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This paper reports on the design of an innovative Electrochemistry course, part of a Chemical Science Engineering programme. Teachers have observed that their students' understanding of electrochemical concepts and phenomena is insufficient to attempt connections to further concepts, and to generate new knowledge in scientific problem-solving. A new course was required aiming to contribute to students' building mastery of concepts and deep insight into phenomena, both in and beyond the learning context. The course was designed tapping on ideas of inquiry-based learning and involving of conceptual modelling as a scaffold to learning. This pedagogical intervention departs from the consideration that conceptual modelling is an essential reasoning ability of (engineering) scientists and, consistently, it constitutes a key learning objective. Concurrent with the implementation of this course, an accompanying empirical investigation was set up to grasp how students learn electrochemistry with this novel pedagogical approach, and whether (and to what extent) there is any observable effect on the learning and transfer of electrochemical concepts. Both the instructional design of the course and the educational research design integrate considerations from Chemical Science, Philosophy of Science in Practice, and Education Sciences. The implications for engineering education and for educational research are discussed. [ABSTRACT FROM AUTHOR]

The most advantageous method for detecting dangerous gases and reducing the risk of potential environmental toxicity effects is the use of innovative gas sensing systems. However, designing effective sensors requires a complex process of synthesizing functional nanoparticles, which is a costly process. Additionally, practical operation of the toxic gas sensors always carries a significant cost along with a considerable risk of hazardous gas emissions. Machine learning algorithms may be used to accurately automate the behavior of the sensors to eliminate the abovementioned deficiencies. In the present research, there are three different factors involved in the optimization of NO2 sensing by means of the response surface methodology (RSM). Two main functions of sensor efficiency, namely sensitivity and response time, are predicted according to the Fe3O4 additive (%), input NO2 (ppm), and response time/sensitivity, and moreover, the execution of a controlling system of the sensor network using the Jacobson model is proposed. The machine learning computations are implemented by Meta.RegressionByDiscretization, M5.Rules, Lazy KStar, and Gaussian Processes algorithms. The outcomes illustrate that the best gas sensor efficiency predictions are related to M5.Rules and Lazy KStar, with a correlation coefficient of more than 96%. The best performance of machine learning computations can be found in the range of 8–10-fold in training and testing arrangements. Meanwhile, the ANOVA assessment confirmed that the most important features in the prediction of response time and sensitivity are NO2 concentration and response time, respectively, with the lowest p-value recorded. The outcomes illustrated that with combinations of RSM, machine learning, and the Jacobson model as a controller, a decision support system can be presented for the NO2 gas sensor system. [ABSTRACT FROM AUTHOR]

The RESTORE project (smaRt accESs TO digital heRitage and mEmory) started in June 2020 with a duration of 2 years. The project consortium, coordinated by the Istituto Opera del Vocabolario Italiano - CNR (National Research Council of Italy), includes the State Archives and the Museum of Palazzo Pretorio in Prato, the Archival and Bibliographic Superintendency of Tuscany as well as the SPACE SpA company. The project - co-financed by the Region of Tuscany - has as its main purpose the recovery, integration and accessibility of data and digital objects produced in the last twenty years by its partners, in order to build a knowledge base with information on the history of the city and of its institutions, on the development of its economic and entrepreneurial system, and on the role of women in the development of a welfare state and network. Starting from the figure of the merchant Francesco di Marco Datini, his family and his entourage, and broadening the focus from the local dimension it will be possible to reconstruct a significant part of the history of European and Mediterranean cities of the 14th century, including commercial and economical aspects. This paper presents a focused overview on the mapping of the archival and museum digital resources provided by the partners (encoded with different standards, such as: EAD, EAC-CPF, TEI, ICCD etc.) to the CIDOC Conceptual Reference Model, the ontology chosen within the RESTORE project as the “common language” for semantic data integration. The landscape for the scientific and infrastructural development of the project’s activities consists in the collaboration (via the CNR- OVI) with some key players in the EU RIs environment, such as DARIAH-ERIC (ESFRI Landmark for the Humanities and Social Sciences) and E-RIHS (ESFRI project for the Heritage Science), as well as other actors within the EOSC Framework.

The article focuses on an algorithm for modelling the main components of engineering thinking in the context of the convergence that the disciplines of the natural-and-mathematical cycle, computer science, information and material technologies gain. The algorithm makes it possible apply the methodological foundations of activity regulation to the practice of forming the principal components of secondary school students’ engineering culture. The procedure is based on the information approach with an emphasis on the students’ mental experience, which they acquire while solving creative engineering problems according to the TRIZ methodology. Grounded on the study worked out, it is established that the procedure of “soft” modelling, as a kind of the cognitive method of building educational models, reveals general trends in the way to form and develop high school students’ natural science-and-design thinking. The tools proposed in the article can use the obtained results when study the tasks of conceptual modelling of various aspects of human intellectual activity.

Information integration across multiple event-based surveillance (EBS) systems has been shown to improve global disease surveillance in experimental settings. In practice, however, integration does not occur due to the lack of a common conceptual framework for encoding data within EBS systems. We aim to address this gap by proposing a candidate conceptual framework for representing events and related concepts in the domain of public health surveillance. [ABSTRACT FROM AUTHOR]

This paper addresses the ontological differences between modern water, the dominant way of thinking about water in the industrialised West, and Living Waters, a concept used by Aboriginal Australians to talk about water(s) and water bodies. Working collaboratively with Aboriginal experts and state government water planners in the Kimberley region of Western Australia we developed two social-ecological conceptual models to compare propositions about the ontological character of water. One model represents a modern water paradigm, upon which state systems of water management are based, and the other represents a customary system of management: a Living Waters paradigm that depicts the relationships between people, other beings, and the waters of the Martuwarra/Fitzroy River catchment. The models are intended to inform water governance processes and influence negotiations between State and customary institutions. We use the models here to analyse water policy tools and their ability to support the relationships particular groups have with their waters. We identify gaps in existing policy for the sustainability of Living Waters. We discuss the implications of the differences between these two conceptual models for water justice, as well as for sustainability research and socio-ecological modelling as it applies to rivers and their waters in Australia and elsewhere, or to other human-environment relationships. [ABSTRACT FROM AUTHOR]

Groundwater systems are social-ecological systems (SES) in which human communities, groundwater dependent ecosystems and groundwater resources are linked, and therefore, cannot be studied in isolation. Complex adaptive systems are characterised by non-linear relationships and feedbacks between the system variables. Modeling feedbacks between social and ecological variables of groundwater systems requires a shift from traditional hydrogeological studies to more holistic approaches that simulate groundwater as a SES. A framework was developed to study and manage groundwater as a social-ecological system. Operationalizing the framework to develop empirical models that consider the social and ecological aspects of groundwater dynamics requires translating the framework components into measurable model inputs that capture the key relationships between social and ecological components. Causal loop diagrams (CLD) are an ideal tool for translating between a conceptual framework and an operational, empirical model. Causal loop diagrams can reveal system complexity associated with the interaction of social, ecological and hydrological components, and identify key inputs that need to be considered to model groundwater as a SES. This paper applies CLD to conceptualize the feedbacks between the social, ecological and hydrological components of a groundwater system. The concept is applied to the groundwater system in the island of Nauru, and shows that two balancing feedbacks help maintain the system in a precarious state of usability while several reinforcing feedbacks in the social subsystem apply constant pressure to the system. The CLD reveals that the social subsystem is large and complex, even though the island population is small (approximately 10,000 people). It also demonstrates that the social system is by far the larger disruptor and has the greater potential to alter system states.

Conceptual modelling is a continuous and iterative process that is essential for simulation projects. However, it often does not receive due consideration. In this sense, many projects document and/or perform the conceptual modelling without following good practices, which ends up affecting the next stages of the simulation. This work aims to identify the state of the art and patterns of conceptual modelling through a Systematic Literature Review (SLR), especially regarding the articles' adherence to good practices. We identified 13 good practices for conceptual modelling in the literature, which aims to develop effective conceptual models, i.e., models with suitable syntactic, semantic, and pragmatic quality. The results of the SLR indicate that although some good practices are consolidated, others need more attention, especially regarding semantic, pragmatic and report quality. Finally, we establish future directions to guide and improve projects that use conceptual modelling. [ABSTRACT FROM AUTHOR]

Academic libraries have a long tradition of collecting, preserving, and offering access to information about doctoral dissertations through the repositories of electronic theses and dissertations. In recent years libraries have turned their attention to Wikidata as a knowledge graph for publishing structured data on the Web. These initiatives include the publication of metadata about theses and dissertations. However, Wikidata is a sociotechnical infrastructure, where the responsibility for its development is in the hands of its users. This includes asynchronous collaborative work on its ontology by many non-expert users. This may cause tensions between traditional metadata control and socially developed schemas. The goal of this article is to develop a conceptual understanding of the representation patterns of doctoral dissertations in a socially constructed Wikidata knowledge graph. This study uses an interpretative approach guided by the method of the close reading of the infrastructure to develop an understanding of what a doctoral dissertation is in this socially constructed representation of reality and how doctoral dissertations are described using Wikidata ontology. The ontological status of the doctoral dissertation is revealed by interpreting the place of this concept in the taxonomic structure of the Wikidata ontology. The expressiveness of Wikidata ontology for the description of doctoral dissertations is limited to three selected properties, authorship, doctoral advisor, and the institution to which the dissertation was submitted. The results of this study show on the one hand the redundancies in modelling doctoral dissertations, and on the other inconsistencies in the descriptions. This calls for close attention from libraries to metadata curatorial practices in Wikidata sociotechnical infrastructure. [ABSTRACT FROM AUTHOR]

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Product configuration systems (PCSs) are increasingly being used in various industries to manage product knowledge and create the required specifications of customized products. Companies applying PCS face significant challenges in modelling, structuring and documenting the systems. Some of the main challenges related to PCSs are formalising product knowledge conceptually and structuring the product features. The modelling techniques predominantly used to visualise and structure PCSs are the Unified Modelling Language (UML) notations, Generic Bill of Materials (GBOM) and Product Variant Master (PVM), associated with class collaboration cards (CRC-cards). These methods are used to both analyse and model the products and create a basis for implementation to a PCS by using an object-oriented approach. However, the modelling techniques do not consider that most commercial PCSs are not fully object-oriented, but rather, they are expert systems with an inference engine and a knowledge base; therefore, the constructed product models require modifications before implementation in the configuration software. The consequences are that what is supposedly a feasible structure of the product model is not always appropriate for the implementation in standard PCS software. To address this challenge, this paper investigates the best practice in modelling and implementation techniques for PCSs in standard software and alternative structuring methods used in object-oriented software design. The paper proposes a method for a modular design of a PCS in not fully object-oriented standard PCS software using design patterns. The proposed method was tested in a case company that suffered from a poorly structured product model in a not fully object-oriented PCS. The results show that its maintainability can be improved by using design patterns in combination with an agile documentation approach.

The most advantageous method for detecting dangerous gases and reducing the risk of potential environmental toxicity effects is the use of innovative gas sensing systems. However, designing effective sensors requires a complex process of synthesizing functional nanoparticles, which is a costly process. Additionally, practical operation of the toxic gas sensors always carries a significant cost along with a considerable risk of hazardous gas emissions. Machine learning algorithms may be used to accurately automate the behavior of the sensors to eliminate the abovementioned deficiencies. In the present research, there are three different factors involved in the optimization of NO2 sensing by means of the response surface methodology (RSM). Two main functions of sensor efficiency, namely sensitivity and response time, are predicted according to the Fe3O4 additive (%), input NO2 (ppm), and response time/sensitivity, and moreover, the execution of a controlling system of the sensor network using the Jacobson model is proposed. The machine learning computations are implemented by Meta.RegressionByDiscretization, M5.Rules, Lazy KStar, and Gaussian Processes algorithms. The outcomes illustrate that the best gas sensor efficiency predictions are related to M5.Rules and Lazy KStar, with a correlation coefficient of more than 96%. The best performance of machine learning computations can be found in the range of 8–10-fold in training and testing arrangements. Meanwhile, the ANOVA assessment confirmed that the most important features in the prediction of response time and sensitivity are NO2 concentration and response time, respectively, with the lowest p-value recorded. The outcomes illustrated that with combinations of RSM, machine learning, and the Jacobson model as a controller, a decision support system can be presented for the NO2 gas sensor system.

Multiple logic-based reconstructions of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exist. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, we specify minimal logic profiles availing of this extended process, including the ontological commitments embedded in the languages, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL). The profiles characterise the essential logic structure needed to handle the semantics of conceptual models, therewith enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable DL ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models.