With the expansion of green energy, more and more data show that wind turbines can pose a significant threat to some endangered bird species. The birds of prey are more frequently exposed to collision risk with the wind turbine blades due to their unique flight path patterns. This paper shows how data from a stereovision system can be used for an efficient classification of detected objects. A method for distinguishing endangered birds from common birds and other flying objects has been developed and tested. The research focused on the selection of a suitable feature extraction methodology. Both motion and visual features are extracted from the Bioseco BPS system and retested using a correlation-based and a wrapper-type approach with genetic algorithms (GAs). With optimal features and fine-tuned classifiers, birds can be distinguished from aeroplanes with a 98.6% recall and 97% accuracy, whereas endangered birds are delimited from common ones with 93.5% recall and 77.2% accuracy.

The study addresses the challenge of bird collisions with wind turbines by developing an autonomous risk assessment method. The research uses data from the stereoscopic Bird Protection System (BPS) to anticipate potential collision threats by analysing flight parameters and distance from turbines. The danger factor depends on the flight characteristics of the identified bird species and the parameters of the wind turbine control system. The paper proposes an online quantitative risk assessment model that operates in real time, with the aim of minimising unnecessary turbine shutdowns while improving bird conservation. The model is validated through field data from bird flights. The findings suggest that adaptive management of turbine operations based on real-time bird flight data can significantly reduce collision risks without compromising energy production efficiency. The research underscores the balance between ecological considerations and the economic viability of wind energy, proposing an adaptive strategy that reduces unnecessary turbine stoppages while ensuring the safety of avian species.

Emerging digital transformation in industry is noticeable among others in Supply Chain Management (SCM). For instance, applying new-generation digitalized technologies in the Dairy Supply Chain (DSC) enables an increase of manufacturing productivity, improves planning and forecasting, and also enhances competitive capabilities according to Industry 4.0 assumptions. It is worth mentioning, that in modern DSC, high visibility of raw materials, components, products, and processes by all contributors on all stages of DSC is crucial. This article focuses on the transparency aspect of the DSC supported by IoT-based technologies enabling interoperability among all DSC participants. The paper addresses the problem of effective integration of heterogeneous data sources, i.e., deployed new technological IoT solutions with traditional SCM systems and a third-party software component. The main objective of this report is to propose the IoT-based DSC model comprising four chain stages: milk production, milk transportation, milk processing, and dairy products distribution. Moreover, the comprehensive DSC domain ontology as a knowledge model is formulated and described. The ontology aims on improvement of the DSC management efficiency by facilitating interoperability within DSC. The applicability of the proposed ontological model is verified using a sustainable-oriented case study, which estimates the environmental footprint at the milk transportation stage of the DSC.

The Internet of Things (IoT) is a jeopardized ecosystem in which heterogeneity is intrinsic at all levels, from physical devices to communication protocols till high-level application semantics. The absence of IoT standards increases the complexity of integration and interoperability among heterogeneous platforms. This generates a strong demand for proper methodologies in order to fully support the development of heterogeneous, yet interoperable, IoT systems. To fill this gap, in this chapter the INTER-METH engineering methodology is presented. Developed in the context of the European H2020 INTER-IoT project, INTER-METH supports the integration of heterogeneous IoT platforms from the analysis to the maintenance phase. Its abstract and instantiated process schema are described, with particular focus on the analysis and design phases that are fundamental drivers of the whole integration process. Relevant interoperability design patterns, the building blocks of the design phase, will be discussed. The chapter also presents the INTER-CASE tool associated to the methodology which is useful to guide integrator designers in properly following the INTER-METH workflow. Finally, the chapter shows the proposed methodology and its tool in action, with the practical integration of BodyCloud and UniversAAL platforms adopted in the INTER-Health pilot of the INTER-IoT project.

While Internet of Things (IoT) systems/applications/ platforms/devices materialize with increasing speed, software engineering "reflection" does not follow "fast enough". The situation is particularly "unbalanced" when one considers integration of independently developed IoT artifacts. To address this problem, we attempt at cataloging software design patterns that materialize in the context of interoperability of/within IoT ecosystems. The aim of this contribution is to briefly describe most common patterns (based on results of the INTER-IoT project), including analysis of common issues, and elaboration of a need for the creation of new (or extending existing) patterns in order to achieve solutions applicable for IoT artifact integration.

One of interesting problems, arising with deployment of large-scale systems, is integration of its nodes (systems / devices). In this work, we discus how to apply semantic technologies, as a mechanism to support node integration and facilitate interoperability within the developed ecosystem. We focus on pragmatic aspects of the proposed solution, discussed from the perspective of the Dependable Embedded Wireless Infrastructure (DEWI) EU project. In this context, a brief analysis of typical integration problems and reasons to apply solution based on semantic technologies is presented. Moreover, the integration procedure is outlined. Here, the key aspect that is discussed in considerable detail, is conversion from the DEWI nodes (based on a traditional relational database approach) towards universal cooperative nodes, which use semantic technologies.

The aim of the paper is to describe the AgentPlanner, an agent-based timetabling system. After its initial implementation (described in [1]), based on results of experiments, we have modified the design (to eliminate discovered shortcomings). Here, we describe the improved AgentPlanner and compare its performance with the state-of-the-art, Free Timetabling Software (FET).

The aim of this note is to present the initial design and preliminary evaluation of the AgentPlanner, an agent-based timetabling system. The primary advantage of the agent approach is the relative ease of schedule modification. This is particularly important in the proposed application area: scheduling university courses. Experimental results, comparing the performance of the AgentPlanner with a state-of-the-art genetic algorithms based software, obtained for a single department and a single building, are presented and analyzed.