Abstract
Photovoltaic (PV) panels serve as a standard solution for the collection of solar energy. The flat photovoltaic solar plate design has been the most adopted by the market for its ease of installation. However, this design faces limitations due to geometric constraints and the sun's trajectory through the day. Inspiration was drawn from nature to overcome these limitations by utilizing the tridimensional hexagonal shape observed in honeycomb structures. The used approach aimed to explore a novel design that can reduce the constraints of flat PV panels while maximizing energy output. The unique 3D arrangement of the hexagonal pyramid enables the installation of mirrors inside to ease the reflection of photons and to increase energy production compared to flat panels. Furthermore, this design presents an opportunity to incorporate a water capture and heating system, thereby increasing the system's overall usage.

Abstract
Net Zero Energy Building (NZEB) is a concept that promotes the reduction of energy consumption in buildings by applying energy efficiency measures. The energy supply for the remaining demand should only come from sources with low CO2 emissions. Despite abundant research on NZEB for new buildings, only a small number of studies address its application to those already existing. This study aims to bridge this research gap by organizing the proposed methods to transform existing buildings into NZEB. The research method is a systematic literature review covering the methodological development and the application of the concept. We conducted a bibliometric and Scientometric analysis of 117 articles and a content analysis of 48 of them. The results highlighted that the methods identified follow similar stages: (i) planning, (ii) data collection, (iii) pre-design, (iv) design, and (v) delivery. The sub-stage with the highest frequency (88%) was the presentation of the efficiency measure package, making it an essential step in the transformation process. The review did not find specific topics, such as equipment listing and performance, occupant engagement, and charrette design. Finally, the study established guidelines for future research.

Abstract
Digital Transformation Models (DTM) and Digital Maturity Models (DMM) are two artefacts that guide the planning and implementation of Digital Transformation (DT) initiatives. When used in a combined approach, a DTM-DMM pairing could support DT managers and practitioners, as DTs are holistic and complex initiatives with high failure rates. However, no study has yet systematically addressed the compatibility amongst artefacts. This paper, therefore, aims to analyse the compatibility between academic DTMs and DMMs. Based on architectural compatibility and conceptual similarity, we provide a structured and replicable mixed methods approach to assessing artefact compatibility. To achieve this, we start with a systematic literature review to identify existing academic DTMs and DMMs, analyse the models and group them according to their scope. After, we employ quantitative similarity analysis techniques (Term Frequency-Inverse Document Frequency and Bidirectional Encoder Representations from Transformers combined with Cosine Similarity) and perform a qualitative compatibility analysis to establish ground truth. Based on this analysis, we apply the Receiver Operating Characteristic Curve technique to define threshold values for compatibility assessment. The threshold values were used to suggest compatible DTM-DMM pairings, resulting in nine DTM-DMM binomials for Small and Medium-sized Enterprises. The findings support managers and practitioners in selecting DTM-DMM pairs to guide DT initiatives while offering academics a mixed-methods approach based on the similarity analysis field to evaluate artefact compatibility systematically. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

Abstract
This paper contributes to developing a Method for Creating Persona Templates (MCPT), addressing a significant gap in user-centred design methodologies. Utilising qualitative data collection and analysis techniques, MCPT offers a systematic approach to developing robust and context-oriented persona templates. MCPT was created by applying the Design Science Research (DSR) methodology, and it incorporates multiple iterations for template refinement and validation among project stakeholders; all of the proposed steps of this method were based on theoretical contributions. Furthermore, MCPT was tested and refined within a real-life R&D project focusing on developing a digital platform e-marketplace for short agrifood supply chains in two iteration cycles. MCPT fills a critical void in persona research by providing detailed instructions for each step of template development. By involving the target audience, users, and project stakeholders, MCPT adds rigour to the persona creation process, enhancing the quality and relevance of personae casts. This paper contributes to the body of knowledge by offering an initial proposal of a comprehensive method for creating persona templates within diverse projects and contexts. Further research should explore MCPT's adaptability to different settings and projects, thus refining its effectiveness and extending its utility in user-centred design practices.

Abstract
The efficient last-mile delivery of goods involves complex challenges in optimizing driver sectors and routes. This problem tends to be large-scale and involves several criteria to meet simultaneously, such as creating compact sectors, balancing the workload among drivers, minimizing the number of undelivered packages and reducing the dissimilarity of sectors on different days. This work proposes a Decision Support System (DSS) that allows decision-makers to select improved allocation strategies to define sectors. The main contribution is an interactive DSS tool that addresses a many-objective (more than 3 objectives) sectorization problem with integrated routing. It establishes a global allocation strategy and uses it as a benchmark for the created daily allocations and routes. A Preference-Inspired Co-Evolutionary Algorithm with Goal vectors using Mating Restriction (PICEA-g-mr) is employed to solve the many-objective optimization problem. The DSS also includes a visualization tool to aid decision-makers in selecting the most suitable allocation strategy. The approach was tested in a medium-sized Metropolitan Area and evaluated using resource evaluation metrics and visualization methods. The proposed DSS deals effectively and efficiently with the sectorization problem in the context of last-mile delivery by producing a set of viable and good-quality allocations, empowering decision-makers in selecting better allocation strategies. Focused on enhancing service efficiency and driver satisfaction, the DSS serves as a valuable tool to improve overall service quality.

Abstract
Multi -objective optimization (MOO) considers several objectives to find a feasible set of solutions. Selecting a solution from Pareto frontier (PF) solutions requires further effort. This work proposes a new classification procedure that fits into the analytic hierarchy Process (AHP) to pick the best solution. The method classifies PF solutions using pairwise comparison matrices for each objective. Sectorization is the problem of splitting a region into smaller sectors based on multiple objectives. The efficacy of the proposed method is tested in such problems using our instances and real data from a Portuguese delivery company. A non -dominated sorting genetic algorithm (NSGA-II) is used to obtain PF solutions based on three objectives. The proposed method rapidly selects an appropriate solution. The method was assessed by comparing it with a method based on a weighted composite single -objective function.