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Circular supply chain management with large scale group decision making in the big data era: The macro-micro model
مدیریت زنجیره تأمین دایره ای با تصمیم گیری گروهی در مقیاس بزرگ در عصر داده های بزرگ: مدل خرد خرد-2021 Today, achieving the circular economy is a common goal for many enterprises and governments all around the world. In the big data era, decision making is well-supported and enhanced by a massive amount of data. In particular, large scale group decision making (LSGDM), which refers to the case in which a lot of decision makers join the decision making process, has emerged. Social network analyses are known to be relevant to LSGDM. In this paper, we examine the literature on LSGDM and highlight the current methodological advances in the area. We review the works focusing on applications of LSGDM. We study how big data can be used in circular supply chains. Based on the reviewed studies, we further construct the three-stage LSGDM CSCM micro framework as well as the five-step LSGDM CSCM macro framework (with a feedback loop) and form the Macro-Micro Model. We discuss how the Macro-Micro Model can help to support circular supply chain management (CSCM). We propose future research directions and areas. This paper contributes by being the first study uncovering systematically how LSGDM can be applied to support CSCM in the big data era using the Macro-Micro Model. Keywords: Large scale group decision making (LSGDM) | Circular supply chains | Research agenda | Literature review | Frameworks | Macro-micro model |
مقاله انگلیسی |
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“Familiar strangers” in the big data era: An exploratory study of Beijing metro encounters
"غریبه های آشنا" در عصر داده های بزرگ: یک مطالعه اکتشافی از برخورد مترو پکن-2020 Traditionally, familiar strangers are defined as those we encounter and observe repeatedly in the city but never
interact with. They are common to most urban dwellers. They also have various socioeconomic, sociopsychological
and public-policy implications, which have only been sporadically mentioned and/or examined in
existing studies across different disciplines. In this manuscript, we first summarize fragmental existing studies on
familiar strangers that are defined in the traditional manner based on “small data” such as survey responses.
Then we reconceptualize “familiar strangers” against the backdrop of the emergence and increased availability
of big and open data. Such familiar strangers are called “familiar strangers in the big data era” (FSiBDE). After
this, we have done the following: (a) synthesized and hypothesized factors influencing the distribution and
quantity of the FSiBDE; (b) conducted an empirical study in the context of Beijing to embody and operationalize
a special type of the FSiBDE among metro riders and to study its possible influencers. We find that across metro
stations, it is spatial structure, population distribution, and transport network that significantly influence the
count and odds of FSiBDE among millions of metro riders. In addition, the FSiBDE also can have important policy
and planning implications for operating metro services and managing metro station. Keywords: Familiar stranger | Big data era | Implications | Odds | Distribution | Beijing |
مقاله انگلیسی |
3 |
Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500m spatial resolution
سنجش از دور و سنجش اجتماعی برای سیستمهای اقتصادی اقتصادی: مطالعه مقایسه ای بین چراغ های شب و رسانه های اجتماعی مبتنی بر مکان در وضوح مکانی 500 متر-2020 With the advent of “social sensing” in the Big Data era, location-based social media (LBSM) data are increasingly
used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind
of LBSM data, geo-tagged tweets, into raster images at the 500m spatial resolution and compares them with the
new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS)
Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly
correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability
on estimating electric power consumption and better performance on assessing personal incomes and population
than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived
built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of
human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of
tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study
explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic
factors as an alternative to NTL images in the United States Keywords: Nighttime lights imagery | Geo-tagged tweets | Socioeconomic factors | Social sensing |
مقاله انگلیسی |
4 |
Forecasting crude oil price with multilingual search engine data
پیش بینی قیمت نفت خام با داده های موتور جستجو چند زبانه-2020 In the big data era, search engine data (SED) has presented new opportunities for
improving crude oil price prediction; however, the existing research were confined to
single-language (mostly English) search keywords in SED collection. To address such a
language bias and grasp worldwide investor attention, this study proposes a novel
multilingual SED-driven forecasting methodology from a global perspective. The proposed
methodology includes three main steps: (1) multilingual index construction, based on
multilingual SED; (2) relationship investigation, between the multilingual index and crude oil
price; and (3) oil price prediction, with the multilingual index as an informative predictor.
With WTI spot price as studying samples, the empirical results indicate that SED have a
powerful predictive power for crude oil price; nevertheless, multilingual SED statistically
demonstrate better performance than single-language SED, in terms of enhancing prediction
accuracy and model robustness. Keywords: Big data | Multilingual search engine index | Crude oil price forecasting | Google Trends | Artificial intelligence |
مقاله انگلیسی |
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DQPFS: Distributed quadratic programming based feature selection for big data
DQPFS: انتخاب ویژگی های مبتنی بر برنامه نویسی درجه دوم برای داده های بزرگ-2020 With the advent of the Big data, the scalability of the machine learning algorithms has become more
crucial than ever before. Furthermore, Feature selection as an essential preprocessing technique can
improve the performance of the learning algorithms in confront with large-scale dataset by removing
the irrelevant and redundant features. Owing to the lack of scalability, most of the classical feature
selection algorithms are not so proper to deal with the voluminous data in the Big Data era. QPFS is
a traditional feature weighting algorithm that has been used in lots of feature selection applications.
By inspiring the classical QPFS, in this paper, a scalable algorithm called DQPFS is proposed based on
the novel Apache Spark cluster computing model. The experimental study is performed on three big
datasets that have a large number of instances and features at the same time. Then some assessment
criteria such as accuracy, execution time, speed-up and scale-out are figured. Moreover, to study more
deeply, the results of the proposed algorithm are compared with the classical version QPFS and the
DiRelief, a distributed feature selection algorithm proposed recently. The empirical results illustrate
that proposed method has (a) better scale-out than DiRelief, (b) significantly lower execution time
than DiRelief, (c) lower execution time than QPFS, (d) better accuracy of the Naïve Bayes classifier in
two of three datasets than DiRelief. Keywords: Big data | Apache Spark | Feature selection | Feature ranking | Quadratic programming |
مقاله انگلیسی |
6 |
Big data analytics as an operational excellence approach to enhance sustainable supply chain performance
تجزیه و تحلیل داده های بزرگ به عنوان یک رویکرد برتری عملیاتی برای افزایش عملکرد پایدار زنجیره تأمین-2020 Operations management is a core organizational function involved in the management of activities to produce
and deliver products and services. Appropriate operations decisions rely on assessing and using information; a
task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along
with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics
and organizational learning in support of sustainable supply chain management outcomes. The current study
uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational
excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the
emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least
Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics
management capabilities have a strong and significant effect on innovative green product development and
sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect
on employee development and sustainable supply chain outcomes. Innovation and learning performance affect
sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A
contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain
outcomes in the mining industry, based on big data analytics capabilities. Keywords: Big data analytics | Operational excellence | Dynamic capability view | Supply chain sustainability | Learning performance |
مقاله انگلیسی |
7 |
A novel deep learning method based on attention mechanism for bearing remaining useful life prediction
یک روش جدید یادگیری عمیق مبتنی بر مکانیسم توجه برای تحمل پیش بینی عمر مفید باقیمانده-2020 Rolling bearing is a key component in rotation machine, whose remaining useful life (RUL) prediction
is an essential issue of constructing condition-based maintenance (CBM) system. However, recent
data-driven approaches for bearing RUL prediction still require prior knowledge to extract features,
construct health indicate (HI) and set up threshold, which is inefficient in the big data era. In this paper,
a pure data-driven method for bearing RUL prediction with little prior knowledge is proposed. This
method includes three steps, i.e., features extraction, HI prediction and RUL calculation. In the first step,
five band-pass energy values of frequency spectrum are extracted as features. Then, a recurrent neural
network based on encoder–decoder framework with attention mechanism is proposed to predict HI
values, which are designed closely related with the RUL values in this paper. Finally, the final RUL
value can be obtained via linear regression. Experiments carried out on the dataset from PRONOSTIA
and comparison with other novel approaches demonstrate that the proposed method achieves a better
performance. Keywords: Remaining useful life prediction | Recurrent neural network | Attention mechanism |
مقاله انگلیسی |
8 |
What can the news tell us about the environmental performance of tourist areas? A text mining approach to China’s National 5A Tourist Areas
چه خبرهایی می تواند از عملکرد زیست محیطی مناطق توریستی به ما بگوید؟ یک رویکرد استخراج متن به مناطق گردشگری ملی 5A در چین-2020 This study aims to evaluate the environmental performance status of tourist areas and explore the influencing
factors using text mining of web news. As the leading tourist attractions in China, the National 5A Tourist Areas
face severe environmental challenges, and were hence chosen to exemplify the rapid assessment approach in the
big data era. This study used over 1,300,000 words from online news sources and assessed the environmental
performance of 120 National 5A Tourist Areas to conclude that (1) water is the most impacted environmental
resource; (2) tourist area environmental performance can be classified into (a) environmental pollution, (b)
ecological and resource pressure, (c) landscape character issues and (d) others; and (3) the primary factors
influencing the environment are tourism and business operating activities, with the tourist areas’ environmental
performance types being strongly related to their spatial locations and weakly related to their resource types. By
comparing the environmental performance types in this paper with related research the effectiveness of this
study’s approach is validated. These conclusions and this approach can provide guidelines and tools for environmental
assessment and promote tourist area management Keywords: Environmental impact assessment | Environmental pollution | Tourist environment | Web content | Text mining | China’s National 5A Tourist Areas |
مقاله انگلیسی |
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Frei-Chen bases based lossy digital image compression technique
پایگاه های Frei-Chen مبتنی بر تکنیک فشرده سازی تصویر دیجیتال از دست رفته-2020 In the big data era, image compression is of significant importance in today’s world. Importantly, compression
of large sized images is required for everyday tasks; including electronic data communications
and internet transactions. However, two important measures should be considered for any compression
algorithm: the compression factor and the quality of the decompressed image. In this paper, we use Frei-
Chen bases technique and the Modified Run Length Encoding (RLE) to compress images. The Frei-Chen
bases technique is applied at the first stage in which the average subspace is applied to each 3 3 block.
Those blocks with the highest energy are replaced by a single value that represents the average value of
the pixels in the corresponding block. Even though Frei-Chen bases technique provides lossy compression,
it maintains the main characteristics of the image. Additionally, the Frei-Chen bases technique
enhances the compression factor, making it advantageous to use. In the second stage, RLE is applied to
further increase the compression factor. The goal of using RLE is to enhance the compression factor without
adding any distortion to the resultant decompressed image. Integrating RLE with Frei-Chen bases
technique, as described in the proposed algorithm, ensures high quality decompressed images and high
compression rate. The results of the proposed algorithms are shown to be comparable in quality and performance
with other existing methods. Keywords: Big data problem | Compression factor | Frei-Chen basis | Run Length Encoding |
مقاله انگلیسی |
10 |
Mega-Archive and the EURONEAR tools for data mining world astronomical images
Mega-Archive و ابزارهای EURONEAR برای داده نویسی تصاویر نجومی جهان-2020 The world astronomical image archives offer huge opportunities to time-domain astronomy sciences
and other hot topics such as space defense, and astronomical observatories should improve this wealth
and make it more accessible in the big data era. In 2010 we introduced the Mega-Archive database and
the Mega-Precovery server for data mining images serendipitously containing Solar system bodies, with
focus on near Earth asteroids (NEAs). This paper presents the improvements and introduces some
new related data mining tools developed during the last years. Currently, Mega-Archive indexed 15
million images available from six major collections and other instrument archives and surveys. This
meta-data index collection is daily updated by a crawler which performs automated query of five
major collections. Since 2016, these data mining tools are installed on the new dedicated EURONEAR
server, and the database migrated to SQL which supports robust and fast queries. To constrain the
area to search for moving or fixed objects in images taken by large mosaic cameras, we built the
graphical tools FindCCD and FindCCD for Fixed Objects which overlay the targets across one of seven
mosaic cameras, plotting the uncertainty ellipse for poorly observed NEAs. In 2017 we improved
Mega-Precovery, which offers now two options for the ephemerides and three options for the input
(objects defined by designation, orbit or observations). Additionally, we developed Mega-Archive for
Fixed Objects (MASFO) and Mega-Archive Search for Double Stars (MASDS). We include a few use case
scenarios and we compare our data mining tools with other few similar services. The huge potential
of science imaging archives is still insufficiently exploited. Their use could be strongly enhanced by
defining a standard format needed to index the image archives. We recommend to the IAU to define
such a standard, asking the observatories to index their image archives in a homogeneous manner. Keywords: Data mining | Asteroids | Near Earth asteroids (NEAs) | Image archives | Mega-Archive | Mega-Precovery |
مقاله انگلیسی |