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Lead halide perovskite nanocrystals (LHP-NCs) embedded in a plastic matrix are highly promising for a variety of photonic technologies and are quickly gaining attention as ultrafast, radiation-resistant nanoscintillators for radiation detection. However, advancements in LHP-NC-based photonics are hindered by their well-known thermal instability, which makes them unsuitable for industrial thermally activated mass polymerization processes - crucial for creating polystyrene-based scintillating nanocomposites. In this study, we address this challenge by presenting the first thermal nanocomposite scintillators made from CsPbBr3 NCs passivated with fluorinated ligands that remain attached to the particles surfaces even at high temperatures, enabling their integration into mass-cured polyvinyl toluene without compromising optical properties. Consequently, these nanocomposites demonstrate scintillation light yields reaching 10,400 photons/MeV, sub-nanosecond scintillation kinetics, and remarkable radiation resilience, able to withstand gamma radiation doses of up to 1 MGy. This performance not only meets but also exceeds the scintillation of plastic scintillators, despite the radiation-induced damage to the host matrix.

The rapid advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have shown great potential in medical diagnostics, particularly in radiology, where datasets such as X-rays are paired with human-generated diagnostic reports. However, a significant research gap exists in the neuroimaging field, especially for conditions such as Alzheimer's disease, due to the lack of comprehensive diagnostic reports that can be utilized for model fine-tuning. This paper addresses this gap by generating synthetic diagnostic reports using GPT-4o-mini on structured data from the OASIS-4 dataset, which comprises 663 patients. Using the synthetic reports as ground truth for training and validation, we then generated neurological reports directly from the images in the dataset leveraging the pre-trained BiomedCLIP and T5 models. Our proposed method achieved a BLEU-4 score of 0.1827, ROUGE-L score of 0.3719, and METEOR score of 0.4163, revealing its potential in generating clinically relevant and accurate diagnostic reports., Comment: The paper has been accepted by the conference: "2024 International Conference on Big Data (IEEE Big Data 2024)"

This paper proposes a framework for time series generation built to investigate anomaly detection in cloud microservices. In the field of cloud computing, ensuring the reliability of microservices is of paramount concern and yet a remarkably challenging task. Despite the large amount of research in this area, validation of anomaly detection algorithms in realistic environments is difficult to achieve. To address this challenge, we propose a framework to mimic the complex time series patterns representative of both normal and anomalous cloud microservices behaviors. We detail the pipeline implementation that allows deployment and management of microservices as well as the theoretical approach required to generate anomalies. Two datasets generated using the proposed framework have been made publicly available through GitHub., Comment: The paper has been accepted by the 10th International Conference on Machine Learning, Optimization and Data Science

Deep learning techniques have shown significant potential in many applications through recent years. The achieved results often outperform traditional techniques. However, the quality of a neural network highly depends on the used training data. Noisy, insufficient, or biased training data leads to suboptimal results. We present a hybrid method that combines deep learning with iterated graph Laplacian and show its application in acoustic impedance inversion which is a routine procedure in seismic explorations. A neural network is used to obtain a first approximation of the underlying acoustic impedance and construct a graph Laplacian matrix from this approximation. Afterwards, we use a Tikhonov-like variational method to solve the impedance inversion problem where the regularizer is based on the constructed graph Laplacian. The obtained solution can be shown to be more accurate and stable with respect to noise than the initial guess obtained by the neural network. This process can be iterated several times, each time constructing a new graph Laplacian matrix from the most recent reconstruction. The method converges after only a few iterations returning a much more accurate reconstruction. We demonstrate the potential of our method on two different datasets and under various levels of noise. We use two different neural networks that have been introduced in previous works. The experiments show that our approach improves the reconstruction quality in the presence of noise.

The escalating challenges of traffic congestion and environmental degradation underscore the critical importance of embracing E-Mobility solutions in urban spaces. In particular, micro E-Mobility tools such as E-scooters and E-bikes, play a pivotal role in this transition, offering sustainable alternatives for urban commuters. However, the energy consumption patterns for these tools are a critical aspect that impacts their effectiveness in real-world scenarios and is essential for trip planning and boosting user confidence in using these. To this effect, recent studies have utilised physical models customised for specific mobility tools and conditions, but these models struggle with generalization and effectiveness in real-world scenarios due to a notable absence of open datasets for thorough model evaluation and verification. To fill this gap, our work presents an open dataset, collected in Dublin, Ireland, specifically designed for energy modelling research related to E-Scooters and E-Bikes. Furthermore, we provide a comprehensive analysis of energy consumption modelling based on the dataset using a set of representative machine learning algorithms and compare their performance against the contemporary mathematical models as a baseline. Our results demonstrate a notable advantage for data-driven models in comparison to the corresponding mathematical models for estimating energy consumption. Specifically, data-driven models outperform physical models in accuracy by up to 83.83% for E-Bikes and 82.16% for E-Scooters based on an in-depth analysis of the dataset under certain assumptions., Comment: 7 pages, 5 figures, 4 tables. This manuscript has been accepted by the IEEE ITEC 2024

With the rising concern over transportation emissions and pollution on a global scale, shared electric mobility services like E-cars, E-bikes, and E-scooters have emerged as promising solutions to mitigate these pressing challenges. However, existing shared E-mobility services exhibit critical design deficiencies, including insufficient service integration, imprecise energy consumption forecasting, limited scalability and geographical coverage, and a notable absence of a user-centric perspective, particularly in the context of multi-modal transportation. More importantly, there is no consolidated open-source platform which could benefit the E-mobility research community. This paper aims to bridge this gap by providing an open-source platform for shared E-mobility. The proposed platform, with an agent-in-the-loop approach and modular architecture, is tailored to diverse user preferences and offers enhanced customization. We demonstrate the viability of this platform by providing a comprehensive analysis for integrated multi-modal route-optimization in diverse scenarios of energy availability, user preferences and E-mobility tools placement for which we use modified Ant Colony Optimization algorithm so called Multi-Model Energy Constrained ACO (MMEC-ACO) and Q-Learning algorithms. Our findings demonstrate that Q-learning achieves significantly better performance in terms of travel time cost for more than 90\% of the instances as compared to MMEC-ACO for different scenarios including energy availability, user preference and E-mobility tools distribution. For a fixed (O, D) pair, the average execution time to achieve optimal time cost solution for MMEC-ACO is less than 2 seconds, while Q-learning reaches an optimal time cost in 20 seconds on average. For a run-time of 2 seconds, Q-learning still achieves a better optimal time cost with a 20\% reduction over MMEC-ACO's time cost., Comment: 15 pages, 5 figures

Battery Electric Vehicles (BEVs) are increasingly significant in modern cities due to their potential to reduce air pollution. Precise and real-time estimation of energy consumption for them is imperative for effective itinerary planning and optimizing vehicle systems, which can reduce driving range anxiety and decrease energy costs. As public awareness of data privacy increases, adopting approaches that safeguard data privacy in the context of BEV energy consumption modeling is crucial. Federated Learning (FL) is a promising solution mitigating the risk of exposing sensitive information to third parties by allowing local data to remain on devices and only sharing model updates with a central server. Our work investigates the potential of using FL methods, such as FedAvg, and FedPer, to improve BEV energy consumption prediction while maintaining user privacy. We conducted experiments using data from 10 BEVs under simulated real-world driving conditions. Our results demonstrate that the FedAvg-LSTM model achieved a reduction of up to 67.84\% in the MAE value of the prediction results. Furthermore, we explored various real-world scenarios and discussed how FL methods can be employed in those cases. Our findings show that FL methods can effectively improve the performance of BEV energy consumption prediction while maintaining user privacy., Comment: This paper is accepted by IEEE Transactions on Transportation Electrification (TTE) on December 4, 2023. (13 pages, 6 figures, and 6 tables)

E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns in the transportation sector. The depletion of fossil fuels, escalating greenhouse gas emissions, and the imperative to combat climate change underscore the significance of transitioning to electric vehicles (EVs). This paper seeks to explore the potential of artificial intelligence (AI) in addressing various challenges related to effective energy management in e-mobility systems (EMS). These challenges encompass critical factors such as range anxiety, charge rate optimization, and the longevity of energy storage in EVs. By analyzing existing literature, we delve into the role that AI can play in tackling these challenges and enabling efficient energy management in EMS. Our objectives are twofold: to provide an overview of the current state-of-the-art in this research domain and propose effective avenues for future investigations. Through this analysis, we aim to contribute to the advancement of sustainable and efficient e-mobility solutions, shaping a greener and more sustainable future for transportation., Comment: 8 pages, 4 tables, 1 figure

Pollution in urban areas can have significant adverse effects on the health and well-being of citizens, with traffic-related air pollution being a major concern in many cities. Pollutants emitted by vehicles, such as nitrogen oxides, carbon monoxide, and particulate matter, can cause respiratory and cardiovascular problems, particularly for vulnerable road users like pedestrians and cyclists. Furthermore, recent research has indicated that individuals living in more polluted areas are at a greater risk of developing chronic illnesses such as asthma, allergies, and cancer. Addressing these problems is crucial to protecting public health and maximising environmental benefits. In this project, we explore the feasibility of tackling this challenge by leveraging big data analysis and data-driven methods. Specifically, we investigate the recently released Google Air Quality dataset and devise an optimisation strategy to suggest green travel routes for different types of active transportation users in Dublin. To demonstrate our achievement, we have developed a prototype and have shown that citizens who use our model to plan their outdoor activities can benefit notably, with a significant decrease of 17.87% on average in pollutant intake, from the environmental advantages it offers., Comment: The manuscript has been accepted by the IEEE ITSC 2023

The Intelligent Transportation System (ITS) is an important part of modern transportation infrastructure, employing a combination of communication technology, information processing and control systems to manage transportation networks. This integration of various components such as roads, vehicles, and communication systems, is expected to improve efficiency and safety by providing better information, services, and coordination of transportation modes. In recent years, graph-based machine learning has become an increasingly important research focus in the field of ITS aiming at the development of complex, data-driven solutions to address various ITS-related challenges. This chapter presents background information on the key technical challenges for ITS design, along with a review of research methods ranging from classic statistical approaches to modern machine learning and deep learning-based approaches. Specifically, we provide an in-depth review of graph-based machine learning methods, including basic concepts of graphs, graph data representation, graph neural network architectures and their relation to ITS applications. Additionally, two case studies of graph-based ITS applications proposed in our recent work are presented in detail to demonstrate the potential of graph-based machine learning in the ITS domain.

Electric Shared Micromobility Services (ESMS) has become a vital element within the Mobility as a Service framework, contributing to sustainable transportation systems. However, existing ESMS face notable design challenges such as shortcomings in integration, transparency, and user-centred approaches, resulting in increased operational costs and decreased service quality. A key operational issue for ESMS revolves around parking, particularly ensuring the availability of parking spaces as users approach their destinations. For instance, a recent study illustrated that nearly 13% of shared E-Bike users in Dublin, Ireland, encounter difficulties parking their E-Bikes due to inadequate planning and guidance. In response, we introduce U-Park, a user-centric smart parking recommendation system designed for ESMS, providing tailored recommendations to users by analysing their historical mobility data, trip trajectory, and parking space availability. We present the system architecture, implement it, and evaluate its performance using real-world data from an Irish-based shared E-Bike provider, MOBY Bikes. Our results illustrate U-Park's ability to predict a user's destination within a shared E-Bike system, achieving an approximate accuracy rate of over 97.60%, all without requiring direct user input. Experiments have proven that this predictive capability empowers U-Park to suggest the optimal parking station to users based on the availability of predicted parking spaces, improving the probability of obtaining a parking spot by 24.91% on average and 29.66% on maximum when parking availability is limited., Comment: The manuscript has been accepted by the IEEE Transactions on Artificial Intelligence. This manuscript includes 15 pages with 11 figures and 6 tables