Cyber-Physical-Social-Thinking Modeling and Computing forGeological Information Service System

Yueqin Zhu∗†, Yongjie Tan∗†, Ruixin Li†‡§, Xiong Luo†‡§∗Development and Research Center, China Geological Survey, Beijing 100037, China.

†Key Laboratory of Geological Information Technology,Ministry of Land and Resources, Beijing 100037, China.‡School of Computer and Communication Engineering,

University of Science and Technology Beijing (USTB), Beijing 100083, China.§Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China.

Abstract:The serious geological hazards occurred frequently in the last few years. They haveinflicted heavy casualties and property losses. Hence, it is necessary to design a geological in-formation service system to analyze and evaluate geological hazards. With the development ofcomputer and Internet service model, it is now possible to obtain rich data and process the datawith some advanced computing techniques under network environment. Then, some technologies,including cyber-physical system (CPS), Internet of Things (IoT), and cloud computing, have beenused in geological information management. Furthermore, the concept of cyber-physical-social-thinking (CPST) as a broader vision of the IoT was presented through the fusion of those advancedcomputing technologies. Motivated by it, in this article a novel modeling and computing method forgeological information service system is developed in consideration of the complex data processingrequirement of geological service under dynamic environment. Specifically, some key techniquesof modeling the information service system and computing geological data via CPS and IoT areanalyzed. Moreover, to show the efficiency of proposed method, two application cases are providedduring the CPST modeling and computing for geological information service system.

Keywords:cyber-physical-social-thinking (CPST); Internet of Things (IoT); geological data pro-cessing; cloud computing.

I. INTRODUCTION

Recently, a geological hazard as one of the most important issues presents an obstacle to the social devel-

opment. It is necessary to design a geological information service system to analyze and evaluate geological

hazards. Among the available geological information service applications, a geographic information system

(GIS) and its location intelligence play an important role in analyzing and visualizing geological data for

the public. Furthermore, with the continuous improvement of Internet technology, the concept of cyber-

physical system (CPS) gradually appears in front of everyone [1], [2], [3]. CPS is a hyperspace system, in

which comprehensive computing, networking, and physical environment can be integrated. Meanwhile, CPS

can also achieve real-time sensing, dynamic control, and information services for large-scale engineering

Corresponding authors: yueqinzhu@163.com (Yueqin Zhu); xluo@ustb.edu.cn (Xiong Luo)This work was partially supported by the Special Funds Project for Scientific Research of Public Welfare Industry from the

Ministry of Land and Resources of China under Grant 201511079, and the National Key Technologies R&D Program of Chinaunder Grant 2015BAK38B01.

systems. Specifically, some techniques related to CPS, e.g., Internet-service-based multi-user accessing and

data service, have been developed in geological information service system (GISS) [4]. As an effective

architecture with the potential powerful capability of addressing complex issues, there are some research

topics to explore along this direction for GISS. Beyond the scope of the CPS, the cyber-physical society

system (CPSS) is developed. In addition to the cyber space and the physical space, it is deployed with

emphasis on humans, knowledge, society, and culture. Hence, it can connect nature, cyber space, and society

with certain rules [5].

With the development of computer and Internet service model, the Internet of Things (IoT) is becoming

increasingly common. Under the IoT architecture, objects can be sensed and controlled remotely across

existing network structure, while integrating the physical world and computer-based systems [27], [7],

[8]. Consequently, the efficiency, accuracy, and economic benefit are all improved. Meanwhile, the cloud

computing technology plays a prominent part in the IoT while making it easier for users to store and process

information. Furthermore, it also improves the computing performance by making the information available

over the web or other terminal ends. Hence, cloud computing is so popular in recent years due to its technical

benefits of the on-demand capacity management model used to efficiently share the resources, software, and

information among multiple devices [9].

More recently, through the fusion of CPS and IoT on the basis of cloud computing technology, a concept

cyber-physical-social-thinking (CPST) as a broader vision of the IoT was presented by merging the thinking

space into the CPS and highlighting the importance of the cognitive intelligence and social organization [10].

Generally, the paradigms of artificial intelligence and cognitive computing have been widely applied to many

fields. Those emerging technologies therefore have brought new development opportunity for IoT. In this

way, both the human cognition capacities and intelligent learning techniques from the society activities

are integrated into the implementation of IoT. Considering it, through the extension and mergence of

thinking space within the traditional CPS space, the CPST is accordingly developed to address more complex

application requirements in practice. Motivated by it, in this article a novel modeling and computing method

for GISS is developed. Considering the complex data processing requirement of geological service under

dynamic environment, it imposes very challenging obstacles to the implementation of GISS with the help

of those current computing technologies. Hence, on the basis of some successful applications of CPS in

GISS, CPST may play an important role in dealing with those difficult issues of designing an effective GIS

system.

The remainder of this article is organized as follows. Section 2 gives a description for related works,

including CPS, CPSS, CPST, IoT, and cloud computing. Section 3 and Section 4 respectively present

the CPST modeling and computing schemes for GISS while providing some application cases. Finally,

a conclusion and brief discussion regarding the open challenging science and technology issues along this

research direction in GISS are discussed in Section 5.

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II. RELATED WORKS

A. CPS, CPSS, and CPST

1) CPS: CPS is a networked, component-based, real-time system that controls and monitors the physical

world [11]. From the structural point of view, a CPS includes the following parts: i) sensors designed to

perceive the physical world; ii) controllers or actuators designed to operate the physical entity; iii) computing

components designed to analyze and process physical information; iv) communication network designed to

integrate the above units and related information, objects, and events.

Currently, with the rise in popularity of smart phones, there is a growing interest in the mobile application

for CPS. Then, mobile cyber-physical system (MCPS) as a prominent subcategory of CPS is developed, in

which the physical system has inherent mobility [12].

2) CPSS: Generally speaking, the society environment is considered an autonomous, living, sustainable,

and intelligent variable within the CPS. As a result, the CPSS gathers and organizes resources into se-

mantically rich forms that both machines and people can easily use [13]. Such a system includes globally

distributed resources, including devices, information, and knowledge. In addition, it also includes services

that could dynamically collaborate to provide some effective just-in-time services to cope with an emerging

crisis.

The key issue of designing a CPSS is how to describe and integrate the interaction of physical, social,

and hardware in an efficient way. Some methods are presented. To improve computational and networking

contextual complexity in deploying a CPSS, a smart services framework in CPSS, called dynamic social

structure of things, was developed aiming at boosting sociality and narrowing down the contextual complexity

based on situational awareness [14]. In one sense, CPSS can promote the interconnection of distributed CPS.

3) CPST: CPST hyperspace is accordingly established through the mergence of a new dimension of

thinking space into the CPS space [10]. Therefore, the wisdom along with the intelligent interactions of

data, information, and knowledge are through the CPST hyperspace. Currently, the hyperworld is integrated

into CPST hyperspace by coupling data, information, knowledge, and cognition related cyber interactions,

physical perceptions, social correlations, and human thinking. The CPST hyperspace has the following

characteristics.

• Integration. The CPST hyperspace may be a combination of different spaces.

• Interconnection. The CPST hyperspace is connected with other same type or different type of spaces.

• Interaction. The CPST hyperspace can exchange information through cyber spaces to achieve ubiq-

uitous intelligence.

B. Cloud Computing

Cloud computing is a computing style in which dynamically scalable and virtualized resources are provided

as a service over the Internet [9]. Then, the traditional system architecture should be extended to meet the

requirement of software development in cloud computing scheme. Under the cloud computing framework,

users can employ a variety of terminal access services in any positions. The requested resources from the

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cloud is virtual, rather than a fixed physical entity. Then, the user may enjoy all kinds of super services

through Internet [15]. Due to its automatic management feature, cloud computing is cost-effective while

greatly reducing the cost for data center management.

Cloud service provides some solutions or real-time software services through Internet. Existing cloud

service can be categorized into three major groups: infrastructure-as-a-service (IaaS), platform-as-a-service

(PaaS), and software-as-a-service (SaaS) [16].

C. IoT

Currently, the IoT achieves a great success with the development of wireless technology [17]. Among the

available key technologies in the IoT, the radio frequency identification (RFID) and wireless sensor network

(WSN) play an important role in implementation applications. Through the use of RFID technology, there

are many real-time monitoring applications in the IoT.

Meanwhile, a WSN uses spatially distributed autonomous sensors to monitor physical or environmental

conditions [18] and to cooperatively pass their data through the network to a main location. Today such

network is used widely in many industrial and consumer applications, such as industrial process monitoring

and control, machine health monitoring, and so on [19]. Moreover, with the development of high performance

computing technologies, the IoT becomes an attractive system paradigm to realize universal interactions

through the cloud computing [20].

III. CYBER-PHYSICAL-SOCIAL-THINKING MODELING FOR GEOLOGICAL INFORMATION SERVICE

SYSTEM

A. Features of Geological Data

In the era of big data, data is being collected to serve the purpose of making decisions in a data-driven

way. Then, such big data analysis now drives nearly every aspect of society, including financial services,

manufacturing, and mobile services.

Emergency data is an important resource from geological disaster emergency command system. And

those data in the emergency command and management of the geological disasters include three types,

i.e., emergency event data, emergency service data, and emergency basic data. Here, emergency basic data

include geographic information, document, knowledge, and many others. Emergency event data is the basic

information of the incidents, including video, audio, and other environmental information. Emergency service

data is in the process of emergency management, and it is generated after analyzing and making decision.

Recently, big data is revolutionizing all aspects of society ranging from science to government. Geological

data is a typical case of big data. Geological big data include geological data produced by multi-source and

multi-mode from geological survey as well as data from public service and management support [21].

Actually, geological data has three characters, i.e., rich sources, wide area, and great capacity of data [22].

So organization and management of those data is a key issue while realizing GISS. Geological data is a series

of important results in geological work, including geological documents, electronic data, specimens, and so

on. Then, it is obtained from the geological scientific research, engineering exploration, and production

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Fig. 1. CPST model of geological information service system.

process. Essentially, the geological data is considered the outcome of the wisdom of experts in geological

areas. Through the use of geological data with an effective scheme during the collecting, integrating, sharing,

and developing processes, the public can obtain an orderly and long-term service.

Practically speaking, geological data is mainly composed of structured and unstructured data. And the

unstructured data, including reports and files generated by word, PDF, excel, graphics, video, and PPT,

is all stored in a traditional mode. Since it is inefficient with traditional storage mode while conducting

data retrieval, data querying, statistic analysis, and data mining, thus resulting in extremely low data service

capability [23]. Hence, it is urgent that geological big data should be addressed with a new modeling scheme

by integrating some of the technologies presented in the state-of-the-art analysis. And the CPST modeling

scheme developed here may be a competitive choice.

B. System Modeling

As mentioned above, GISS confronts some challenging issues, which requires the GISS should address the

geological big data with a more effective mode during data acquisition, search, analysis, and visualization.

Actually, although the diversity and completeness of source data are strengthened during the geological

data acquisition, it makes data analysis difficult due to the huge amounts of geological data. Then, it is

necessary to make a synthesis of all data through the combination of users requirement, geological context

knowledge, and application background. During this process, it hardly achieves such object without the help

of intelligent thinking and learning capabilities from human. As a result, CPST modeling scheme may be

a good choice and is accordingly employed to GISS on the basis of its intelligence and social computing

paradigm.

The CPST includes the cyber space, physical space, social space, and thinking space. Fig. 1 shows the

GISS and its key components involving the cyber, physical, social, and thinking modules described by CPST.

The functions of every part are given as follows, and the main equipments and technologies are presented

in Fig. 2.

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Fig. 2. Key equipments and technologies.

1) Cyber Space: In the cyber space, it refers to the generalized information resources, including virtual

and digital abstractions to achieve interconnections among cyber entities [10]. In this space, a cognitive

information framework is designed to provide the geological data, resource, and service in an intelligent

self-adaptive learning way while implementing the cyber interactions. Considering the big data characteristics

in geological data, it should provide some effective data services, including holistic data management for

massive geological data storage, on-demand geological resource management in virtual cloud computing

environment, and online spontaneous management during distributed interactions for geological data.

2) Physical Space: Physical space represents the real world in which geological objects are respectively

perceived and controlled by semantic sensors or cooperative actuators to collect the real-time data to achieve

interactions remote collaboration through the use of the communication channels in context-aware networks.

Here, in order to realize interconnections in the cyber space mentioned above, a geological object should

be transformed into single or multiple cyber entities under virtual working framework.

3) Social Space: Social space is designed as a logical component in CPST modeling scheme. It is

responsible for collecting those social attributes and social relationships from the human beings and other

geological objects or cyber entities. Firstly, in the social space those information are from human activities

and social events, including supervision, organization, and coordination addressed by geological experts and

public users. Secondly, the inherent relationships among the geological objects, including direct and indirect

correlations, are also integrated into the social space in a semantic representation way.

4) Thinking Space: Within CPST, the thinking space plays a special role in addresses a high-level thought

or idea raised during the intellectual activities of geological experts and public users. Specifically, those

thinking information can be obtained through a self-adaptive learning way, e.g., analysis, synthesis, judgment,

reasoning, and some computational intelligence-based learning algorithms, e.g., the popular deep learning

method [24], reinforcement learning method [25], and many other state-of-the-art techniques.

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Fig. 3. Architecture of geological information service system.

5) System Architecture: Considering the background of geological service and complex features of

geological big data, a system architecture for GISS is designed on the basis of the modeling idea of CPST.

Here, the administration can use web and mobile devices in this system to collect and manage the geological

data. Due to some limitations of network technology, it is not possible to connect all the devices directly to

the next generation network. Then, to make full use of geological data through some technologies of CPS

and IoT, a novel information platform system framework for geological data service is shown in Fig. 3.

This system architecture is composed of three layers, including application layer, network layer, and

perceptual layer. Meanwhile, some specific functions are implemented within this architecture. Here, the

network layer focus on the interconnection and interoperability of equipments, and it is specialized for

data transmission and resource sharing while ensuring that the next generation of network characteristics is

effectively integrated in the transmission processing. The network layer aims at transmitting various real-

time and un-real-time geological data for users through the special-purpose connection network. In the

proposed architecture, the perceptual layer is a front-end of information collection. The application layer is

the business logic layer in the integrated information platform, which provides some business to meet the

geological information service needs of different users.

6) An Application Case: Here, a simple case regarding the geological data analysis in thinking space is

provided under the CAST framework.

Currently, there are some ways used to help us to make decisions, such as mapping knowledge domain

and semantic relation analysis. Here, the term mapping knowledge domains was chosen to describe a newly

evolving interdisciplinary area of science during the process of charting, mining, analyzing, sorting, and

displaying knowledge. Scientists, academics, and librarians have historically worked to codify, classify, and

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organize knowledge, thereby making it useful and accessible [26]. In addition, perhaps even more important,

the new analysis techniques that are being developed to process extremely large databases give promise of

revealing implicit knowledge that is presently known only to domain experts.

Some of these techniques are now being applied, aiming to identify and organize research areas according

to experts, publications, text, and figures; discover interconnections among these; establish the import of

research; reveal the export of research among fields; examine dynamic changes such as speed of growth and

diversification; highlight key factors in information production and dissemination; find and map scientific

and social networks. The new techniques support and complement human judgment.

Geological data is a kind of expression form of geological significance in the long history of the earth. The

traditional geological data includes geological map, structural map, mineral map, geological hazard map,

and rock phase diagram, and many others. Classification of rocks and lithofacies is an important part of it.

Here, an example of a rock classification and lithofacies relationship is provided. And the user interface is

demonstrated in Chinese.

Firstly, the statistics and classification of the existing geological data text are sorted, including the size

and type of data, the document number, the paragraph number, and numerical data type.

Then, semantic relation analysis is conducted to identify and extract subjective information by the natural

language processing (NLP), text analysis, and other machine learning methods [27]. In this example,

the hidden-Markov-model (HMM)-Viterbi-based standard word segmentation is employed to segment the

document, so that the text can be divided into different parts. Then, the TextRank algorithm is applied to

filter out the those words, such as the irrelevant adverbs and adjectives behind the participle, so as to achieve

the effect of keyword extraction.

The result of word segmentation is accordingly analyzed, and the relationship between words is shown

through the use of a visual programming tool, which stores the structured data on the web instead of the

table. Specifically, through use of Neo4j map database tool, geological information processing and retrieval

are implemented. And a good semantic knowledge mapping is thus developed to show the magmatic rock

classification and attribute better. A demonstration result is shown in Fig. 4.

In this figure, the nodes represent entities, and the edges represent the relationships between entities.

And different classes of entities are distinguished by different colors, and each one has a specific semantic

meaning. Here, the semantic meaning denotes classification and attribute relationships. The former includes

the shape classification (i.e., plutonic intrusive rock, hypabyssal intrusive rock, and extrusive rock) and the

acidity classification (i.e., ultrabasic rock, mafic rock, intermediate rock, and acidic rock). The latter includes

the color, mineral component, accessory mineral, structure, alteration, alkalinity, and SiO2 content. From

Fig. 4, the hierarchy result of classification for magmatic rock can be clearly found with a strong visual

effect. Furthermore, it can help the user to find more accurate information, make a more comprehensive

summary, and provide more depth information. Meanwhile, it also can systematically display the keywords

and the related knowledge system. Complex areas of knowledge are shown through data mining, information

processing, knowledge measurement, and graphics rendering. Hence, it provides a practical and valuable

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Fig. 4. Relational mapping of geological information.

reference for the geological research and decision.

IV. CYBER-PHYSICAL-SOCIAL-THINKING COMPUTING FOR GEOLOGICAL INFORMATION SERVICE

SYSTEM

Computing technologies play an elemental role in facilitating the intelligent circumstance monitoring,

semantic analysis, social behavior modeling, and insight generation. In our developed system, computing

technologies include traditional data computing and the CPST computing. Specifically, the security of the

system is inevitable to be considered in the computing technologies.

A. The Computing for Geological data

1) Management of Distributed Database: The management on distributed database is one of the important

issues in the geological service system. To ensure the high reliability of data, distributed database often

employs backup strategy to achieve fault tolerance. Therefore, when reading data, the client can concurrently

read simultaneously from multiple backup server, so as to improve the speed of data access and provide

higher concurrent traffic.

2) Data Processing and Analysis: Generally, the collected geological information data is abundant, and

those data can be fused to analyze and evaluate geologic events. The issue on how to preprocess data to

improve the data analysis quality is very important. Data processing extracts valuable information from large

amounts of data. There exists many methods to process and analyze data [12]. In the practical applications,

the combination of two or more methods is implemented to achieve satisfactory data processing and analysis

performance. Fig. 5 shows a typical distributed implementation framework for geological data processing and

sharing. In this figure, it can be observed that in the cloud-based distributed computing environment, many

data sources are organized to form different databases through the data sharing and exchange specification.

Furthermore, the advanced data processing, exchanging, and presentation services are provided on the basis

of those databases.

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Fig. 5. Framework of geological data sharing and processing.

B. The CPST Computing

1) A Multi-level Computing Framework: The CPS computing has been designed to support human-centric

paradigms, and address data and information to provide contextually relevant knowledge for human beings

[28]. Moreover, the CPST computing could provide a holistic treatment of human thought and thinking to

achieve supportive wisdom. Generally speaking, the CPST computing is a multi-level computing framework.

Cyber-physical computing is an essential mechanism of abstracting physical resources while achieving

CPST in the computing framework [29]. Some methods, e.g., cloud computing-assisted big data analysis,

can achieve this purpose. They are seen as an extension and deep application for physical infrastructure in

GISS. In general, a cloud computing environment is composed of physical servers that contain resources

shared by many users and applications. Within this environment, the big-data-analysis-centric computing

strategy can copy with the application requirements of geological big data.

Within this framework, in addition to cyber-physical computing, the social computing in the cybermatics

is a key part. More recently, it has become a focus in computing application fields. By integrating social

dynamics and mining social knowledge, the social intelligence is thus highlighted in this computing mode

[30]. Then, the valuable geological information and knowledge can be obtained by interacting with social

networks and recommendations from geological experts while carrying out social computing.

It should be pointed out that thinking computing arisen from emotions also plays an important role in

CPST computing. It is emerged to recognize, interpret, and learn human cognitive states. Consequently,

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Geological data text

Text preprocess

Chinese word segmentation

Deleting stop words

Deleting low-frequency words

A collection of text

(vector representation)

Map1( )

Map2( )

Mapn( )

Classifier1

Classifier2

Classifiern

Boosting-based

weighted ensemble

result

Result evaluation

··· ···

Mapper Reducer

Sequence

File

Geological

expert

Hadoop-based geological data text computing framework

Fig. 6. Geological data text computing framework under Hadoop architecture.

it enhances self-adaptive and self-understanding performance while addressing geological data for GISS.

In thinking computing, there exists many analysis techniques, including deep learning, fuzzy logic, kernel

learning, brain-inspired intelligence methods [24], [25], [31].

2) An Application Case: Here, an application case is analyzed while implementing cyber-physical com-

puting in CPST multi-level computing framework.

Aiming at the large amount of structured and unstructured geological big data, the computational perfor-

mance of traditional computing approaches is not satisfactory. Considering that there are huge volume of

geological data texts and the size of some texts is relatively not big, the Hadoop-based Sequence File method

is employed while merging a large number of small files into one big file stored with the form of key. After

completing the store processing for geological texts, some big data analysis applications can be conducted.

For example, the text classification prediction is expected to be implemented. A specific implementation

process is shown in Fig. 6.

Hadoop is known as a distributed system infrastructure [32]. It uses cluster computing and high-speed

storage technologies to address huge amount of data. The key of Hadoop is MapReduce computing framework

and Hadoop distributed file system (HDFS). Here, the data are stored by using HDFS, and the parallel

computing is implemented by using MapReduce. In Fig. 6, the working process of text classification is

summarized as follows. Firstly, the preprocessing for those collected geological texts is conducted, including

Chinese word segmentation, deleting stop words, and deleting low-frequency words. Secondly, the vector

representation for a collection of text is achieved using vector space model (VSM) model [33]. Thirdly,

within the MapReduce mechanism, the text vectors are classified using K-nearest neighbor (KNN) algorithm

method [34]. Then, all of the classification results are integrated with a Boosting-based weighted ensemble.

Finally, the classification is evaluated by some geological experts in social computing mode.

C. System Security

Security is one of the most concerned problems for system. Due to open network structure and service

sharing scheme in cloud, it imposes very challenging obstacles to the security guarantee. In the system

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proposed here, through monitoring abnormal behavior of software used in network environment of clients,

the cloud can intercept malicious program and Trojan. Security analysis shows that the third party provider

chosen in the proposed system could guarantee data availability to a certain degree, and thus effectively

enhance data confidentiality. Furthermore, data security is one of the most concerned issues for the user.

Some effective protection measures for data have been developed to ensure the security of the data, such

as multiple copies of data storage, encryption [35]. A cloud security policy focuses on managing users,

protecting data, securing virtual machines, and specifying the infrastructure usage policies. The developed

system in this article has data backup and data restore functions to ensure data security. Meanwhile, the

classification strategy for modules in the system can also make the system manager effectively check access

permissions on system resource, and accordingly display content.

V. CONCLUSION

Currently, the complexity of geological data imposes very challenging obstacles to the traditional manage-

ment service of geological information. With the rapid development of computer technologies and Internet,

many software tools and solutions are developed to design a GISS to improve the application performance.

Through the deep understanding of the applications of IoT in the field of geology, the construction require-

ment of GISS is accordingly analyzed, and the overall architecture and physical deployment of the geological

things are proposed. In this article, RFID, sensors, and other things are employed to achieve the orderly

and intelligent management of geological data. Specifically, based on the management and application of

geological equipments, this article presents a modeling and computing scheme using CPST. Moreover, two

application cases are provided to show the efficiency of using CPST modeling and computing scheme for

GISS. In addition, the data security of the developed system is also analyzed. As an effective solution for

GISS management using CPST, there are several interesting research topics to be explored in the future. For

instance, further GISS can be developed using some advanced open source software.

CONFLICT OF INTERESTS

The authors declare that there is no conflict of interests regarding the publication of this article.

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