Abstract
Mutation testing maybe used to guide test case generation and as a technique to assess the quality of test suites. Despite being used frequently, mutation testing is not so commonly applied in the mobile world. One critical challenge in mutation testing is dealing with its computational cost. Generating mutants, running test cases over each mutant, and analyzing the results may require significant time and resources. This research aims to contribute to reducing Android mutation testing costs. It implements mutation testing operators (traditional and Android-specific) according to mutant schemata (implementing multiple mutants into a single code file). It also describes an Android mutation testing framework developed to execute test cases and determine mutation scores. Additional mutation operators can be implemented in JavaScript and easily integrated into the framework. The overall approach is validated through case studies showing that mutant schemata have advantages over the traditional mutation strategy (one file per mutant). The results show mutant schemata overcome traditional mutation in all evaluated aspects with no additional cost: it takes 8.50% less time for mutant generation, requires 99.78% less disk space, and runs, on average, 6.45% faster than traditional mutation. Moreover, considering sustainability metrics, mutant schemata have 8,18% less carbon footprint than traditional strategy.

Abstract
The rapid integration of Machine Learning (ML) in organizational practices has driven demand for substantial computational resources, incurring both high economic costs and environmental impact, particularly from energy consumption. This challenge is amplified in dynamic data environments, where ML models must be frequently retrained to adapt to evolving data patterns. To address this, more sustainable Machine Learning Operations (MLOps) pipelines are needed for reducing environmental impacts while maintaining model accuracy. In this paper, we propose a model reuse approach based on data similarity metrics, which allows organizations to leverage previously trained models where applicable. We introduce a tailored set of meta-features to characterize data windows, enabling efficient similarity assessment between historical and new data. The effectiveness of the proposed method is validated across multiple ML tasks using the cosine and Bray-Curtis distance functions, which evaluate both model reuse rates and the performance of reused models relative to newly trained alternatives. The results indicate that the proposed approach can reduce the frequency of model retraining by up to 70% to 90% while maintaining or even improving predictive performance, contributing to more resource-efficient and sustainable MLOps practices.

Abstract
This paper proposes a multi-objective stochastic optimization framework that can be used by governments to run auctions and select the best community energy storage system (CESS) projects to support. The framework enables CESS providers and energy community members to equitably benefit from the economic value generated by CESSs. The auction accepts offers from competing CESS providers that constitute the data of the CESS location, size, install time, technology, provider, investment cost, and energy trading price. The auction is run by a government agency which selects CESS projects that maximize the economic benefits and distribute them equitably among CESS providers and community members. The multi-objective stochastic optimization accounts for the multi-year uncertainties of photovoltaic (PV) generation, real and reactive energy consumption, energy trading prices, and PV installations. We exploit the Monte Carlo simulation and scenario trees to model the aforementioned uncertainties. The K-Means clustering method is used to reduce the number of scenarios, and thereby, lessen the computational burden of the optimization problem. Our experiments on an Australian low-voltage network with a community of prosumers and consumers demonstrate that government financial support can accelerate the installation of CESSs and enhance their business viability. This can be achieved by boosting the economic benefits shared between CESS providers and communities and ensuring these benefits are distributed equitably. Also, our experiments show that the economic benefits of all stakeholders are further improved with a high growth of the number of PV installations, and a slight reduction of energy import and export prices over the planning period.

Abstract

Abstract
Since publication of the 2020 survey of surveys, Finding the gaps about uses of immersive learning environments: a survey of surveys, the field of immersive learning environments has experienced substantial growth and diversification. This updated review systematically maps recent developments by analyzing 64 new literature surveys published after the original corpus date, significantly expanding the corpus from 47 to 111 reviews. Through thematic content analysis, our study identifies and integrates five new educational use themes-Games, Observation, Personification, Storytelling, and Student Authoring-and revises existing categories based on recent research. We observed shifts in the prevalence of themes, most notably an increase in uses related to data collection, interactive exploration and manipulation, contextual/media integration, and physical world simulation. We also discussed these changes in relation to recent technological advancements and the influence of emergency remote teaching during the COVID-19 pandemic. Moreover, our results provide an updated representation of immersive learning uses within the conceptual framework of immersion dimensions (system, narrative, agency), updating current research clusters and persistent gaps. By illustrating areas with limited exploration, such as highly interactive narrative experiences, or low-technology interactive uses, this paper informs future research directions and contributes to an understanding of how immersive environments are being employed for learning. This comprehensive mapping thus serves as a resource for researchers and educators aiming to leverage immersive learningenvironments. This paper builds on a shorter version accepted for inclusion in the proceedings of the iLRN 2025 conference, offering expanded results, additional analyses, and extended discussion that clarifies and deepens the original findings.

Abstract
Due to the increase in energy consumption based on fossil fuels, sustainable alternatives have emerged, and green hydrogen (H-2) is one of them. This fuel is a promising eco-friendly energy source but is highly flammable. Therefore, continuous monitoring is essential, where optical sensors can contribute with a fast and remote sensing capability. In this field, plasmonic sensors have demonstrated high sensitivity, but with the plasmonic band in the visible range and low definition in the infrared. It presents a sensing structure for H-2 sensing composed of inexpensive materials (SiO2 and TiO2) and Pd as a sensitive medium, which supports Tamm Plasmon Resonance. The structure is numerically optimized to obtain a plasmonic band around 1550nm, which was experimentally validated with a sensitivity of 9.5nm in the presence of 4 vol% H2 and a response time of 30 seconds. This work aims to emphasize the advantages of this plasmonic technique for gas sensing at the infrared spectral range, allowing remote sensing.