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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Academy of Nutrition and Dietetics: Revised 2021 Standards of Professional Performance for Registered Dietitian Nutritionists (Competent, Proficient, and Expert) in Management of Food and Nutrition Systems
آکادمی تغذیه و رژیمشناسی: استانداردهای 2021 اصلاحشده عملکرد حرفهای برای متخصصان تغذیه ثبتشده (لایق، ماهر و متخصص) در مدیریت سیستمهای غذایی و تغذیه-2021 Management of food and nutrition systems (MFNS) encompasses the varied roles of registered dietitian nutritionists (RDNs) with
administrative responsibilities for food and nutrition services within an organization. RDNs in MFNS are frequently employed in acute
care, but also expand into a multitude of other settings in which management of nutrition and foodservice is required, for example,
foodservice departments in assisted living and post-acute and long-term care; colleges and universities, kindergarten through grade 12
and pre-kindergarten schools and childcare; retail foodservice operations; correctional facilities; and companies that produce,
distribute, and sell food products. RDNs in MFNS aim to create work environments that support high-quality customer-centered care
and services, attract and retain talented staff, and foster an atmosphere of collaboration and innovation. The Management in Food and
Nutrition Systems Dietetic Practice Group, with guidance from the Academy of Nutrition and Dietetics Quality Management Committee,
has revised the Standards of Professional Performance (SOPP) for RDNs in MFNS for 3 levels of practice: competent, proficient, and
expert. The SOPP describes 6 domains that focus on professional performance: Quality in Practice, Competence and Accountability,
Provision of Services, Application of Research, Communication and Application of Knowledge, and Utilization and Management of
Resources. Indicators outlined in the SOPP depict how these standards apply to practice. The standards and indicators for RDNs in MFNS
are written with the leader in mind—to support an individual in a leadership role or who has leadership aspirations. The SOPP is
intended to be used by RDNs for self-evaluation to assure competent professional practice.
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مقاله انگلیسی |
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Two Decades of AI4NETS - AI/ML for Data Networks: Challenges & Research Directions
دو دهه AI4NETS - AI / ML برای شبکه های داده: چالش ها و دستورالعمل های تحقیق-2020 The popularity of Artificial Intelligence (AI) –
and of Machine Learning (ML) as an approach to AI, has
dramatically increased in the last few years, due to its outstanding
performance in various domains, notably in image,
audio, and natural language processing. In these domains, AI
success-stories are boosting the applied field. When it comes
to AI/ML for data communication Networks (AI4NETS), and
despite the many attempts to turn networks into learning agents,
the successful application of AI/ML in networking is limited.
There is a strong resistance against AI/ML-based solutions, and
a striking gap between the extensive academic research and the
actual deployments of such AI/ML-based systems in operational
environments. The truth is, there are still many unsolved complex
challenges associated to the analysis of networking data through
AI/ML, which hinders its acceptability and adoption in the
practice. In this positioning paper I elaborate on the most
important show-stoppers in AI4NETS, and present a research
agenda to tackle some of these challenges, enabling a natural
adoption of AI/ML for networking. In particular, I focus the
future research in AI4NETS around three major pillars: (i) to
make AI/ML immediately applicable in networking problems
through the concepts of effective learning, turning it into a useful
and reliable way to deal with complex data-driven networking
problems; (ii) to boost the adoption of AI/ML at the large scale
by learning from the Internet-paradigm itself, conceiving novel
distributed and hierarchical learning approaches mimicking the
distributed topological principles and operation of the Internet
itself; and (iii) to exploit the softwarization and distribution of
networks to conceive AI/ML-defined Networks (AIDN), relying on
the distributed generation and re-usage of knowledge through
novel Knowledge Delivery Networks (KDNs). Index Terms: Machine Learning | Artificial Intelligence | Data Communication Networks | Data-driven networking | Knowledge Delivery Networks (KDNs) | AI/ML-defined networking (AIDN) |
مقاله انگلیسی |
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الگوریتم تکاملی چند هدفه 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 |
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Multi-energy-storage energy management with the robust method for distribution networks
مدیریت انرژی ذخیره سازی چند منظوره با روش قوی برای شبکه های توزیع-2020 The randomness, volatility and anti-peaking characteristic from distributed renewable energy generation rise
great challenges for the safe and economic operation of the distribution networks (DNs). To address this problem,
this paper proposes a novel multi-energy-storage energy management system (EMS) to co-optimize the
electricity-driven mobile energy storage (MES) and inverter air-conditioning (AC)-based thermal energy storage
(TES). To facilitate the energy management of the DN, the MES that considers the delay factors and the TES that
regulates reactive power have been developed into a unified analytic model capable of charging and discharging.
In addition, considering the impact of the forecasting uncertainties and the risk-aversion of the dispatcher, a
novel robust optimization method is proposed to obtain more accurate “worst scenario”. The dispatching model
is then converted into a mixed integer second-order cone programming problem (MI-SOCP) and a mixed integer
linear programming problem (MILP), and linearized techniques and an iteration method are used to efficiently
solve these problems. Simulation studies on a 41-node DN in Ontario indicate that the operational cost and
power loss of the DN can be reduced by no less than 1% and 8% using the proposed EMS, respectively, while a
safer voltage level with a voltage deviation of 5% can be obtained. The results confirm the effectiveness of the
MES and TES for peak shaving, valley filling and voltage supporting. Keywords: Distribution network | Mobile energy storage | Thermal energy storage | Energy management system | Iterative method |
مقاله انگلیسی |
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Data-driven software defined network attack detection : State-of-the-art and perspectives
تشخیص حمله به شبکه تعریف شده نرم افزار داده محور: حالت پیشرفته و چشم انداز-2020 SDN (Software Defined Network) has emerged as a revolutionary technology in network, a substantial amount of researches have been dedicated to security of SDNs to support their various applications. The paper firstly analyzes State-of-the-Art of SDN security from data perspectives. Then some typical network attack detection (NAD) methods are surveyed, in- cluding machine learning based methods and statistical methods. After that, a novel tensor based network attack detection method named tensor principal component analysis (TPCA) is proposed to detect attacks. After surveying the last data-driven SDN frameworks, a ten- sor based big data-driven SDN attack detection framework is proposed for SDN security. In the end, a case study is illustrated to verify the effectiveness of the proposed framework. Keywords: Network attack detection | Data-driven | Tensor | Network security | Software defined network (SDN) |
مقاله انگلیسی |
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The use of the Reynolds force vector in a physics informed machine learning approach for predictive turbulence modeling
استفاده از بردار نیرو رینولدز در یک روش یادگیری ماشین با علم به فیزیک برای مدل سازی تلاطم پیش بینی-2019 Data-driven turbulence modeling is receiving considerable attention specially when Direct Numerical Simulations (DNS) are the physics-informed learning environment and Reynolds average Navier–Stokes (RANS) simulations are the injected environment. A caveat of such approach, is that some studies indicate the existence of an intrinsic error in the Reynolds stress tensor provided by reputable DNS databases that, although small, lead to a reconstructed mean velocity field with a flagrant inaccuracy. This fact imposes a huge challenge in data-driven and traditional RANS models and is becoming a concern in the very recent literature. In the present work, we propose to replace the Reynolds stress tensor by its divergence, the Reynolds force vector , as a target for the machine learning technique. Since the Reynolds force vector can be computed from first order statistics, this estimate is not contaminated by the errors associated with applying the divergence onto the Reynolds stress tensor available in the DNS databases, circumventing the problem exposed above. The turbulent flow through a square duct was chosen as the case to be ana- lyzed. Employing a κ–RANS model as injection environment, the non-persistence-of-straining tensor to compose the set of inputs, and a neural network architecture as the ML technique to bridge the injected and learning environments, we compared the proposed strategy with the approach commonly used in the literature, i.e. to correct the Reynolds stress tensor. The results demonstrate that the Reynolds force vector correction is able to reconstruct the mean velocity field with a higher fidelity with respect to the DNS data. Keywords: Reynolds force vector | Turbulent flows | Machine learning |
مقاله انگلیسی |
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TD-Root: A trustworthy decentralized DNS root management architecture based on permissioned blockchain
TD-Root: یک مدیریت معماری ریشه غیرمتمرکز DNS قابل اعتماد و مبتنی بر بلاکچین مجاز-2019 DNS root faces security vulnerabilities and trust risks due to centralized management architecture.
Several related schemes have been proposed to alleviate the security vulnerabilities and trust risks.
However, there are still some open issues, including tamper-proofing, security, deployability, etc. To
address these issues, this paper presents a trustworthy decentralized DNS root management architecture
called TD-Root based on permissioned blockchain. TD-Root is a tamper-proofing architecture
and can tolerate one-third of malicious root servers behaving arbitrarily. Different from the current
centralized distribution method, every root server maintains the consistent same root zone file through
consensus algorithm, eliminating the security vulnerabilities and trust risks in current centralized
management architecture. Correspondingly, we design a novel consensus algorithm, in which credence
value and penalty mechanism are introduced to ensure the strong consistency, scalability, and security
of TD-Root. Furthermore, a compatible deployment scheme which optimizes the lookup performance
based on the blockchain data structure is proposed, reducing the deployment complexity and difficulty.
Finally, TD-Root is implemented in Golang and validated through simulation using Google Cloud. Keywords: DNS root | Trustworthy | Decentralized | Tamper-proofing | Blockchain |
مقاله انگلیسی |
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Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers
پتانسیل ها، گرایشات و چشم اندازها در روشهای لبه ای: مراکز داده ای مات، تکه ابر، لبه ای سیار و میکرو-2018 Advancements in smart devices, wearable gadgets, sensors, and communication paradigm have enabled the vision of smart cities, pervasive healthcare, augmented reality and interactive multimedia, Internet of Every Thing (IoE), and cognitive assistance, to name a few. All of these visions have one thing in common, i.e., delay sensitivity and instant response. Various new technologies designed to work at the edge of the network, such as fog computing, cloudlets, mobile edge computing, and micro data centers have emerged in the near past. We use the name ``edge computing for this set of emerging technologies. Edge computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of ``being near to the users or terminal devices. Edge computing aims to bring cloud resources and services at the edge of the network, as a middle layer between end user and cloud data centers, to offer prompt service response with minimal delay. Two major aims of edge computing can be denoted as: (a) minimize response delay by servicing the users’ request at the network edge instead of servicing it at far located cloud data centers, and (b) minimize downward and upward traffic volumes in the network core. Minimization of network core traffic inherently brings energy efficiency and data cost reductions. Downward network traffic can be minimized by servicing set of users at network edge instead of service providers data centers (e.g., multimedia and shared data) Content Delivery Networks (CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud. This survey presents a detailed overview of potentials, trends, and challenges of edge computing. The survey illustrates a list of most significant applications and potentials in the area of edge computing. State of the art literature on edge computing domain is included in the survey to guide readers towards the current trends and future opportunities in the area of edge computing.
keywords: Edge computing| Fog computing| Internet of Things |
مقاله انگلیسی |
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داده های مربوط به غلظت فلوراید و ارزیابی ریسک بهداشت آب آشامیدنی استان خراسان رضوی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 8 در حالی که فلوراید (F) آنیون ضروری برای حفظ سلامت بدن انسان است، مصرف زیاد فلوراید می تواند منجر به مشکلات جدی سلامتی شود. نظارت بر میزان فلوراید آب آشامیدنی در مسیر اصلی مصرف فلوراید ، یک عامل کلیدی در پیشگیری از پیامدهای منفی بهداشتی آن است. در این مقاله به سطوح فلوراید در شبکه های توزیع آب آشامیدنی استان خراسان رضوی دسترسی داریم که طی 2016 تا 2017 جمع آوری شده است. محاسبه خطر ابتلا به سرطان ناشی از فلوراید در مناطق شهری و روستایی نیز با محاسبه¬ی مصرف روزانه مزمن (CDI) و فاکتور خطر (HQ) برای بزرگسالان و کودکان انجام شد. نمونه برداری از شبکه توزیع آب آشامیدنی در 112 منطقه مختلف خراسان رضوی صورت گرفت و غلظت فسفر ، با روش استاندارد SPADNS تعیین شد. حداقل سطح فلوراید در نمونه های شهرداری و روستایی به ترتیب 0.09 و 0.16 میلی گرم در لیتر، حداکثر آن نیز به ترتیب 1.7 و 1.1 و میانگین آن به ترتیب 0.74 و 0.59 میلی گرم در لیتر است. میانگین مقادیر میانگین CDI فلوراید در نمونه های شهری برای مردان، زنان و کودکان به ترتیب 2-10 × 1.3، 4-10 × 3.34 و 6-10 × 6.56 میلی گرم/کیلوگرم/ روز بدست امد. CDIبرای نمونه های روستایی به ترتیب برای مردان، زنان و کودکان به ترتیب2-10 × 1.51، 4-10 × 3.34 و 6-10 × 8.56 میلی گرم/کیلوگرم/ روز بدست آمد. میانگین HQ فلوراید برای مردان، زنان و کودکان در نمونه های شهری و روستایی به ترتیب 1-10 × 2.17، 3-10 × 5.56 ، 4-10 × 1.43 و 1-10 × 2.44، 3-10 × 6.26 ، 4-10 × 1.61 است. به منظور جلوگیری از خطرات بالقوه سلامتی در مناطق دارای HQ>1 به استراتژی مناسب برای کاهش سطح فلوراید آب آشامیدنی نیاز است.
کليدواژه: فلورايد | ارزيابي ريسك سلامتی | آب آشاميدني | خراسان رضوي |
مقاله ترجمه شده |
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Optimal Decision Making for Big Data Processing at Edge-Cloud Environment: An SDN Perspective
تصمیم گیری بهینه برای پردازش داده های بزرگ در محیط لبه-ابر: چشم انداز SDN-2018 With the evolution of Internet and extensive usage of smart devices for computing and storage, cloud computing has become popular. It provides seamless services
such as e-commerce, e-health, e-banking, etc., to the end
users. These services are hosted on massive geodistributed
data centers (DCs), which may be managed by different service providers. For faster response time, such a data explosion creates the need to expand DCs. So, to ease the load on DCs, some of the applications may be executed on the edge
devices near to the proximity of the end users. However,
such a multiedge-cloud environment involves huge data
migrations across the underlying network infrastructure,
which may generate long migration delay and cost. Hence,
in this paper, an efficient workload slicing scheme is proposed for handling data-intensive applications in multiedgecloud environment using software-defined networks (SDN).
To handle the inter-DC migrations efficiently, an SDN-based
control scheme is presented, which provides energy-aware
network traffic flow scheduling. Finally, a multileader multifollower Stackelberg game is proposed to provide costeffective inter-DC migrations. The efficacy of the proposed
scheme is evaluated on Google workload traces using various parameters. The results obtained show the effectiveness of the proposed scheme.
Index Terms: Cloud data centers, edge computing, energy efficiency, software-defined networks (SDNs), Stackel berg game |
مقاله انگلیسی |