timber. These two incidents led to two companion
court cases that made it all the way to the Washington Supreme Court. The court rulings, both made on
May 31, 2012, held that the railway company was not liable for timber damages under Washington’s
timber trespass statute, despite having acted negligently. As a consequence of these decisions, economic
theory predicts a decrease in the value of timber in those areas associated with higher risk of fire, and
an increase in the value of Washington railway companies. Using a triple difference model and an event
study, we test and find evidence supporting this prediction.
Keywords: Liability | Property rights | Law and economics | Event study |
مقاله انگلیسی |
92 |
Improving robot dual-system motor learning with intrinsically motivated meta-control and latent-space experience imagination
بهبود یادگیری حرکتی سیستم دوگانه ربات با انگیزه ذاتی متا کنترل و تجربه فضای پنهان تخیلی-2020 Combining model-based and model-free learning systems has been shown to improve the sample
efficiency of learning to perform complex robotic tasks. However, dual-system approaches fail to
consider the reliability of the learned model when it is applied to make multiple-step predictions,
resulting in a compounding of prediction errors and performance degradation. In this paper, we present
a novel dual-system motor learning approach where a meta-controller arbitrates online between
model-based and model-free decisions based on an estimate of the local reliability of the learned
model. The reliability estimate is used in computing an intrinsic feedback signal, encouraging actions
that lead to data that improves the model. Our approach also integrates arbitration with imagination
where a learned latent-space model generates imagined experiences, based on its local reliability, to
be used as additional training data. We evaluate our approach against baseline and state-of-the-art
methods on learning vision-based robotic grasping in simulation and real world. The results show
that our approach outperforms the compared methods and learns near-optimal grasping policies in
dense- and sparse-reward environments. Keywords: Meta-control | Arbitration | Experience imagination | Intrinsic motivation | Reinforcement learning | Robotic grasping |
مقاله انگلیسی |
93 |
A causation mechanism for coal bursts during roadway development based on the major horizontal stress in coal: Very specific structural geology causing a localised loss of effective coal confinement and Newton’s second law
مکانیسم سببی برای انفجار زغال سنگ در حین توسعه جاده بر اساس فشار عمده افقی در زغال سنگ: زمین شناسی ساختاری بسیار خاص باعث از بین رفتن موضعی سلول زغال سنگ موثر و قانون دوم نیوتن-2020 In 2017, one of the international authorities on coal bursts, Mark Christopher, published a paper entitled
‘‘Coal bursts that occur during development: A rock mechanics enigma”, in which several relevant technical
issues were identified. This paper outlines what is considered to be a credible, first-principles,
mechanistic explanation for these three current development coal burst conundrums by reference to
early published coal testing work examining the significance of a lack of ‘‘constraint” to coal stability
and an understanding of how very specific structural geology and other geological features can logically
cause this to occur in situ, albeit on a statistically very rare basis. This basic model is examined by reference
to published information pertaining to the development coal-burst that occurred at the Austar Coal
Mine in New South Wales, Australia, in 2014 and from the Sunnyside District in Utah, the United States.
The ‘‘cause and effect” model for development of coal bursts presented also offers a meaningful explanation
for the statistical improbability for what are nonetheless potentially highly-destructive events, being
able to explain the statistical rarity being just as important to the credibility of the model as explaining
the local conditions associated with burst events. The model could also form the basis for a robust, riskbased
approach utilising a ‘‘hierarchy of controls”, to the operational management of the development
coal burst threat. Specifically, the use of pre-mining predictions for likely burst-prone and non-burstprone
areas, the use of the mine layout to avoid or at least minimise mining within burst-prone areas
if appropriate, and finally the development of an operational Trigger Action Response Plan (TARP) that
reduces the likelihood of inadvertent roadway development into a burst-prone area without suitable
safety controls already being in place. Keywords: Development coal burst | Wing-cracks | Austar Mine | Sunnyside Mine | Major horizontal stress in coal |
مقاله انگلیسی |
94 |
Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big dat
آیا می توان از طریق استعلام هوشمند تحت شرایط تجارت الکترونیکی ، وضعیت یک بیمار را پیش بینی کرد؟ پیش بینی خطر ابتلا به بیماری مبتنی بر ویژگی های توالی بر ویژگی های انتخاب شده از تشخیص شناختی داده های بزرگ-2020 The data-driven mode has promoted the researches of preventive medicine. In prediction of disease risks,
physicians’ clinical cognitive diagnosis data can be used for early prevention of diseases and, therefore, to reduce
medical cost, to improve accessibility of medical services and to lower medical risk. However, researches involved
no physicians’ cognition of patients’ conditions in intelligent inquiry under e-health business mode,
offered no diagnosis big data, neglected the values of the fused text information generated by joint activities of
online and offline medical data, and failed to thoroughly analyze the phenomenon of redundancy-complementarity
dispersion caused by high-order information shortage from the online inquiry data-driven perspective.
Besides, the risk prediction simply based on offline clinical cognitive diagnosis data undoubtedly reduces
prediction precision. Importantly, relevant researches rarely considered temporal relationships of different
medical events, did not conduct detailed analysis on practical problems of pattern explosion, did not offer a
thought of intelligent portrayal map, and did not conduct relevant risk prediction based on the sub-maps obtained
from the map. In consequence, the paper presents a disease risk prediction method with the model for
redundancy-complementarity dispersion-based feature selection from physicians’ online cognitive diagnosis big
data to realize features selection from the cognitive diagnosis big data of online intelligent inquiry; the obtained
features were ranked intelligently for subsequent high-dimensional information shortage compensation; the
compensated key feature information of the cognitive diagnosis big data was fused with offline electronic
medical record (EMR) to form the virtual electronic medical record (VEMR). The formed VEMR was combined
with the method of the sequential feature map for modelling, and a sequential feature map-based model for
disease risk prediction was presented to obtain online users’ medical conditions. A neighborhood-based collaborative
prediction model was presented for prediction of an online intelligent medical inquiry user’s possible
diseases in the future and to intelligently rank the risk probabilities of the diseases. In the experiments, the online
intelligent medical inquiry users’ VEMRs were used as the foundation of the simulation experiments to predict
disease risks in chronic obstructive pulmonary disease (OCPD) population and rheumatic heart disease (RHD)
population. The experiments demonstrated that the presented method showed relatively good metric performances
in the VEMR and improved disease risk prediction. Keywords: Cognitive diagnosis big data | Online intelligent inquiry | Sequential feature map | Disease risk prediction | Redundancy and complementarity dispersion |
مقاله انگلیسی |
95 |
Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems
مدیریت داده های همکاری مشترک زوج دیجیتال برای سیستم های تولید مواد افزودنی فلز-2020 Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great
advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter
development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness,
stability and repeatability caused by the unsolved complex relationships between material properties, product
design, process parameters, process signatures, post AM processes and product quality has significantly impeded
its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal
AM, which would support the development of intelligent process monitoring, control and optimisation, this
paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM
systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A
metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to
support the collaborative data management. The feasibility and advantages of the proposed framework are
validated through the practical implementation in a distributed metal AM system developed in the project
MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer
defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great
potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction
models, reducing development times and costs, and improving product quality and production efficiency. Keywords: Metal Additive Manufacturing | Digital Twin | data management | data model | machine learning | product lifecycle management |
مقاله انگلیسی |
96 |
Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux
مدل سازی فرآیند اسمزوز رو به جلو با استفاده از شبکه های عصبی مصنوعی (ANN) برای پیش بینی شار نفوذ-2020 Artificial neural networks (ANN) are black box models that are becoming more popular than transport-based
models due to their high accuracy and less computational time in predictions. The literature shows a lack of ANN
models to evaluate the forward osmosis (FO) process performance. Therefore, in this study, a multi-layered
neural network model is developed to predict the permeate flux in forward osmosis. The developed model is
tested for its generalization capability by including lab-scale experimental data from several published studies.
Nine input variables are considered including membrane type, the orientation of membrane, molarity of feed
solution and draw solution, type of feed solution and draw solution, crossflow velocity of the feed solution, and
the draw solution and temperature of the feed solution and the draw solution. The development of optimum
network architecture is supported by studying the impact of the number of neurons and hidden layers on the
neural network performance. The optimum trained network shows a high R2 value of 97.3% that is the efficiency
of the model to predict the targeted output. Furthermore, the validation and generalized prediction capability of
the model is tested against untrained published data. The performance of the ANN model is compared with a
transport-based model in the literature. A simple machine learning technique such as a multiple linear regression
(MLR) model is also applied in a similar manner to be compared with the ANN model. ANN demonstrates its
ability to form a complex relationship between inputs and output better than MLR. Keywords: Artificial neural network | Forward osmosis | Water treatment | Desalination | Machine learning |
مقاله انگلیسی |
97 |
Bayesian networks + reinforcement learning: Controlling group emotion from sensory stimuli
شبکه های بیزی + یادگیری تقویتی : کنترل احساسات گروهی از محرک های حسی-2020 As communication technology develops, various sensory stimuli can be collected in service spaces. To enhance the service effectiveness, it is important to determine the optimal stimuli to induce group emo- tion in the service space to the target emotion. In this paper, we propose a stimuli control system to adjust the group emotion. It is a stand-alone system that can determine optimal stimuli by utility ta- ble and modular tree-structured Bayesian networks designed for emotion prediction model proposed in the previous study. To verify the proposed system, we collected data using several scenarios at a kinder- garten and a senior welfare center. Each space is equipped with sensors for collection and equipment for controlling stimuli. As a result, the system shows a performance of 78% in the kindergarten and 80% in the senior welfare center. The proposed method shows much better performance than other classifica- tion methods with lower complexity. Also, reinforcement learning is applied to improving the accuracy of stimuli decision for a positive effect on system performance. Keywords: Adjusting emotion | Group emotion | Bayesian networks | Reinforcement Learning | IoT |
مقاله انگلیسی |
98 |
Allocating supervisory responsibilities to central bankers: Does national culture matter?
اختصاص مسئولیت های نظارتی به بانک های مرکزی: آیا فرهنگ ملی اهمیت دارد؟-2020 Central banks play an important role in the economy. They are responsible for the conduct of monetary
policy, and in several countries, they get involved in the supervision of the financial sector. We derive
a simple theoretical model to illustrate how culture may influence a politician’s choice of regulatory
architecture and the assignment of responsibilities when anticipating the impact of that regime on the
regulatory agencies’ incentives to cooperate. Using a sample of around 70 countries during the period
1996–2013 we confirm that the extent of supervisory duties that are allocated to the central bank are
influenced by national culture. More specifically, consistent with the theoretical predictions, we find that
individualism is positively associated, and power distance is negatively associated, with the likelihood
of higher central bank involvement in supervision.
Keywords: Central banks | Culture | Politicians | Supervision | Regulatory competition |
مقاله انگلیسی |
99 |
Determinants of Cone and Rod Functions in Geographic Atrophy: AI-Based Structure- Function Correlation
عوامل تعیین کننده عملکردهای مخروطی و میله ای در آتروفی جغرافیایی: همبستگی عملکردی مبتنی بر هوش مصنوعی-2020 PURPOSE: To investigate the association between
retinal microstructure and cone and rod function in
geographic atrophy (GA) secondary to age-related macular
degeneration (AMD) by using artificial intelligence
(AI) algorithms.
DESIGN: Prospective, observational case series.
METHODS: A total of 41 eyes of 41 patients (75.8 ± 8.4
years old; 22 females) from a tertiary referral hospital
were included. Mesopic, dark-adapted (DA) cyan and
red sensitivities were assessed by using funduscontrolled
perimetry (‘‘microperimetry’’); and retinal
microstructure was assessed by using spectral-domain optical-
coherence-tomography (SD-OCT), fundus autofluorescence
(FAF), and near-infrared-reflectance (IR)
imaging. Layer thicknesses and intensities and FAF and
IR intensities were extracted for each test point. The
cross-validated mean absolute error (MAE) was evaluated
for random forest-based predictions of retinal sensitivity
with and without patient-specific training data and percentage
of increased mean-squared error (%IncMSE) as
measurement of feature importance.
RESULTS: Retinal sensitivity was predicted with a
MAE of 4.64 dB for mesopic, 4.89 dB for DA cyan,
and 4.40 dB for DA red testing in the absence of
patient-specific data. Partial addition of patient-specific. |
مقاله انگلیسی |
100 |
Encouraging domestic innovation by protecting foreign intellectual property
تشویق نوآوری داخلی با حمایت از مالکیت معنوی خارجی-2020 This paper examines the relationship of respect for foreign intellectual property (IP) and domestic innovation. In a global economy, countries may choose to protect the IP of their own citizens, foreigners,
or both or neither. We develop a model that shows that countries will have higher levels of innovation
when respecting both domestic and foreign intellectual property. We test this prediction and show that
domestic innovation is positively related to respect for both foreign and domestic IP. Intuitively, respect
for domestic IP encourages innovation. We demonstrate the less intuitive case that protection of foreign
IP further incentivizes domestic innovation Keywords: Intellectual property rights | Innovation | Intellectual property | International trade | Technology policy |
مقاله انگلیسی |