Abstract
This paper introduces a comprehensive methodology for day-ahead planning of renewable energy systems geared toward green hydrogen and ammonia production. This approach is a forecasting algorithm that uses synthetic data, which feeds a short-term load forecasting (STLF) algorithm to predict the 24-hour hydrogen demand. This capability enables the optimization of hourly system operations, with the goal of maximizing profitability while maintaining system efficiency. The case study presented includes a renewable energy source - photovoltaic power plant (PV) - and a grid connection, which supply power to an electrolyser. Essential supporting infrastructure such as the auxiliary system of the electrolyser is incorporated into the model. Additionally, an electrochemical battery - a battery energy storage system (BESS) - is incorporated, which helps to keep a high electrolyser load factor and creates smoother operating profiles. This BESS also allows the system to contribute to the energy reserves market, enhancing its economic and operational viability.

Abstract
This paper explores the main challenges and barriers to VR/AR adoption and categorizes common activities performed with these technologies, explaining each specific factor affecting them. After reviewing literature on metaverse retailing, channel strategies, VR/AR technologies, and user experiences, a conceptual framework was developed. Data from the Voice of the Consumer: Digital Survey (20202024) in over 20 countries was analyzed, using Pearson's correlation, factor analysis, and multiple linear regressions. The results point that key challenges for VR/AR adoption include security, privacy, content, price, headset-free experiences, digital fatigue, and poor experiences. Gaming is the most common VR/AR activity, while metaverse retailing activities like shopping and virtual try-ons have fewer users. Practical considerations drive metaverse retailing, unlike gaming, which is mainly hedonic. Privacy concerns, safety risks, poor experiences, and lack of knowledge surprisingly increase VR/AR usage for metaverse retailing, indicating informed consumers or threshold characterization of these variables. Additional insights were found for tourism, hospitality, and gaming activities. Theoretical implications, insights, and potential actions for retailers and tech companies are discussed, along with limitations and suggestions for further research.

Abstract
This work proposes a robust methodology for the location and sizing of grid forming (GFM) converters that simultaneously considers the solution costs and the security gains while accounting for the TSO nonlinear cost-security sensitivity. Such methodology, which includes a collection of techniques to reduce the problem dimensionality, formulates the placement problem as a non-linear multi-criteria decision support problem and uses a solution-seeking algorithm based on Bayesian Optimisation to determine the solution. To ease comprehension, a modified version of the IEEE 39 Test System is used as a case study throughout the method's detailed explanation and application example. A sensitivity analysis of the GFM converter's over-current capacity in the solution of the formulated placement problem is also performed. The results show that the proposed method is successful in finding solutions with physical meaning and that respect the decision agent preferences.

Abstract
This paper discusses the potential effects of Attention-Capture Damaging Patterns (ACDPs) in designing socially and culturally sensitive interfaces based on their mechanisms and psychological impacts on users. Building on the concept of “dark patterns” and examining how they contribute to social polarization, this study explores the intersection between digital interface design, digital wellbeing, and polarization. The paper analyzes several examples of ACDPs present in popular social media apps and platforms such as Instagram, TikTok, WhatsApp, and Facebook, proposing a new taxonomic approach based on three main categories. In addition, a set of alternative design strategies that promote healthier interactions on digital platforms are discussed to mitigate the negative effects of these patterns and promote a more balanced digital environment.

Abstract
This chapter aims to analyze and map the behaviors and strategies employed by organizations recognized for their innovation, with the goal of developing a comprehensive innovation management framework. This framework is designed to merge innovation practices with elements of traditional management, creating a hybrid model to support companies, universities, and research institutes in fostering innovation. Rooted in an understanding of human evolution, the framework will reflect changes in needs, skills, and behaviors over time, enabling institutions to adapt their innovation strategies to align with societal and individual development. Adopting an interdisciplinary approach, it will integrate concepts from innovation, organizational management, and the human sciences to establish a structure that supports sustainable innovation while addressing contemporary challenges. © 2025, IGI Global Scientific Publishing.

Abstract
Reduced-impact logging (RIL) has been recognized as a promising strategy for biodiversity conservation and carbon sequestration within sustainable forest management (SFM) areas. However, monitoring the forest understory-a critical area for assessing logging impacts-remains challenging due to limitations in conventional methods such as field inventories and global navigation satellite system (GNSS) surveys, which are time-consuming, costly, and often lack accuracy in complex environments. Additionally, aerial and satellite imagery frequently underestimate the full extent of disturbances as the forest canopy obscures understory impacts. This study examines the effectiveness of the relative density model (RDM), derived from airborne LiDAR data, for mapping and monitoring understory disturbances. A field-based validation of LiDAR-derived RDM was conducted across 25 sites, totaling 5504.5 hectares within the Jamari National Forest, Rond & ocirc;nia, Brazil. The results indicate that the RDM accurately delineates disturbances caused by logging infrastructure, with over 90% agreement with GNSS field data. However, the model showed the greatest discrepancy for skid trails, which, despite their lower accuracy in modeling, accounted for the largest proportion of the total impacted area among infrastructure. The findings include the mapping of 35.1 km of primary roads, 117.4 km of secondary roads, 595.6 km of skid trails, and 323 log landings, with skid trails comprising the largest proportion of area occupied by logging infrastructure. It is recommended that airborne LiDAR assessments be conducted up to two years post-logging, as impacts become less detectable over time. This study highlights LiDAR data as a reliable alternative to traditional monitoring approaches, with the ability to detect understory impacts more comprehensively for monitoring selective logging in SFM areas of the Amazon, providing a valuable tool for both conservation and climate mitigation efforts.