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Sustainability in systems refers to applying sustainable principles and practices to create more resilient, efficient, and equitable systems that promote the well-being of people and the planet. Sustainability is an essential topic in contemporary software engineering, and its relationship with the characteristics and properties of a system or product called quality attributes is still an open question since each researcher has established their definition of sustainability in software. This has created diverse terms and concepts for distinct application environments and scopes, creating ambiguity and misconceptions. This work defines a domain ontology of Sustainability in Software named SinSO to address these issues. SinSO was implemented in OWL, using competency-based questions to validate. The findings show that this proposal satisfies several quality and content requirements. Also, using Protégé and the Hermit reasoner, we verified that SinSO is consistent since the ontology statements are coherent and do not lead to conflicting or contradictory conclusions. In addition, competency questions allowed us to demonstrate that SinSO does fulfill its purpose. FOCA methodology allowed us to evaluate SinSO quality. Also, SinSO was used in two case studies, one about software for senior-citizen smart-home, and the other, a simulator to develop and test smart-city applications, achieving positive outcomes. To verify its accuracy, completeness, and maintainability, further evaluations of SinSO are needed in real case studies. We conclude that SinSO can significantly contribute to reducing ambiguity and enhancing comprehension in this area. Furthermore, SinSO can be an effective tool for engineers to recognize the concepts and relationships in the sustainable domain to consider in the systems development life cycle to build sustainable systems. [ABSTRACT FROM AUTHOR]

This article proposes a method for ontology construction in the field of ancient architecture digitization with the aim of addressing the lack of formalization, sharing, and reusable unified description mechanisms currently observed in the field of ancient architecture digitization. This method defines the related concepts, attributes, and relationships between concepts in the digitization of ancient architecture. It employs the network ontology language OWL to model the ontology in the digitization domain of ancient architecture and realizes the visualization of the ontology in the digitization domain of ancient architecture, thereby providing effective support for the sharing and reuse of digitization knowledge of ancient architecture. Finally, an example of a wooden tower is taken to verify the effectiveness and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

Email is an excellent technique for connecting users at low cost. Spam emails pose the risk of collecting a user's personal information by fooling them into clicking on a link or engaging in other fraudulent activities. Furthermore, when a spam message is delivered, the user may read the entire message before deciding it is spam and deleting it. Most approaches to email classification proposed by other authors use natural language processing (NLP) methods to analyze the content of email messages. One of the biggest shortcomings of NLP-based methods is their dependence on the language in which a message is written. To construct an effective email cyber threat intelligence (CTI) sharing framework, the privacy of a message's content must be preserved. This article proposes a novel domain-specific ontology and method for emails that require only the metadata of email messages to be shared to preserve their privacy, making them applicable to solutions for sharing email CTI. To preserve privacy, a new semantic parser was developed for the proposed email domain-specific ontology to populate email metadata and create a dataset. Machine learning algorithms were examined, and experiments were conducted to identify and classify spam messages using the newly created dataset. Feature-ranking algorithms, chi-squared, ANOVA (analysis of variance), and Kruskal–Wallis tests were used. In all experiments, the kernel naïve Bayes model demonstrated acceptable results. The highest accuracy of 92.28% and an F1 score of 95.92% for recognizing spam email messages were obtained using the proposed domain-specific ontology, the newly developed semantic parser, and the created metadata dataset. [ABSTRACT FROM AUTHOR]

The article discusses models and mechanisms of transdisciplinary research in the perspective of creating clusters of disciplinary convergence and scientific theories, formal representation of knowledge, and the formation of the knowledge industry using a unified ontology engineering devkit. Special attention is focused on the possibilities of research-related design for the creation of new knowledge and technologies. A conceptual analysis of ontology engineering is conducted, indicating the importance and efficiency of applying ontology knowledge and mechanisms in solving user problems. The domain ontology, its ontology graph, and their essential differences and advantages from owl ontologies are examined in depth. An example of the application of task ontology to the design of programmable microchip computing devices is proposed. [ABSTRACT FROM AUTHOR]

Wheat sharp eyespot is a soil-borne fungal disease commonly found in wheat areas in China, which can occur throughout the entire reproductive period of wheat and has a great impact on the yield and quality of wheat in China. By constructing a domain ontology for wheat sharp eyespot control and modeling the domain knowledge, we aim to integrate and share the knowledge in the field of wheat sharp eyespot control, which can provide important support and guidance for agricultural decision-making and disease control. In this study, the literature in the field of wheat sharp eyespot control was used as a data source, the KeyBERT keyword extraction algorithm was used to mine the core concepts of the ontology, and the hierarchical relationships among the ontology concepts were extracted through clustering. Based on the constructed ontology of wheat sharp eyespot control, the schema of knowledge extraction was formed, and the knowledge extraction model was trained using the ERNIE 3.0 knowledge enhancement pretraining model. This study proposes a model and algorithm to realize knowledge extraction based on domain ontology, describes the construction method and process framework of wheat sharp eyespot control domain ontology, and details the training and reasoning effect of the knowledge extraction model. The knowledge extraction model constructed in this study for wheat sharp eyespot control contains a more complete conceptual system of wheat sharp eyespot. The F1 value of the model reaches 91.26%, which is a 17.86% improvement compared with the baseline model, and it can satisfy the knowledge extraction needs in the field of wheat sharp eyespot control. This study can provide a reference for domain knowledge extraction and provide strong support for knowledge discovery and downstream applications such as intelligent Q&A and intelligent recommendation in the field of wheat sharp eyespot control. [ABSTRACT FROM AUTHOR]

The article discusses models and mechanisms of transdisciplinary research in the perspective of creating clusters of disciplinary convergence and scientific theories, formal representation of knowledge, and the formation of the knowledge industry using a unified ontology engineering devkit. Special attention is focused on the possibilities of research-related design for the creation of new knowledge and technologies. A conceptual analysis of ontology engineering is conducted, indicating the importance and effectiveness of applying ontology knowledge and mechanisms in solving user tasks. The domain ontology, its ontology graph, and their essential differences and advantages from owl ontologies are examined in depth. An example of the application of task ontology to the design of programmable microchip computing devices is proposed. [ABSTRACT FROM AUTHOR]

In their anthology Everything Flows: Towards a Processual Philosophy of Biology, Daniel J. Nicholson and John Dupré argue that modern theories of biology imply that the fundamental structure of reality is processual at its core. In the present work, I first examine the implicit and explicit metaphysical presuppositions the editors make in order to allow for such an inference from scientific theory to ontology. After showing the difficulties of a naïve transfer of theoretical entities to fundamental ontology, I argue that the editors can nevertheless extend their claims beyond the mere articulation of different domain ontologies. This leads to the idea of a scientifically informed induction base for an ontology of processes. [ABSTRACT FROM AUTHOR]

Domain ontologies facilitate the organization, sharing, and reuse of subject areas. Building a software ontology is labor-intensive and time-consuming. In the process of obtaining a software ontology, it is required to analyze a huge number of scientific publications relevant to the software being modeled. The process of ontology replenishing with information from a huge number of scientific publications can be facilitated and accelerated through the use of lexical-syntactic patterns of ontological design. In this paper, we consider the possibility of automated construction of scientific subject area ontologies based on a heterogeneous patterns system of ontological design. This system includes ontological design patterns and is intended for ontology developers. System also includes automatically built lexical and syntactic patterns, which help to automatic replenishment of the ontology with information extracted from natural language texts. [ABSTRACT FROM AUTHOR]

Already in the early 1990s, one could encounter the opinion that the state in which the field of qualitative research finds itself bears the hallmarks of the ‘curse of abundance’. Since then, the phenomenon of proliferation in the field has continued to gain momentum. Due to the dynamic growth of qualitative variants of research methodologies, methods and techniques, as well as the enormous internal diversity of the field, qualitative researchers are struggling to orient themselves in the field of their own research practice. Increasingly, many researchers signal the need to systematize their knowledge of the numerous contemporary variants of qualitative research practice. This article responds to this need. It presents a model of the field of contemporary qualitative research based on the IT concept of domain ontology, developed based on a multidimensional content analysis of five dominant methodological journals presented in the form of a semantic network. The proposed model gives an insight into the essential elements of the field (epistemological approaches, data collection and analysis methods, classified into 369 ontological classes), as well as shows their clusters and inter-class relationships. It indicates the existence of three sub-fields characterized by the presence of different approaches and research methods, which differ in density and the strength of relationships. The ontological model of the qualitative research field is an important step toward the development of a domain qualitative research knowledge base, i.e., an information system organizing methodological knowledge that allows for trend monitoring, knowledge management, and effective use of knowledge in research practice.

Abstract Online rumors are widespread and difficult to identify, which bring serious harm to society and individuals. To effectively detect and govern online rumors, it is necessary to conduct in-depth semantic analysis and understand the content features of rumors. This paper proposes a TFI domain ontology construction method, which aims to achieve semantic parsing and reasoning of the rumor text content. This paper starts from the term layer, the frame layer, and the instance layer, and based on the reuse of the top-level ontology, the extraction of core literature content features, and the discovery of new concepts in the real corpus, obtains the core classes (five parent classes and 88 subclasses) of the rumor domain ontology and defines their concept hierarchy. Object properties and data properties are designed to describe relationships between entities or their features, and the instance layer is created according to the real rumor datasets. OWL language is used to encode the ontology, Protégé is used to visualize it, and SWRL rules and pellet reasoner are used to mine and verify implicit knowledge of the ontology, and judge the category of rumor text. This paper constructs a rumor domain ontology with high consistency and reliability.

Copyright of Przegląd Socjologii Jakościowej is the property of Redakcja Przegladu Socjologii Jakosciowej and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Online rumors are widespread and difficult to identify, which bring serious harm to society and individuals. To effectively detect and govern online rumors, it is necessary to conduct in-depth semantic analysis and understand the content features of rumors. This paper proposes a TFI domain ontology construction method, which aims to achieve semantic parsing and reasoning of the rumor text content. This paper starts from the term layer, the frame layer, and the instance layer, and based on the reuse of the top-level ontology, the extraction of core literature content features, and the discovery of new concepts in the real corpus, obtains the core classes (five parent classes and 88 subclasses) of the rumor domain ontology and defines their concept hierarchy. Object properties and data properties are designed to describe relationships between entities or their features, and the instance layer is created according to the real rumor datasets. OWL language is used to encode the ontology, Protégé is used to visualize it, and SWRL rules and pellet reasoner are used to mine and verify implicit knowledge of the ontology, and judge the category of rumor text. This paper constructs a rumor domain ontology with high consistency and reliability. [ABSTRACT FROM AUTHOR]

The complexity of diagnosing and treating gait-related diseases necessitates the existence of a domain ontology that can support intelligent decision-making by gait experts and medical personnel. This study describes the development of a domain ontology for gait analysis and decision support on gait-related diseases. The process for developing the ontology followed a custom ontology development process that is based on the hybridisation of the Ontology 101 (OD-101) development methodology and Methontology, ensuring a systematic and replicable construction of the ontology. The design of the Gait Analysis Domain Ontology (GADO) embraced thirteen dimensions, making it the most comprehensive domain ontology for gait analysis compared to previous efforts. The GADO was created using the Protégé ontology editor and was evaluated by using ontology verification and validation procedures. Ontology verification was conducted by using the Framework for Ontology Conformance Analysis (FOCA) to assess domain task fit through competency questions and content richness of the ontology. The results demonstrated GADO’s suitability in representing the domain effectively. Ontology validation involved checking the structural and logical consistency of the ontology by using reasoners such as HermiT and Pellet and employing Description Logic and SPARQL queries to assess the ontology’s ability to respond to domain-specific queries accurately. The validation process confirmed the GADO’s effectiveness in facilitating the retrieval of relevant information. Thus, the GADO is positioned to enhance clinical decision support for gait-related diseases, thereby advancing the applications of ontology-enabled decision support systems.

This contribution reflects on the current role of ontologies in terminology research and practice and their future role, especially with a view to the creation of fully digital terminographic resources. The very notion of (domain) ontology, its concept and term, is discussed, highlighting metaterminological differences and substantial ambiguities arising from the interdisciplinary contact between Ontology Engineering and Terminology. Major challenges in ontology building, e.g. subjectivity, are mentioned, also with respect to the distinction between realist and non-realist ontologies and their relevance in Terminology. In addition, this contribution presents some examples of terminology resources with a distinct ontological component, showing a diversity of approaches depending on the purpose of the resource and its scope. In this context, more specific topics are addressed, such as the acquisition of ontological data and suitable formats and models for representing domain knowledge. The contribution ends with a vision of the integration of complex concept systems such as ontologies in future terminology work: here, the development of models based on terminology-specific requirements and typical users will be fundamental. [ABSTRACT FROM AUTHOR]

Solving the Arithmetic Word Problems (AWPs) using AI techniques has attracted much attention in recent years. We feel that the current AWP solvers are under-utilizing the relevant domain knowledge. We present a knowledge- and learning-based system that effectively solves AWPs of a specific type--those that involve transfer of objects from one agent to another (Transfer Cases (TC)). We represent the knowledge relevant to these problems as TC Ontology. The sentences in TC-AWPs contain information of essentially four types: before-transfer, transfer, after-transfer, and query. Our system (KLAUS-Tr) uses statistical classifier to recognize the types of sentences. The sentence types guide the information extraction process used to identify the agents, quantities, units, types of objects, and the direction of transfer from the AWP text. The extracted information is represented as an RDF graph that utilizes the TC Ontology terminology. To solve the given AWP, we utilize semantic web rule language (SWRL) rules that capture the knowledge about how object transfer affects the RDF graph of the AWP. Using the TC ontology, we also analyze if the given problem is consistent or otherwise. The different ways in which TC-AWPs can be inconsistent are encoded as SWRL rules. Thus, KLAUS-Tr can identify if the given AWP is invalid and accordingly notify the user. Since the existing datasets do not have inconsistent AWPs, we create AWPs of this type and augment the datasets. We have implemented KLAUS-Tr and tested it on TC-type AWPs drawn from the All-Arith and other datasets. We find that TC-AWPs constitute about 40% of the AWPs in a typical dataset like All-Arith. Our system achieves an impressive accuracy of 92%, thus improving the state-of-theart significantly. We plan to extend the system to handle AWPs that contain multiple transfers of objects and also offer explanations of the solutions. [ABSTRACT FROM AUTHOR]

Industry 4.0 (I4.0) implementation is a hot topic among manufacturing organizations to reach smart factory status and integrate a fully connected ecosystem. Achieving such a transition presents notable challenges for Small and Medium Enterprises (SMEs) since they often face resource and skilled personnel limitations. This study developed a domain ontology to represent various stages of maturity toward I4.0 implementation. Ontology provides a tool for SMEs to self-assess in situations of machines, processes, and factories for the dimensions of control, integration, and intelligence. This study focused on the identification of classes and relationships according to I4.0 implementation situations in the context of a manufacturing setting, the reuse of ontologies related to the domain of observations to model situations, and the creation and validation of the ontology through the information obtained from the questionnaires applied to SMEs. Finally, the ontology delivers a tool to understand SMEs' current state concerning I4.0 implementation and plan based on informed decisions about the maturity state and the technology required to advance to the next stage in their manufacturing processes. [ABSTRACT FROM AUTHOR]

Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it. [ABSTRACT FROM AUTHOR]

This article proposes a method for ontology construction in the field of ancient architecture digitization with the aim of addressing the lack of formalization, sharing, and reusable unified description mechanisms currently observed in the field of ancient architecture digitization. This method defines the related concepts, attributes, and relationships between concepts in the digitization of ancient architecture. It employs the network ontology language OWL to model the ontology in the digitization domain of ancient architecture and realizes the visualization of the ontology in the digitization domain of ancient architecture, thereby providing effective support for the sharing and reuse of digitization knowledge of ancient architecture. Finally, an example of a wooden tower is taken to verify the effectiveness and reliability of the proposed method.

The traditional education model gradually reveals the corresponding problems and deficiencies in the face of the update of the concept of education frontier and the change of talent cultivation mode. In this paper, we first sorted out the relevant features of the big data and data mining process, mined out the specific needs and realistic development dilemmas of musicology professional certification, and explored the appropriate mechanisms and strategies of UGS collaborative education and training. Using big data Hadoop technology as the infrastructure, a platform for music discipline services is designed to support interoperability and sharing of music education resources among universities, governments, and primary and secondary schools. Specifically, semantic similarity calculation is performed with music resource domain ontology to improve the intelligent retrieval ability, and the recommendation method of user dynamic preference is introduced to provide more accurate music teaching resources for platform users. The effect of cultivating musicology majors in colleges and universities is analyzed in terms of emotional attitudes, knowledge, and skills through the collaborative cultivation mechanism. The accuracy of the semantic similarity intelligent search method based on domain ontology is above 50%, and the mean value of students’ satisfaction with the intelligently recommended music learning resources is 4.3 points. 83.42% of the teaching carried out based on the subject service platform indicated that it met their learning needs, and the overall performance of the students who carried out the platform teaching was 16 points higher than that of the traditional classes. By using big data technology, the UGS collaborative cultivation mechanism can be utilized as a tool to enhance the quality of talent cultivation for musicology majors.

Copyright of Architectural Journal / Jian Zhu Xue Bao is the property of Architectural Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies only rely on numerical simulation, which cannot provide adequate support for decision-making in indoor fire scenarios. The knowledge graph is a knowledge base that can fully utilize massive heterogeneous data to form a sound knowledge system; however, it has not been effectively applied in the fire emergency domain. This study is a preliminary attempt to construct a knowledge graph for indoor fire emergency evacuation. We constructed the indoor fire domain ontology and proposed a four-tuple knowledge representation model. A knowledge graph was constructed with 1852 nodes and 2364 relations from 25 indoor fire events. The proposed method was tested for the case study of Henan Pingdingshan '5.25' Fire Accident in China. Results show that the proposed knowledge representation model and the corresponding knowledge graph can represent complicated indoor fire events and support indoor fire emergency evacuation. [ABSTRACT FROM AUTHOR]

The use of modular, yet reusable and interchangeable components offers flexibility for system designers and thus leads to a higher degree of reconfigurability with cost effective, adaptable and scalable solutions. The development of a robotic system remains, however, a complex task with increasing demands depending on the targeted capabilities, functionalities and operational constraints of the robot. This requires expert knowledge in mechanical, electrical and software engineering, as well as tools that can collect, process and use relevant data. As a result, the amount of available and managed information is increasing, while its relevance, usability, and knowledge that can be extracted from it are consequently decreasing: a situation described as information overload paradox. The design of a modular system can be based on the use of standardized modules with universal interconnects, but it should be aligned with a strong formalism, e. g. the semantic representation of components, to allow for a better management of the overall complexity. An ontology-based knowledge representation, for instance, which provides domain and application specific knowledge for the design of robotic systems, can be used as a method of storing and sharing data in a uniform, machine-readable and standardized way. In this paper, we introduce the Knowledge-based Open Robot voCabulary as Utility Toolkit (korcut) as a core component to support the ontology-driven development of robotic systems as part of a reference implementation of the Q-Rock development cycle. Q-Rock gathers methods to assist users in designing robot configuration by: (a) automatically exploring robot capabilities based on robot hardware, (b) proposing robot designs to meet the user's needs, (c) refining the proposed robot designs. We use korcut to improve the robot design process in orbital and planetary robotics from the requirements definition to the development phases of complex modular robotic structures (e. g. composition of an unmanned ground vehicle with robotic arms). This is implemented by embedding semantic component descriptions in a state-of-the-art open-source 3D modeling software. Furthermore, the korcut ontology family includes various sub-ontologies to address specific space-related tasks, such as the modeling of a Standard Interconnect (SI) for On Orbit Services (OOS) and Orbital Factory. The ontology design follows criteria that have been derived from a survey conducted as part of this work. This paper also covers an evaluation of its current applications from a methodological, knowledge representation, and software tool perspectives. In the application part, we introduce the use of the ontology to model a multi-functional SI that enables mechanical connections between various (modular) robotic components, as well as transfer of power and data. Finally, we provide a critical analysis of our work and outline its future work. • The development of complex robotic systems in space domain needs expert knowledge. • Increasing information about a robotic system can lead to an information paradox. • The information paradox can be improved by standardized and inferable knowledge. • Ontology is used in a case study to facilitate the robot development workflow. • Ontology is used to provide a metric for evaluating a space interface. [ABSTRACT FROM AUTHOR]

In line with increasing attention to the scope of Business Process Management (BPM) over the past two decades, many techniques and tools have been introduced. Finding the proper technique and tool in each phase of the business process management life cycle takes time and effort. This study aims to design and develop an ontology to facilitate the selection of suitable techniques and tools at each step of the BPM life cycle. This ontology provides a common understanding of concepts of this domain for computers. The study results showed that two taxonomies for techniques and software tools for business process management were created based on BPM life cycle steps. Then, an ontology was developed for them. Noy & McGuinness methodology was applied to implement this ontology, and Protégé 5.2 and owl language were used. Also, the quality criteria-based approach was used for the evaluation of ontology. All the main concepts in the domain of BPM techniques and tools were extracted from previous studies. There are 298 terms. 58 of them are domain concepts or classes, 2 are about taxonomic relations, 2 are related to data property, and 224 are instances. This research used these terms, and the deployed ontology with the quality criteria-based approach was evaluated.

The purpose of developing an ontology for Thai’s indigenous rice knowledge was to create vocabularies that clearly represented the scope and knowledge structure for the production, processing, and cultural practices of rice in Thailand. The terminology could be described comprehensively by integrating domain ontology outline and the analytico-synthetic method of facet analysis. The process of developing ontology was accomplished through the Hozo program then evaluated by selected experts. It was found that the ontology of indigenous rice culture knowledge could be classified to 20 knowledge groups comprising 3 classes: (1) Rice production; (2) Rice culture; and (3) Special contexts of indigenous rice knowledge beyond 8 sub-classes: (1) Rice varieties; (2) Rice production process; (3) Rice rituals; (4) Rice local scholars; (5) Local wisdom; (6) Periods; (7) Ethnic groups; and (8) Geographic labels. Other beneath layers of the ontology consisted of 17 sub-classes, 244 types of relationships, 155 characteristics, and 10 types of associated relationships. Moreover, 11 groups of associated concepts were found: (1) Production resources; (2) Principles and methods; (3) Traditional technologies; (4) Processes; (5) Products; (6) Value; (7) Belief; (8) Safety; (9) Security; (10) Continuity; and (11) Social identity.

Pulmonary Tuberculosis (PTB) is an infectious disease caused by a bacterium called Mycobacterium tuberculosis. This paper aims to create Symbolic Artificial Intelligence (SAI) system to diagnose PTB using clinical and paraclinical data. Usually, the automatic PTB diagnosis is based on either microbiological tests or lung X-rays. It is challenging to identify PTB accurately due to similarities with other diseases in the lungs. X-ray alone is not sufficient to diagnose PTB. Therefore, it is crucial to implement a system that can diagnose based on all paraclinical data. Thus, we propose in this paper a new PTB ontology that stores all paraclinical tests and clinical symptoms. Our SAI system includes domain ontology and a knowledge base with performance indicators and proposes a solution to diagnose current and future PTB also abnormal patients. Our approach is based on a real database of more than four years from our collaborators at Pondicherry hospital in India. [ABSTRACT FROM AUTHOR]