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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A dynamic model of knowledge management in innovative technology companies: A case from the energy sector
یک مدل پویا مدیریت دانش در شرکت های فن آوری نوآورانه: یک مورد از بخش انرژی-2021 This paper presents fresh insights into how medium to large innovative technology companies in the
energy business evolve their knowledge management (KM) capability. To date existing models of KM
have been static, while this work provides a more dynamic approach. The primary data is analysed using
a combination of an operational research (OR) approach (causal mapping) with a well-established generic
qualitative research method (the Gioia method). This paper contributes to KM literature by developing a
dynamic model of KM, which shows how KM capability evolves over time within an organisation. In
this model, KM evolves from managing explicit knowledge through knowledge sharing to creating new
knowledge. Such understanding of KM as a process can help managers in decision making with respect
to both KM and innovation activities.
© 2020 Elsevier B.V. All rights reserved. keywords: سیستم های مبتنی بر دانش | مدیریت دانش | نوآوری | به اشتراک گذاری دانش | نقشه برداری علمی | Knowledge-based systems | Knowledge management | Innovation | Knowledge sharing | Causal mapping |
مقاله انگلیسی |
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Reliability assessment of measurement accuracy for FBG sensors used in structural tests of the wind turbine blades based on strain transfer laws
ارزیابی قابلیت اطمینان از دقت اندازه گیری سنسورهای FBG مورد استفاده در تست های ساختاری تیغه های توربین بادی بر اساس قوانین انتقال فشار-2020 FBG sensors are often packaged within composites before they are pasted on the blade surface,
and many studies have shown that the materials, fatigue properties, geometric parameters, etc. of
intermediate layer have influences on the measuring accuracy of the FBG sensors. Thus, this
paper established an reliability calculation model based on strain transfer efficiency for the
measuring accuracy of FBG sensors packaged by composites, analyzed the influences of material
properties and geometric parameters of the adhesive layer on the performance of FBG sensors
based on finite element analysis (FEA) method, and then compared the differences of strain
transfer efficiency and reliability of the FBG sensors under different load conditions. The results
show that the bond length and the bond thickness of the adhesive layer have greater influences
on the performance of the FBG sensors compared with other parameters, both the strain transfer
efficiency and the reliability of the FBG sensors will reduce over time under suddenly applied
load and increase with increasing frequency of the alternating load. Keywords: FBG sensors | Reliability assessment | Strain transfer law | Static load | Suddenly applied load | Alternating load |
مقاله انگلیسی |
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مدلسازی و بررسی تأثیر اینرسی آسنکرون موتور القایی بر پاسخ فرکانسی سیستم قدرت
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 29 افزون بر اینرسی سنکرون، اینرسی آسنکرون موتور القایی نیز بطور گسترده در سیستم¬های قدرت وجود دارد، اما تأثیر آن بر پاسخ فرکانسی سیستم نیز به خوبی مورد بررس ی قرار نگرفته است. یک مدل تابع انتقال ساده¬شده برای موتور القایی براساس مدل دقیق به دست آمد. این مدل به عنوان بخش میراشونده¬ی توان مصرفی در انحراف فرکانسی شناخته می¬شود. سه ویژگی کلیدی مدل اینها هستند: بخش آنی، بخش حالت ماندگار و ثابت زمانی میراشونده. عوامل اثرگذار آنها نیز مورد بررسی قرار گرفتند. سپس این مدل به مدل پاسخ فرکانسی سیستم افزوده شد تا تأثیر موتور القایی بررسی شود. سه عامل کلیدی موتور القایی که بر پاسخ فرکانسی اثر می¬گذارند، اینها هستند: حساسیت گشتاور الکترومغناطیسی به لغزش، حساسیت گشتاور بار مکانیکی به سرعت روتور، و اینرسی. اثرات آنها به صورت تحلیلی آنالیز می¬شوند. اینرسی آسنکرون صرفاً بر انحراف فرکانسی بیشینه تأثیر می¬گذارد، و اینرسی بزرگ موجب کاهش انحراف فرکانسی بیشینه می¬شود. بااینحال، نادیده گرفتن اینرسی آسنکرون و نمایش موتور القایی با مدل استاتیک با در نظر
گرفتن حساسیت فرکانسی حالت ماندگار، قابل قبول است. نتایج به دست آمده، بر اساس نتایج شبیه-سازی، اعتبارسنجی می¬شوند.
کلیدواژه: اینرسی آسنکرون | انحراف فرکانسی | پاسخ فرکانسی | موتور القایی | نرخ تغییر فرکانس |
مقاله ترجمه شده |
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تست بارهای استاتیک از یک نسب منظری بالا به جعبه بال دو-رشته ای
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 21 - تعداد صفحات فایل doc فارسی: 29 تست بارهای استاتیک بر روی جعبه بال با نسبت ابعادی بالا و ۳۹ فوتی متشکل از پوسته های دو رشته ای (Tow-steered) فیبر کربن انجام شد که برای بازده سوخت هواپیما تحت بارهای آیروالاستیک مناسب بودند. این مقاله آزمایشی به عنوان بال تایلور غیر فعال (PAT) در نظر گرفته شده است. تا به امروز، بال PAT، که نسبت ابعاد آن ۱۳.۵ است، بزرگترین جعبه بال طراحی شده و برای استفاده از الیاف کربن با جهت متغیر در طول دهانه بال ساخته شده است. در طول آزمایش، بارهای نقطهای توزیع شده به جعبه بال اعمال شدند تا هر دو بارهای مانور را شبیهسازی کنند. برای تعیین موقعیت محور خمشی جعبه بال، بارهای نقطهای جداگانه اعمال شدند. پاسخ سراسری بال (اندازهگیری جابجایی و چرخش) در مقایسه با پیشبینیهای مدل المان محدود، روندهای مشابهی را نشان داد، گرچه هنگامی که مقادیر واقعی بین مدل و آزمایش مقایسه شدند، اختلاف تا ۱۷ % مشاهده شد. بر این اساس نتیجه گرفته شد که شرایط مرزی و ویژگیهای غیرساختاری جعبه - بال احتمالا دلیل ناسازگاری هستند. پاسخ محلی جعبه بال (اندازهگیری کرنش)، که کمتر تحتتاثیر عوامل غیر مرتبط با دو رشته ای بود، توافق خوبی با پیشبینیهای مدل نشان داد، تکنیکهای مدلسازی به کار گرفته شده برای کامپوزیت دو رشته ای را کنترل کرد. |
مقاله ترجمه شده |
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Experimental studies on reaction laws during the process of air injection into the oil reservoirs with low permeability
مطالعات تجربی در مورد قوانین واکنش در طی فرآیند تزریق هوا به مخازن روغن با نفوذپذیری کم-2020 The low permeability oil reservoirs are widely distributed in the world, but difficult to develop with the traditional
methods due to the low permeability and poor injection properties. Air injection technique not only has
the common characteristics of other gas injection techniques, but also can improve the quality of crude oil in situ
because of the oxidative ability of the injected air, with a unique advantage in developing low permeability
reservoirs. In order to study the reaction regularity between the hydrocarbon components in crude oil under
different conditions with the oxygen in the injected air, this experiment is designed for a low permeability
sandstone reservoir. In this experiment, one set of self-designed static low-temperature oxidation equipment
(including injection pump and high-temperature high–pressure reactor with sampling outlets) has been used.
Through analysis of the results of the chromatography for over 60 oil and gas samples, conclusions can be made
that after low temperature oxidation (LTO) reactions between the crude oil and the oxygen in the injected air, the
concentration of the heavy components in crude oil decreased, and the concentration of the light components
increased. By injecting oxygen-reduced air with 8% of oxygen, the concentration of the heavy components of
crude oil in the target reservoir reduced most obviously. The formation water and associated gas can hinder the
pyrolysis of heavy components, while the minerals in the formation can accelerate the pyrolysis process. The
relationship between the carbon atoms number in each of the most consumed hydrocarbon molecule and
different LTO reaction conditions has been proposed. According to the sensitivity analysis, it is believed that the
influence of the formation water on the LTO reactions is greater than that of minerals in the formation, and
influence of associated gas is less than that of minerals. Finally, combined the related physical and chemical
theories, a LTO chemical reaction equation has been put forward for the targeted low permeability oil reservoir.
The conclusions of the studies can provide some references for the indoor experiments and field applications of
air injection in the future. Keywords: Low permeability sandstone oil reservoir | Air injection | Static experiments | Low temperature oxidation | Formula |
مقاله انگلیسی |
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Static malware detection and attribution in android byte-code through an end-to-end deep system
شناسایی بدافزارهای استاتیکی و انتساب در بایت کد اندرویدی از طریق یک سیستم عمیق انتها به انتها-2020 Android reflects a revolution in handhelds and mobile devices. It is a virtual machine based, an
open source mobile platform that powers millions of smartphone and devices and even a larger no.
of applications in its ecosystem. Surprisingly in a short lifespan, Android has also seen a colossal
expansion in application malware with 99% of the total malware for smartphones being found in
the Android ecosystem. Subsequently, quite a few techniques have been proposed in the literature
for the analysis and detection of these malicious applications for the Android platform. The increasing
and diversified nature of Android malware has immensely attenuated the usefulness of prevailing
malware detectors, which leaves Android users susceptible to novel malware. Here in this paper,
as a remedy to this problem, we propose an anti-malware system that uses customized learning
models, which are sufficiently deep, and are ’End to End deep learning architectures which detect
and attribute the Android malware via opcodes extracted from application bytecode’. Our results
show that Bidirectional long short-term memory (BiLSTMs) neural networks can be used to detect
static behavior of Android malware beating the state-of-the-art models without using handcrafted
features. For our experiments in our system, we also choose to work with distinct and independent
deep learning models leveraging sequence specialists like recurrent neural networks, Long Short Term
Memory networks and its Bidirectional variation as well as those are more usual neural architectures
like a network of all connected layers(fully connected), deep convnets, Diabolo network (autoencoders)
and generative graphical models like deep belief networks for static malware analysis on Android. To
test our system, we have also augmented a bytecode dataset from three open and independently
maintained state-of-the-art datasets. Our bytecode dataset, which is on an order of magnitude large,
essentially suffice for our experiments. Our results suggests that our proposed system can lead to
better design of malware detectors as we report an accuracy of 0.999 and an F1-score of 0.996 on a
large dataset of more than 1.8 million Android applications. Keywords: End-to-end architecture | Malware analysis | Deep neural networks | Android and big data |
مقاله انگلیسی |
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Kernel-based template attacks of cryptographic circuits using static power
حملات الگوی مبتنی بر هسته از مدارهای رمزنگاری شده با استفاده از توان استاتیک-2019 Side-channel attacks using static power have been shown to be successful against cryptographic circuits in
different environments. This class of attacks exploits the power leakage when the circuit is in a static state, during
which the power leakage is expected to be a fixed value. Due to the low signal-to-noise ratio of static power,
usually more traces are needed for a static power attack to reach the same success rate as a dynamic power attack.
The probabilistic distribution pattern of static power varies significantly in different devices, which further poses
challenges to the accurate modeling of static power. In this paper we propose non-parametric template attacks
which use a kernel methodology to improve the accuracy of modeling static power consumption. The proposed
template attacks are tested using transistor-level simulations of circuits designed with a 45-nm standard cell
library. Our test results show that our approach improves the success rate of template attacks using static power
in cases where the distribution of static power consumption cannot be accurately modeled by Gaussian models. Keywords: Cryptographic circuits | Block ciphers | Side-channel attacks | Power analysis attacks | Static power | Template attacks |
مقاله انگلیسی |
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Personalised modelling with spiking neural networks integrating temporal and static information
مدل سازی شخصی شبکه های عصبی spiking با یکپارچه سازی اطلاعات موقت و استاتیک-2019 This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction
and pattern recognition when both static and dynamic/spatiotemporal features are presented
in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal
selection of neighbouring samples to an individual with respect to the integration of both static
(vector-based) and temporal individual data. The most relevant samples to an individual are selected
to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to
capture the space and time association patterns. The generated time-dependant patterns resulted in
a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling
and conventional methods. In addition, the PSNN models can support interpretability by creating
personalised profiling of an individual. This contributes to a better understanding of the interactions
between features. Therefore, an end-user can comprehend what interactions in the model have led
to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several
real-life health applications, where different data domains describe a person’s health condition. The
system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the
investigation of individual response to treatment and (2) prediction of risk of stroke with respect to
temporal environmental data. For both datasets, besides the temporal data, static health data were also
available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN
clustering parameters, are optimised for each individual Keywords: Integrated data domains | Prediction | Classification | Personalised modelling | Spiking neural networks | Pattern recognition |
مقاله انگلیسی |
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بهبود کیفیت توان و تزریق توان PV توسط DSTATCOM با کنترل ولتاژ متغیر لینک dc RSC-MLC
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 34 این مطالعه روشی را برای بهینهسازی ولتاژ لینک dc جبران کننده استاتیکی توزیع براساس تقاضای جبران بار با استفاده از مبدل کاهش یافته چند سطحی چند سوئیچه (RSC-MLC) با ادغام سیستم فتوولتائیک (PV) پشنهاد می کند . روش پیشنهادی قادر به جبران توان راکتیو ، نامتعادلی و هارمونیک های مورد نیاز برای سه فاز نامتعادل و غیرخطی است که به سمت توزیع متصل شده اند و منجر به بهبود کیفیت توان می شود. هم چنین قادر به فراهم نمودن توان اکتیو متحمل شده بار است و بنابراین از بارگیری بیش از حد منبع در زمان نیاز جلوگیری میکند . در طول بارهای زمان کم مصرف (off-peak) ، ولتاژ لینک DC میتواند به مقدار پایینتری آورده شود ، تا تنش ولتاژ بین سوییچهای اینورتر را کاهش دهد و تلفات سوییچینگ را به حداقل برساند. ولتاژ dc-link متغیر با استفاده از RSC-MLC تامین می شود ، که نیاز به تامین ولتاژ DC دارد. این روش از منابع انرژی تجدیدپذیر مانند سلولهای خورشیدی به عنوان منبع ولتاژ DC استفاده میکند. ولتاژ خروجی پنل PV با استفاده از مبدل تقویت کننده بوست گین بالا به مقدار بیشتری افزایش می یابد و به RSC-MLC داده می شود. ردیابی نقطه ماکزیمم توان پنل های PV با استفاده از الگوریتم Perturb و Observe بدست می آید. نتایج از طریق شبیه سازی و مطالعات تجربی تأیید شده است.
کلمات کلیدی: ولتاژ DC-link | DSTATCOM | کیفیت برق | سیستم PV | مبدل چند سطحی تعداد سوئیچ کاهش یافته (RSC-MLC) | تلفات سوئیچینگ |
مقاله ترجمه شده |
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Decoding and targeting the molecular basis of MACC1-driven metastatic spread: Lessons from big data mining and clinical-experimental approaches
رمزگشایی و هدف قرار دادن اساس مولکولی گسترش متاستاتیک MACC1 محور: درسهایی از کاوش داده های بزرگ و رویکردهای بالینی-تجربی-2019 Metastasis remains the key issue impacting cancer patient survival and failure or success of cancer therapies.
Metastatic spread is a complex process including dissemination of single cells or collective cell migration, penetration
of the blood or lymphatic vessels and seeding at a distant organ site. Hundreds of genes involved in
metastasis have been identified in studies across numerous cancer types. Here, we analyzed how the metastasisassociated
gene MACC1 cooperates with other genes in metastatic spread and how these coactions could be
exploited by combination therapies: We performed (i) a MACC1 correlation analysis across 33 cancer types in the
mRNA expression data of TCGA and (ii) a comprehensive literature search on reported MACC1 combinations and
regulation mechanisms. The key genes MET, HGF and MMP7 reported together with MACC1 showed significant
positive correlations with MACC1 in more than half of the cancer types included in the big data analysis.
However, ten other genes also reported together with MACC1 in the literature showed significant positive
correlations with MACC1 in only a minority of 5 to 15 cancer types. To uncover transcriptional regulation mechanisms that are activated simultaneously with MACC1, we isolated pan-cancer consensus lists of 1306
positively and 590 negatively MACC1-correlating genes from the TCGA data and analyzed each of these lists for
sharing transcription factor binding motifs in the promotor region. In these lists, binding sites for the transcription
factors TELF1, ETS2, ETV4, TEAD1, FOXO4, NFE2L1, ELK1, SP1 and NFE2L2 were significantly enriched,
but none of them except SP1 was reported in combination with MACC1 in the literature. Thus, while
some of the results of the big data analysis were in line with the reported experimental results, hypotheses on
new genes involved in MACC1-driven metastasis formation could be generated and warrant experimental validation.
Furthermore, the results of the big data analysis can help to prioritize cancer types for experimental
studies and testing of combination therapies. Keywords: MACC1 | Big data analyses | Cancer prognosis and prediction | Biomarker combination | Combinatorial therapy |
مقاله انگلیسی |