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Adoption of dynamic energy tariffs by households is crucial for the transition to carbon-neutral energy systems. Influencing the adoption patterns of these tariffs necessitates an examination of the drivers, decision components, and contextual factors influencing household decisions. Few computational models address this comprehensively, often omitting non-financial decision variables. Moreover, methodologically robust integrative reviews on this topic are scarce. To address this gap, this paper develops a concept-centered integrative review methodology aimed at deriving computer models for socio-techno-economic simulations of household adoption of sustainable technologies. The methodology encompasses five sequential phases: Setup, Literature Search, Analysis, Synthesis and Conceptual Model, and Discussion. To illustrate the methodology, it is applied to the case of household adoption of dynamic energy tariffs, resulting in an abstract conceptual model adaptable to local contexts. The review reveals a lack of consensus on modeled tariffs but highlights the significance of tariff complexity, relative advantage, household heterogeneity, and various agent properties. It also identifies potential improvements in model fundamentals, particularly spatial modeling. The developed process model focuses on the stages 'knowledge', 'decision', and 'reevaluation'. The article contributes by presenting a comprehensive review scheme and delivering a concept-centered integrative review along with an explicit conceptual model derived from it. [ABSTRACT FROM AUTHOR]

Background: Familiar cardiopathies are genetic disorders that affect the heart. Cardiologists face a significant problem when treating patients suffering from these disorders: most DNA variations are novel (i.e., they have not been classified before). To facilitate the analysis of novel variations, we present CardioGraph, a platform specially designed to support the analysis of novel variations and help determine whether they are relevant for diagnosis. To do this, CardioGraph identifies and annotates the consequence of variations and provides contextual information regarding which heart structures, pathways, and biological processes are potentially affected by those variations. Methods: We conducted our work through three steps. First, we define a data model to support the representation of the heterogeneous information. Second, we instantiate this data model to integrate and represent all the genomics knowledge available for familiar cardiopathies. In this step, we consider genomic data sources and the scientific literature. Third, the design and implementation of the CardioGraph platform. A three-tier structure was used: the database, the backend, and the frontend. Results: Three main results were obtained: the data model, the knowledge base generated with the instantiation of the data model, and the platform itself. The platform code has been included as supplemental material in this manuscript. Besides, an instance is publicly available in the following link: https://genomics-hub.pros.dsic.upv.es:3090. Conclusion: CardioGraph is a platform that supports the analysis of novel variations. Future work will expand the body of knowledge about familiar cardiopathies and include new information about hotspots, functional studies, and previously reported variations. [ABSTRACT FROM AUTHOR]

The paper proposes universal conceptual modeling, conceptual modeling that strives to be as general-purpose as possible and accessible to anyone, professionals and non-experts alike. The idea of universal conceptual modeling is meant to catalyze new thinking in conceptual modeling and be used to evaluate and develop conceptual modeling solutions, such as modeling languages, approaches for requirements elicitation, or modeling tools. These modeling solutions should be usable by as many people and design agents as possible and for as many purposes as possible, aspiring to the ideals of universal conceptual modeling. We propose foundations of universal conceptual modeling in the form of six principles: flexibility, accessibility, ubiquity, minimalism, primitivism, and modularity. We then demonstrate the utility of these principles to evaluate existing conceptual modeling languages and understand conceptual modeling practices. Finally, we propose future research opportunities meant to realize the ideals of universal conceptual modeling. [ABSTRACT FROM AUTHOR]

Value driver trees (VDTs) are abstract, indicator‐based representations of a business model. Although they are conceptual models by nature, no systematic and unified approaches to their modeling exist to date. In fact, a heterogeneous understanding of their conception and methodological implementation has been established. The goal of this study is to provide more clarity in this regard by examining the semantics (the question of "what?") and syntax (the question of "how?") with respect to VDT modeling. For this purpose, a structured literature review was conducted in which a collection of 161 VDTs was evaluated. Based on an extended taxonomy design process, the typical model constructs of VDTs were extracted. As a result, a so‐called VDT Model Classification was derived, which structures 34 model constructs into three dimensions with eight categories. This classification establishes a clearer understanding of the model constructs and their representation, thereby providing a conceptual framework for a unified and more substantiated "vocabulary" for VDT modeling. Finally, a research agenda has been formulated that generally addresses the role and future application potential of VDTs and, in particular, describes the next steps toward a sound notation for modeling VDTs. [ABSTRACT FROM AUTHOR]

Abstract Background Familiar cardiopathies are genetic disorders that affect the heart. Cardiologists face a significant problem when treating patients suffering from these disorders: most DNA variations are novel (i.e., they have not been classified before). To facilitate the analysis of novel variations, we present CardioGraph, a platform specially designed to support the analysis of novel variations and help determine whether they are relevant for diagnosis. To do this, CardioGraph identifies and annotates the consequence of variations and provides contextual information regarding which heart structures, pathways, and biological processes are potentially affected by those variations. Methods We conducted our work through three steps. First, we define a data model to support the representation of the heterogeneous information. Second, we instantiate this data model to integrate and represent all the genomics knowledge available for familiar cardiopathies. In this step, we consider genomic data sources and the scientific literature. Third, the design and implementation of the CardioGraph platform. A three-tier structure was used: the database, the backend, and the frontend. Results Three main results were obtained: the data model, the knowledge base generated with the instantiation of the data model, and the platform itself. The platform code has been included as supplemental material in this manuscript. Besides, an instance is publicly available in the following link: https://genomics-hub.pros.dsic.upv.es:3090 . Conclusion CardioGraph is a platform that supports the analysis of novel variations. Future work will expand the body of knowledge about familiar cardiopathies and include new information about hotspots, functional studies, and previously reported variations.

Over the last thirty years, In the field of digital philology, several generic IT tools have been developed to produce digital scholarly editions (DSE). However, the majority of DSEs continue to be produced using custom-developed tools. This trend is unsustainable, but it is because generic tools are often less usable, difficult to customize, and unsuitable for the scientific objectives of scholars. The scientific community has been exploring how to create flexible and effective tools for digital philology. Two main responses have emerged in the literature: the need for shared editorial models that represent philologists’ desiderata, and the adoption of software engineering practices to produce more robust, durable, and easily maintainable tools. This article presents a modeling strategy that considers both perspectives and is based on the creation of a network of editorial models, each one theoretically framed within the philological approach in which it has emerged. The editorial models, formalized as ontologies, can be applied as conceptual models to determine the logical organization and functioning of a tool for digital philology, thanks to the principles of domain-driven design.

Despite the public availability of legal documents, there is a need for finding specific information contained in them, such as paragraphs, clauses, items and so on. With such support, users could find more specific information than only finding whole legal documents. Some research efforts have been made in this area, but there is still a lot to be done to have legal information available more easily to be found. Thus, due to the large number of published legal documents and the high degree of connectivity, simple access to the document is not enough. It is necessary to recover the related legal framework for a specific need. In other words, the retrieval of the set of legal documents and their parts related to a specific subject is necessary. Therefore, in this work, we present a proposal of a RDF-based graph to represent and search parts of legal documents, as the output of a set of terms that represents the pursued legal information. Such a proposal is well-grounded on an ontological view, which makes possible to describe the general structure of a legal system and the structure of legal documents, providing this way the grounds for the implementation of the proposed RDF graph in terms of the meaning of their parts and relationships. We posed several queries to retrieve parts of legal documents related to sets of words and the results were significant. [ABSTRACT FROM AUTHOR]

Conceptual modeling plays a central role in planning, designing, developing and maintaining software-intensive systems. One of the goals of conceptual modeling is to enable clear communication among stakeholders involved in said activities. To achieve effective communication, conceptual models must be understood by different people in the same way. To support such shared understanding, conceptual modeling languages are defined, which introduce rules and constraints on how individual models can be built and how they are to be understood. A key component of a modeling language is an ontology, i.e., a set of concepts that modelers must use to describe world phenomena. Once the concepts are chosen, a visual and/or textual vocabulary is adopted for representing the concepts. However, the choices both of the concepts and of the vocabulary used to represent them may affect the quality of the language under consideration: some choices may promote shared understanding better than other choices. To allow evaluation and comparison of alternative choices, we present Peira, a framework for empirically measuring the domain and comprehensibility appropriateness of conceptual modeling language ontologies. Given a language ontology to be evaluated, the framework is based on observing how prospective language users classify domain content under the concepts put forth by said ontology. A set of metrics is then used to analyze the observations and identify and characterize possible issues that the choice of concepts or the way they are represented may have. The metrics are abstract in that they can be operationalized into concrete implementations tailored to specific data collection instruments or study objectives. We evaluate the framework by applying it to compare an existing language against an artificial one that is manufactured to exhibit specific issues. We then test if the metrics indeed detect these issues. We find that the framework does offer the expected indications, but that it also requires good understanding of the metrics prior to committing to interpretations of the observations. [ABSTRACT FROM AUTHOR]

The ethics of artificial intelligence (AI) is a research topic with both theoretical and practical significance. However, the ethical and moral aspects of conceptual, ontological, semantic, and knowledge modeling, more specifically, and which are sometimes found in AI applications, is not being given sufficient attention. I argue that it should. Whether considering using or developing these meaning-focused models, there are ethical aspects. This paper offers a preliminary outline about this potentially new research field, discussing: some questions and areas of concern, their significance; scenarios of ethical import, and initial ethical guidelines. A focus area, potential case study, and lens through which to see the ethical significance at hand, is ontologies, with emphasis on the most abstract ontologies (so-called generic, foundational, top-level or upper ontologies). This would aim to show the ethical import of highly abstract (often philosophical) ontological choices, assumptions, and their effects on data, developers, users, and society. In particular, I stress the need for critical examination of such models, their content, their intended meanings, assumptions, ideologies, as well as the development process. Both the ethics of, and ethics in, these activities are, therefore, of interest. Topics in the ethics of AI that are relevant include: transparency, bias, data-sharing, privacy, control over data, etc. This paper thereby contributes to the ethics of AI, as well as the ethics of information, of knowledge managment and relevant topics by developing and discussing the ethics of ontology (and similar systems). Future development will refine and add to the ideas expressed in this paper. [ABSTRACT FROM AUTHOR]

Molecular case studies (MCSs) provide educational opportunities to explore biomolecular structure and function using data from public bioinformatics resources. The conceptual basis for the design of MCSs has yet to be fully discussed in the literature, so we present molecular storytelling as a conceptual framework for teaching with case studies. Whether the case study aims to understand the biology of a specific disease and design its treatments or track the evolution of a biosynthetic pathway, vast amounts of structural and functional data, freely available in public bioinformatics resources, can facilitate rich explorations in atomic detail. To help biology and chemistry educators use these resources for instruction, a community of scholars collaborated to create the Molecular CaseNet. This community uses storytelling to explore biomolecular structure and function while teaching biology and chemistry. In this article, we define the structure of an MCS and present an example. Then, we articulate the evolution of a conceptual framework for developing and using MCSs. Finally, we related our framework to the development of technological, pedagogical, and content knowledge (TPCK) for educators in the Molecular CaseNet. The report conceptualizes an interdisciplinary framework for teaching about the molecular world and informs lesson design and education research. [ABSTRACT FROM AUTHOR]

This study examines the use of conceptual modeling to aid in selecting a power generation system concept in the maritime industry. The research objective is to understand how conceptual modeling can enhance decision‐making during the early phases of concept evaluation. The study was conducted at a world‐leading maritime technology company to address the need for more formal processes to support decision‐making in complex development projects. The study applied a conceptual modeling approach in an industry case to facilitate decision‐making in the early phases of a development project. The study shows that conceptual modeling is effective in supporting early‐phase decision‐making in the development project. Conceptual models effectively manage complexity, enhance understanding, and enable effective communication among team members. By combining conceptual modeling with a Pugh matrix, informed decision‐making is facilitated, aligning with stakeholders' objectives. Overall, conceptual modeling provides a structured representation of the problem domain, guiding early‐phase decision‐making. [ABSTRACT FROM AUTHOR]

Decision-making in complex production environments is challenging as the information and knowledge requirements must be constantly observed since the ecosystems they operate in are continuously changing. Artificial intelligence (AI) can tackle complexity in decision-making by making machines more intelligent. But reacting to changing or new problems and related decision processes to facilitate the understanding of the involved humans is an equally important problem. Therefore, decision support systems are required to assist complex decisions and enable flexibility to support the decision-makers. Within this scope, we will introduce the Democratic AI-based Decision Support System (DAI-DSS), which is designed and implemented within the EU-funded FAIRWork project, considering both human and machine actors during decision-making. The FAIRWork project proposes a model-based approach to both express high-level decision scenarios and formally describe the decision processes, which are then used as input for configuring the decision support system to meet concrete decision problems.

Regarding competency-oriented teaching in higher education, lecturers face the challenge of employing aligned task material to develop the intended competencies. What is lacking in many disciplines are well-founded guidelines on what competencies to develop and what tasks to use to purposefully promote and assess competency development. Our work aims to create an empirically validated framework for competency-oriented assessment in the area of graphical modeling in computer science. This article reports on the use of the think-aloud method to validate a competency model and a competencyoriented task classification. For this purpose, the response processes of 15 students during the processing of different task types were evaluated with qualitative content analysis. The analysis shed light on the construct of graphical modeling competency and the cognitive demand of the task types. Evidence was found for the content and substantive aspect of construct validity but also for the need to refine the competency model and task classification. [ABSTRACT FROM AUTHOR]

Data-intensive applications aim at discovering valuable knowledge from large amounts of data coming from real-world sources. Typically, workflow languages are used to specify these applications, and their associated engines enable the execution of the specifications. However, as these applications become commonplace, new challenges arise. Existing workflow languages are normally platform-specific, which severely hinders their interoperability with other languages and execution engines. This also limits their reusability outside the platforms for which they were originally defined. Following the Design Science Research methodology, the paper presents SWEL (Scientific Workflow Execution Language). SWEL is a domain-specific modeling language for the specification of data-intensive workflows that follow the model-driven engineering principles, covering the high-level definition of tasks, information sources, platform requirements, and mappings to the target technologies. SWEL is platform-independent, enables collaboration among data scientists across multiple domains and facilitates interoperability. The evaluation results show that SWEL is suitable enough to represent the concepts and mechanisms of commonly used data-intensive workflows. Moreover, SWEL facilitates the development of related technologies such as editors, tools for exchanging knowledge assets between workflow management systems, and tools for collaborative workflow development. [ABSTRACT FROM AUTHOR]

This study discusses the integration of artificial intelligence (AI) and machine learning (ML) in medical reasoning and decision-making, with a focus on the challenges and opportunities associated with the massive consumption of data required for training AI systems, and contrasts this with the limited data typically available to medical practitioners. We advocate for a balanced approach that includes small data and emphasize the importance of maintaining the art of clinical reasoning amid technological advancements. Finally, we highlight the potential of multidisciplinary research in addressing the complexities of medical reasoning and suggest the necessity of careful abstraction and conceptual modeling in AI applications.

Adoption of dynamic energy tariffs by households is crucial for the transition to carbon-neutral energy systems. Influencing the adoption patterns of these tariffs necessitates an examination of the drivers, decision components, and contextual factors influencing household decisions. Few computational models address this comprehensively, often omitting non-financial decision variables. Moreover, methodologically robust integrative reviews on this topic are scarce. To address this gap, this paper develops a concept-centered integrative review methodology aimed at deriving computer models for socio-techno-economic simulations of household adoption of sustainable technologies. The methodology encompasses five sequential phases: Setup, Literature Search, Analysis, Synthesis and Conceptual Model, and Discussion. To illustrate the methodology, it is applied to the case of household adoption of dynamic energy tariffs, resulting in an abstract conceptual model adaptable to local contexts. The review reveals a lack of consensus on modeled tariffs but highlights the significance of tariff complexity, relative advantage, household heterogeneity, and various agent properties. It also identifies potential improvements in model fundamentals, particularly spatial modeling. The developed process model focuses on the stages ‘knowledge’, ‘decision’, and ‘reevaluation’. The article contributes by presenting a comprehensive review scheme and delivering a concept-centered integrative review along with an explicit conceptual model derived from it.

ISO/IEC/IEEE 15288:2015 is one of the most fundamental systems engineering international standards. In this work, the major system lifecycle processes specified in 15288 and, equally importantly, the objects interacting through them, are modeled meticulously using OPM ISO 19450. The conceptual model, based on this standard's text, reflects the implied authors' intent, bringing up ambiguities that arise from the informality of natural language text and reference to related figures. The resulting OPM model is an exact, formal, and detailed expression of the processes and related objects in the first part of 15288, making it machine interpretable. The gaps discovered during the modeling process are testimony to the value of the model‐based standards authoring approach and the centrality of a formal yet humanly accessible model as the underlying backbone of international standards and key technical documents in general. [ABSTRACT FROM AUTHOR]

This paper explains conceptual modeling within the framework of Frame-Based Terminology (Faber, 2012; 2015; 2022), as applied to EcoLexicon (ecolexicon.ugr.es), a specialized knowledge base on the environment (León-Araúz, Reimerink &, Faber, 2019; Faber & León-Araúz, 2021). It describes how a frame-based terminological resource is currently being restructured and reengineered as an initial step towards its formalization and subsequent transformation into an ontology. It also explains how the information in EcoLexicon can be integrated in environmental ontologies such as ENVO (Buttigieg, Morrison, Smith, Mungall & Lewis, 2013; Buttigieg, Pafilis, Lewis, Schildhauer, Walls & Mungall, 2016), particularly at the bottom tiers of the Ontology Learning Layer Cake (Cimiano, 2006; Cimiano, Maedche, Staab & Volker, 2009). The assumption is that frames, as a conceptual modeling tool, and information extracted from corpora can be used to represent the conceptual structure of a specialized domain. [ABSTRACT FROM AUTHOR]

The reform of the European academic landscape with the introduction of bachelor's and master's degree programs has brought about several profound changes for teaching and assessment in higher education. With regard to the examination system, the shift towards output-oriented teaching is still one of the most significant challenges. Assessments have to be integrated into the teaching and learning arrangements and consistently aligned towards the intended learning outcomes. In particular, assessments should provide valid evidence that learners have acquired competences that are relevant for a specific domain. However, it seems that this didactic goal has not yet been fully achieved in modeling education in computer science. The aim of this study is to investigate whether typical task material used in exercises and exams in modeling education at selected German universities covers relevant competences required for graphical modeling. For this purpose, typical tasks in the field of modeling are first identified by means of a content-analytical procedure. Subsequently, it is determined which competence facets relevant for graphical modeling are addressed by the task types. By contrasting a competence model for modeling with the competences addressed by the tasks, a gap was identified between the required competences and the task material analyzed. In particular, the gap analysis shows the neglect of transversal competence facets as well as those related to the analysis and evaluation of models. The result of this paper is a classification of task types for modeling education and a specification of the competence facets addressed by these tasks. Recommendations for developing and assessing student's competences comprehensively are given. [ABSTRACT FROM AUTHOR]

Students often see college courses as the presentation of disconnected facts, especially in the life sciences. Student‐created Structure Mechanism/Relationship Function (SMRF) models were analyzed to understand students' abilities to make connections between genotype, phenotype, and evolution. Students were divided into two sections; one section received instructions that included a specific gene as an example related to larger issues like human disease or the environment. The other section was only given generic examples, like gene X and phenotype Y. Coding of exam models and a comprehensive (extensive) model reveled students were able to make links and work within and between biological scales of organization. Modeling provided a way to show and allow students to practice and demonstrate the ability to build step‐by‐step causal relationships that link ideas together. We also observed a small differing with students receiving the specific prompt performing better than students receiving generic prompt at the point in the semester where linking across many biological scales was required to be successful. [ABSTRACT FROM AUTHOR]

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The 38th issue of CSIMQ comprises four articles selected by guest issue editors on topics related to novel decision support methods or model-based frameworks for evolving or evaluating information systems. Design-oriented research is the dominant approach in these works, balancing technical design decisions insights with empirical evaluation cases. Three of the selected articles contribute with decision-support methods or frameworks – for ESG (Environment-Social-Governance) accounting, for democratized decision services, and for information security management. The fourth article revisits UML-based model-driven software development from a new perspective.