Abstract
This research addresses the challenge of generating synthetic data that resembles real-world data while preserving privacy. With privacy laws protecting sensitive information such as healthcare data, accessing sufficient training data becomes difficult, resulting in an increased difficulty in training Machine Learning models and in overall worst models. Recently, there has been an increased interest in the usage of Generative Adversarial Networks (GAN) to generate synthetic data since they enable researchers to generate more data to train their models. GANs, however, may not be suitable for privacy-sensitive data since they have no concern for the privacy of the generated data. We propose modifying the known Conditional Tabular GAN (CTGAN) model by incorporating a privacy-aware loss function, thus resulting in the Private CTGAN (PCTGAN) method. Several experiments were carried out using 10 public domain classification datasets and comparing PCTGAN with CTGAN and the state-of-the-art privacy-preserving model, the Differential Privacy CTGAN (DP-CTGAN). The results demonstrated that PCTGAN enables users to fine-tune the privacy fidelity trade-off by leveraging parameters, as well as that if desired, a higher level of privacy.

Abstract
Battery degradation remains a major challenge in electric vehicle (EV) adoption, directly affecting long-term performance, cost, and user satisfaction. This paper proposes a data-driven charging strategy that reduces battery wear while meeting the user's daily range needs. By integrating manufacturer guidelines, battery aging models, and thermal dynamics, the proposed optimization algorithm dynamically adjusts the charging current and timing to minimize stressors, such as high temperatures and prolonged high state of charge (SoC). The methodology is responsive to user inputs such as departure time and required driving range, enabling personalized charging behavior. Simulation results show that this approach can reduce battery degradation by up to 2.7% over a 30-day period compared to conventional charging habits, without compromising usability. The framework is designed for integration into Battery Management Systems (BMS), with applications for both private EV users and fleet operators. We address EV battery aging driven by high core temperature and prolonged high state of charge (SoC) during overnight/home charging. Given a user-specified departure time and required driving range, we schedule charging power over time to minimize predicted degradation exposure while still meeting the range requirement. The scheduler optimizes charging timing/current under SoC dynamics, thermal constraints, and charger/ BMS limits.

Abstract
The fast global expansion of online public services has transformed how governments interact with citizens, offering convenience and efficiency. However, this digital transformation also introduces significant security risks, as sensitive data exchanged between users and service providers over public networks are exposed to cyber threats. Thus, ensuring the security and trustworthiness of these services is critical to the success of Electronic Government (EGOV) initiatives. This study evaluates the information security posture of 3068 public service platforms across all 193 UN Member States through non-intrusive assessments conducted in 2023 and 2024. The evaluation focuses on three key dimensions: (i) the adoption of secure end-to-end communication protocols, (ii) the trustworthiness of digital certificate chains, and (iii) the exposure of hosting servers to known vulnerabilities. The findings reveal that while some progress has been made in securing online public services, substantial gaps remain in the implementation of international security standards and best practices. Many platforms continue to rely on outdated cryptographic protocols, misconfigured certificates, and unpatched vulnerabilities, leaving citizens and services vulnerable to cyber threats due to weaknesses that malicious actors can easily and inconspicuously identify. These insights emphasize the need for effective implementation of more comprehensive cybersecurity policies, proactive security assessments, and improved regulatory compliance checks. Additionally, this work provides actionable guidance for governments and system administrators to enhance the security of EGOV infrastructures by addressing persistent vulnerabilities and adopting robust cybersecurity practices.

Abstract
The importance of time series forecasting drives continuous research and the development of new approaches to tackle this problem. Typically, these methods are introduced through empirical studies that frequently claim superior accuracy for the proposed approaches. Nevertheless, concerns are rising about the reliability and generalizability of these results due to limitations in experimental setups. This paper addresses a critical limitation: the number and representativeness of the datasets used. We investigate the impact of dataset selection bias, particularly the practice of cherry-picking datasets, on the performance evaluation of forecasting methods. Through empirical analysis with a diverse set of benchmark datasets, our findings reveal that cherry-picking datasets can significantly distort the perceived performance of methods, often exaggerating their effectiveness. Furthermore, our results demonstrate that by selectively choosing just four datasets - what most studies report - 46% of methods could be deemed best in class, and 77% could rank within the top three. Additionally, recent deep learning-based approaches show high sensitivity to dataset selection, whereas classical methods exhibit greater robustness. Finally, our results indicate that, when empirically validating forecasting algorithms on a subset of the benchmarks, increasing the number of datasets tested from 3 to 6 reduces the risk of incorrectly identifying an algorithm as the best one by approximately 40%. Our study highlights the critical need for comprehensive evaluation frameworks that more accurately reflect real-world scenarios. Adopting such frameworks will ensure the development of robust and reliable forecasting methods.

Abstract
When each patient of a kidney exchange program has a preference ranking over its set of compatible donors, questions naturally arise surrounding the stability of the proposed exchanges. We extend recent work on stable exchanges by introducing and underlining the relevance of a new concept of locally stable, or L-stable, exchanges. We show that locally stable exchanges in a compatibility digraph are exactly the so-called local kernels (L-kernels) of an associated blocking digraph (whereas the stable exchanges are the kernels of the blocking digraph), and we prove that finding a nonempty L-kernel in an arbitrary digraph is NP-complete. Based on these insights, we propose several integer programming formulations for computing an L-stable exchange of maximum size. We conduct numerical experiments to assess the quality of our formulations and to compare the size of maximum L-stable exchanges with the size of maximum stable exchanges. It turns out that nonempty L-stable exchanges frequently exist in digraphs which do not have any stable exchange. All the above results and observations carry over when the concept of (locally) stable exchanges is extended to the concept of (locally) strongly stable exchanges.

Abstract
This chapter presents the fundamentals and state-of-the-art hosting capacity (HC), including its concept, historical development, considerations, applications, impact factors, and technologies for increasing the HC. It discusses the basics and importance of grid HC, the basic flow chart for HC analysis, the use of HC as a component of integrated distribution planning, and the HC as a process encompassing the input data, analysis, and application of results for informing interconnection and planning. Moreover, this chapter depicts two types of HC analysis based on the number of distributed energy resources (DERs) and load conditions (i.e., operating scenarios): static and dynamic HC analysis. It presents feeder and node HC levels based on the number of considered DERs. The main impact factors that influence the HC results and hinder the connection of additional DERs to the grid are also described. These impact factors include grid characteristics, DER characteristics, and other considerations such as time, performance metrics, and HC methods. Several review articles on HC and studies that explore the use of battery energy storage systems, electric vehicles, and smart inverters as strategies to increase HC in power distribution networks are presented. Finally, this chapter outlines the conclusion and future search directions for HC analysis. © 2025 Elsevier Inc. All rights reserved.