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Big Data and AI for Process Innovation in the Industry...
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Big Data and AI for Process Innovation in the Industry 4.0 Era

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 50781

Special Issue Editors

Prof. Dr. Hyerim Bae

E-Mail Website
Guest Editor
Department of Industrial Engineering, Pusan National University, Busan 46241, Korea
Interests: big data analysis; process science; AI and applications; smart port; logistics information systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jaehun Park

E-Mail Website
Guest Editor
Major in Industrial Quality Engineering, Daegu Haany University, Daegu 38610, Korea
Interests: smart factory; big data analysis; performance evaluation; multi-criteria decision-making

Special Issue Information

Dear Colleagues,

With the rapid development of innovative technologies, such as artificial intelligence (AI), big data, Internet of Things, and cloud computing, the new concept of Industry 4.0 has been revolutionizing production and logistics systems by introducing distributed, collaborative, and automated processes. The objective of Industry 4.0 is a drastic enhancement of productivity, which depends on the processes of the enterprise. In order to innovate processes, big data and AI have been considered key solutions. Big data analytics is a process of examining data to discover knowledge, such as unknown patterns and correlations, market insights, and customer preferences, which can be useful to make various business decisions. Significant advances in deep learning, machine learning, and data mining have improved to the point where these techniques can be used in analyzing big data in any kind of industry. Big data is also recognized as a fundamental technology for advancing AI with sophisticated algorithms and advanced computing power. In this sense, big data and AI are becoming core assets of Industry 4.0 and process innovation. Thus, we invite academic communities and relevant industrial partners to submit papers on “Big Data and AI for Process Innovation in the Industry 4.0 Era” to this Special Issue. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Operational big data analytics;
  • AI and big data applications for Industry 4.0;
  • AI and big data for smart port and logistics;
  • Algorithms for process analysis;
  • Reinforcement learning for real-time decision-making;
  • Cloud computing and IoT for operational intelligence;
  • Deep learning for business intelligence and data mining;
  • Cyber physical systems and cyber-physical production systems;
  • Advanced manufacturing and smart factories;
  • Advanced data mining and process mining;
  • Process modeling and simulation;
  • Industrial Internet of Things;
  • Performance analysis of automated systems.
Prof. Dr. Hyerim Bae
Prof. Dr. Jaehun Park
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Process analysis
  • Big data
  • AI
  • Industry 4.0
  • Manufacturing and logistics process

Published Papers (15 papers)

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Editorial

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4 pages, 173 KiB  
Editorial
Big Data and AI for Process Innovation in the Industry 4.0 Era
by Jaehun Park and Hyerim Bae
Appl. Sci. 2022, 12(13), 6346; https://doi.org/10.3390/app12136346 - 22 Jun 2022
Cited by 3 | Viewed by 1275
Abstract
The fourth industrial revolution or what can be referred to as Industry 4 [...] Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)

Research

Jump to: Editorial

16 pages, 1220 KiB  
Article
CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems
by Hyeonjong Ha and Jongpil Jeong
Appl. Sci. 2021, 11(14), 6378; https://doi.org/10.3390/app11146378 - 09 Jul 2021
Cited by 17 | Viewed by 3096
Abstract
Currently, the development of automated quality inspection is drawing attention as a major component of the smart factory. However, injection molding processes have not received much attention in this area of research because of product diversity, difficulty in obtaining uniform quality product images, [...] Read more.
Currently, the development of automated quality inspection is drawing attention as a major component of the smart factory. However, injection molding processes have not received much attention in this area of research because of product diversity, difficulty in obtaining uniform quality product images, and short cycle times. In this study, we proposed a defect inspection system for injection molding in edge intelligence. Using data augmentation, we solved the data shortage and imbalance problem of small and medium-sized enterprises (SMEs), introduced the actual smart factory method of the injection process, and measured the performance of the developed artificial intelligence model. The accuracy of the proposed model was more than 90%, proving that the system can be applied in the field. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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17 pages, 3343 KiB  
Article
Quality-Aware Resource Model Discovery
by Minsu Cho, Gyunam Park, Minseok Song, Jinyoun Lee and Euiseok Kum
Appl. Sci. 2021, 11(12), 5730; https://doi.org/10.3390/app11125730 - 21 Jun 2021
Cited by 1 | Viewed by 1874
Abstract
Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this [...] Read more.
Context-aware process mining aims at extending a contemporary approach with process contexts for realistic process modeling. Regarding this discipline, there have been several attempts to combine process discovery and predictive process modeling and context information, e.g., time and cost. The focus of this paper is to develop a new method for deriving a quality-aware resource model. It first generates a resource-oriented transition system and identifies the quality-based superior and inferior cases. The quality-aware resource model is constructed by integrating these two results, and we also propose a model simplification method based on statistical analyses for better resource model visualization. This paper includes tooling support for our method, and one of the case studies on a semiconductor manufacturing process is presented to validate the usefulness of the proposed approach. We expect our work is practically applicable to a range of fields, including manufacturing and healthcare systems. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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21 pages, 4139 KiB  
Article
Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems
by Ana Pajić Simović, Slađan Babarogić, Ognjen Pantelić and Stefan Krstović
Appl. Sci. 2021, 11(12), 5476; https://doi.org/10.3390/app11125476 - 12 Jun 2021
Cited by 4 | Viewed by 2420
Abstract
Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event [...] Read more.
Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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15 pages, 3344 KiB  
Article
Dual-Kernel-Based Aggregated Residual Network for Surface Defect Inspection in Injection Molding Processes
by Hwaseop Lee and Kwangyeol Ryu
Appl. Sci. 2020, 10(22), 8171; https://doi.org/10.3390/app10228171 - 18 Nov 2020
Cited by 12 | Viewed by 3295
Abstract
Automated quality inspection has been receiving increasing attention in manufacturing processes. Since the introduction of convolutional neural networks (CNNs), many researchers have attempted to apply CNNs to classification and detection of defect images. However, injection molding processes have not received much attention in [...] Read more.
Automated quality inspection has been receiving increasing attention in manufacturing processes. Since the introduction of convolutional neural networks (CNNs), many researchers have attempted to apply CNNs to classification and detection of defect images. However, injection molding processes have not received much attention in this field of research because of product diversity, difficulty in obtaining uniform-quality product images, and short cycle times. In this study, two types of dual-kernel-based aggregated residual networks are proposed by utilizing a fixed kernel and a deformable kernel to detect surface and shape defects of molded products. The aggregated residual network is selected as a backbone, and a fixed-size, deformable kernel is applied for extracting surface and geometric features simultaneously. Comparative studies are conducted by including the existing research using the Weakly Supervised Learning for Industrial Optical Inspection dataset, which is a DAGM dataset. A case study reveals that the proposed method is applicable for inspecting the quality of injection molding products with excellent performance. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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18 pages, 6253 KiB  
Article
Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting
by Jaehong Yu, Seoung Bum Kim, Jinli Bai and Sung Won Han
Appl. Sci. 2020, 10(20), 7351; https://doi.org/10.3390/app10207351 - 20 Oct 2020
Cited by 12 | Viewed by 4428
Abstract
Recently, a number of data analysists have suffered from an insufficiency of historical observations in many real situations. To address the insufficiency of historical observations, self-starting forecasting process can be used. A self-starting forecasting process continuously updates the base models as new observations [...] Read more.
Recently, a number of data analysists have suffered from an insufficiency of historical observations in many real situations. To address the insufficiency of historical observations, self-starting forecasting process can be used. A self-starting forecasting process continuously updates the base models as new observations are newly recorded, and it helps to cope with inaccurate prediction caused by the insufficiency of historical observations. This study compared the properties of several exponentially weighted moving average methods as base models for the self-starting forecasting process. Exponentially weighted moving average methods are the most widely used forecasting techniques because of their superior performance as well as computational efficiency. In this study, we compared the performance of a self-starting forecasting process using different existing exponentially weighted moving average methods under various simulation scenarios and real case datasets. Through this study, we can provide the guideline for determining which exponentially weighted moving average method works best for the self-starting forecasting process. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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18 pages, 1175 KiB  
Article
Mining Shift Work Operation from Event Logs
by Nur Ichsan Utama, Riska Asriana Sutrisnowati, Imam Mustafa Kamal, Hyerim Bae and You-Jin Park
Appl. Sci. 2020, 10(20), 7202; https://doi.org/10.3390/app10207202 - 15 Oct 2020
Cited by 5 | Viewed by 2043
Abstract
Event logs are records of events that are generally used in process mining to determine the manner in which various processes are practically implemented. Previous studies on process mining attempted to combine the results based on different perspectives such as control flow, data, [...] Read more.
Event logs are records of events that are generally used in process mining to determine the manner in which various processes are practically implemented. Previous studies on process mining attempted to combine the results based on different perspectives such as control flow, data, performance, and resources (organizational) to create a simulation model. This study focuses on the resource perspective. A prior study from the resource perspective focused on clustering the resources into organizational units. Implementing the results of the above study in a simulation model will yield inaccurate results because the resources are assumed to always be available if no task is performed. In a practical scenario, resources (particularly humans) tend to work based on shifts. Thus, we propose mining the shift work operation of resources from event logs to tackle this issue. We utilized a self-organizing map and k-means clustering to incorporate the shift work information from the event logs into the simulation model. Moreover, we introduce a distance function and weight-centroid updating rule in the clustering technique to realize our objective. We conducted extensive experiments with artificial data sets to assess the effectiveness of the proposed method. The simulation shows that introducing the shift work operation time of resources can yield more accurate results. Furthermore, the proposed distance function can capture the shift work operation of the resources more precisely compared with the general distance function. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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20 pages, 3468 KiB  
Article
Smart Grid for Industry Using Multi-Agent Reinforcement Learning
by Martin Roesch, Christian Linder, Roland Zimmermann, Andreas Rudolf, Andrea Hohmann and Gunther Reinhart
Appl. Sci. 2020, 10(19), 6900; https://doi.org/10.3390/app10196900 - 01 Oct 2020
Cited by 33 | Viewed by 3808
Abstract
The growing share of renewable power generation leads to increasingly fluctuating and generally rising electricity prices. This is a challenge for industrial companies. However, electricity expenses can be reduced by adapting the energy demand of production processes to the volatile prices on the [...] Read more.
The growing share of renewable power generation leads to increasingly fluctuating and generally rising electricity prices. This is a challenge for industrial companies. However, electricity expenses can be reduced by adapting the energy demand of production processes to the volatile prices on the markets. This approach depicts the new paradigm of energy flexibility to reduce electricity costs. At the same time, using electricity self-generation further offers possibilities for decreasing energy costs. In addition, energy flexibility can be gradually increased by on-site power storage, e.g., stationary batteries. As a consequence, both the electricity demand of the manufacturing system and the supply side, including battery storage, self-generation, and the energy market, need to be controlled in a holistic manner, thus resulting in a smart grid solution for industrial sites. This coordination represents a complex optimization problem, which additionally is highly stochastic due to unforeseen events like machine breakdowns, changing prices, or changing energy availability. This paper presents an approach to controlling a complex system of production resources, battery storage, electricity self-supply, and short-term market trading using multi-agent reinforcement learning (MARL). The results of a case study demonstrate that the developed system can outperform the rule-based reactive control strategy (RCS) frequently used. Although the metaheuristic benchmark based on simulated annealing performs better, MARL enables faster reactions because of the significantly lower computation costs for its own execution. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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14 pages, 582 KiB  
Article
Sustainable Competitive Advantage Driven by Big Data Analytics and Innovation
by Muawia Ramadan, Hana Shuqqo, Layalee Qtaishat, Hebaa Asmar and Bashir Salah
Appl. Sci. 2020, 10(19), 6784; https://doi.org/10.3390/app10196784 - 28 Sep 2020
Cited by 36 | Viewed by 6798
Abstract
Big data analytics (BDA) is one of the main pillars of Industry 4.0. It has become a promising tool for supporting the competitive advantages of firms by enhancing data-driven performance. Meanwhile, the scarcity of resources on a worldwide level has forced firms to [...] Read more.
Big data analytics (BDA) is one of the main pillars of Industry 4.0. It has become a promising tool for supporting the competitive advantages of firms by enhancing data-driven performance. Meanwhile, the scarcity of resources on a worldwide level has forced firms to consider sustainable-based performance as a critical issue. Additionally, the literature confirms that BDA and innovation can enhance firms’ performance, leading to competitive advantage. However, there is a lack of studies that examine whether or not BDA and innovation alone can sustain a firm’s competitive advantage. Drawing on previous studies and dynamic capability theory, this study proposes that big data analytics capabilities (BDAC), supported by a high level of data availability (DA), can improve innovation capabilities (IC) and, hence, lead to the development of a sustainable competitive advantage (SCA). This study examines the proposed hypotheses by surveying 117 manufacturing firms and analyzing responses via partial least squares–structural equation modeling (PLS-SEM) statistical software. Findings reveal that BDAC relies significantly on the degree of DA and has a significant role in increasing IC. Furthermore, the analysis confirms that IC has a significant and direct effect on a firm’s SCA, while BDAC has no direct effect on SCA. This study provides valuable insights for manufacturing firms to improve their sustainable business performance and theoretical and practical insights into BDA implementation issues in attaining sustainability in processes. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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14 pages, 3949 KiB  
Article
Study on Reverse Logistics Focused on Developing the Collection Signal Algorithm Based on the Sensor Data and the Concept of Industry 4.0
by Si-Il Sung, Young-Sun Kim and Hyun-Soo Kim
Appl. Sci. 2020, 10(14), 5016; https://doi.org/10.3390/app10145016 - 21 Jul 2020
Cited by 12 | Viewed by 2447
Abstract
Reverse logistics include all operations related to the reuse of products and materials. In this study, we focus on collection, which is the first operation of reverse logistics, and on the strength of using the sensor data and the concept of Industry 4.0. [...] Read more.
Reverse logistics include all operations related to the reuse of products and materials. In this study, we focus on collection, which is the first operation of reverse logistics, and on the strength of using the sensor data and the concept of Industry 4.0. Previously, the collection activities of electronic wastes (e-wastes) was conducted by a fixed schedule without consideration of the fulfillment level of the collection boxes. However, due to the progress of IoT(internet of things) technology and sensor technology, it is possible to consider the fulfillment level of the collection boxes in order to make the collection schedule. To utilize the sensor data and IoT technology in reverse logistics, a collection signal algorithm is required to treat the rate of fulfilment of collection boxes. However, the collection signal algorithm for the disposal of small and medium (S&M)-sized e-wastes have not yet been developed in South Korea. This study uses a collection box to develop the collection algorithm based on an experimental design method with multiple sensors. The proposed algorithm can be utilized to solve the current collection problems and to save logistics costs. Furthermore, proper collection of e-wastes will lead to more recycling activities, which can further create and sustain a safer environment on Earth. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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17 pages, 2867 KiB  
Article
Reconfiguration Decision-Making of IoT based Reconfigurable Manufacturing Systems
by Sumin Han, Tai-Woo Chang, Yoo Suk Hong and Jinwoo Park
Appl. Sci. 2020, 10(14), 4807; https://doi.org/10.3390/app10144807 - 13 Jul 2020
Cited by 9 | Viewed by 3054
Abstract
With the recent diversification of demands, manufacturing systems that can respond to multiple types of goods have become more important. In this circumstance, reconfigurable manufacturing systems (RMSs) that can provide flexible manufacturing with limited machine tools through reconfiguration have gained a lot of [...] Read more.
With the recent diversification of demands, manufacturing systems that can respond to multiple types of goods have become more important. In this circumstance, reconfigurable manufacturing systems (RMSs) that can provide flexible manufacturing with limited machine tools through reconfiguration have gained a lot of attention. As an RMS supports flexibility through layout reconfiguration, reconfiguration decision-making is very important and difficult. The development of IoT technology has made it possible to collect hidden information inside systems. This study focused on the reconfiguration decision-making system with the data acquisition system based on IoT technology. The decision-making system detected a reconfiguration situation and built a reconfiguration plan using the data collected by IoT sensors. The performance of the algorithm proposed in this study was verified in a simulation experiment. It was found that the algorithm had a stable performance under various reconfigurable situations. It is expected that the proposed system will help to improve the performance of RMS. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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16 pages, 3320 KiB  
Article
Passive Radio-Frequency Identification Tag-Based Indoor Localization in Multi-Stacking Racks for Warehousing
by Jaehun Park, Yong-Jeong Kim and Byung Kwon Lee
Appl. Sci. 2020, 10(10), 3623; https://doi.org/10.3390/app10103623 - 23 May 2020
Cited by 18 | Viewed by 3152
Abstract
Radio-frequency identification (RFID) technology-based real-time indoor location awareness has been widely studied. In this paper, a passive RFID-based indoor inventory localization method for small and medium-sized enterprises (SMEs) is proposed to effectively manage their indoor inventory tracking in terms of the multi-stacking racking [...] Read more.
Radio-frequency identification (RFID) technology-based real-time indoor location awareness has been widely studied. In this paper, a passive RFID-based indoor inventory localization method for small and medium-sized enterprises (SMEs) is proposed to effectively manage their indoor inventory tracking in terms of the multi-stacking racking (MSR). To achieve this, we introduce a concept of reference tags and a calculation of measurement for the distance between the RFID reader and reference tag to improve the accuracy of the item location recognition. To illustrate the efficacy and applicability of the method, an empirical case study that applies it to an electronic device manufacturing company is conducted. It was noted that there was no significant difference in the location awareness rate of the proposed system compared with the existing active RFID-based methods. Also, it is demonstrated that the construction can be relatively inexpensive in terms of identifying the location of the items loaded in MSR and relatively narrow areas using a passive tag. This advantage makes it suitable for SMEs that have issues with large-scale facility investment, applying the method to compare the location difference between the registered location information in the inventory system and the actual location of the item in the rack. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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13 pages, 3022 KiB  
Article
PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management
by Aekyung Kim, Josue Obregon and Jae-Yoon Jung
Appl. Sci. 2020, 10(10), 3521; https://doi.org/10.3390/app10103521 - 20 May 2020
Cited by 5 | Viewed by 3393
Abstract
Most organizations need to monitor and assess their business activities. In order to support the performance analysis of the business activities in a more systematic manner, in this research, we introduce a PRocess ANalytics System, called PRANAS. The system adopts process warehouses and [...] Read more.
Most organizations need to monitor and assess their business activities. In order to support the performance analysis of the business activities in a more systematic manner, in this research, we introduce a PRocess ANalytics System, called PRANAS. The system adopts process warehouses and process cubes to support process-oriented analysis, as well as data-oriented analysis. In this research, the process warehouse and cube were designed to assess business performances for supply chain management, specifically under the SCOR standard models. Furthermore, the process cube was constructed based on process-related dimensions such as time, case type, and event class to support process mining. Finally, we exemplify how the system can be applied to process analytics with three use cases of process discovery, data analytics, and decision point analysis. It is expected that the proposed system can be a helpful reference model when business process analyst designs process analytics systems in the process-oriented perspective, as well as in the data-oriented perspective. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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16 pages, 2417 KiB  
Article
A Data-Driven Analysis on the Impact of High-Speed Rails on Land Prices in Taiwan
by Joyce M.W. Low and Byung Kwon Lee
Appl. Sci. 2020, 10(10), 3357; https://doi.org/10.3390/app10103357 - 12 May 2020
Cited by 8 | Viewed by 3092
Abstract
High-speed rail (HSR) networks boost inter-city accessibility across a country and stimulate economic growth in inner cities. These economic gains, however, can often be accompanied by sharp increases in land and property prices along the lines that raise governmental concerns. This study examined [...] Read more.
High-speed rail (HSR) networks boost inter-city accessibility across a country and stimulate economic growth in inner cities. These economic gains, however, can often be accompanied by sharp increases in land and property prices along the lines that raise governmental concerns. This study examined the effect of the introduction of HSR on land prices in Taiwan and how the extent of such an effect varied with the stages of economic, societal, and infrastructural developments in different cities in Taiwan. Based on extensive published data, an empirical study was conducted using an integrated methodology comprising system dynamics, multivariate regression, and principal component analysis to examine the interacting relationships between the presence of HSR transportation and other important dimensions of city development in determining land prices. The study found that while land prices correlated with the greater locational accessibility brought about HSR, the extent of land price increases depended significantly on economic, societal, and infrastructural considerations such as the unemployment rate, risk-free interest rate, population density, and the existence of free trade zones, etc. This understanding of system behavior will be helpful for policy makers in devising ways to curb the escalation of property price while enjoying the benefits of HSR. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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15 pages, 4049 KiB  
Article
Implementation of a Blood Cold Chain System Using Blockchain Technology
by Seungeun Kim, Joohyung Kim and Dongsoo Kim
Appl. Sci. 2020, 10(9), 3330; https://doi.org/10.3390/app10093330 - 11 May 2020
Cited by 26 | Viewed by 5108
Abstract
As the population structure changes due to lower fertility rates and rapid aging, the blood supply available for blood transfusion decreases and demand increases. In most countries, blood management information systems, led by national institutions, operate centrally. However, existing centralized blood management systems [...] Read more.
As the population structure changes due to lower fertility rates and rapid aging, the blood supply available for blood transfusion decreases and demand increases. In most countries, blood management information systems, led by national institutions, operate centrally. However, existing centralized blood management systems have limitations in that they lack detailed blood information and, moreover, information is not reflected in real time. To solve this problem, this paper presents an innovative blood cold chain system based on blockchain technology. The proposed system aims to increase information visibility by recording the overall information on the blood supply and providing detailed blood information such as blood consumption and disposal to the distributed ledger. In addition, this paper proposes direct blood transactions between medical institutions in cases of emergency. Currently, blockchain technologies are being actively employed in the supply chain management and medical fields in addition to financial systems. Particularly, private blockchain techniques with limited participants are relatively fast and reliable, making them suitable for B2B (Business-to-Business) transactions. Therefore, the proposed system is based on the architecture of Hyperledger Fabric, a private blockchain technology implemented by the Hyperledger Composer tool. Information in the proposed blood cold chain system cannot be forged or tampered with, and information recorded and shared in real time is kept transparent. In addition, allowing for B2B blood transaction in special circumstances will minimize the blood supply time and enable patients to be transfused quickly. Moreover, the surplus blood of medical institutions will be used to increase the usage rate relative to the supply amount. Full article
(This article belongs to the Special Issue Big Data and AI for Process Innovation in the Industry 4.0 Era)
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