In this article, we consider the modelling of stationary incompressible and isothermal one-dimensional fluid flow through a long pipeline. The approximation of the average pressure in the developed model by the arithmetic mean of inlet and outlet pressures leads to the known empirical Darcy–Weisbach equation. Most importantly, we also present another improved approach that is more accurate because the average pressure is estimated by integrating the pressure along the pipeline. Through appropriate transformation, we show the difference between the Darcy–Weisbach equation and the improved model that should be treated as a Darcy–Weisbach model error, in multiplicative and additive form. This error increases when the overall pressure drop increases. This symptomatic phenomenon is discussed in detail. In addition, we also consider four methods of estimating the coefficient of friction, assess the impact of pressure difference on the estimated average flow velocity and, based on experimental data, we show the usefulness of new proposals in various applications.

The objective of this review is to conduct a critical analysis of the current literature pertaining to segmentation techniques and provide a methodical summary of their impact on forestry-related activities, emphasizing their applications using LiDAR and imagery data. This review covers the challenges, progress, and application of these strategies in ecological monitoring, forest inventory, and tree species classification. Through the process of synthesizing pivotal discoveries from multiple studies, this comprehensive analysis provides valuable perspectives on the present status of research and highlights prospective areas for further exploration. The primary topics addressed encompass the approach employed for executing the examination, the fundamental discoveries associated with semantic segmentation and instance segmentation in the domain of forestry, and the ramifications of these discoveries for the discipline. This review highlights the effectiveness of semantic and instance segmentation techniques in forestry applications, such as precise tree species identification and individual tree monitoring. However, challenges such as occlusions, overlapping branches, and varying data quality remain. Future research should focus on overcoming these obstacles to enhance the precision and applicability of these segmentation methodologies.

A system for haptic interaction with physical, virtual, and augmented realities, founded on drive and measurement elements (DMEs), is considered. The system consists of eight DME rolls equipped with linear actuators, able to measure their angular velocity, drive the sphere, and adjust downforce (pressing the roll against the sphere). Two modeling issues are addressed. Special effort is put in to compensate for various technical issues. Analytic derivation of the relation between the angular velocities of the rolls and the sphere is presented. On this basis, the importance of control over the downforce applied to an individual roll is indicated with the aim of minimizing the wear of the roll. The selection of the proper downforce for each specific position (angle) of a DME roll can extend its life cycle by reducing its grinding on the sphere. The issue of modeling the relation between the angular velocity of a given DME and the sphere is addressed. In a simplified case, such a relation can be obtained analytically. However, for a nonuniform distribution of reaction forces on the contact area, a numerical approach is necessary. Two numerical methods for the estimation of the angular velocity of the DME roll are presented and discussed. Moreover, an algorithm for the estimation of the sphere's rotational motion parameters is described and implemented, proving the usefulness of the presented method.

Forests are critical ecosystems, supporting biodiversity, economic resources, and climate regulation. The traditional techniques applied in forestry segmentation based on RGB photos struggle in challenging circumstances, such as fluctuating lighting, occlusions, and densely overlapping structures, which results in imprecise tree detection and categorization. Despite their effectiveness, semantic segmentation models have trouble recognizing trees apart from background objects in cluttered surroundings. In order to overcome these restrictions, this study advances forestry management by integrating depth information into the YOLOv8 segmentation model using the FinnForest dataset. Results show significant improvements in detection accuracy, particularly for spruce trees, where mAP50 increased from 0.778 to 0.848 and mAP50-95 from 0.472 to 0.523. These findings demonstrate the potential of depth-enhanced models to overcome the limitations of traditional RGB-based segmentation, particularly in complex forest environments with overlapping structures. Depth-enhanced semantic segmentation enables precise mapping of tree species, health, and spatial arrangements, critical for habitat analysis, wildfire risk assessment, and sustainable resource management. By addressing the challenges of size, distance, and lighting variations, this approach supports accurate forest monitoring, improved resource conservation, and automated decision-making in forestry. This research highlights the transformative potential of depth integration in segmentation models, laying a foundation for broader applications in forestry and environmental conservation. Future studies could expand dataset diversity, explore alternative depth technologies like LiDAR, and benchmark against other architectures to enhance performance and adaptability further.

Engineering tools support the process of creating, operating, maintaining, and evolving systems throughout their lifecycle. Toolchains are sequences of tools that build on each others’ output during this procedure. The complete chain of tools itself may not even be recognized by the humans who utilize them, people may just recognize the right tool being used at the right place in time. Modern engineering processes, however, do not value such ad-hoc choice of tooling, because of their uncontrolled nature. Building upon the Extended Automation Engineering Model defined by the IEC 81346 standard, this paper proposes to automate the toolchain building and execution process for Cyber-Physical System of Systems (CPSoS), utilizing key principles of the Eclipse Arrowhead framework. The proposed toolchain automation solution addresses issues such as tool interoperability, interaction, automation, and dynamic choreography. The feasibility of this set of integrated concepts is validated through an Arrowhead-based toolchain choreography demonstration. Note to Practitioners —The paper discusses approaches to the automated execution of various industry-related processes. As the processes are becoming more complex and involve numerous systems which have to be orchestrated, a simple and preprogrammed workflow is not enough anymore. Therefore, building on top of the principles of the Eclipse Arrowhead framework, an adequate model of toolchains, allowing for their automated execution, is proposed. Different approaches to supervision of toolchain execution are discussed showing the benefits of reaching higher automation levels. Further, four adoption levels are introduced, which are a measure of the toolchain automation progress. Finally, a simplified demonstrator is shown and steps to elevate it to higher adoption levels are highlighted. To ensure that the approach is industry-oriented, several examples of how the proposed methodology can be used in the industrial context are discussed.