Candidate agent platforms.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"intechopen-signs-new-contract-with-cepiec-china-for-distribution-of-open-access-books-20210319",title:"IntechOpen Signs New Contract with CEPIEC, China for Distribution of Open Access Books"},{slug:"150-million-downloads-and-counting-20210316",title:"150 Million Downloads and Counting"},{slug:"intechopen-secures-indefinite-content-preservation-with-clockss-20210309",title:"IntechOpen Secures Indefinite Content Preservation with CLOCKSS"},{slug:"intechopen-expands-to-all-global-amazon-channels-with-full-catalog-of-books-20210308",title:"IntechOpen Expands to All Global Amazon Channels with Full Catalog of Books"},{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"}]},book:{item:{type:"book",id:"4605",leadTitle:null,fullTitle:"Lipoproteins - From Bench to Bedside",title:"Lipoproteins",subtitle:"From Bench to Bedside",reviewType:"peer-reviewed",abstract:"In mammalian blood plasma there exist some 6 major lipoprotein classes. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"63309",title:"Leveraging Internet-of-Things to Support Circular Economy Paradigm in Manufacturing Industry",doi:"10.5772/intechopen.80453",slug:"leveraging-internet-of-things-to-support-circular-economy-paradigm-in-manufacturing-industry",body:'\nCircular economy represents a fundamental alternative to the currently predominating linear economic model, based on the take-make-consume-dispose paradigm. The linear model has conducted to economic growth and welfare but ran its course. New economic models are needed in order to reduce material inputs and waste generation leveraging on ecodesign, recycling and reusing of products, new business models, and new technologies. Products and production systems need to be designed to be “circular”: materials need to be efficiently processed and waste needs to be arranged and recycled.
\nLooking from a technological perspective, the circular economy generates needs in the field of manufacturing, processing, networking, identification, and recycling of materials and products. As Bányai discussed [1], the success of the operation of these functions is based on the optimal design and control. Most of the supply chain models include only the traditional procurement-production-distribution sub-processes used for converting raw materials to final products and deliver them to the wholesalers, retailers or directly to end users. In this case study is showed a complex green supply chain model in which sophisticated operation research heuristics has to be used to find the optimal solution in order to minimize the costs.
\nThe main needs generated by circular economy are as follows:
Advanced collection, sorting, and recycling technologies—apps, sensors, robots, etc.
Efficient materials processing technologies—machine learning and artificial intelligence.
Production technologies that support design for circularity—3D printing, disassembly, and repairability.
Interactive platforms for enhanced connectivity—apps, websites, databases, and Internet-of-Things.
Industry 4.0 is fundamental to move from linear to circular economy model. Industry 4.0 refers to the introduction of new technologies, such as the Internet-of-Things, 3D printing, artificial intelligence, cloud computing, machine-to-machine communication, etc., in the traditional industrial domain in order to support the improvement of the efficiency and automation of the processes happening in the industry.
\nThe introduction of circular economy generates new technological and nontechnological needs. The change in ownership and material management concepts, both at a consumer and at business level, generates a need for upscaling and acceleration of business concepts such as: products as a service, sharing platforms, peer-to-peer interactions, and industrial symbiosis [2, 3]. Industrial symbiosis approach focuses on the hidden value of waste resources within an industrial network which can be exploited through the cooperation [4].
\nWasted resources are unused or reusable energy, water, materials, and residues of production processes. Industrial Symbiosis can help improve the overall efficiency of the industrial system: companies within an industrial symbiosis network establish beneficial business relationships among them based on these exchanges of resources. However, these relationships are not always easy to establish due to the difficulty in creating the network or lack of a communication platform for the network [5].
\nThe vision of the circular economy is well suited to the concept expressed by the manifesto of the Physical Internet (PI) Initiative [6], a recent concept of breakthrough innovation aiming to improve by an order of magnitude; the economical, environmental, and societal efficiency and sustainability of the way physical objects are moved, deployed, realized, supplied, designed, and used. It attempts to achieve this scope by applying concepts from Internet data transfer to real-world shipping processes [7, 8].
\nPrevious research has already proven that new inventory models enabled by and applied in PI could help reduce inventory levels, thanks to its high flexibility [9]. The vision of the physical Internet involves encapsulating goods in smart, ecofriendly, and modular containers ranging from the size of a maritime container to the size of a small box. That new inventory models enabled by and applied to PI could help reduce inventory levels, thanks to its high flexibility. These modular containers will be continuously monitored and routed, exploiting their digital interconnection through the Internet-of-Things.
\nCircular economy concepts, and more specifically industrial symbiosis, are vehiculating lot of research in the Internet-of-Things (IoT) domain. Indeed, waste characterization and identification can be considered as the most challenging steps in waste management, that is, the ones that kick off the subsequent valorization and exploitation. Once characterized and identified the waste, the IoT-domain literature proposes a huge number of infrastructures and platforms (e.g., see [10, 11, 12]), which helps to monitor features of interest, in real time or on historical base, communicating information only to subjects who are subscribed to specific topics of interest, just when they are available.
\nThe aim of this chapter is to present how Internet-of-Things and artificial intelligence can support the circular economy paradigm, through the development of a marketplace for connecting buyers and sellers of manufacturing services, raw materials, and products toward building global supply chains. The core component of this marketplace is a novel, agent-based, brokering module that will apply both syntactic and semantic matching in terms of manufacturing capabilities, in order to find the best possible supplier to fulfill a request for a service, raw materials or products involved in the supply chain.
\nSection 2 provides the analysis of the background and state of the art of the multi-agent systems.
\nSection 3 of this chapter is dedicated to the description of a typical industrial symbiosis case study that the proposed solution intends to address.
\nSection 4 introduces the solution proposed by this work to support the circular economy, which integrates traditional IoT platforms with an agent-based approach to support a bidding system through the implementation of a marketplace.
\nSection 5 specifies the type of agents implemented in the marketplace, and finally Section 6 concludes the chapter addressing the future works to extend the platform.
\nMulti-agent systems (MAS) [13] have been widely studied in the literature, and some existing options have been evaluated before deciding to implement the marketplace. A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Agent platforms are typically designed with a container paradigm, where all agents live in a well-defined agent container. Among the many platforms available on the Internet, a few subsets of candidates that might cover a good subset of composition features and requirements have been selected.
\nJIAC (Java-based Intelligent Agent Componentware) [14] is a Java-based agent architecture and framework that allows an easy development as well as the operation of large-scale, distributed applications, and services. The framework supports the design, implementation, and deployment of software agent systems. Then, the entire software development process is supported by JIAC. It also allows the possibility of reusing applications and services, also by modifying them during runtime. The strength points of JIAC are distribution, scalability, adaptability, and autonomy. In fact, JIAC V applications can be developed using extensive functionality that is provided in a library. This library has already-prepared services, components, and agents which can be integrated into an application. For example, individual agents are based on a component architecture which already provides the basic functionality for communication and process management. However, application-specific functionality can be provided by the developer and be interactively integrated.
\nSARL [15] is a general-purpose agent-oriented language. SARL focuses on providing the fundamental abstractions for dealing with interaction, concurrency, distribution, reactivity, decentralization, autonomy, and dynamic reconfiguration. These are high-level features considered so far as the major requirements for a good and practical implementation of modern complex software applications. SARL approach remains as generic as possible and highly extensible to easily integrate new concepts and features comparing to the variety of existing approaches and meta-models in the field of agent-oriented engineering and multi-agent systems. The language is platform and architecture independent.
\nJADE (Java Agent DEvelopment Framework) [16] is a software framework that eases the implementation of multi-agent systems through a middle-ware complies with the FIPA [17] specifications and a set of graphical tools that help the debugging and deployment process. Another helpful characteristic concerning JADE-based system is that it can also be distributed across machines with different OS. In a distributed scenario, the configuration of each system deployed can be controlled remotely thought a GUI. The configuration can be even changed at run time by moving agents from one machine to another, if required. Finally, JADE is completely implemented in Java language, and the minimal system requirement is the version 5 of JAVA.
\nSPADE (Smart Python multi-Agent Development Environment) [18] is a multiagent and organizations platform based on the XMPP/Jabber technology and written in the Python language. This environment offers features and facilities that simplifies the construction of MAS, examples are: the concepts of users (agents) and servers (platforms); an existing communication channel and an extensible communication protocol based on XML, just like FIPA-ACL [19]. Many other exist, but SPADE has been the first agent platform based on XMPP technology.
\nIn Table 1, a comparative summary of the candidate agent platforms is reported, where respective pros and cons are addressed.
\nPlatform | \nPros | \nCons | \nLicense | \n
---|---|---|---|
JIAC | \nExploits Apache ActiveMQ as transport declarative agent definition through spring | \nNot FIPA compliant | \nApache v2.0 | \n
SARL | \nExploits ZeroMQ as transport | \nNot FIPA compliant | \nApache v2.0 | \n
JADE | \nFIPA compliant | \nSupports only RMI transport. Few extensions for JMS/XMPP transports | \nLGPL | \n
SPADE | \nFIPA compliant | \nUses a custom XMPP server | \nLGPL | \n
Candidate agent platforms.
As it is possible to notice from the presented analysis, none of the platforms offers both a distributed approach and a general purpose implementation for the communication back end.
\nThe platform proposed in this work, therefore, represents a unique example of a marketplace based on semi-automatic multi-agent collaboration.
\nInternet-of-Things can support the circular economy in many ways. A typical case study consists on the early and automated detection of scrap material levels and its management, also identifying a network of companies which take waste materials as raw material for their production process.
\nWhenever a desired level (e.g., 50, 75 or 90%) is detected by the scrap material storage area sensors, it will be annotated, reported, and recorded through IoT platform automatically. Getting accurate information about the scrap material storage area levels during the day will now be possible and the company producing the scrap material will stop occupying the staff time to manually detect scrap material levels at each storage area, with the system taking care of such procedure in each factory. Real time data will help optimize scrap material management procedures. Scrap material recycling companies will be either automatically contacted by the platform or they will receive continuous information on the current scrap material level. In response to such an interaction, they will be ready to put their offers to the system, once a new bidding process will be started by the company producing the scrap material. Such process can take place once the scrap material specification and the bidding specifications have been identified, with the company inserting the bid details in the platform. The system registers the new bid request and contacts the waste management companies for getting bids on scrap material. Companies contacted by the system typically belong to a selected subset of contractors. However, according to the company’s policies, in some cases, the bid could be open to new contractors, for example, to find better offers fostering new collaborations. When the system contacts contractors for bidding, a deadline is provided on when the last bid will be accepted. Once the deadline is reached, the platform system will select the best (or n-best) offer according to the bidding process criteria (entered when the process started) and present it to the relevant manager (this automation will reduce the time required for processing bids). Upon human approval, bidders will be automatically informed about the outcomes of their bids. The company will then arrange a pick-up date and time in agreement with the selected scrap material recycling company; such an arrangement will be automatized to the greatest extent (see Figure 1).
\nScrap material collection and bidding process.
The criteria to award the contract will be price, company reputation, and the data agreement for the collection of the material. The literature presents several well-known algorithms that can be leveraged for automatic negotiations, such as contract net protocol [20], while many protocols are nowadays considered de facto standards for supporting secure peer-to-peer communication in IoT (such as, XMPP, MQTT, CoAP, and AMQP).
\nThis section introduces the solution proposed to support the circular economy in the use case presented in Section 3. The proposed platform integrates traditional IoT platforms with an agent-based approach to support a bidding system through the implementation of a marketplace. The marketplace is a fully distributed multi-agent system designed to support Industry 4.0 exchanges between involved stakeholders. It is particularly aimed to supporting automatic supply chain formation and negotiation of goods/data exchanges. The marketplace exploits a microservice architecture, based on Docker, and relies upon a scalable messaging infrastructure. Trust and security are granted in every negotiation step undertaken by automated agents on behalf of involved stakeholders.
\nThe marketplace includes the following elements: (a) a marketplace management portal; (b) a marketplace core based on Docker; and (c) a set of “default” agent implementations ready to be adopted by interested stakeholders.
\nThe main building blocks of the marketplace are displayed in Figure 2.
\nMarketplace components overview.
According to FIPA specifications [21], an Agent Management System (AMS) is a mandatory component of every agent platform and only one AMS should exist in every platform. It offers the White Pages service to other agents on the platform by maintaining a directory of the agent identifiers currently active on the platform. Prior to any operation, every agent should register to the AMS to get a valid agent identifier, which will be unique within the agent platform.
\nA White Pages service is a mandatory component of any MAS system. It is required to locate and name agents on the system, making it possible for one agent to connect with one another. In the current implementation of the Agent Management System, the agent identifiers are stored in a MySQL database. MySQL has been chosen because it offers relevant features for the project such as on-demand scalability, high availability, and reliability. Other agent platforms, like SPADE, use MySQL as well for the database underlying the White Pages service.
\nAgents are stored according to the schema depicted in (Table 2):
\nAgent_id | \nAgent_owner | \nAgent_role | \n
---|---|---|
The unique identifier for the agent. It is the primary key for the table, having the constraint of being unique. | \nThe name of the company owning the agent. | \nThe role of the agent on the marketplace can be either ‘Requester’ or ‘Supplier’. | \n
Table schema.
Since the directory service is offered by the AMS, the latter is the only agent allowed to directly interact with the database. When the AMS is executed, it needs the following configurations:
IP address of the host running the MySQL instance.
Valid username and password for performing CRUD operations on the database.
Name of the database to operate on, since the same instance may run different databases.
There are two main categories of agents that can be defined a priori, depending on the kind of services provided:
Marketplace agents (MA)
Stakeholder agents (SA)
The first category groups all the agents providing services that are crucial for the marketplace to operate. The second category, instead, groups agents developed and deployed by the marketplace stakeholders to take part in chain formation rounds.
\nFrom an implementation point of view, they are very similar and share a large set of features, especially the communication protocols used for the interaction with stakeholders and other agents.
\nThe communication protocols used by the MA for the interaction with the stakeholder on left side and with other agents on the marketplace on the right side are shown in Figure 3. Any agent can be controlled by the stakeholder via RESTful APIs which will be listed and explained in the following sections. Agents on the marketplace can exchange messages using Advanced Message Queuing Protocol (AMQP) as transport layer.
\nSimplified agents’ communication logic.
Stakeholder agents are instead deployed at the stakeholder’s premises, and their purpose is to fulfill the stakeholder’s interests. Stakeholder agents can be further divided into two different categories: requester and supplier. In the following, the reference implementations for the two different kinds of stakeholder agents will be described. However, both kinds of agents share a set of features allowing them to respond in different ways to the same kind of solicitations (such as input from GUI, messages from other agents) according to the state of their current behavior (when the solicitation is received).
\nCurrently, agents support two different behaviors:
Authentication behavior: This is triggered upon the each agent activation. It consists of all the procedures that are necessary for the agent to correctly operate on the marketplace.
Negotiation behavior: This is the behavior each agent needs to support in order to be able to participate in marketplace negotiations. It is designed in a way that allows different negotiation protocols to be adopted.
The authentication behavior is the first one engaged by any agent since:
It takes care of getting the agent identifier from the AMS.
It authenticates the agent with the messaging broker, allowing secure message exchange over SSL.
Both these phases are mandatory for any agent to operate on the marketplace. After the authentication process, the negotiation behavior comes to play. It allows the agent to perform transactions and interactions with the other agents on the marketplace. Its behavior is constrained to the negotiation protocol that is being used.
\nAgents might or might not support a certain level of “intelligence” in their decisions regarding certain negotiation protocols. For example, an agent might have the capability of self-evaluation for the received offers, whereas another agent might not have such capability and, therefore, it will involve an external agent (providing such intelligence) during the decision process. An agent having such level of intelligence will be described as “smart,” while it will be described as “dumb” in the absence of that.
\nThe requester agent is the agent used by a factory to request the execution of an existing supply chain or to initiate a new supply chain. Due to the dynamics of exchanges pursued in a factory-oriented marketplace, there is no actual distinction between the two processes, that is, for any supply need a new chain is formed, and a new execution of the chain is triggered. The requester agent may act according to several negotiation protocols, which can possibly be supported by only a subset of the agents active on a specific marketplace instance. In the proposed implementation, it exists a baseline protocol, which must be supported by any agent, called CONTRACT-NET. In such protocol, a requester agent plays the role of “Initiator.”
\nAs described by Shoham [22], any agent can pass through a different set of states; the state of an agent consists of components such as beliefs, decisions, capabilities, and obligations. In the CONTRACT-NET, protocol requester agents go through different sets of states, performing different actions upon the receipt of a message (either from the UI or from other agents) according to their current state. In Figure 4, the states for requester agent are shown with arrows indicating the allowed transitions between one state and another.
\nRequester states.
One of our factory-oriented marketplace strengths is the tight connection existing between intra-factory and inter-factory components. This connection allows the agent on the marketplace to operate in the optimum way to fulfill the stakeholder’s needs.
\nConcerning the requester agent, such connection is the starting point of a new negotiation protocol: the IIMS notifies, through the learning agent (an agent available at the stakeholder’s premises, in charge of intra-inter factory communication), that a certain good/service is needed. Upon receipt of such notification, the requester agent can react accordingly, using the most suitable protocol to fulfill the request. A possible interaction is here described from the requester agent point of view:
Marketplace system automatically notifies the stakeholder about the scrap metal bin fill level, through a notification from the learning agent (which receives the data from the sensors installed in the factory premises) to the requester agent.
Requester agent has the logic for starting a new bidding process through default parameters (set by the stakeholder).
Requester agent starts the bidding process, looking for the list of the supplier agents currently available on the marketplace that are capable in replying to such offer.
Offers come to requester agent, which can either evaluate them locally (smart agent behavior) or send them out for evaluation toward an external agent.
Once the deadline for receiving proposals has expired, requester agent notifies the UI about the best one(s) that have been received.
Stakeholder can now choose the preferred offer, select it, and notify the requester agent that can close the deal with the corresponding supplier agent.
The same input that is needed for a bidding process to start, that is, the fill level, can be used by the requester agent to update the supplier agents interested in receiving such information in order to be ready when a new bidding process will take place. The latest described scenario is here described from the requester agent’s point of view:
The fill level information is received from the learning agent.
The requester agent forwards such information, in the marketplace, only to the supplier agent interested in receiving it.
The supplier agent is the counterpart of the requester agent on marketplace. It is usually adopted by actual suppliers to respond to supply requests coming from other stakeholders in the marketplace. Factories transforming goods typically employ at least one requester agent to get prime goods and one supplier agent to sell intermediate products to other factories.
\nIn the CONTRACT-NET protocol, supplier agents go through different sets of states, performing different actions upon receipt of a message (either from the UI or from other agents) according to their current state. In Figure 5, the states for supplier agent are shown with arrows indicating the allowed transitions between one state and another.
\nSupplier states.
A typical supplier agent is registered on the marketplace remaining “silent” while waiting for a new call for proposal, issued by a requester agent. Once this message arrives, the supplier agent:
Evaluates the call for proposal message according to the policies set by the agent owner.
If the proposal is compliant with such policies, an offer is sent from the supplier agent, otherwise it goes back to the “silent” (IDLE) state.
Once the offer has been sent, the supplier agent waits for the requester’s decision.
In the end, if the requester agent has accepted the proposal, a notification from the supplier agent is sent to the UI for notifying the stakeholder and waiting for his decision.
The final message for acceptance is sent back to the requester agent in case the stakeholders accept the deal, otherwise a reject message will be sent.
As already mentioned for the requester agents, the tight connection between intra-factory and inter-factory systems is a strength for the marketplace. For the supplier agent, this connection is exploited to dynamically adapt to the market needs.
\nFor the supplier agent, this connection is here briefly described from the supplier agent point of view:
The supplier agent subscribes, via AMQP, to the queues where the fill level notification related to a good of a certain company will be received.
When such information arrives, the supplier agent can store it to exploit it for predictions of future fill levels.
The same behavior can be used for predicting the prices of a certain good/service. The supplier agent can, in fact, store information about the past bidding process and apply deep learning techniques to have a prediction about the possible future values of interest, that is, what will be the selling price for a certain good at a certain point of time in the future.
\nThe proposed marketplace platform shows an approach that enables the paradigm switch from the linear economy model to the circular one. The circular economy paradigm is based upon reuse, reparation, refurbishment, and recycling of existing materials and products and, as shown in the presented case study, these activities can be facilitated through the proposed marketplace approach. Particularly, a company can order the raw materials exploiting a certain supply chain available on the marketplace, process them, and sell the residual production waste through another dedicated supply chain available on the marketplace. On the other hand, a recycling company can buy (or simply collect) the offered waste, properly process it, and sell it again as raw material.
\nWhat is important to notice is that, even though existing collaboration models can be exploited on the marketplace, the proposed implementation has been designed to foster new collaboration models within and across the supply chain, making it easier for new actors to participate and collaborate with the existing ones. In order to increase the overall market competitivity while trying to maximize the participant profits, new protocol interactions based on different kinds of auctions are being studied. For instance, Vickrey auction [23] is being investigated, since it has been proved to be efficient, under certain circumstances, in maximizing the expected utility of each bidder.
\nOne of the next important issues to be carefully addressed concerns the trust. Platforms like eBay imply the complete trust of strangers who take money before sending any wished goods. The reputation of sellers is built according to feedback provided by previous buyers and often is very hard for newcomers to sell items on the marketplace because they have to build up a good reputation. With this trust system is very easy to fake reviews in such a way to increase the reputation. Nowadays, Distributed Ledger Technologies (DLT), such as blockchain, are analyzed to be applied in many different application domains. Marketplaces can benefit from the power of DLT since it is built to create trust and transparency. In order to enhance the proposed marketplace approach, the authors are planning to leverage on DLT. DLT have received more and more attention in recent years as innovative methods to store and update data within and among organizations. Multiple copies of the ledger are held by different parties, with data added by consensus and without the need for a third party. This means that DLT are able to offer an immutable record, which means that data added to the ledger is in theory unchangeable, secure, and preserved for the life of the ledger, with the agreement of all participants. Additions to the ledger or changes to the governing structure are decided on a consensus basis by multiple participants. So, these systems provide a transparent and verifiable record of transactions. As a result, DLT can provide gains in trust among bidding participants.
\nThis work was supported by the European Union’s Horizon 2020 research and innovation program through the COMPOSITION project (Grant No. 723145).
\nThe outbreak of the novel COVID-19 (SARS-CoV-2) which the World Health Organization declared a pandemic in March 2020 was first detected in Wuhan, China [1]. It has endangered millions of lives, especially individuals with chronic conditions such as high blood pressure and diabetes [2]. COVID-19 causes severe respiratory tract infections. This chapter focuses on the current status of coronavirus cases and deaths worldwide, and the underway efforts to develop a vaccine or a therapeutic drug to the pandemic.
\nOn the 12th of December 2020, the World Health Organization (WHO) reports that there were 69 808 588 confirmed cases and 1 588 854 deaths worldwide [3]. From Figure 1. America and Europe had the highest number of confirmed daily COVID-19 cases of 29 770 479 and 21 688 472 respectively on the 30th of November 2020. The least numbers of daily confirmed cases are recorded in Africa (1 610 874) and the Western Pacific (952 629).
\nNumber of COVID-19 daily confirmed cases by WHO region from 31 January 2020 up to 30 November 2020 (WHO, 2020).
From Figure 2. America and Europe recorded the highest numbers of deaths by the 30th of November 2020 while Africa and the Western Pacific recorded the least numbers of deaths.
\nNumber of COVID-19 daily confirmed deaths by WHO region from 31 January 2020 up to 30 November 2020 (WHO, 2020).
The COVID-19 pandemic has affected the health, economy, and social mobility of people in countries around the world. Limited mobility through a lockdown and banning international travelers and social distancing, wearing face masks, and health officials and agencies undertook frequent hand washing with soap to slow down the rate of infection. These measures delay the spread of viral infections, reduce the acute pressure on hospital beds, frontline medical personnel, and resources [4]. The measures, thus, prevent the collapse of the national healthcare systems.
\nCountries like South Korea and Singapore were able to contain the virus due to the rapid and extensive tests carried out accompanied by preventive measures, including early school closures [5]. Furthermore, interviews were conducted by the South Korean government followed by cellphone contact tracing to track down the contacts of new coronavirus patients and ordered those contacts to self-isolate [6]. However, the United States of America’s failure to contain COVID-19 is attributed to the slow rate of testing during the first onset of the pandemic due to the shortage of testing kits [7]. A huge proportion of the population in the USA has chronic conditions [8]. Hence, they easily succumb to the virus. Measures such as lockdown are important to slow down the rate of transmission. However, there is a need to develop a vaccine or a drug that can treat the coronavirus. It is hoped that Biotechnology companies will come with a suitable vaccine or therapeutic drug to ease the pressure of the pandemic.
\nBiotechnology research is essential in the development of key interventions such as vaccines and drugs to fight off the pandemic [9]. The new pandemic opened up opportunities for medical specialists and biological researchers to look for different ways of managing and containing the spread of the virus using biotechnology. Rosales-Mendoza, Comas-García and Korban (2020, p.283) assert that the onset of Covid-19 opened up opportunities for researchers:
\nUnder these unusual circumstances, academic researchers can work on pending manuscripts, analyze data, conduct experiments left behind on the back burner, and more importantly pursue new ideas for discovery: developing vaccines and monoclonal antibodies and screening antiviral compounds, such as secondary metabolites and peptides; thereby, actively participating in finding solutions to ongoing real-time global human and economic crises.\n
\nMore research groups in academia and in private industry are now offering free webinars to share and demonstrate new technologies or experimental protocols [9]. Young researchers will have valuable new opportunities to acquire and develop new skills and ideas to make the best out of this situation.
\nEfforts were made to develop diagnostic assays, prophylactic vaccines and therapeutics using biotechnology to combat the Covid-19 pandemic. Significant efforts have been made by biotechnology companies and organizations to develop new types of diagnostic kits, ventilators and therapeutic regimens or antivirals [1]. Several centres were made available for clinical trials.
\nShortages of Test kits prompted Local companies such as CapeBio Technologies in South Africa to develop Covid-19 test kits that were more efficient than the imported kits [1]. To speed up the production of reagents and test kits for Covid-19, the organizations such Department of Science and Innovation, the South African Medical Research Council and the Technology Innovation Agency funded companies, organizations and researchers for Covid-19 projects. The University of the Witwatersrand collaborated with the University of Oxford and started the Ox1Cov-19 vaccine trial in South Africa in June 2020 [1]. Human Research Ethics Committee had to approve the vaccine to ensure the safety of participants. The Covid-19 pandemic has demonstrated that some developing countries such as South Africa have the biotechnological expertise needed to address their health challenges.
\nPlants can play an essential role in producing diagnostic reagents to identify infected and recovered individuals, vaccines to combat infection, and antivirals to treat symptoms [4]. Plants have been used as a platform for the production of plant-derived proteins as diagnostic reagents for more than 30 years [10]. Assays for the detection of the virus can either detect the virus genomic RNA or virus proteins. The RNA based assay lacks a universal positive control, which would allow standardization across different testing laboratories. Scientists are using biotechnology to develop a diagnostic control reagent for COVID-19 based on virus-like particles (VLPs) derived from Cowpea mosaic virus [11]. Thus, plant biotechnology is playing an important role in the development of diagnostic reagents. In addition, the plant is also being used to produce vaccines and antivirals that are being tested using mice. According to Capell et al., antiviral drugs interfere with the viral replication cycle and slow down the infection [4]. The slowed infection gives the immune system more time to respond to the virus.
\nHealthcare professional researchers are trying different types of drugs existing in the market [2]. Such drugs have proven effective in the treatment of other respiratory illnesses. They are hopeful that some of the drugs may also be effective against COVID-19.
\nLockdowns due to the COVID 19 pandemic can only be implemented for a reasonably short period as they disrupt industrial production, the global supply chain as well as international trade [2]. Hence, there is need to develop thereupetic drugs and vaccines through biotechnology.
\nAs a result, the demand for the vaccine, drugs and other medical products is rising exponentially as the new COVID-19 cases rise throughout the world. The biotechnology industry, such as pharmaceutical companies and research organizations across the world have the challenge to develop vaccines and drug therapies to combat the spread of COVID-19 [2]. Different organizations are working together in tracking the pandemic, providing advice on essential interventions, distributing vital medical supplies to the needy [12].
\nVaccines save millions of lives by boosting the immune system to produce the necessary antibodies that recognize and fight off the pathogens they target [12]. If the body is exposed to the pathogens later, the immune system immediately destroys them, preventing illness. Vaccines consist of small amounts of weakened harmless versions of the pathogen that cause the disease.
\nSeveral companies including non-governmental entities and the World Health Organization are working tirelessly to identify the vaccines and treatment drugs to prevent the spread of COVID-19 [1, 12, 13]. WHO is working in collaboration with different partners, with more than 50 COVID-19 vaccine in trials to speed up the pandemic response. A safe vaccine will first be distributed to people who are most at risk while the public health actions continue to suppress transmission and reduce mortality. The people who are at risk include health workers, older age groups and people with underlying medical conditions. It has not been established how long the vaccines will last in terms of protection. Therefore, all measures being taken now to reduce the transmission should continue.
\nThe National Institute of Allergy and Infectious Diseases and the Coalition for Epidemic Preparedness Innovations – developed a vaccine using mRNA biotechnology with hopes to begin human tests of their product in April 2020 [14]. The Regional Centre for Biotechnology (RCB), a top research institute in Faridabad, India, is actively contributing to the global research efforts to combat the COVID-19 pandemic. RCB is conducting research activities such as potential inhibitors, markers and transmission reduction through the virucidal coating [14]. The virucidal coating will help destroy or deactivate the virus on different surfaces like glass, plastic and textiles, including cotton, nylon, and polyester that can potentially hinder the viral transmission [14]. Several companies have begun the development of vaccines and antiviral therapies.
\nThe work previously done on medical countermeasures (MCMs) against other coronaviruses, including SARS, are being tested against the novel COVID-19. Various other antiviral drugs and biologics are being investigated, and several have entered clinical trials to test for efficacy and safety against COVID-19.
\nBy the 18th of November 2020, Pfizer and BioNTech conclude phase 3 study of the covid-19 vaccine candidate. Their vaccine, BNT162b2, has proved to be 95% effective against COVID-19 beginning 28 days after the first dose. The vaccine has met the following conditions:
Efficacy was consistent across age, gender, race and ethnicity demographics; observed efficacy in adults over 65 years of age was over 94%
Safety data milestone required by the U.S. Food and Drug Administration (FDA) for Emergency Use Authorization (EUA) has been achieved
Data demonstrate vaccine was well tolerated across all populations with over 43,000 participants enrolled; no serious safety concerns observed; the only Grade 3 adverse event greater than 2% in frequency was fatigue at 3.8% and headache at 2.0% [15].
The following plans are under way:
Companies plan to submit within days to the FDA for EUA and share data with other regulatory agencies around the globe
The companies expect to produce globally up to 50 million vaccine doses in 2020 and up to 1.3 billion doses by the end of 2021
Pfizer is confident in its vast experience, expertise and existing cold-chain infrastructure to distribute the vaccine around the world [15].
The vaccines by Pfizer-BioNTech and Moderna are nucleic acid/genetic vaccines [16]. Nucleic acid/genetic vaccines use synthetic virus genes to initiate the immune response. The genetic material causes the body’s cells to produce the antigen, which in turn stimulates the production of antibodies [16, 17]. Although the immune response is long-lasting, the genetic instructions are short-lived. Nucleic acid vaccine technology allows the creation of vaccines and prompt manufacture of thousands of doses once the viral genetic sequence is known [18].
\nSimilarly, viral vector-based vaccines provide the body cells with genetic instructions [19]. They use a harmless virus to transport the instructions [19]. The body’s cells produce the antigen, which triggers antibody production. The Covid-19 vaccine Sputnik and the vaccine made by AstraZeneca and Oxford University use this biotechnology [16].
\nThe third type of vaccines is called Inactivated vaccines. Inactivated vaccines contain viruses with destroyed genetic material, but they can still stimulate the immune system to produce antibodies [20]. The vaccine by Sinopharm and the Beijing Institute of Biological Products use this biotechnology [16].
\nFinally, the Protein-based vaccines use either fragment of or the entire virus protein, which triggers an immune response. Coronavirus vaccines produced by Novavax, Sanofi and Bektop, use this biotechnology although they are currently not yet approved [16].
\nOnce a vaccine has been produced, it is critical to identify the dose that best balances safety and efficacy. Age is also another important factor that should be considered since the immune functions decline with age leading to poor vaccine responses. Currently, it has not been proved whether older adults would need high doses of Covid-19 vaccine as is the case with influenza vaccines [21]. After passing all the trial phases, each vaccine must be licensed and then production must be increased to meet the global demands. It is critical to ensure that vaccines are affordable and accessible to both low income and middle-income countries for immunization programmes can be implemented.
\nEfforts are being made to use monoclonal antibodies in the treatment of COVID-19. Currently, cocktail REGN-CoV 2 and Lilly’s LY-CoV555 which target the viral spike glycoprotein (S-protein) are under trial for the treatment of the coronavirus [22]. Eleven other monoclonal antibodies that target the S-protein are under trial [22]. Monoclonal antibodies are vital especially for the elderly who may not respond well to the vaccine.
\nAlthough there is no cure for COVID-19, patients have been found to benefit from the use of remdesivir [23]. However, the solidarity trials done in October 2020 indicated that the remdesivir and other drugs (hydroxychloroquine, lopinavir and interferon) had little or no effect on mortality and duration of hospital stay for patients in hospital[Who 2020]. The use of corticosteroids has been found to be effective against severe COVID-19 [24].
\nBiotechnology has raised hope to the world through the production of several vaccines. However, until there is an efficacious vaccine and an effective treatment drug that is accessible to everyone, we should keep in mind that SARS-CoV-2 or coronavirus is likely to remain a seasonal pathogen. The current measures that are in place to prevent the spread of infection should be maintained because it will take a long time for the vaccine to be accessible to all nations. The efforts that are being made to come up with more vaccines and therapeutic drugs should continue to be supported. The governments and the private sector should collaborate in providing the necessary funding to companies and researchers who are working tirelessly to develop new vaccines and treatment drugs.
\nI am very grateful to Dr. Cronje, who edited and improved the chapter. I am also grateful to the reviewers who provided constructive comments to improve the chapter. I am also very thankful to the funders who paid the publication fees.
\nThe author declare no conflict of interest.
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