Abstract
Traditional retail business models price electricity using volumetric tariffs, which charge customers for the unit of energy consumed. These tariffs were designed for passive consumers with low flexibility. In this paper, we argue that these volumetric tariffs are unsuitable for prosumers with high flexibility since they are unable to adequately value the flexibility of their distributed energy resources in multiple electricity markets. This reduces the interest of prosumers participating in aggregators' business models. To address this issue, we propose a new business model for aggregators of prosumers, based on the concept of energy-as-a-service. In this business model, prosumers pay a monthly fee for aggregators to represent and optimize them in multiple wholesale electricity markets, including in energy and ancillary service markets. The monthly fee is computed by a new technoeconomic simulation framework proposed in this paper, which can also be used to estimate the profitability of the new business model from the perspectives of both the aggregator and prosumers. Our experiments on a portfolio of real prosumers from Australia show that the new business model maximizes the economic benefits of both the aggregator and prosumers by increasing the average profit of the aggregator by 438% and reducing the average electricity cost of prosumers from $583/year to $0 when compared to two of the most common retail business models available in the Australian market. In other words, the economic benefit for prosumers is free electricity. In addition to this benefit, the new business model also provides simplicity and predictability to prosumers, as they are offered a guaranteed outcome before providing the services.

Abstract

Abstract
In the evolving landscape of global education, Artificial Intelligence's (AI) integration into Learning Management Systems (LMS) promises a transformative shift. This paper presents Mozambique's journey in this domain, comparing it with global advancements. While the Mozambican higher education sector stands at the cusp of a digital revolution, its engagement with AI in LMS remains foundational. This is juxtaposed against the global trend where AI tools, such as ChatGPT, are rapidly becoming standard in many educational platforms, enhancing personalization, efficiency, and data-driven insights. The benefits of AI integration, such as tailored learning experiences and administrative automation, are counterbalanced by challenges, including data privacy concerns and over-reliance on technology. Drawing from real-world case studies, the paper highlights pioneering endeavours that showcase AI's potential in reshaping educational paradigms. As Mozambique navigates its unique challenges, insights from global best practices offer a roadmap for harnessing the transformative potential of AI in LMS, aiming to elevate its higher education sector to new heights.;Na evolução da educação global, a integração da Inteligência Artificial (IA) nos Sistemas de Gestão de Aprendizagem (LMS) promete uma transformação significativa. Este artigo investiga a jornada de Moçambique neste domínio, comparando-a com os avanços globais. Enquanto o setor de ensino superior moçambicano está à beira de uma revolução digital, seu envolvimento com a IA em LMS ainda está em uma fase inicial. Isso é contrastado com a tendência global, onde ferramentas de IA, como o ChatGPT, estão rapidamente se a se tornar padrão em muitas plataformas educativas, aprimorando a personalização, eficiência e insights baseados em dados. Os benefícios da integração da IA, como experiências de aprendizagem adaptadas e automação administrativa, são equilibrados por desafios, incluindo preocupações com a privacidade dos dados e excesso de dependência da tecnologia. Através de estudos de caso do mundo real, o artigo destaca esforços pioneiros que mostram o potencial da IA em remodelar os paradigmas educacionais. Enquanto Moçambique navega pelos seus desafios únicos, os insights das melhores práticas globais oferecem um roteiro para aproveitar o potencial transformador da IA em SGA, com o objetivo de elevar seu setor de ensino superior a novos patamares.

Abstract
AOB-1 is an Adaptive Optics (AO) facility currently designed to feed the Gemini infrared Multi Object Spectrograph (GIRMOS) on the GEMINI North 8m class telescope located in Hawaii. This AO system will be made of two AO modes. A laser tomography AO (LTAO) mode using 4 LGS (laser guide stars) and [1-3] NGS (natural guide stars) for high performance over a narrow field of view (a few arcsec). The LTAO reconstruction will benefit from the most recent developments in the field, such as the super-resolution concept for the multi-LGS tomographic system, the calibration and optimization of the system on the sky, etc. The system will also operate in Ground Layer Adaptive Optics (GLAO) mode providing a robust solution for homogeneous partial AO correction over a wide 2’ FOV. This last mode will also be used as a first step of a MOAO (Multi-object adaptive optics) mode integrated in the GIRMOS instrument. Both GLAO and LTAO modes are optimized to provide the best possible sky coverage, up to 60% at the North Galactic Pole. Finally, the project has been designed from day one as a fast-track, cost effective project, aiming to provide a first scientific light on the telescope by 2027 at the latest, with a good balance of innovative and creative concepts combined with standard and well controlled components and solutions. In this paper, we will present the innovative Phase A concepts, design and performance analysis of the two AO modes (LTAO and GLAO) of the AOB-1 project. © 2023 7th Adaptive Optics for Extremely Large Telescopes Conference, AO4ELT7 2023. All rights reserved.

Abstract
BackgroundCognitive impairment is a critical aspect of our aging society. Yet, it receives inadequate intervention due to delayed or missed detection. Dual-task gait analysis is currently considered a solution to improve the early detection of cognitive impairment in clinical settings. Recently, our group proposed a new approach for the gait analysis resorting to inertial sensors placed on the shoes. This pilot study aimed to investigate the potential of this system to capture and differentiate gait performance in the presence of cognitive impairment based on single- and dual-task gait assessments.MethodsWe analyzed demographic and medical data, cognitive tests scores, physical tests scores, and gait metrics acquired from 29 older adults with mobility limitations. Gait metrics were extracted using the newly developed gait analysis approach and recorded in single- and dual-task conditions. Participants were stratified into two groups based on their Montreal Cognitive Assessment (MoCA) global cognitive scores. Statistical analysis was performed to assess differences between groups, discrimination ability, and association of gait metrics with cognitive performance.ResultsThe addition of the cognitive task influenced gait performance of both groups, but the effect was higher in the group with cognitive impairment. Multiple dual-task costs, dual-task variability, and dual-task asymmetry metrics presented significant differences between groups. Also, several of these metrics provided acceptable discrimination ability and had a significant association with MoCA scores. The dual-task effect on gait speed explained the highest percentage of the variance in MoCA scores. None of the single-task gait metrics presented significant differences between groups.ConclusionsOur preliminary results show that the newly developed gait analysis solution based on foot-worn inertial sensors is a pertinent tool to evaluate gait metrics affected by the cognitive status of older adults relying on single- and dual-task gait assessments. Further evaluation with a larger and more diverse group is required to establish system feasibility and reliability in clinical practice.

Abstract
Gastric Intestinal Metaplasia (GIM) is one of the precancerous conditions in the gastric carcinogenesis cascade and its optical diagnosis during endoscopic screening is challenging even for seasoned endoscopists. Several solutions leveraging pre-trained deep neural networks (DNNs) have been recently proposed in order to assist human diagnosis. In this paper, we present a comparative study of these architectures in a new dataset containing GIM and non-GIM Narrow-band imaging still frames. We find that the surveyed DNNs perform remarkably well on average, but still measure sizeable interfold variability during cross-validation. An additional ad-hoc analysis suggests that these baseline architectures may not perform equally well at all scales when diagnosing GIM.