SUPPORTING CORAL REEF ECOSYSTEMS RESEARCH THROUGH MODELLING A REUSABLE ONTOLOGY FRAMEWORK

Coral reefs around the world are facing serious threats. These fragile ecosystems are in need for conservation. The coastal state of Bahia hosts the most extensive and richest area of coral reefs in the South Atlantic Ocean. Assessment, planning and management of coral reef ecosystems are particularly challenging tasks. This work shows how the creation of a GIS improves the process of management, monitoring and conservation of the Bahian reef environments The initial data input started by the vectorization of 1) bathymetric data from the Bureau of Hydrography and Navigation (DHN), 2) shoreline and mangrove areas from Landsat 7 ETM + images, 3) near surface reefs from Quickbird images, and 4) coastal and marine protected areas of federal, state and local administrations. Geological, physical, biological and social information was then included in order to create a suitable marine GIS for conservation aims. The data includes information on sediment granulometry and transport patterns, rocky substrate outcrops, sea surface temperature, wave direction, rain precipitation, major contributing river discharge, artisanal fishery, benthic cover and bleaching data. ReefBahia GIS has provided essential information for a better understanding of coral reefs of the state of Bahia geological and ecological characteristics such as mapping, representation, connectivity and biodiversity of coral reefs, geological facies, Quaternary sedimentation, numeric modeling of wave refraction and monitoring of bleaching events.

Although current efforts have indicated the need for design verification and evaluation systems, little has been done to develop knowledge representation systems for implementing this class of expert systems. In this paper, we describe CANDLE, a knowledge representation system built to solve design verification and evaluation problems. We define the class of design verification and evaluation systems and the functionality required of knowledge representation systems used to develop these systems. We present an example design verification and evaluation problem, illustrating how this problem imposes these functionality requirements on the knowledge representation system used. In the remainder of the paper we discuss CANDLE, a knowledge representation system created to develop design verification and evaluation systems. We show that CANDLE meets the knowledge representation requirements for this class of systems. We also present some of CANDLE'S functionality that exceeds the defined knowledge representation requirements and provides a knowledge engineer with the capability to expand the functionality of a design verification and evaluation system.

Relevance. The growing integration of Internet of Things (IoT) technologies into all areas of human life – from intelligent households to smart city infrastructure – is accompanied by an exponential increase in the volume of data being collected, transmitted, and processed in real time. When combined with artificial intelligence technologies, this data becomes the foundation for making autonomous decisions, predicting user behavior, and adapting environments to the needs of specific individuals. However, it is precisely in this context that the critically important issue of personal data protection arises. Many IoT devices operate in uncontrolled environments, have limited resources for cryptographic protection, and are vulnerable to cyberattacks and unauthorized data collection. Meanwhile, artificial intelligence algorithms used to analyze this data often exhibit the “black box” problem, where it is impossible to fully explain how and why a particular decision was made based on personalized data. The lack of transparency, combined with broad access to sensitive information, threatens fundamental human rights to privacy. The relevance of this topic is driven by the need to find balanced technical solutions that enable both effective analysis of large-scale data in IoT environments and a high level of data security. In this regard, the study of modern methods for processing, analyzing, and protecting data in IoT systems, adapted to the requirements of ethical artificial intelligence and digital privacy standards, represents one of the key challenges of contemporary digital science. The object of research: the processes of data collection, processing, analysis, and protection in IoT systems, particularly those components related to the use of users' personal information and its processing through artificial intelligence methods. Purpose of the article: research of modern methods for data processing and analysis in IoT systems. The objective of the work is to identify the most effective approaches to secure data handling, characterize existing privacy threats, and assess the potential for integrating protected analytical algorithms that meet both the technical and ethical requirements of the digital environment. Research results. A comprehensive analysis of modern approaches to data collection, processing, analysis, and protection in IoT systems has been conducted, particularly in the context of the growing role of artificial intelligence. The technological foundations of IoT functionality were examined, key architectural components identified, and their role in creating digital ecosystems for monitoring, management, and decision-making across various sectors – from household systems to critical infrastructure – was investigated. Special attention was paid to data preprocessing methods, which help reduce information load, improve the quality of analysis, and adapt data flows to the requirements of intelligent algorithms. It was demonstrated that the use of edge processing and local-level aggregation enhances both system performance and security. The main types of databases for IoT – especially those optimized for time series – were analyzed, along with tools for handling large volumes of data in cloud and hybrid environments. Conclusions. Data collection methods in IoT are multilayered and closely linked to the requirements for energy efficiency, security, latency, and system scalability. The quality and reliability of the collected information form the foundation for subsequent processing, analysis, and decision-making; therefore, the selection of sensors, communication protocols, and architectural models is of strategic importance for any IoT system. Preprocessing and efficient data storage in IoT are critical stages that ensure the quality, security, and usability of information for further analysis. They determine not only the accuracy of analytics but also the stability, scalability, and compliance with regulatory standards. This creates a demand for the development of adaptive, intelligent data processing and storage systems capable of dynamically responding to changes in device operation context and user requirements. The successful implementation of secure IoT solutions requires an integrated approach that combines technical expertise, legal knowledge, and ethical responsibility.

This experience report describes the successful application of statistical software testing to a large-scale realworld equipment replacement project. The Data Processing System at Dungeness B nuclear power station is obsolescent and needs to be replaced by a new system involving different technology. The new Data Processing and Control System (DPCS) involves a large amount of software code, with the potential for software failures, and therefore confidence needs to be established in the dependability of the new software-based system. Statistical software testing (ST) was used as one technique to do this. The aim was to support the claim that with high confidence the higher integrity parts of the replacement software have a failure probability of better than 10-2 failures per demand (fpd). This paper describes how this has been achieved by careful design of the statistical tests and by the development of a large number of supporting tools and procedures. A total of 395 statistical tests with no failure was achieved providing at least 98% confidence that the safety claim has been met.

Base on the ZigBee technology, a wireless sensor networks for remote home environment monitoring and health care monitoring was built in this paper, and the applications of data fusion algorithms in system of front & back end data processing were deeply discussed. The experiment is proved that the applications of data fusion algorithm, which is a combination of Bayesian estimation and fuzzy reasoning, could effectively reduced the network information redundancy and Internet delay of wireless sensor networks. It is also improved the accuracy and reliability of data and the network lifetime of this system.

The paper presents an approach to the development of a software environment for creation of intelligent systems. This environment is based on an integrated knowledge representation model that combines both classic knowledge representation means and constraint programming methods with agent-based techniques. In contrast to other systems following constraint programming methods, the environment presented here allows one to manipulate objects with imprecise (subdefinite) values of attributes. The process of refining such subdefinite attributes is performed by means of a special data-driven mechanism. Another feature of the environment is the use of an agent-based technique instead of the traditional production rule technique, in order to increase efficiency of the processes of logical inference and data processing. Due to a natural combination of the data-driven and event-driven mechanisms, the environment can be used for the development of efficient intelli-gent systems for various applications.

Retrieving and composing individuals' capabilities are the matter of several research fields, like competence-based management, human resources management, enterprise knowledge management systems, knowledge representation systems, etc. This talk focuses on capability representation, discovery and composition in heterogeneous, and possibly distributed, knowledge representation environments. A driving motivation of this work is to provide a contribution to the satisfaction of the need for retrieving individuals who may carry out actions, as well as the need for retrieving individuals who, when putting their competences together, may carry out actions. We outline some formal capability description languages, and we propose approaches and algorithms for capability management, discovery and composition. In addition, we outline how our proposals are implemented in mediator-based prototype systems under a service oriented architecture and a Peer-to-Peer architecture.

In order to represent the design knowledge of complex product, logical descriptions of different knowledge are conducted by character analysis for the generalized design knowledge. Then a knowledge representation system is proposed in this chapter utilizing the three-layer structure of “design object ontology—design process ontology—data resource” (OPR). And the semantic description frame is defined based on the suggested upper merged ontology (SUMO) to implement the ontology collaborative construction using the ontology web language (OWL). Based on the constructed ontology, the storage structure for generalized knowledge is determined to actualize the generalized knowledge representation. Finally, the feasibility and effectiveness of the proposed technique in this chapter is demonstrated by the design knowledge representation for the self-propelled gun.

The ASTRI Mini-Array is an international project led by the Italian National Institute for Astrophysics (INAF) to build and operate an array of nine 4-m class Imaging Atmospheric Cherenkov Telescopes (IACTs) at the Observatorio del Teide (Tenerife, Spain). The system is designed to perform deep observations of the galactic and extragalactic gamma-ray sky in the TeV and multi-TeV energy band, with important synergies with other ground-based gamma-ray facilities in the Northern Hemisphere and space-borne telescopes. As part of the overall software system, the ASTRI (Astrofisica con Specchi a Tecnologia Replicante Italiana) Team is developing dedicated systems for Data Processing, Simulation, and Archive to achieve effective handling, dissemination, and scientific exploitation of the ASTRI Mini-Array data. Thanks to the high-speed network connection available between Canary Islands and Italy, data acquired on-site will be delivered to the ASTRI Data Center in Rome immediately after acquisition. The raw data will be then reduced and analyzed by the Data Processing System up to the generation of the final scientific products. Detailed Monte Carlo simulated data will be produced by the Simulation System and exploited in several data processing steps in order to achieve precise reconstruction of the physical characteristics of the detected gamma rays and to reject the overwhelming background due to charged cosmic rays. The data access at different user levels and for different use cases, each one with a customized data organization, will be provided by the Archive System. In this contribution we present these three ASTRI Mini-Array software systems, focusing on their main functionalities, components, and interfaces.

We present a step-by-step methodology for the systematic extraction, alignment, and analysis of peer review data from Crossref to enhance the OpenCitations Index. This process aims to address the following research questions: Is it possible to create a new index of citations which contains typed citations where a peer review (citing entity) reviews (specific citation function) a publication (cited entity)? What is the transformation of the Crossref dump necessary to create such an index to be compliant with the OpenCitations Data Model ? What are the top publication venues in terms of the number of peer reviews received? How many peer reviews in Crossref are included in OpenCitations Meta? How many articles that have been reviewed by a peer review are included in OpenCitations Meta? The protocol delineates four key phases: (1) data extraction, (2) data processing, (3) post-processing, and (4) data analysis. The first two research questions were addressed during the data processing and post-processing phases, while the answers to the third, fourth, and fifth questions were found during the data analysis phase.

In recent years both the number and the size of organisational databases have increased rapidly. However, although available processing power has also grown, the increase in stored data has not necessarily led to a corresponding increase in useful information and knowledge. This has led to a growing interest in the development of tools capable of harnessing the increased processing power available to better utilise the potential of stored data. The terms Knowledge Discovery in Databases and Mining have been adopted for a field of research dealing with the automatic discovery of knowledge implicit within databases. Data mining is useful in situations where the volume of data is either too large or too complicated for manual processing or, to a lesser extent, where human experts are unavailable to provide knowledge. The success already attained by a wide range of data mining applications has continued to prompt further investigation into alternative data mining techniques and the extension of data mining to new domains. This paper surveys, from the standpoint of the database systems community, current issues in data mining research by examining the architectural and process models adopted by knowledge discovery systems, the different types of discovered knowledge, the way knowledge discovery systems operate on different data types, various techniques for knowledge discovery and the ways in which discovered knowledge is used.

Sickle cell disease (SCD) is one of the most common monogenic diseases in humans with multiple phenotypic expressions that can manifest as both acute and chronic complications. Although described more than a century ago, challenges in comprehensive disease management and collaborative research on this disease are compounded by the complex molecular and clinical phenotypes of SCD, environmental and psychosocial factors, limited therapeutic options and ambiguous terminology. This ambiguous terminology has hampered the integration and interoperability of existing SCD knowledge, and SCD research translation. The SCD Ontology (SCDO), which is a community-driven integrative and universal knowledge representation system for SCD, overcomes this issue by providing a controlled vocabulary developed by a group of experts in both SCD and ontology design. SCDO is the first and most comprehensive standardized human- and machine-readable resource that unambiguously represents terminology and concepts about SCD for researchers, patients and clinicians. It is built around the central concept ‘hemoglobinopathy’, allowing inclusion of non-SCD haemoglobinopathies, such as thalassaemias, which may interfere with or influence SCD phenotypic manifestations. This collaboratively developed ontology constitutes a comprehensive knowledge management system and standardized terminology of various SCD-related factors. The SCDO will promote interoperability of different research datasets, facilitate seamless data sharing and collaborations, including meta-analyses within the SCD community, and support the development and curation of data-basing and clinical informatics in SCD.

In today's world, the increase in the volume and variety of data used in the process of military management leads to an increase in the requirements for their storage and processing. To effectively manage military operations, it is necessary to have information about various aspects of military activity, such as the location of military units, the availability of resources, the assessment of potential threats, and much more. Modern military operations require the processing and analysis of a large amount of data generated in real time. Data collected from sensors, satellites, probes, sensors, and other sources can be vast in volume and diverse in structure. The increase in the volume and heterogeneity of data used in the process of military management leads to an increase in the requirements for their storage and processing. This presents military organizations with the task of creating effective and reliable data storage and processing systems. The article considers the possibilities of using «Big Data» technology, which allow collecting, storing, processing and analyzing large volumes of various data in real time. The use of modern data management technologies can help increase the efficiency of military activities and ensure fast and accurate decision-making at various levels of management. This can be achieved through the use of artificial intelligence, machine learning and data analysis technologies that can help military structures perform calculations and forecasts, optimize the use of resources and increase the efficiency of solving various tasks. Also, an important component of the effective use of «Big Data» technologies in the military sphere is ensuring the protection of data from unauthorized access, as well as ensuring the reliability and stability of data storage and processing systems in the face of possible cyberattacks. The article analyzes the Hadoop system, which can be used to store, process and analyze «Big Data» in the military sphere.

The functioning of modern information and communication networks is impossible without data processing. With the emergence of new network services, the amount of information that needs to be processed increases, while the requirements to the data processing quality become more and more stringent. Therefore, the problem of designing and maintaining a scalable data processing system with a flexible quality of service management is becoming more and more important for a network operator. Such data processing systems have a complex internal structure with many interrelated parameters, which makes them difficult to analyze, manage, and expand. This study proposes to use an ontological model to store, represent, and manipulate the information in the operator’s data processing system. The ontological model allows to structure and systematize the data of an information processing system, and transparently reflects the relationships between the parameters of the system to simplify its analysis and scaling. The proposed ontology of a data processing system consists of three related subsystems. The paper describes the proposed ontological model and additionally analyzes the sources of information that needs to be processed in the information and communication network.

Knowledge Organization in editorial policies for titles, abstracts and keywords in JCR-indexed journals: an exploratory study in the areas of Information and Communication Sciences