Development of a new software application for supporting research of thermo-mechanical behavior of agri-food and forest products

Development of applications and services conforming to recent standards and perspectives of ICT is important for increasing productivity in agri-food and forestry sectors to deliver desired quantities of safe and quality products to end-users. Therefore a field of study combining two national curricula: informatics and agricultural engineering was developed by the Department of Applied Informatics of the Poznan University of Life Sciences. The scope of studies corresponds to the area of research conducted in the Department and focuses on development of Web-based advisory systems for agriculture and information systems supporting research in the agri-bio-engineering. In the paper two exemplary systems are presented. They support analysis of thermo-mechanical behavior of agri-food and forest products subjected to heating, cooling, drying and storing operations. Development of the systems resulted in a significant increase in accuracy and efficiency of estimating properties of biomaterials and in more accurate predictions of the processes investigated.</jats:p

The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

In this article, we discuss existing literature on DSS in agriculture, on DSS that use data available in the Semantic Web, and on Semantic Web initiatives focusing on agriculture information. Our goal is to assess the readiness of the Semantic Web as a platform to empower DSS that can keep risk and uncertainty in agriculture under control. Key agricultural activities targeted by DSS reported in literature are nutrient management, insect and pest management, land use and planning, environmental change and forecasting, and water and drought management. The most relevant use of Semantic Web in DSS, is in data analysis, as a means of making DSS more intelligent. There are initiatives to produce vocabularies and semantic repositories in the domain of agriculture. However, data and models are still isolated in specific domain repositories, and interoperability is still weak.IFIP Advances in Information and Communication Technology, vol. 506.Laboratorio de Investigación y Formación en Informática Avanzad

The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

In this article, we discuss existing literature on DSS in agriculture, on DSS that use data available in the Semantic Web, and on Semantic Web initiatives focusing on agriculture information. Our goal is to assess the readiness of the Semantic Web as a platform to empower DSS that can keep risk and uncertainty in agriculture under control. Key agricultural activities targeted by DSS reported in literature are nutrient management, insect and pest management, land use and planning, environmental change and forecasting, and water and drought management. The most relevant use of Semantic Web in DSS, is in data analysis, as a means of making DSS more intelligent. There are initiatives to produce vocabularies and semantic repositories in the domain of agriculture. However, data and models are still isolated in specific domain repositories, and interoperability is still weak.IFIP Advances in Information and Communication Technology, vol. 506.Laboratorio de Investigación y Formación en Informática Avanzad

An Ontology-Based Decision Support System to Foster Innovation and Competitiveness Opportunities of Health Tourism Destinations

The competitiveness of nature-based Health Tourism (NHT) industry, especially in the Alpine regions, is increasingly linked to the sustainability and exploitation of unique natural resources of tourism destinations, which often lack the access to knowledge and networks of stakeholders to improve their offerings. In this sense, the use of digital tools can open up further opportunities to reconsider value offerings and better access different knowledge resources and relationships within the industry network. This Chapter illustrates the collaborative design approach adopted in HEALPS2 for the development of an ontology-based Decision Support System for health tourism destinations. The resulting ontology aims to model the relationships between the available natural resources, the value offerings and the target groups of NHT destinations. Moreover, the Collaborative Design approach foresees the involvement of end-users (i.e. not only tourism destinations, but also the network of stakeholders, and the actual and potential future tourists) as both sources of knowledge and validators of the ontology and its outputs, aiming to inform decision-making processes in a shared knowledge model that leverages on digital tools

Semantic Web Tools and Decision-Making

Semantic Web technologies are intertwined with decision-making processes. In this paper the general objectives of the semantic web tools are reviewed and characterized, as well as the categories of decision support tools, in order to establish an intersection of utility and use. We also elaborate on actual and foreseen possibilities for a deeper integration, considering the actual implementation, opportunities and constraints in the decision-making context.info:eu-repo/semantics/publishedVersio

PADTUN - using semantic technologies in tunnel diagnosis and maintenance domain

YesA Decision Support System (DSS) in tunnelling domain deals with identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key area in diagnosing pathologies is to identify regions of interest (ROI). In practice, tunnel experts intuitively abstract regions of interest by selecting tunnel portions that are susceptible to the same types of pathologies with some distance approximation. This complex diagnosis process is often subjective and poorly scales across cases and transport structures. In this paper, we introduce PADTUN system, a working prototype of a DSS in tunnelling domain using semantic technologies. Ontologies are developed and used to capture tacit knowledge from tunnel experts. Tunnel inspection data are annotated with ontologies to take advantage of inferring capabilities offered by semantic technologies. In addition, an intelligent mechanism is developed to exploit abstraction and inference capabilities to identify ROI. PADTUN is developed in real-world settings offered by the NeTTUN EU Project and is applied in a tunnel diagnosis use case with Société Nationale des Chemins de Fer Français (SNCF), France. We show how the use of semantic technologies allows addressing the complex issues of pathology and ROI inferencing and matching experts’ expectations of decision support

A Semantic Decision Support System to optimize the energy use of public buildings

Cities are expected to play a key role in the implementation of Europe 2020 strategy, leading to relevant actions towards energy-efficient neighbourhoods. Although there are plenty of energy data and other related data sets available at the city level, their appropriate integration to support decision making processes for local authorities, still remains a challenge. To fill this gap, a web-based Decision Support System (DSS) has been developed within the framework of the OPTIMUS project to support the decision making process, improving the energy efficiency of buildings, by optimizing the energy use in their premises, and reducing CO 2 emissions. In this paper, we presents the semantic framework that has been developed to provide the required interoperability, between the DSS and the different data sources, using Semantic Web technologies. In this framework, the OPTIMUS ontology has been designed to capture and model the information from these data sources. Experimental results derived from the adoption of the ontology are discussed in this paper

A Fuzzy Approach to the Synthesis of Cognitive Maps for Modeling Decision Making in Complex Systems

The object of this study is fuzzy cognitive modeling as a means of studying semistructured socio-economic systems. The features of constructing cognitive maps, providing the ability to choose management decisions in complex semistructured socio-economic systems, are described. It is shown that further improvement of technologies necessary for developing decision support systems and their practical use is still relevant. This work aimed to improve the accuracy of cognitive modeling of semistructured systems based on a fuzzy cognitive map of structuring nonformalized situations (MSNS) with the evaluation of root-mean-square error (RMSE) and mean average squared error (MASE) coefficients. In order to achieve the goal, the following main methods were used: systems analysis methods, fuzzy logic and fuzzy sets theory postulates, theory of integral wavelet transform, correlation and autocorrelation analyses. As a result, a new methodology for constructing MSNS was proposed—a map of structuring nonformalized situations that combines the positive properties of previous fuzzy cognitive maps. The solution of modeling problems based on this methodology should increase the reliability and quality of analysis and modeling of semistructured systems and processes under uncertainty. The analysis using open datasets proved that compared to the classical ARIMA, SVR, MLP, and Fuzzy time series models, our proposed model provides better performance in terms of MASE and RMSE metrics, which confirms its advantage. Thus, it is advisable to use our proposed algorithm in the future as a mathematical basis for developing software tools for the analysis and modeling of problems in semistructured systems and processes. Doi: 10.28991/ESJ-2022-06-02-012 Full Text: PD

An Ontology-Based Framework for a Telehealthcare System to Foster Healthy Nutrition and Active Lifestyle in Older Adults

In recent years, telehealthcare systems (TSs) have become more and more widespread, as they can contribute to promoting the continuity of care and managing chronic conditions efficiently. Most TSs and nutrition recommendation systems require much information to return appropriate suggestions. This work proposes an ontology-based TS, namely HeNuALs, aimed at fostering a healthy diet and an active lifestyle in older adults with chronic pathologies. The system is built on the formalization of users' health conditions, which can be obtained by leveraging existing standards. This allows for modeling different pathologies via reusable knowledge, thus limiting the amount of information needed to retrieve nutritional indications from the system. HeNuALs is composed of (1) an ontological layer that stores patients and their data, food and its characteristics, and physical activity-related data, enabling the inference a series of suggestions based on the effects of foods and exercises on specific health conditions; (2) two applications that allow both the patient and the clinicians to access the data (with different permissions) stored in the ontological layer; and (3) a series of wearable sensors that can be used to monitor physical exercise (provided by the patient application) and to ensure patients' safety. HeNuALs inferences have been validated considering two different use cases. The system revealed the ability to determine suggestions for healthy, adequate, or unhealthy dishes for a patient with respiratory disease and for a patient with diabetes mellitus. Future work foresees the extension of the HeNuALs knowledge base by exploiting automatic knowledge retrieval approaches and validation of the whole system with target users