Abstract
Fleet logistics management requires reliable monitoring of temperature-sensitive goods and asset utilization to meet regulatory requirements and operational efficiency targets. This paper presents an integrated framework combining blockchain technology and IoT sensors to enhance internal process efficiency and data integrity in logistics operations. The research develops and deploys a permissioned blockchain system within a Portuguese logistics company, enabling empirical evaluation of core performance metrics including end-to-end latency, transaction throughput, and audit traceability under operational conditions. A pilot study involving 12 sensors distributed across transport operations demonstrates measurable improvements in audit preparation efficiency. Analysis indicates that low-latency event registration (meaning 5 seconds) supports operational monitoring requirements, whilst automated evidence generation with cryptographic proofs reduces manual verification overhead in internal and external audit processes. The study establishes performance benchmarks and cost-benefit analysis comparing blockchain adoption against centralized logging solutions with digital signatures. The architecture enhances decision-making transparency by providing logistics managers with cryptographically verifiable operational data, whilst governance insights support organizations implementing blockchain-based integrity mechanisms in regulated environments. © 2025 IEEE.

Abstract

Abstract
Electric vehicles (EVs) allow a significant reduction in harmful gas emissions, thus improving urban air quality. However, the widespread adoption of this technology is limited by several factors, resulting in heterogeneous deployment in urban areas. This raises challenges regarding the planning of public electric vehicle charging infrastructure (EVCI), requiring adaptive strategies to ensure comprehensive and efficient coverage. This study introduces an innovative method that leverages geographic information systems to pinpoint appropriate sizes and suitable locations for public EVCI within urban environments. Initially, a Bass diffusion model is employed to estimate EV adoption rates by regions, enabling the determination of the appropriate sizes of EVCI necessary for each of them. Subsequently, a multi-criteria decision-making approach is applied to identify the suitable locations for EV charger installation within each region. In this way, EVCI locations are selected using spatial criteria, which ensure they are near common areas of interest and easily accessible through the road network. To validate the effectiveness and applicability of the proposed method, tests using geospatial data from a city in Brazil were carried out. The findings suggest that EVCI planning without proper spatial analysis may result in inefficient locations and inadequate sizes, which may discourage potential EV adopters and hinder widespread adoption of this technology.

Abstract
The increasing integration of Artificial Intelligence (AI) into Building Energy Management Systems (BEMS) is revolutionizing energy optimization by enabling real-time monitoring, predictive analytics, and automated control. While these advancements improve energy efficiency and sustainability, the opacity of AI models poses challenges in interpretability, limiting user trust and hindering widespread adoption in operational decisionmaking. Ensuring transparency is crucial for aligning AI insights with building performance requirements and regulatory expectations. This paper presents EI-Build, a novel Explainable Artificial Intelligence (XAI) framework designed to enhance the interpretability of intelligent automated BEMS. EI-Build integrates multiple XAI techniques, including Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Anchors, Partial Dependence Plots, Feature Permutation Importance, and correlation-based statistical analysis, to provide comprehensive explanations of model behavior. By dynamically tailoring the format and depth of explanations, EI-Build ensures that insights remain accessible and actionable for different user profiles, from general occupants to energy specialists and machine learning experts. A case study on photovoltaic power generation forecasting applied to a real BEMS context evaluates EI-Build's capacity to deliver to deliver both global and local explanations, validate feature dependencies, and facilitate cross-comparison of interpretability techniques. The results highlight how EI-Build enhances user trust, facilitates informed decision-making, and improves model validation. By consolidating diverse XAI methods into a single automated framework, EI-Build represents a significant advancement in bridging the gap between complex AI energy models and real-world applications.

Abstract
In this paper is presented a mixed-integer linear programming (MILP) model that maximizes the Photovoltaic-based (PV-based) hosting capacity (HC) in unbalanced and active distribution networks. The model takes into account the controlled charge of electric vehicles (EVs) and incorporates a demand-response program (DRP), for demand-side load shifting. The model's solution determines the optimal operation of distributed generators (DGs), switched capacitor banks (SCBs), energy storage devices (ESDs), coordination of the EVs charging, and DRP. Linear formulation is obtained from a mixed-integer non-linear programming (MINLP) model, ensuring tractability and guarantee convergence, since it can be efficiently solved using commercial optimization solvers of convex optimization. The model's effectiveness is demonstrated through tests on a 123-bus, three-phase unbalanced distribution system. Four case studies are conducted to assess the effect of different distributed energy resources (DERs). Results show that the simultaneous optimization of DERs, EVs charging and DR scheduling can significantly increase the PV-based HC -reaching up more than the substation capacity- while reducing total power losses. These findings demonstrate the technical potential of integrated DER coordination in enhancing PV penetration and improving the operational efficiency of active distribution systems.

Abstract
[No abstract available]