Search Results (64)

Air pollution poses a growing threat to public health and the environment, highlighting the need for continuous and precise urban air quality monitoring. The aim of this study was to implement and evaluate an autonomous air quality monitoring platform developed by the University of Ruse, “Angel Kanchev”, under Bulgaria’s National Recovery and Resilience Plan (project BG-RRP-2.013-0001), co-financed by the European Union through the NextGenerationEU initiative. The system, based on Libelium’s mobile sensor technology, was installed at a height of two meters on the university campus near Rodina Boulevard and operated continuously from 1 March 2024 to 30 March 2025. Every 15 min, it recorded concentrations of CO, CO₂, NO₂, SO₂, PM₁, PM_2.5, and PM₁₀, along with meteorological parameters (temperature, humidity, and pressure), transmitting the data via GSM to a cloud-based database. Analyses included a distributional assessment, Spearman rank correlations, Kruskal–Wallis tests with Dunn–Sidak post hoc comparisons, and k-means clustering to identify temporal and meteorological patterns in pollutant levels. The results indicate the high operational stability of the system and reveal characteristic pollution profiles associated with time of day, weather conditions, and seasonal variation. The findings confirm the value of combining calibrated IoT systems with advanced statistical methods to support data-driven air quality management and the development of predictive environmental models. Full article

The World Health Organization reports approximately 1.35 million fatalities annually due to road traffic accidents, with pedestrians constituting 23% of these deaths. This highlights the critical need to enhance pedestrian safety, especially given the significant role human error plays in road accidents. Autonomous vehicles present a promising solution to mitigate these fatalities by improving road safety through advanced prediction of pedestrian behavior. With the autonomous vehicle market projected to grow substantially and offer various economic benefits, including reduced driving costs and enhanced safety, understanding and predicting pedestrian actions and intentions is essential for integrating autonomous vehicles into traffic systems effectively. Despite significant advancements, replicating human social understanding in autonomous vehicles remains challenging, particularly in predicting the complex and unpredictable behavior of vulnerable road users like pedestrians. Moreover, the inherent uncertainty in pedestrian behavior adds another layer of complexity, requiring robust methods to quantify and manage this uncertainty effectively. This review provides a structured and in-depth analysis of pedestrian intention prediction techniques, with a unique focus on how uncertainty is modeled and managed. We categorize existing approaches based on prediction duration, feature type, and model architecture, and critically examine benchmark datasets and performance metrics. Furthermore, we explore the implications of uncertainty types—epistemic and aleatoric—and discuss their integration into autonomous vehicle systems. By synthesizing recent developments and highlighting the limitations of current methodologies, this paper aims to advance the understanding of Pedestrian intention Prediction and contribute to safer and more reliable autonomous vehicle deployment. Full article

This paper explores the evolving relationship between Digital Twins (DT) and the Metaverse, two foundational yet often conflated digital paradigms in digital architecture. While DTs function as mirrored models of real-world systems—integrating IoT, BIM, and real-time analytics to support decision-making—Metaverses are typically fictional, immersive, multi-user environments shaped by social, cultural, and speculative narratives. Through several research projects, the team investigate the divergence between DTs and Metaverses through the lens of their purpose, data structure, immersion, and interactivity, while highlighting areas of convergence driven by emerging technologies in Artificial Intelligence (AI) and Extended Reality (XR).This study aims to investigate the convergence of DTs and the Metaverse in digital architecture, examining how emerging technologies—such as AI, XR, and Large Language Models (LLMs)—are blurring their traditional boundaries. By analyzing their divergent purposes, data structures, and interactivity modes, as well as hybrid applications (e.g., data-integrated virtual environments and AI-driven collaboration), this study seeks to define the opportunities and challenges of this integration for architectural design, decision-making, and immersive user experiences. Our research spans multiple projects utilizing XR and AI to develop DT and the Metaverse. The team assess the capabilities of AI in DT environments, such as reality capture and smart building management. Concurrently, the team evaluates metaverse platforms for online collaboration and architectural education, focusing on features facilitating multi-user engagement. The paper presents evaluations of various virtual environment development pipelines, comparing traditional BIM+IoT workflows with novel approaches such as Gaussian Splatting and generative AI for content creation. The team further explores the integration of Large Language Models (LLMs) in both domains, such as virtual agents or LLM-powered Non-Player-Controlled Characters (NPC), enabling autonomous interaction and enhancing user engagement within spatial environments. Finally, the paper argues that DTs and Metaverse’s once-distinct boundaries are becoming increasingly porous. Hybrid digital spaces—such as virtual buildings with data-integrated twins and immersive, social metaverses—demonstrate this convergence. As digital environments mature, architects are uniquely positioned to shape these dual-purpose ecosystems, leveraging AI, XR, and spatial computing to fuse data-driven models with immersive and user-centered experiences. Full article

The surge in electric vehicles (EVs) in Denmark challenges the country’s residential low-voltage (LV) distribution system. In particular, it increases the demand for home EV charging significantly and possibly overloads the LV grid. This study analyzes the impact of EV charging integration on Denmark’s residential distribution networks. A residential grid comprising 67 households powered by a 630 kVA transformer is studied using DiGSILENT PowerFactory. With the assumption of simultaneous charging of all EVs, the transformer can be heavily loaded up to 147.2%. Thus, a voltage-droop based autonomous control approach is adopted, where the EV charging power is dynamically adjusted based on the point-of-connection voltage of each charger instead of the fixed rated power. This strategy eliminates overloading of the transformers and cables, ensuring they operate within a pre-set limit of 80%. Voltage drops are mitigated within the acceptable safety range of ±10% from normal voltage. These results highlight the effectiveness of the droop control strategy in managing EV charging power. Finally, it exemplifies the benefits of intelligent EV charging systems in Horizon 2020 EU Projects like SERENE and SUSTENANCE. The findings underscore the necessity to integrate smart control mechanisms, consider reinforcing grids, and promote active consumer participation to meet the rising demand for a low-carbon future. Full article

To validate microgrid systems, precise simulations are necessary beforehand. Traditional Hardware-in-the-Loop Simulation (HILS) is used to validate systems by creating a digital twin environment that integrates software and hardware to mimic reality. However, HILS requires high investment costs for hardware, posing a significant hurdle for companies. To address this issue, this study proposes a Software-in-the-Loop Simulation (SILS) framework using SCADA/EMS and MATLAB/Simulink(R2024a). The proposed SILS framework is highly compatible with Energy Management Systems (EMSs) and Supervisory Control and Data Acquisition (SCADA), allowing near real-time data exchange and scenario-based analysis without relying on physical hardware. According to the simulation results, SILS effectively replicates the dynamic behavior of microgrid components such as solar power generation systems, energy storage systems (ESSs), and diesel generators. Solution providers can quickly conduct feasibility tests through systems that simulate actual power systems. They can test the operation of SCADA/EMS at a lower cost and reduce on-site time, thereby reducing business costs and preemptively addressing potential issues in the field. This paper demonstrates how SILS can contribute to establishing optimal operation strategies and power supply stability through case studies, including daily operation optimization and autonomous operation scenarios for microgrids. This research provides a foundation for the feasibility of microgrid solution construction by enabling software performance evaluations and the verification of economic expected returns in the early stages of a project. Full article

Maintenance of offshore wind farms requires the transportation of tools and spare parts in close coordination with the deployment of technicians and the cost-intensive shutdown of the wind turbines. In addition to ships and helicopters, drones are envisioned to support the offshore transportation system in the future. For cost-efficient and scalable offshore drone operations, autonomy is key to minimize the required infrastructure and personnel. In this work, we present a system architecture that integrates the key onboard capabilities for autonomous offshore drone operations: onboard mission and contingency management, en-route trajectory planning, robust flight control, safe landing, communication management, and runtime monitoring. We also present technical solutions for each of these capabilities and discuss their integration and interaction within the autonomy architecture. Furthermore, remaining challenges and the feasibility of autonomous drone operations for offshore wind farm cargo delivery are addressed, contributing to the realization of this vision in the near future. The work presented here summarizes the results of autonomous cargo drone operations within the UDW research project, a joint project between the German Aerospace Center (DLR) and the energy supplier EnBW. Full article

The autonomy of transport systems presents a transformative opportunity to enhance logistics efficiency, improve safety, and support decarbonization. In the maritime sector, the International Maritime Organization (IMO) has been working since 2016 to develop a mandatory regulatory framework for Maritime Autonomous Surface Ships (MASSs), aiming to finalize a comprehensive code. Simultaneously, pilot projects are underway in national waters under the oversight of national administrations. Naval applications of autonomous ships demonstrate their potential, as emerging doctrines highlight their strategic and operational advantages. Although the military sector is not governed at the international level, safely managing interactions between military and commercial MASSs is crucial for ensuring safe navigation. Classification societies play a vital role in achieving high safety standards and ensuring regulatory compliance. This study aims to propose a framework for certifying maritime autonomous vessels. Through a thorough analysis of the existing literature and by identifying gaps, this study outlines a structured pathway to facilitate the certification and operation of MASSs, addressing key technical, operational, and safety considerations. This research contributes to designing a risk-informed approach for the development of autonomous surface vehicles. Full article

Computer simulations of processes are increasingly used in business practice to improve the results of an enterprise and maximise its value. Designing process models and simulating their behaviour provide the opportunity to analyse economic and operational results before appropriate organisational, location, and investment decisions are made. This article presents the possibilities of using simulation modelling in intralogistics systems. In the presented article, a decision-making support tool based on the DES simulator developed by the authors was proposed. This tool supports the decision-making process based on the analysis of parameters that affect the energy efficiency of the analysed process and its sustainability. The possibilities of the proposed tool were presented by giving an example of the analysis of the implementation of the automation of intralogistics processes. As part of the implementation, the use of Autonomous Mobile Robot (AMR) vehicles was proposed. By conducting experiments of the intralogistics system model and analysing the obtained results also in terms of energy consumption by AMR vehicles, the proposed project can be verified and improvements can be proposed. The results obtained in this research confirmed the possibility of using the proposed tool for supporting the decision-making process for assessing the energy efficiency of the designed intralogistics system. The proposed method is a cost-free element of analysis that helps the management staff of a given enterprise make appropriate decisions. Full article

This paper discusses SESAR’s Auto-Steer Taxi at Airport (ASTAIR) project, which seeks to advance airport ground operations including engine-off taxiing to move towards sustainable airports. The ASTAIR concept integrates human–AI teaming to optimize aircraft movement from gates to runways, with the primary objectives of improving predictability, efficiency, and environmental sustainability at large airports. Building on previous initiatives such as SESAR’s AEON, ASTAIR brings high-level automation to tasks like autonomous taxiing and vehicle routing. The system assists operators by calculating conflict-free routes for vehicles and dynamically adjusting operations based on real-time data. Based on workshops with several stakeholders, we describe the operational challenges involved in implementing ASTAIR, including managing parking stand availability and adapting to unforeseen events. A significant challenge highlighted is the human–automation partnership, where AI plays a supportive role but humans retain control over critical decisions, particularly in cases of system failure. The need for clear and consistent collaboration between AI and human operators is emphasized to ensure safety, efficiency, and improved compliance with take-off schedules, which in turn facilitates in-flight optimization. Full article

This paper explores and compares two participatory management approaches—the Company Democracy Model and Holacracy—for their application within the Indian Ministry of Education. It emphasizes the need for innovative organizational techniques in the management of the public sector, particularly in light of the dynamic demands posed by the New Education Policy (NEP) 2020. The study evaluates how these approaches enhance employee engagement and improve the quality of deliverables. Lewin’s Field Force Analysis is utilized to examine the organization’s strategy. The study employs Kotter’s Change Model to assess the applicability of Holacracy—a decentralized, project-oriented system, characterized by its dynamic and self-organizing structures. This model is analyzed for its potential to meet the Ministry’s shifting priorities and to foster adaptability through autonomous teams. Conversely, the Company Democracy Model, which emphasizes employee-centric growth and decision-making within a tiered, spiral framework, is evaluated using the ADKAR Change Model. This model’s compatibility with the Ministry’s hierarchical structure and its potential to enhance participatory governance are key areas of focus. The study contributes novel insights by integrating change management theories with a refined presentation of the CDM pyramid and by introducing specific performance metrics for both models. By combining theoretical frameworks with practical applications, this paper offers a sustainable governance model suited to dynamic organizational environments. Full article

This study presents a multi-drone proof of concept for efficient forest mapping and autonomous operation, framed within the context of the OPENSWARM EU Project. The approach leverages state-of-the-art open-source simultaneous localisation and mapping (SLAM) frameworks, like LiDAR (Light Detection And Ranging) Inertial Odometry via Smoothing and Mapping (LIO-SAM), and Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm (DCL-SLAM), seamlessly integrated within the MRS UAV System and Swarm Formation packages. This integration is achieved through a series of procedures compliant with Robot Operating System middleware (ROS), including an auto-tuning particle swarm optimisation method for enhanced flight control and stabilisation, which is crucial for autonomous operation in challenging environments. Field experiments conducted in a forest with multiple drones demonstrate the system’s ability to navigate complex terrains as a coordinated swarm, accurately and collaboratively mapping forest areas. Results highlight the potential of this proof of concept, contributing to the development of scalable autonomous solutions for forestry management. The findings emphasise the significance of integrating multiple open-source technologies to advance sustainable forestry practices using swarms of drones. Full article

The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by The Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM’s efficiency as a DSS. Full article

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles. Full article

With the development of advanced driver assistance systems (ADAS) and autonomous vehicles (AV), recent years have seen an increasing evolution of onboard sensors and communication interfaces capable of interacting with available infrastructures, including satellite constellations, road structures, modern and heterogeneous network systems (e.g., 5G and beyond) and even adjacent vehicles. Consequently, it is essential to develop architectures that cover data fusion (multi–sensor approach), communication, power management, and system monitoring to ensure accurate and reliable perception in several navigation scenarios. Motivated by the EMERGE project, this paper describes the definition and implementation of an On Board Unit (OBU) dedicated to the navigation process. The OBU is equipped with the Xsens MTi–630 AHRS inertial sensor, a multi–constellation/multi–frequency Global Navigation Satellite System (GNSS) receiver with the u–blox ZED–F9P module and communication interfaces that afford access to the PointPerfect augmentation service. Experimental results show that GNSS, with corrections provided by augmentation, affords centimetre accuracy, with a Time To First Fix (TTFF) of about 30 s. During the on–road tests, we also collect: the output of fusion with inertial sensor data, monitoring information that assess correct operation of the module, and the OBU power consumption, that remains under 5 W even in high–power operating mode. Full article

Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity. Full article