The shift to battery electric vehicles (BEVs) is a key step toward reducing greenhouse gas (GHG) emissions and decreasing dependence on fossil fuels, ultimately achieving sustainability development goals (SDGs) and, more specifically, a more sustainable form of transportation. However, while BEVs help eliminate tailpipe emissions, understanding and consequently optimizing their energy efficiency is still an ongoing task, as battery life is often affected by various driving conditions, including driving behavior, terrain, and sometimes weather. Traditional linear modeling techniques often lack the ability to capture the nonlinear and complex relationships found in real-world scenarios. For this reason, this study introduces a comprehensive statistical framework for energy consumption modeling for BEVs, specifically developed with the purpose to explain and estimate energy consumption through an in-depth exploratory analysis. Building on the acquired knowledge from the data, two distinct energy consumption modeling approaches are developed, namely, a multiple principal component (PC) regression and a custom generalized additive model (GAM), incorporating PCs and smoothing splines polynomials. The experimental results showcase that the integration of smoothing splines further refines the proposed model, achieving an accuracy of around 90% and improving performance by approximately 20% on average across all clusters, compared to the simpler linear variant.

Weather forecasting, given the ever-increasing occurrence of climate change-induced events, has been widely introduced as a method to offer accurate and timely forecasts for proactive measures and risk mitigation. Artificial intelligence of things (AIoT) offers promising solutions for short-term weather forecasting, contributing to the advancement of sustainable and efficient weather monitoring technologies. This work presents everWeather_2.0, a significantly enhanced low-cost and self-powered AIoT-based weather forecasting station, which addresses key challenges in power consumption, user engagement and forecasting accuracy. The proposed end-to-end Cloud-Edge-IoT (CEI) proof-of-concept solution improves upon its predecessor by combining a more robust renewable energy subsystem for complete power autonomy with a series of lightweight, adaptive statistical models for on-device forecasting and an integrated display for on-site user engagement. Deployed in a real-world scenario, the station demonstrated seamless operation and high short-term forecasting accuracy for the thermodynamic variables during the pilot deployment period, with model errors observed as low as 2% for 30 min forecasts to 4.3% for 120 min intervals, validating its applicability in real-time and continuous physical weather monitoring. While wind speed and rainfall were monitored, they were excluded from the current accuracy metrics due to their high volatility and the insufficient number of events recorded during the pilot period to ensure reliable modeling.

Cooperative, Connected, and Automated Mobility (CCAM) constitutes a viable solution toward sustainable future mobility in order to achieve the target of decarbonization. Artificial Intelligence (AI) and Big Data (BD) have altered several industrial sectors providing novel and affordable solutions that facilitate and improve existing operations in these sectors. Hence, the combination of the CCAM paradigm with AI methodologies based on BD could ever increase the potential benefits of CCAM in the contemporary society. For this reason, three CCAM services, which are based on AI and BD, are introduced in the current research work in order to tackle three well-known issues of mobility such as i) the estimated time of arrival, ii) the passenger demand prediction and iii) the mobility patterns identification. The proposed CCAM services were tested on various pilot sites of the EU-funded SHOW project, thus demonstrating the potential of BD and AI in future mobility services.

Achieving the Sustainable Development Goals (SDG) requires a transition from conventional fossil-fuel-powered vehicles to alternative energy sources, such as electricity. However, accurately forecasting energy consumption remains a critical challenge in the widespread adoption of Electric Vehicles (EVs), as it directly impacts operational efficiency, route planning, and charging strategies. To address this, a novel approach is proposed, combining advanced machine learning models—such as XGBoost, Random Forest, and regression-based techniques—with innovative dataset manipulation using statistical methods. The methodology integrates feature engineering to incorporate vehicle-specific metrics, including driving patterns and environmental conditions, ensuring models dynamically adapt to real-world scenarios. The proposed framework demonstrates high accuracy and robustness in predicting energy consumption, providing valuable insights for sustainable transportation and efficient energy management toward SDG achievement.

The rapid evolution and usage of Electric Vehicles (EVs) and bidirectional Vehicle-to-Grid (V2G) technologies is reshaping the energy ecosystem, creating new opportunities for data-driven optimization while exposing charging infrastructures to evolving cybersecurity risks. This paper presents a comprehensive architectural framework for Securing Data-Driven Cognitive V2G Charging that leverages edge intelligence, distributed machine learning and 5G-enabled IoT microservices to enable trusted EV energy exchange. Building on prior knowledge through European Union (EU) funded projects, the proposed approach addresses two complementary scenarios: (i) cognitive edge optimization of power flows for intelligent and cost-efficient EV charging under volatile renewable generation, and (ii) cybersecurity and trust enhancement in V2G data exchange through continuous monitoring, vulnerability detection and secure, auditable data workflows. By integrating cognitive decision-making with big data analytics, the framework enables measurable cost savings across the EV charging value chain, while simultaneously ensuring grid stability, power quality and resilience against cyber threats. Furthermore, the paper discusses open challenges and research directions for building secure, scalable and trustworthy V2G charging infrastructures, highlighting the role of big data-driven cognitive intelligence in bridging energy efficiency with cybersecurity.

The Internet of Things (IoT) paradigm has been rapidly adopted in a plethora of application domains, promoting the development of a variety of smart services relying on data from different sources. As a consequence, Sensing-as-a-Service is also gaining popularity, enabling IoT data providers and consumers to share, exchange and trade IoT data. In such a data-driven ecosystem, data marketplaces play a pivotal role, which besides the need for interoperable solutions, necessitates also sophisticated market mechanisms that ensure trusted and secure data exchange. In this paper, we present the IoTFeds solution which offers a complete open source interoperability framework combined with blockchain technologies, and thus provides a decentralized federation management and marketplace platform. The IoTFeds platform enables trustworthy and secure transactions allowing the creation and monetization of composite IoT data services offered by multiple federated data providers.

Cooperative, Connected, and Automated Mobility (CCAM) is set to play a key role in the future of transportation, contributing to the achievement of sustainable development goals. Moreover, Artificial Intelligence (AI), a transformative technology with applications across various industries, can significantly enhance CCAM operations. Additionally, passenger demand forecasting, a critical aspect of mobility research, will become even more essential as CCAM adoption continues to grow in the next years. Therefore, the present research study, in order to deal with the issue of passenger demand forecasting in CCAM, proposes the Principal Component Random Forest (PCRF) methodology, which is based on AI, as it leverages a well-established statistical methodology such as the Principal Components Analysis with a flagship traditional machine learning technique, which is Random Forest. The application of PCRF in four European pilot sites within the European Union-funded SHOW project demonstrated its high accuracy and effectiveness as reflected by the average normalized error of approximately 15%.

With the global transportation sector being a major contributor to greenhouse gas (GHG) emissions, transitioning to cleaner and more efficient forms of transportation is essential for mitigating climate change and improving air quality. Toward sustainable mobility, Fuel Cell Electric Vehicles (FCEVs) have emerged as a promising solution offering zero-emission transportation without sacrificing performance or range. However, FCEV adoption still faces significant challenges regarding refueling infrastructure. This work proposes an innovative refueling automation service for FCEVs to facilitate the refueling procedure and to increase the fuel cell lifetime, by leveraging (i) Big Data, namely, real-time mobility data and (ii) Machine Learning (ML) for the energy consumption forecasting to dynamically adjust refueling priorities. The proposed service was evaluated on a simulated FCEV energy consumption dataset, generated using both the Future Automotive Systems Technology Simulator and real-time data, including traffic information and details from a real-world on demand Public Transportation service in the Geneva Canton region. The experimental results showcased that all three ML algorithms achieved high accuracy in forecasting the vehicle’s energy consumption with very low errors on the order of 10% and below 20% for the normalized Mean Absolute Error and normalized Root Mean Squared Error metrics, respectively, indicating the high potential of the suggested service.

The e-commerce and digital technologies growth, has led to the emergence of various electronic marketplaces having the ability to connect parties across geographical locations, thus offering convenience and flexibility. The European Union recognizes the prowess of digital marketplaces and for this reason, many EU-funded projects presented e-marketplaces in various sectors. For this reason, a Systematic Literature Review (SLR) is proposed to summarize recent studies in the field, providing a comprehensive overview of specific business and technical characteristics, and extracting valuable insights. From the SLR, 26 primary studies have been extracted during 2013-2023. The analysis highlighted that there are five marketplace types in terms of market offerings, catering to multiple sectors of economy. Moreover, the emergence of the blockchain technology has led to the development of decentralized marketplaces, offering greater security, and transparency. This trend is also reflected by the results alongside with some useful outcomes regarding implementation technologies, interoperability and deployment. Finally, the results highlighted that the exploitation of these marketplace is an open issue.

Weather constitutes a crucial factor that impacts many of the human outdoor activities, whether they are related to obligations or pleasure. In the contemporary era, due to climate change, the weather is more unstable and the forecasting task is more challenging than ever. By combining the Internet of Things (IoT) with Artificial Intelligence (AI), a new research field emerges that is called Artificial Intelligence of Things (AIoT) and could offer significant possibilities for the research community in order to efficiently tackle the short-term weather forecasting. Renewable energy sources constitute solutions for the achievement of sustainability development goals and could also offer power autonomy in a weather forecasting station. In the present research study, everWeather is proposed as a low-cost, self-powered weather forecasting station based on the AIoT paradigm and renewable energy. The proposed solution combines a variety of low-cost environmental sensors, the prowess of solar energy and an appropriate lightweight Machine Learning (ML) algorithm such as the Multiple Linear Regression (MLR) in order to forecast physical weather for the next half hour. Preliminary experiments have been conducted for the proposed solution validation and the corresponding results highlighted that the performance of the everWeather station is quite satisfactory, in terms of reliability and forecasting accuracy.

Autonomous Vehicles (AVs) will be the future of automotive including both the Public and the Private Transportation. One of the major concerns of the corresponding research community is the safety of the AVs. Considering this, a lightweight accident detection model for autonomous fleets is presented, utilizing only GPS data. The proposed accident detection model combines well-known statistical and machine learning techniques such as data normalization, PCA transformation, and DBSCAN clustering. In order to validate the proposed methodology simulated data were utilized exploiting well-established techniques, such as Dead-Reckoning, accident speed profiles, and pre-crash acceleration models. The preliminary results highlighted that the proposed methodology managed to achieve its accurate accident detection purpose presenting accuracy higher than 98%.

Autonomous Vehicles are expected to play a pivotal role in the future of transportation. While huge accomplishments have been conducted in this field during the recent years, the main field of relevant research is primarly private transportation. Public transport (PT), especially in urban areas, although equally important, is lagging behind when the technological breakthroughs are considered. One of the main PT desiderata is punctual arrival. Therefore, this paper deals with the Estimated Time of Arrival (ETA) issue, tackled as a time-series problem, using contemporary Gradient Boosting (GB) methods, in order to benefit both commuters and stakeholders. The GB methods used are eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machines (LightGBM), and Categorical Boosting (CatBoost). This study proposes a competitive ETA methodology for Autonomous Shuttles, providing encouraging results both in the field of Research & Innovation for Autonomous Vehicles and Public Transport, being able to be expanded for commercial purposes.

AbstractSeamless integration of new types of mobility such as those realized by Connected Automated Vehicles (CAVs) into the existing public transport service requires new service-oriented orchestration platforms. In this work, we present a framework for automated mobility of passengers and goods, realized by the service-oriented architecture of the EU-funded research project SHOW (GA No 857730). As part of the framework, intra-systems and cloud-to-everything interfaces are proposed, while high-level design specifications of architecture alternatives are critically reviewed. An actual example of how such an inclusive architecture is instantiated in Trikala SHOW pilot site for city-specific SHOW services deployment is presented. Finally, lessons learnt that are relevant to data access, interoperability and cybersecurity, based on the experience from all SHOW pilot sites, are outlined.

The e-commerce and digital technologies growth, has led to the emergence of various electronic marketplaces having the ability to connect parties across geographical locations, thus offering convenience and flexibility. The European Union recognizes the prowess of digital marketplaces and for this reason, many EU-funded projects presented e-marketplaces in various sectors. For this reason, a Systematic Literature Review (SLR) is proposed to summarize recent studies in the field, providing a comprehensive overview of specific business and technical characteristics, and extracting valuable insights. From the SLR, 26 primary studies have been extracted during 2013–2023. The analysis highlighted that there are five marketplace types in terms of market offerings, catering to multiple sectors of economy. Moreover, the emergence of the blockchain technology has led to the development of decentralized marketplaces, offering greater security, and transparency. This trend is also reflected by the results alongside with some useful outcomes regarding implementation technologies, interoperability and deployment. Finally, the results highlighted that the exploitation of these marketplace is an open issue.

The rapid adoption of Electric Vehicles (EVs) in the global pursuit of energy efficiency and carbon neutrality necessitates effective strategies to mitigate their carbon footprint and enhance operational stability. Similarly, in order to achieve Sustainability Development Goals, a promising solution toward green mobility, which is gaining ground nowadays, constitutes Automated Vehicles (AVs), which are EVs having the capability to move autonomously, without the need for a driver. One of the most critical factors regarding energy efficiency is the optimal management of energy consumption of AVs. This research study explores the application of machine learning (ML) models for State-of-Charge (SoC) forecasting in AVs, crucial for addressing challenges such as range anxiety and grid overloading. Leveraging real-life EV data from automated minibuses in Gothenburg, Sweeden, a comprehensive pipeline is proposed for data pre-processing, feature selection, and model training. With a focus on predicting SoC several minutes ahead, various ML techniques, including linear regression, ridge regression, lasso regression, and elastic-net regression are embedded in a pipeline specifically developed to overcome the challenge of training time-series models on discontinuous data segments, corresponding to discharge cycles. This pipeline is called Cross-Segment-Leakage-Free (CSLF). The results demonstrate the efficacy of CSLF, with the best-performing model achieving a Mean Absolute Error (MAE) of 0.92 in a forecasting horizon of 30 minutes, representing a significant improvement over baseline models. The study underscores the importance of meaningful pre-processing and model selection in SoC consumption forecasting for AVs, offering insights into future research directions and deployment strategies for enhancing EV efficiency and grid stability.

AbstractThe perception of comfort and safety among passengers of Autonomous Vehicles (AVs) is crucial and significantly influences their adoption in current Public Transport systems. It is essential to align the objective perception with an analysis of vehicle performance data to identify vulnerabilities and factors affecting passenger comfort and safety. This paper presents the first comprehensive correlation between objective and subjective data from autonomous fleets in three well-established pilot locations (Graz, Madrid, Linköping), each using different technologies and experiencing varying environmental conditions. Our analysis (i) revealed significant differences between the three pilot sites in terms of perceived safety and comfort (both perceived and actual) and (ii) confirmed a strong correlation between safety and comfort levels and the vehicles’ behaviour in terms of speed and acceleration, particularly noting the impact of hard braking events as those were defined by the SHOW consortium.

TransfusionVolume 63, Issue S5 p. 300A-300A SUPPLEMENT ARTICLE P-TS-68 | Presence of Isohemagglutinins in Group O Neonates: Potential Implications for ABO Incompatible Heart Transplants G. Spanos, G. Spanos Department of Pathology, Division Transfusion Medicine, Johns Hopkins All Children's Hospital, St. Petersburg, FloridaSearch for more papers by this authorM. Fusaro, M. Fusaro Department of Pathology, Division Transfusion Medicine, Johns Hopkins All Children's Hospital, St. Petersburg, FloridaSearch for more papers by this authorN. Harb, N. Harb Department of Pathology, Division Transfusion Medicine, Johns Hopkins All Children's Hospital, St. Petersburg, FloridaSearch for more papers by this authorC. Josephson, C. Josephson Department of Pathology, Division Transfusion Medicine, Johns Hopkins All Children's Hospital, St. Petersburg, FloridaSearch for more papers by this author G. Spanos, G. Spanos Department of Pathology, Division Transfusion Medicine, Johns Hopkins All Children's Hospital, St. Petersburg, FloridaSearch for more papers by this authorM. Fusaro, M. Fusaro Department of Pathology, Division Transfusion Medicine, Johns Hopkins All Children's Hospital, St. Petersburg, FloridaSearch for more papers by this authorN. Harb, N. Harb Department of Pathology, Division Transfusion Medicine, Johns Hopkins All Children's Hospital, St. Petersburg, FloridaSearch for more papers by this authorC. Josephson, C. Josephson Department of Pathology, Division Transfusion Medicine, Johns Hopkins All Children's Hospital, St. Petersburg, FloridaSearch for more papers by this author First published: 12 October 2023 https://doi.org/10.1111/trf.405_17554Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume63, IssueS5October 2023Pages 300A-300A RelatedInformation

The topic of in-vehicle and V2X communication in autonomous vehicles consists of a variety of different communication protocols, mechanisms, and devices. The implementation and cooperation between these entities and protocols in such a complex system is a rigorous and complicated process that should not only be efficient, robust, flexible, and scalable, but also secure. The security of critical systems such as autonomous vehicles requires a deep understanding of all the individual and distinct components that compose the system. This paper presents a cybersecurity architecture having as purpose to shield the communication security in the autonomous vehicles. For this reason, several well-established cybersecurity tools (e.g. Keycloak, Cloudflare) and communication mechanisms (e.g. MQTT, Kafka) have been combined in this architecture along with a novel statistical-based Intrusion Detection System. All the aforementioned cybersecurity defense mechanisms were selected to protect the entire system pipeline and meet the requirements for Confidentiality, Integrity, and Availability regarding vehicle communication. To test the performance of the proposed architecture abnormal data have been injected to the system and the results from the experiments conducted highlighted that the proposed solution can achieve its purpose of increased cybersecurity.