Abstract
Purpose This study contributes to the understanding of how cultural organizations are using digital technologies to redesign their business models and enable sustainable and impactful audiovisual digital archives. Design/methodology/approach An inductive multiple case research design was used. Five cases of audiovisual digital archives of independent films were selected. Data collected was based on desk research, onsite visits, interviews with top managers responsible for the digitalization of some of the archives and experimentation with the services provided. Data was collected and analyzed based on a theoretical framework defined from the literature for business models of cultural organizations. Findings The archives analyzed faced the challenge of aligning the commercial viability with a mission of making content available to increase cultural knowledge. A sustainable business model may be achieved by using different revenue models, while guaranteeing to offer a value proposition carefully aligned with stakeholders' expectations. Moreover, an impactful business model, i.e. a business model that enhances the creation of cultural value for customers and reaches wider audiences, requires careful audience management and the use of data analysis about audience behavior to adjust the offering. Finally, the business model must consider the resources, activities and infrastructure that ensure critical capabilities for the business and must be designed to ensure financial resilience of the organization. Originality/value This study contributes with a holistic analysis of business models for the digital transformation of cultural organizations, detailing alternative configurations for the most relevant components of a digital business model for audiovisual archives.

Abstract
We present a literature review that aims to understand the role of the Educational Escape Room (EER) in improving the teaching, learning, and assessment processes through an EER design framework. The main subject is to identify the recent interventions in this field in the last five years. Our study focuses on understanding how it is possible to create an EER available to all students, namely visually challenged users. As a result of the implementation of new learning strategies that promote autonomous learning, a concern arose in adapting educational activities to each student's individual needs. To study the adaptability of each EER, we found the EER design framework essential to increase the student experience by promoting the consolidation of knowledge through narrative and level design. The results of our study show evidence of progress in students' performance while playing an EER, revealing that students' learning can be effective. Research on Procedural Content Generation (PCG) highlighted how important it is to implement adaptability in future studies of EERs. However, we found some limitations regarding the process of evaluating learning through the EERs, showing how important it is to study and implement learning analytics in future studies in this field.

Abstract
Driven by the need for decarbonizing energy carriers across sectors, and the increasing availability of low-cost renewable electricity generation future energy systems will see a rise of power-to-gas technology. For example, hydrogen and its derivates can make enable the usage of carbon-neutral electricity for hard-to abate industry sectors and serve as long-term seasonal storage. Given recent drafts of ambitious political hydrogen strategies around the world, the question arises which power-to-gas configurations provide the highest value for money from a power system perspective. This work provides a flexible framework to compare the performance of current power-to-gas sites all over the world. Power-to-gas technologies are assessed with a benchmarking framework based on Composite Indicators to compare the system value of existing conversion technologies, plant sizes, cost structures, and configurations. Our analysis confirms recent research that suggests that plant performance is higher for larger projects and improves as projects move from research stage over pilot stage to commercial stage. Our findings inform policy makers and electricity system planners who aim to identify the economically and technically most promising solutions for investment.

Abstract
This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application.

Abstract
Collaborative robots are being increasingly used by manufacturing companies due to their potential to help companies cope with market volatility. Before introducing this technology, companies face the decision phase where they determine the investment feasibility. Decision models for cobot adoption can assist decision-makers in this task, but they require previous identification of decision criteria. Since existing literature overlooked this issue, this study aims to provide a list of decision criteria that can be considered in the cobot adoption decision process. These criteria were identified by a literature review of the benefits, advantages, and disadvantages of cobot adoption. Results show that flexibility, competitiveness, ergonomics, quality, safety, space, mobility, ease of programming, technical features, human-robot collaboration, and productivity are important aspects to consider when deciding whether to invest in cobots. The findings of this study provide a better understanding of the decision process for cobot adoption by listing decision criteria along with some indicators, which is an important input for the design of a decision-making process.

Abstract
The Minimum Life on Receipt (MLOR) is a widely used rule that imposes the minimum remaining age a food product must be delivered by the producer to the retailer. In practice, this rule is set by retailers and it is fixed, around 2/3 of the age of products regardless their shelf life. In this work, we study single and two echelon make-to-stock production-inventory problems for fixed-lifetime perishables. Mixed-integer linear optimization models are developed considering the MLOR rule both as decision variable and fixed parameter. When the MLOR rule is a variable, it is considered either a sole decision of the producer or a collaborative decision between retailer and producer. The goal of this work is to compare the supply chain performance considering this innovative setting of optimal MLOR (as a variable) against the traditional setting of fixed MLOR rule. The computational results suggest that allowing flexible MLOR rules according to the shelf life of products and the operational requirements of the producer benefit both entities in the supply chain. In particular, reducing the MLOR requirement in up to 12% does not interfere substantially with the average freshness of products arriving to the retailer, but reduces extensively surplus/waste generation at the producer while keeping a small amount of waste at the retailer.