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).
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61 |
Review of current transfusion therapy and blood banking practices
بررسی روشهای فعلی انتقال خون و روشهای بانکی خون-2019 Transfusion Medicine is a dynamically evolving field. Recent high-quality
research has reshaped the paradigms guiding blood transfusion. As increasing
evidence supports the benefit of limiting transfusion, guidelines have been
developed and disseminated into clinical practice governing optimal transfusion of
red cells, platelets, plasma and cryoprecipitate. Concepts ranging from transfusion
thresholds to prophylactic use to maximal storage time are addressed in guidelines.
Patient blood management programs have developed to implement principles of
patient safety through limiting transfusion in clinical practice. Data from National
Hemovigilance Surveys showing dramatic declines in blood utilization over the
past decade demonstrate the practical uptake of current principles guiding patient
safety. In parallel with decreasing use of traditional blood products, the
development of new technologies for blood transfusion such as freeze drying and
cold storage has accelerated. Approaches to policy decision making to augment
blood safety have also changed. Drivers of these changes include a deeper
understanding of emerging threats and adverse events based on hemovigilance, and
an increasing healthcare system expectation to align blood safety decision making
with approaches used in other healthcare disciplines Keywords: blood transfusion | red blood cells | platelets | plasma | cryoprecipitate | patient blood management | hemovigilance | cold stored platelets | lyophilized plasma | pathogen reduction |
مقاله انگلیسی |
62 |
Online discrete choice models: Applications in personalized recommendations
مدل های انتخاب گسسته آنلاین: برنامه های کاربردی در توصیه های شخصی شده-2019 This paper presents a framework for estimating and updating user preferences in the context of app-based
recommender systems. We specifically consider recommender systems which provide personalized menus of
options to users. A Hierarchical Bayes procedure is applied in order to account for inter- and intra-consumer
heterogeneity, representing random taste variations among individuals and among choice situations (menus) for
a given individual, respectively. Three levels of preference parameters are estimated: population-level, individual-
level and menu-specific. In the context of a recommender system, the estimation of these parameters is
repeated periodically in an offline process in order to account for trends, such as changing market conditions.
Furthermore, the individual-level parameters are updated in real-time as users make choices in order to incorporate
the latest information from the users. This online update is computationally efficient which makes it
feasible to embed it in a real-time recommender system. The estimated individual-level preferences are stored for
each user and retrieved as inputs to a menu optimization model in order to provide recommendations. The
proposed methodology is applied to both Monte-Carlo and real data. It is observed that the online update of the
parameters is successful in improving the parameter estimates in real-time. This framework is relevant to various
recommender systems that generate personalized recommendations ranging from transportation to e-commerce
and online marketing, but is particularly useful when the attributes of the alternatives vary over time. Keywords: Personalization | Intra-consumer heterogeneity | Hierarchical Bayes | Preference updates | recommender systems |
مقاله انگلیسی |
63 |
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
هوش مصنوعی (AI): چشم اندازهای چند رشته ای در مورد چالش ها ، فرصت ها و دستور کار برای تحقیق ، تمرین و سیاست های نوظهور-2019 As far back as the industrial revolution, significant development in technical innovation has succeeded in
transforming numerous manual tasks and processes that had been in existence for decades where humans had
reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for
the augmentation and potential replacement of human tasks and activities within a wide range of industrial,
intellectual and social applications. The pace of change for this new AI technological age is staggering, with new
breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities
for continued innovation. The impact of AI could be significant, with industries ranging from: finance,
healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI
technologies. The study brings together the collective insight from a number of leading expert contributors to
highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda
posed by the rapid emergence of AI within a number of domains: business and management, government, public
sector, and science and technology. This research offers significant and timely insight to AI technology and its
impact on the future of industry and society in general, whilst recognising the societal and industrial influence
on pace and direction of AI development. Keywords: Artificial intelligence | AI | Cognitive computing | Expert systems | Machine learning | Research agenda |
مقاله انگلیسی |
64 |
How crowdfunding platforms change the nature of user innovation – from problem solving to entrepreneurship
How crowdfunding platforms change the nature of user innovation – from problem solving to entrepreneurship-2019 Crowdfunding has become a key research trend in recent years providing a new form of acquiring funding for
innovation projects from users prior to the realization of the product in a ‘market before the market’. In this
paper, we link the concept of crowdfunding with the user innovation phenomenon and show how user innovators
harness crowdfunding to complement their innovative behavior and obtain funding to build firms and
produce products in a more professional way. Conducting three case studies ranging from low- to high-tech
crowdfunding campaigns, we investigate how crowdfunding impacts constituent dimensions of user innovation
theory such as user motivation, user role, user community, collaboration between users and user investments. In
particular, we argue that crowdfunding platforms (CFPs) may give rise to a more widespread occurrence of user
entrepreneurs, who found a firm to commercialize their product or service in a marketplace they have created
for their own need. Hence, we show the development from traditional user innovation to crowdfunding-enabled
user innovation, which democratizes not only the creation but also the more large-scale commercialization of
new products and services. Keywords: User innovation | Crowdfunding | Crowdsourcing | Crowdfunding platform | Entrepreneurship | User communities | User entrepreneur |
مقاله انگلیسی |
65 |
New approach to evaluate a non-grain oriented electrical steel electromagnetic performance using photomicrographic analysis via digital image processing
روش جدید برای ارزیابی عملکرد الکترومغناطیسی فولاد برقی غیر دانه ای با استفاده از آنالیز فوتو میکروگرافی از طریق پردازش تصویر دیجیتال-2019 The growing global demand for energy makes it necessary to adopt measures ranging fromthe exploration of new energy sources to the development of technology for machinery andequipment with greater energy efficiency. Non-grain oriented electrical steels are widelyused in the construction of rotors and stators that form the core of electric motors, and theirmicrostructures are directly related to its electromagnetic performance. This paper presentsa new, fast and efficient method for the classification of non-grain oriented electrical steelmicrostructural states and their electromagnetic performance using photomicrographicanalysis. The study was performed on non-grain oriented electrical steel samples with 1.28%silicon, cold-rolled with reductions of 50% and 70%, annealed in box at 730◦C for 12 h, andsubjected to a subsequent annealing heat treatment for grain growth at 620◦C, 730◦C, 840◦Cand 900◦C for 1, 10, 100 and 1000 min at each temperature. A total of 32 samples were usedto form a database with 192 images. Our approach used a combination of extractor features(GLCM, LBP and moments) with the classifiers (Bayes, K-NN, K-means, MLP and SVM), alsocombined with two data partitioning, and the hold out and leave one out. KNN with 1 neigh-bor using the GLCM extractor showed the highest accuracy rate of 97.44%, and values greaterthan 96.0% for the other validation methods. The time required for the test was only 15.4 ms.The results obtained with this proposed approach, generate a new approach to evaluate anon-grain oriented electrical steel electromagnetic performance. Keywords:Electrical steel | Electromagnetic performance | Digital image processing | Pattern recognition |
مقاله انگلیسی |
66 |
Principal components methodology : A novel approach to forecasting production from liquid-rich shale (LRS) reservoirs
متدولوژی اجزای اصلی: یک روش جدید برای پیش بینی تولید از مخازن سنگ نفت غنی از مایع (LRS)-2019 With increasing global demand for energy, the importance of unconventional shale oil and gas research cannot
be over-emphasized. The oil and gas industry requires rapid and reliable means of forecasting production.
Existing traditional decline curve analysis (DCA) methods have been limited in their ability to satisfactorily
forecast production from unconventional liquid-rich shale (LRS) reservoirs. This is due to several causes ranging
from the complicated production mechanisms to the ultra-low permeability in shales. The use of hybrid (combination)
DCA models can improve results. However, complexities associated with these techniques can still
make their application quite tedious without proper diagnostic plots, correct use of model parameters and some
knowledge of the production mechanisms involved. This work, therefore, presents a new statistical data-driven
approach of forecasting production from LRS reservoirs called the Principal Components Methodology (PCM).
PCM is a technique that bypasses a lot of the difficulties associated with existing methods of forecasting and
forecasts production with reasonable certainty. PCM is a data-driven method of forecasting based on the statistical
technique of principal components analysis (PCA).
In our study, we simulated production of fluids with different compositions for 30 years with the aid of a
commercial compositional simulator. We then applied the Principal Components Methodology (PCM) to the
production data from several representative wells by using Singular Value Decomposition (SVD) to calculate the
principal components. These principal components were then used to forecast oil production from wells with
production histories ranging from 0.5 to 3 years, and the results were compared to simulated data. Application of
the PCM to field data is also included in this work.
This study provides fresh initiatives into how production forecasting from unconventional LRS reservoirs can
be done in a different way. Keywords: Principal components | Liquid-rich shale | Unconventional resources | Production forecasting | Pattern recognition |
مقاله انگلیسی |
67 |
Design and field implementation of an impact detection system using committees of neural networks
طراحی و اجرای میدانی یک سیستم تشخیص ضربه با استفاده از کمیته های شبکه های عصبی-2019 Many critical societal functions depend on uninterrupted service of civil engineering infrastructure. Rail- roads represent important infrastructure components of the transportation sector and provide both pas- senger and freight services. Railroad bridges over roadways are susceptible to impacts from overheight vehicles and equipment, which may damage bridge girders or supports and must be investigated after each event. One method of monitoring for vehicle-bridge collisions utilizes accelerometers to monitor for abnormal bridge vibrations corresponding to abnormal activity. Passing trains under normal operat- ing conditions frequently produce significant bridge responses that have similar response characteristics to bridge strikes, but do not need to be investigated. This paper presents an expert system which com- prises committees of artificial neural networks trained to interrogate data collected from accelerometers mounted on the bridge, assess the nature of the acceleration signal, and classify the event as either a passing train or a potentially damaging impact. This system is trained using acceleration time histories from accelerometers installed on 8 low-clearance rail bridges; no finite element model simulations were used for network training or data stream creation. The presented system accurately detects and classifies impacts with average impact detection performance ranging from 91–100% with average false positive rates limited to 0.00–0.75%. Keywords: Bridge impacts Impact detection | Signal classification | Feature selection | Artificial neural networks |
مقاله انگلیسی |
68 |
How does energy matter? Rural electrification, entrepreneurship, and community development in Kenya
انرژی چطور اهمیت دارد؟ برق سازی روستایی ، کارآفرینی و توسعه جامعه در کنیا-2019 We examine the impact of rural electrification on individuals and businesses within a community in order to test
a resource-based theory of entrepreneurship. We show that access to electricity increases average households’
income and entrepreneurial activities. The impact of electricity on entrepreneurial activity has wide-ranging
implications for development policy in countries where access to electricity is sparse. Results show a significant
difference in entrepreneurial opportunities with respect to firm formation, with the electrified site reporting
more new micro-enterprises (33) than the control site (20) after implementation. Electrification affects both
households’ income, individuals’ perceptions of their social position, and opportunities for business development.
Individuals’ future expectations and entrepreneurial activities are enhanced in the community that receives
electricity. We also find evidence that women-led households benefit from electrification more than menled
ones, but this benefit does not eliminate the difference in income between women and men-led household.
We discuss implications of the study for entrepreneurship and community social development interventions Keywords: Kenya | Entrepreneurship | Rural electrification | Experiment | Microenterprise | Community development |
مقاله انگلیسی |
69 |
Designing secure blockchain-based access control scheme in IoT-enabled Internet of Drones deployment
طراحی امن کنترل دسترسی مبتنی بر بلاکچین در اینترنت با استفاده از IoT از استقرار هواپیماهای بدون سرنشین-2019 In recent years, the Internet of Drones (IoD) has emerged as an important research topic in the academy
and industry because it has several potential applications ranging from the civilian to military. In IoD
environment, several drones, called Unmanned Aerial Vehicles (UAVs), are deployed in different flying zones
that communicate each other to exchange crucial information, and then the information are collected by the
Ground Station Server (????????????). All the drones and the ???????????? are registered with a central trusted authority,
Control Room (????????) prior to their deployment. Since the drones and the ???????????? communicate over open channel
(e.g., wireless medium), there are security and privacy issues in the IoD environment. To handle such issues,
in this paper we introduce a blockchain-based access control scheme in the IoD environment that allows
secure communication among the drones, and also among the drones and the ????????????. Secure data gathered by
the ???????????? form transactions, and those transactions are made into the blocks. The blocks are finally added
in the blockchain by the cloud servers connected with the ???????????? via the Ripple Protocol Consensus Algorithm
(RPCA) in a peer-to-peer cloud server network. Once the blocks are added into the blockchain, the transactions
containing in the blocks cannot be altered, modified or even removed. We provide all sorts of security
analysis including formal security under the random oracle model, informal security and simulation-based
formal security verification to assure that the proposed scheme can resist various potential attacks with high
probability needed in an IoD environment. In addition, a meticulous comparative analysis among the proposed
scheme and other closely related existing schemes shows that our scheme offers more functionality attributes
and better security, and also low communication and computation costs as compared to other scheme. Keywords: Internet of Drones (IoD) | Access control | Blockchain | Consensus | Security | AVISPA |
مقاله انگلیسی |
70 |
A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches
تحقیق مشترک در زمینه اندازه گیری متن از ساختار لفاظی سخنان روسای جمهور ایالات متحده-2019 This work presents a text mining context and its use for a deep analysis of the messages delivered by politicians. Specifically, we deal with an expert systems-based exploration of the rhetoric dynamics of a large collection of US Presidents’ speeches, ranging from Washington to Trump. In particular, speeches are viewed as complex expert systems whose structures can be effectively analyzed through rank-size laws. The methodological contribution of the paper is twofold. First, we develop a text mining-based procedure for the construction of the dataset by using a web scraping routine on the Miller Center website –the repository site collecting the speeches. Second, we explore the implicit structure of the discourse data by implementing a rank-size procedure over the individual speeches, being the words of each speech ranked in terms of their frequencies. The scientific significance of the proposed combination of text- mining and rank-size approaches can be found in its flexibility and generality, which let it be repro- ducible to a wide set of expert systems and text mining contexts. The usefulness of the proposed method and of the speeches analysis is demonstrated by the findings themselves. Indeed, in terms of impact, it is worth noting that interesting conclusions of social, political and linguistic nature on how 45 United States Presidents, from April 30, 1789 till February 28, 2017 delivered political messages can be carried out. Indeed, the proposed analysis shows some remarkable regularities, not only inside a given speech, but also among different speeches. Moreover, under a purely methodological perspective, the presented contribution suggests possible ways of generating a linguistic decision-making algorithm. Keywords: Text mining | Natural Language Processing | Politics | Rank-size laws |
مقاله انگلیسی |