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Research on Quality Construction of National Logistics Hub in Yangtze River Delta Based on DPSIR Model
Wang Chuanlei,
Zhang Chunmeng,
Dong Yin
Issue:
Volume 8, Issue 1, March 2022
Pages:
1-13
Received:
3 March 2022
Accepted:
21 March 2022
Published:
29 March 2022
Abstract: Under the background of double circulation, the national logistics hub will play an important role of node, hub and platform in the national logistics network. As a national strategy, the integration of Yangtze River Delta matters a lot to the national logistics network. This study constructs the evaluation index system of national logistics hubs and studies the construction of national logistics hubs in Yangtze River Delta and central China based on DPSIR theory. DPSIR model integrates the advantages of pressure-state-response (PSR) model and driving-state-response (DSR) model, and evaluates from driving force, pressure, state, influence and response. The results show that the construction of national logistics hubs in Shanghai, Nanjing, Suzhou, Ningbo-Zhoushan, Jinhua and other cities in the Yangtze River Delta is obviously better than that in the central part of China. In order to adapt to the integrated development of Yangtze River Delta and enhance the status of Anhui province in the national logistics hub network, logistics hub cities in Anhui Province should take effective measures from five aspects of the model. For example, concentrate on developing the economy; optimize the urban transportation system; build distinctive industrial clusters; develop multimodal transport facilities; innovate smart logistics models; treasure the construction of cold chain logistics; strengthen macro policy support; building a response mechanism for ecological green development.
Abstract: Under the background of double circulation, the national logistics hub will play an important role of node, hub and platform in the national logistics network. As a national strategy, the integration of Yangtze River Delta matters a lot to the national logistics network. This study constructs the evaluation index system of national logistics hubs a...
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Data Mining and Revealing Hidden Sentiment in Tweets Using Spark
Ameen Abdullah Qaid Aqlan
Issue:
Volume 8, Issue 1, March 2022
Pages:
14-21
Received:
10 December 2021
Accepted:
22 January 2022
Published:
29 March 2022
Abstract: Data science is important and scientific value in our lives because it is multi-fields, it's the science that uses scientific methods, processes, algorithms, and systems for the purpose of extracting knowledge and ideas from data whether this data is organized or not. Data science is called 21st century oil to highlight its importance and scientific value in our lives. We paid great attention in this research paper, where we achieved three main steps in the field of data analysis, collecting data from different sources in the Internet and then storing the data within the system, the second step cleaning the data in order to obtain structured data and then applying the algorithms that are responsible for classifying the data. In pursuit of development, we have collected more than 1600000 tweets about the educational process and the possibility of future online education. We give great attention to this field of Sentiment Analysis (SA) and the use of modern Spark technology, which has achieved great success since its emergence. We have succeeded in using Spark in getting a good result, handling data quickly and accurately, which encouraged us to test it on two algorithms of Machine Learning, Support Vector Machine (SVM) & Maximum Entropy (Max Ent).
Abstract: Data science is important and scientific value in our lives because it is multi-fields, it's the science that uses scientific methods, processes, algorithms, and systems for the purpose of extracting knowledge and ideas from data whether this data is organized or not. Data science is called 21st century oil to highlight its importance and scientifi...
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Customized Learning in Online Tutoring Systems by Mining Learning Units from Tasks and Examples
Ritu Chaturvedi,
Christie I. Ezeife
Issue:
Volume 8, Issue 1, March 2022
Pages:
22-35
Received:
9 February 2022
Accepted:
10 March 2022
Published:
30 March 2022
Abstract: In recent years, technology has enabled Universities and Colleges to offer web-based courses, in which, teachers (or experts) design, curate and upload all course material required to teach the course online so that students can learn at their own pace, time and location. This research proposes a tutoring framework called Example Recommendation System (ERS) that is based on example-based learning (EBL) instructional method. ERS focuses on students devoting their time and cognitive capacity to studying worked-out examples so that they can enhance their learning and apply it to graded tasks assigned to them. ERS uses regular expression analysis to extract basic learning units (LU) (e.g. scanf is a LU in C programming) from all task solutions and worked-out examples and represents this knowledge in vector space. Then, these vectors are mined to generate a customized list of worked-out examples for each assigned task. The prime contribution of ERS’s extraction module is its extendibility to new domains without requiring highly trained experts. Besides extendibility, ERS extracts LUs with 81% correctness for the domain of “Programming in C” and 95% for domain of “Programming in Miranda”. ERS’s data mining model used for customization has 93% accuracy and 88% f score. ERS’s educational impact is also evident from experiments that show that students score an average of 89% in tasks for which they use ERS’s recommended worked-out examples, as opposed to an average of 73% for those tasks that students attempt without ERS’s assistance.
Abstract: In recent years, technology has enabled Universities and Colleges to offer web-based courses, in which, teachers (or experts) design, curate and upload all course material required to teach the course online so that students can learn at their own pace, time and location. This research proposes a tutoring framework called Example Recommendation Sys...
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