Abstract
This paper introduces a mathematical model designed to optimise the operation of natural gas distribution networks, considering the injection of hydrogen in multiple nodes. The model is designed to optimise the quantity of hydrogen injected to maintain pressure, gas flows, and gas quality indexes (Wobbe index (WI) and higher heating value (HHV)) within admissible limits. This study also presents the maximum injection allowable of hydrogen correlated with the gas quality index variation. The model has been applied to a case study of a gas network with four distinct scenarios and implemented using Python. The findings of the case study quantify the maximum permitted volume of hydrogen in the network, the total savings in natural gas, and the reduction in carbon dioxide emissions. Lastly, a sensitivity analysis of injected hydrogen as a function of the Wobbe index (WI) and Higher Heating Value (HHV) limits relaxation.

Abstract
In this work a hybrid simulation-optimization approach is presented to support decision-making towards improved resiliency and sustainability in pharmaceutical supply chain (PSC) operations. In a first step, a simulation model is used to assess the PSC performance under a set of disruptive scenarios to select the best inventory-based strategy for enhanced resiliency. Disruptions addressed in this work are mainly related to unpredicted medium-term production stoppages due to unexpected high-impact events such as accidents in production and transportation, or natural disasters. In a second step, a multi-objective mixed integer linear programming (MO-MILP) model is developed to optimize the selected inventory-based strategy regarding the economic, social, and environmental dimensions. In particular, the social and environmental aspects are introduced by anticipating the expected waste generation of close to expire medicines, redirecting them into a donation scheme. The proposed approach is applied to a representative PSC, with preliminary results showing the relevance of this tool for decision-makers to assess the trade-offs associated to the economic and social dimensions, as well as their impacts on waste generation.

Abstract
This study explores the contribution of serious game teaching technology, such as FLIGBY, to the development of entrepreneurial learning outcomes in the context of an entrepreneurship course in higher education. The sample is composed of 551 students through the construction of a randomized pretest-posttest control group. A quantitative methodology is adopted through the development of a structured equation model that seeks to assess the effectiveness of FLIGBY in the development of three constructs related to the development of entrepreneurial skills, reduction in the perception of barriers associated with entrepreneurial activity and increase the entrepreneurial intention. The findings reveal that FLIGBY can effectively contribute to the development of the first two constructs. However, we found that it has no effect on the third construct because it was not possible to identify significant differences in the entrepreneurial intention of FLIGBY students with those of the control group. The results of this study are relevant in extending the understanding of the impact of adopting a serious game in the context of entrepreneurship education and also supports their role in the development of more immersive and student-centered training.

Abstract
Discrete choice models can forecast market shares and individual choice probabilities with different price and alternative set scenarios. This work introduces a method to personalize choice models involving causal variables, such as price, using rich observational data. The model provides interpretable customer- and context-specific preferences, and price sensitivity, with an estimation procedure that uses orthogonalization. We caution against the nalive use of regularization to deal with the high-dimensional observational data challenge. We experiment with the attended home delivery (AHD) slot choice problem using data from a European online retailer. Our results indicate that while the popular non-personalized multinomial logit (MNL) model does very well at the aggregate (day-slot) level, personalization provides significantly and substantially more accurate predictions at the individual-context level. But the nalive personalization approach using regularization without orthogonalization wrongly predicts that the choice probability will increase if the slot price increases, rendering it unfit for forecasting demand with pricing scenarios. The proposed method avoids this problem. Further, we introduce features based on potential consideration sets in the AHD slot choice context that increase accuracy and allow for more realistic substitution patterns than the proportional substitution implied by MNL.

Abstract
Azobenzenes are a class of compounds presenting photoisomerization capabilities that allow the writing and erasure of birefringence along a desired direction. This feature enables applications requiring polarization control, which although have been extensively investigated in the visible light spectrum, poor emphasis has been paid to the infrared region. In this paper, a systematic characterization of induced birefringence creation and relaxation dynamics has been carried out in azopolymers thin films in the infrared telecommunications region of 1550 nm. This study covers both birefringence characterization in terms of wavelength and irradiance of birefringence writing beams. Preliminary results revealed remarkable maximum birefringence values as high as 0.0465 attained during the recording phase, that stabilized at 0.0424 during the relaxation phase, which is quite promising for many applications.

Abstract
Effective anomaly detection in telecommunication networks is essential for securing digital transactions and supporting the sustainability of our global information ecosystem. However, the volume of data in such high-speed distributed environments imposes strict latency and scalability requirements on anomaly detection systems. This study focuses on distributed heavy hitter detection in telephone networks - a critical component of network traffic analysis and fraud detection. We propose a federated version of the Lossy Counting algorithm and compare it to its centralized version. Our experimental results reveal that the federated approach can detect considerably more unique heavy hitters than the centralized method while enhancing privacy. Furthermore, Federated Lossy Counting does not need a large amount of centralized processing power since it can leverage the networked infrastructure with minimal impact on bandwidth and computing power.