Abstract

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Abstract
Renewable energy resources are crucial for addressing global economic and environmental challenges. Energy communities, which unite consumers to pursue shared energy goals, present a promising solution for reducing energy costs and enhancing sustainability. This study analyzes the optimal sizing and operation of energy community resources, formulating the problem as mixed-integer linear programming (MILP) models. Two tools are employed: one for daily operation, calculating energy setpoints for community assets such as battery energy storage systems (BESS) and electric vehicles (EVs), and another for sizing photovoltaic (PV) panels and BESS capacities to minimize costs while optimizing local energy trades. Due to the high computational demands of MILP, three optimization methods are compared: deterministic, hybrid particle swarm optimization (PSO), and evolutionary PSO (EPSO). The hybrid PSO method handles binary and continuous variables efficiently, while EPSO introduces diversity to improve solution quality in complex scenarios. These metaheuristic approaches address the trade-off between solution accuracy and computational effort, providing reliable tools for decision-makers in energy communities.

Abstract
The development of safety-critical systems is heavily governed by domain-specific standards. In the aerospace industry, the DO-178C-Software Considerations in Airborne Systems and Equipment Certification-serves as the primary certification standard used by agencies such as the FAA and EASA to review and approve software-based systems. Although DO-178C aims to ensure system safety while providing evidence for certification, it does not prescribe a specific software development process, allowing flexibility for traditional Waterfall, Agile, or hybrid methods with appropriate adaptations for the aerospace context. This study proposes Scrum4DO178C, an Agile process based on Scrum, to meet the demanding requirements of aerospace software, including safety, robustness, reliability, and integrity. Scrum4DO178C introduces novel process enhancements specifically tailored to meet these criticality needs, while aligning with the standard. Unlike previous proposals that lack detail, this research presents a comprehensive, validated process applied in a real-world industry project at the highest criticality level (Level A - Catastrophic), offering insights beyond theoretical scenarios. The findings demonstrated that the Scrum4DO178C process improves project performance, allows frequent and manageable requirement changes, reduces Verification & Validation (V&V) effort, and increases efficiency while maintaining full compliance with DO-178C. The study also identifies areas for further improvement and suggests exploring the process in additional case studies, both within the aerospace industry and other domains with similarly stringent safety-critical requirements. Finally, it confirms that appropriate automation, namely for documentation production, is a central element to further improve the process.

Abstract
Deep Inferior Epigastric Perforator (DIEP) flap breast reconstruction relies on the precise identification of perforator vessels supplying blood to transferred tissue. Traditional manual mapping from preoperative imaging is timeconsuming and subjective. To address this, AVA, a semi-automated perforator detection algorithm, was developed to analyze angiography images. AVA follows a three-step process: automated anatomical segmentation, manual annotation of perforators, and segmentation of perforator courses. This approach enhances accuracy, reduces subjectivity, and accelerates the mapping process while generating quantitative reports for surgical planning. To streamline integration into clinical workflows, AVA has been embedded into PACScenter, a medical imaging platform, leveraging DICOM encapsulation for seamless data exchange within a Vendor Neutral Archive (VNA). This integration allows surgeons to interactively annotate perforators, adjust parameters iteratively, and visualize detailed anatomical structures. AVA-PACScenter integration eliminates workflow disruptions by providing real-time perforator analysis within the surgical environment, ultimately improving preoperative planning and intraoperative guidance. Currently undergoing clinical feasibility testing, this integration aims to enhance DIEP flap reconstruction efficiency by reducing manual inputs, improving mapping precision, and facilitating long-term report storage within Dicoogle. By automating perforator analysis, AVA represents a significant advancement toward data-driven, patient-centered surgical planning.

Abstract