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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31 |
Identifying and mapping terrons in Denmark
شناسایی و نقشه برداری از زمینهای وحشی در دانمارک-2020 Spatial assessment of terroir is creating a new possibility for enhancement of high quality agro-food product and
to minimize negative environmental effects such as soil degradation and associated risks. The classification and
mapping of particular terroir units could be a competitive marketing tool with a major impact on farmers’
incomes. For this purpose, Carré and McBratney (2005) proposed the terron concept to establish combined soil
and landscape entities as the first investigative step to identify terroirs. The main objective of the present work
was to assemble various environmental factors (i.e. soil, terrain and climate), to identify and then to map terrons
in Denmark. First, for representing soil factors, a national soil spectral library was utilized to measure taxonomic
distances between 34 Danish reference soil profiles and the Danish national soil profile database (586 soil
profiles). Second, the terrain and climate factors for each soil profile location were then compiled as represented
by relative slope position, valley depth, valley bottom flatness, vertical distance to the channel network, number
of frost days, annual number of growing days, global solar radiation, and precipitation. Third, nine Danish terron
classes were established by fuzzy c-means clustering based on an integrated matrix including all soil, terrain and
climate factors whereby each terron class is characterized by soil, terrain and climate as a whole entity. Finally,
the spatial distribution of Danish terrons was mapped using Cubist regression rules. The results were compared
with a soil map derived from the same profile database. We concluded that the map of terrons described natural
environment quantitatively and formally in terms of soil, landscape and climatic information better than just a
soil class or soil attribute map. Further investigations are needed to discover whether the terron classes give
better predictions of landscape-dynamic processes and allow better management options than soil alone. This
study also demonstrated several advantages of using soil spectral data and ancillary data to identify and map
terrons. The next step will be to validate the terron map by incorporating crop yield data and social factors to
delineate natural Danish terroir units. Keywords: Terron | Vis-NIR | Digital soil mapping | Soils | Terrain | Climate |
مقاله انگلیسی |
32 |
Network properties of healthy and Alzheimer brains
خواص شبکه مغز سالم و آلزایمر-2020 The application of graph theory in diffusion weighted resonance magnetic images have
allowed the description of the brain as a complex network, often called structural network.
For many years, the small-world properties of brain networks have been studied
and reported. However, few studies have gone beyond of clustering and characteristic
path length. In this work, we compare the structural connection network of a healthy
brain and a brain affected by Alzheimer’s disease with artificial small-world networks.
Based on statistical analysis, we demonstrate how artificial networks can be constructed
using Newman–Watts procedure. The network quantifiers of both structural matrices
are identified inside a probabilistic valley. Despite of similarities between structural
connection matrices and artificial small-world networks, increased assortativity can be
found in the Alzheimer brain. Due to limited experimental data, we cannot define
a direct link between Alzheimer’s disease and assortativity. Nevertheless, we intend
to call attention for an important network quantifier that has been neglected. Our
results indicate that network quantifiers can be helpful to identify abnormalities in real
structural connections, for instance Alzheimer’s disease that disrupts the communication
among neurons. One of our main results is to show that the network indicators of
the Alzheimer brain are almost identical with the small-world network, except the
assortativity. Keywords: Network | Human brain | Alzheimer’s disease | Small-world |
مقاله انگلیسی |
33 |
یک سیستم پشتیبانی تصمیم گیری با استفاده از هوش مصنوعی مبتنی بر مدل کیفیت چند تصویر و کاربرد آن در طراحی رنگ
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 31 تصویر رنگ محصول نیازهای رنگ مصرف کنندگان را از طریق شناخت احساسات منتقل میکند . در این مقاله ، یک سیستم پشتیبانی تصمیمگیری براساس الگوریتم هوش مصنوعی ترکیبی پیشنهاد شدهاست . سیستم پیشنهادی به بررسی همبستگی داخلی بین تصویر رنگی و نیاز کاربران میپردازد . در سیستم پیشنهادی ، یک شبکه عصبی مصنوعی براساس تابع پایه شعاعی بکارگرفته می شود. مدل شبکه با بهینهسازی انبوه ذرات در ترکیب با استراتژی انطباقی و تئوری هرج و مرج آموزش داده میشود . مدل پیشنهادی تصاویر رنگی "uses " را پیشبینی میکند . سپس ، رنگها از رنگهای پیشبینیشده توسط خوشهبندی K - هارمونیک استخراج میشوند . نتایج تجربی نشان میدهد که سیستم پشتیبان تصمیم رنگ پیشنهادی در طراحی الگوی رنگ و ارائه راهنمایی تیوری برای تولید - طراحی رنگ امیدوار کننده است .
واژگان کاربردی: سیستم پشتیبانی از تصمیمگیری | هوش مصنوعی | طراحی محصول | تصاویر چند کاربردی |
مقاله ترجمه شده |
34 |
Mechanism of anti-inflammatory effects of volatile compounds ofAi pian based on network pharmacology, in vivo animal experiments,and GC–MS
مکانیسم اثرات ضد التهابی ترکیبات فرار بر اساس داروسازی شبکه ، آزمایشات داخل بدن حیوانات و GC-MS-2020 Ai pian (AP) is a well-known Miao national herb with resuscitative effects. However, pharmacologicaland clinical applications of AP are limited because its precise molecular mechanism remains unclear.This study was conducted to evaluate the anti-inflammatory activities of the volatile compounds of APin in vivo animal models and determine the molecular mechanism underlying the anti-inflammatoryeffects based on network pharmacology and molecular docking. We performed gas chromatography-mass spectrometric analysis of volatile compounds with chemometric methods, including hierarchicalclustering analysis and principal component analysis, to identify AP from different origins. Mouse modelsof xylene-induced ear edema were used to examine the in vivo anti-inflammatory activities of AP withcotton ball-granulation test. The mechanism of AP was determined by network pharmacology analysisand molecular docking. Significant differences in chemical constituents and percentage contents wereobserved among different habitats. We found that AP exerted potent anti-inflammatory effect, and thatmultiple targets and pathways were involved in this effect. These results provided a foundation for furthercomprehensive development and application of AP from Miao national herb. Keywords:Ai pian | Gas chromatography-mass spectrometry | Volatile compound | Anti-inflammatory | Network pharmacology | Molecular docking |
مقاله انگلیسی |
35 |
Improving response of wind turbines by pitch angle controller based on gain-scheduled recurrent ANFIS type 2 with passive reinforcement learning
بهبود پاسخ توربین های بادی توسط کنترل کننده زاویه گام بر اساس ANFIS تکرار شونده نوع 2 با یادگیری تقویتی غیرفعال-2020 In this paper, passive reinforcement learning (RL) solved by particle swarm optimization policy (PSOeP)
is used to handle an adaptive neuro-fuzzy inference system (ANFIS) type-2 structure with unsupervised
clustering for controlling the pitch angle of a real wind turbine (WT). The proposed control scheme is
based on gain-scheduled reinforcement learning recurrent ANFIS type 2 (GS-RL-RANFIST2) pitch angle
controller to maintain the rotor speed at its rated value while smoothing the output power and the
performance of the pitch angle system. The practical application of the proposed controller is evaluated
by using FAST tool for a real 600 kW WT equipped with a synchronous generator with a full-size power
converter (CART3, located at the National Renewable Energy Laboratory, NREL), whose results are
compared with those obtained by a gain corrected proportional integral (GC-PI) controller. The results
demonstrate that the GS-RL-RANFIST2, which sets the nonlinear characteristics of the system automatically
and waves more uncertainties in the windy conditions, allows to increase the energy capture
and smooth the output power fluctuation, and therefore, to improve the control and response of theWT. Keywords: Pith angle controller | Wind turbine | Gain-scheduled | ANFIS type-2 controller | Reinforcement learning (RL) | Unsupervised clustering |
مقاله انگلیسی |
36 |
Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods
شناسایی موقعیت داخلی بیماران برای هدایت مراقبت های مجازی: رویکرد هوش مصنوعی با استفاده از یادگیری ماشین و روش های دانش بنیان-2020 In a digitally enabled healthcare setting, we posit that an individual’s current location is pivotal for supporting
many virtual care services—such as tailoring educational content towards an individual’s current location, and,
hence, current stage in an acute care process; improving activity recognition for supporting self-management in a
home-based setting; and guiding individuals with cognitive decline through daily activities in their home.
However, unobtrusively estimating an individual’s indoor location in real-world care settings is still a challenging
problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and
require the individual’s discrete semantic location; i.e., it is the concrete type of an individual’s location (e.g., exam
vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of
activities. We utilized Machine Learning methods to accurately identify an individual’s discrete location, together
with knowledge-based models and tools to supply the associated semantics of identified locations. We
considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate
sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization
approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application
of our approach in two compelling healthcare use cases, and empirically validated our localization
approach at the emergency unit of a large Canadian pediatric hospital. Keywords: Virtual care | Ambient sensors | Indoor localization | Machine learning | Semantic web | eHealth platform | Data fusion | Self-management | Ambient assisted living | Activities of daily living |
مقاله انگلیسی |
37 |
Reinforcement learning framework for freight demand forecasting to support operational planning decisions
چارچوب یادگیری تقویتی پیش بینی تقاضای حمل بار برای پشتیبانی از تصمیمات برنامه ریزی عملیاتی-2020 Freight forecasting is essential for managing, planning operating and optimizing the use of resources.
Multiple market factors contribute to the highly variable nature of freight flows, which
calls for adaptive and responsive forecasting models. This paper presents a demand forecasting
methodology that supports freight operation planning over short to long term horizons. The
method combines time series models and machine learning algorithms in a Reinforcement
Learning framework applied over a rolling horizon. The objective is to develop an efficient
method that reduces the prediction error by taking full advantage of the traditional time series
models and machine learning models. In a case study applied to container shipment data for a US
intermodal company, the approach succeeded in reducing the forecast error margin. It also allowed
predictions to closely follow recent trends and fluctuations in the market while minimizing
the need for user intervention. The results indicate that the proposed approach is an effective
method to predict freight demand. In addition to clustering and Reinforcement Learning, a
method for converting monthly forecasts to long-term weekly forecasts was developed and tested.
The results suggest that these monthly-to-weekly long-term forecasts outperform the direct long
term forecasts generated through typical time series approaches. Keywords: Freight demand forecasting | Time series | Reinforcement learning | Rolling horizon |
مقاله انگلیسی |
38 |
Managing minority opinions in micro-grid planning by a social network analysis-based large scale group decision making method with hesitant fuzzy linguistic information
مدیریت نظرات اقلیت ها در برنامه ریزی خرد شبکه ای با استفاده از روش تصمیم گیری گروهی مقیاس بزرگ مبتنی بر تحلیل شبکه های اجتماعی با اطلاعات زبانی فازی مردد-2020 The growth of global electricity demand has put forward higher requirements for power distribution
networks. The high cost of the large-scale power system and the voice for the use of renewable
energy impel the birth of the micro-grid which plays a complementary role in the power generation of
large-scale power system. The construction of micro-grid planning is complex and many stakeholders’
opinions should be considered for a comprehensive evaluation. Furthermore, the development of social
big data techniques, such as e-marketplace and e-democracy, makes experts have social relationships
among them. This study aims to develop a consensus model to manage minority opinions for largescale
group decision making with social network analysis for micro-grid planning. To deal with the
vague and uncertain features in complex micro-grid planning problems, experts are supposed to use
hesitant fuzzy linguistic term sets to express their opinions. A social network analysis-based clustering
method is introduced to classify experts. Besides, in a large-scale group decision making problem, the
opinions of experts should be fully considered, especially the minority opinions. This model considers
the minority opinions in a micro-grid planning problem and provides an approach to manage these
opinions. Finally, we use an illustrative example concerning the micro-grid planning decision making
in Ali district in Tibet to demonstrate the effectiveness and practicability of the proposed model. Keywords: Micro-grid planning | Large-scale group decision making | Social network analysis | Minority opinions | Hesitant fuzzy linguistic term sets | Consensus |
مقاله انگلیسی |
39 |
A new fast search algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based check strategy
یک الگوریتم جستجوی سریع جدید برای همسایگان دقیق k-مبتنی بر استراتژی بررسی مبتنی بر مثلث-نابرابری بهینه-2020 The k-nearest neighbor (KNN) algorithm has been widely used in pattern recognition, regression,
outlier detection and other data mining areas. However, it suffers from the large distance computation
cost, especially when dealing with big data applications. In this paper, we propose a new fast search
(FS) algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based (OTI) check
strategy. During the procedure of searching exact k-nearest neighbors for any query, the OTI check
strategy can eliminate more redundant distance computations for the instances located in the marginal
area of neighboring clusters compared with the original TI check strategy. Considering the large space
complexity and extra time complexity of OTI, we also propose an efficient optimal triangle-inequalitybased
(EOTI) check strategy. The experimental results demonstrate that our proposed two algorithms
(OTI and EOTI) achieve the best performance compared with other related KNN fast search algorithms,
especially in the case of dealing with high-dimensional datasets Keywords: Exact k-nearest neighbors | Fast search algorithm | Clustering | Triangle inequality | Optimal check strategy |
مقاله انگلیسی |
40 |
Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example
کاوش مکانیسم طب سنتی چینی با رویکرد هوش مصنوعی با استفاده از یادگیری ماشین بدون نظارت برای شباهت عملکردی سلولی ترکیبات در شبکه های ناهمگن ، گرانول های XiaoErFuPi به عنوان مثال-2020 ‘Polypharmacology’ is usually used to describe the network-wide effect of a single compound, but traditional
Chinese medicine (TCM) has a polypharmacological effect naturally based on the ‘multi-components, multitargets
and multi-pathways’ principle. It is a challenge to investigate the polypharmacology mechanism of TCM
with multiple components. In this study, we used XiaoErFuPi (XEFP) granules as an example to describe an
unsupervised learning strategy for polypharmacology research of TCM and to explore the mechanism of XEFP
polypharmacology against multifactorial disease function dyspepsia (FD). Unsupervised clustering of compounds
based on similarity evaluation of cellular function fingerprints showed that compounds of TCM without similar
targets and chemical structure could also exert similar therapeutic effects on the same disease, as different
targets participate in the same pathway closely associated with the pathological process. In this study, we
proposed an unsupervised machine learning strategy for exploring the polypharmacology-based mechanism of
TCM, utilizing hierarchical clustering based on cellular functional similarity, to establish a connection from the
chemical clustering module to cellular function. Meanwhile, FDA-approved drugs against FD were used as references
for the mechanism of action (MoA) of FD. First, according to the compound-compound network built by
the similarity of cellular function of XEFP compounds and FDA-approved FD drugs, the possible therapeutic
function of TCM may represent a known mechanism of FDA-approved drugs. Then, as unsupervised learning,
hierarchical clustering of TCM compounds based on cellular function fingerprint similarity could help to classify
the compounds into several modules with similar therapeutic functions to investigate the polypharmacology
effect of TCM. Furthermore, the integration of quantitative omics data of TCM and approved drugs (from LINCS
datasets) provides more quantitative evidence for TCM therapeutic function consistency with approved drugs. A
spasmolytic activity experiment was launched to confirm vanillic acid activity to repress smooth muscle contraction;
vanillic acid was also predicted to be active compound of XEFP, supporting the accuracy of our strategy.
In summary, the approach proposed in this study provides a new unsupervised learning strategy for polypharmacological
research investigating TCM by establishing a connection between the compound functional
module and drug-activated cellular processes shared with FDA-approved drugs, which may elucidate the unique
mechanism of traditional medicine using FDA-approved drugs as references, facilitate the discovery of potential
active compounds of TCM and provide new insights into complex diseases. Keywords: Polypharmacology | Traditional Chinese medicine | Unsupervised clustering | Cellular function fingerprints | FDA-approved drugs | Functional dyspepsia |
مقاله انگلیسی |