Abstract
Missing data is an issue that can negatively impact any task performed with the available data and it is often found in real -world domains such as healthcare. One of the most common strategies to address this issue is to perform imputation, where the missing values are replaced by estimates. Several approaches based on statistics and machine learning techniques have been proposed for this purpose, including deep learning architectures such as generative adversarial networks and autoencoders. In this work, we propose a novel siamese neural network suitable for missing data imputation, which we call Siamese Autoencoder-based Approach for Imputation (SAEI). Besides having a deep autoencoder architecture, SAEI also has a custom loss function and triplet mining strategy that are tailored for the missing data issue. The proposed SAEI approach is compared to seven state-of-the-art imputation methods in an experimental setup that comprises 14 heterogeneous datasets of the healthcare domain injected with Missing Not At Random values at a rate between 10% and 60%. The results show that SAEI significantly outperforms all the remaining imputation methods for all experimented settings, achieving an average improvement of 35%. This work is an extension of the article Siamese Autoencoder-Based Approach for Missing Data Imputation [1] presented at the International Conference on Computational Science 2023. It includes new experiments focused on runtime, generalization capabilities, and the impact of the imputation in classification tasks, where the results show that SAEI is the imputation method that induces the best classification results, improving the F1 scores for 50% of the used datasets.

Abstract
This article delves into the intersections of place branding, digital strategic communication, citizenship, and tourism. It explores the dynamic relationship between these concepts, particularly within the context of Brazilian city governments. With an emphasis on reflexivity, the study investigates how governments manage their public image and engage citizens through digital channels. Simultaneously, it examines how these governments strategically position their cities as attractive tourist destinations. By analyzing these tensions and synergies, the article provides insights into the complex landscape of communication strategies employed by Brazilian city governments, which aim to balance citizen engagement and tourism promotion.

Abstract
What causes ineffective external idea evaluation in open innovation (OI) still remains an unsolved puzzle, with most such studies focused on creative idea generation or using samples of untrained evaluators. To help better understand the microfoundations of OI, this article examines the effects of mood on external idea evaluation using a practitioner sample. Drawing on "mood-as-an-input" theory, in two behavioral experiments using music induction, cognitive tasks, and idea framing, we test how one's mood affects the innovativeness rating of an externally developed idea, and examine whether this effect is stable within a mood state regardless of the level of creativity (high and low) of an idea. We found that people in happy and sad mood conditions differ in their evaluation of the same external idea, which is explained by differences in assessment of creativity of an idea and not the perceived certainty of its success. Moreover, a given mood state does not affect how ideas low in creativity are rated in their innovativeness, compared to ideas high in creativity. This article by investigating effects of mood within an OI process augments individual level OI literature, while informing the ways external idea evaluation can be managed toward enhancing OI potential.

Abstract
Long-term storage expansion planning has usually employed representative days and intra-annual time series aggregation methodologies to reduce the computation complexity. This paper proposes a shift on the approach to the economic evaluation of these systems by implementing an intra-annual time series cost evaluation that considers different uncertainty trajectories. This methodology aims to determine the best possible investment strategies for the available computational budget using strategy game-based decision-making models, as Monte Carlo tree search. The proof of concept is illustrated by a single-bus equivalent test system and compared to a deterministic evaluation for a limited uncertainty model.

Abstract
In the GRAVITY+ project, GRAVITY is presently undergoing a series of upgrades to enhance its performance, add wide field capability and thereby expand its sky coverage. Some aspects of these improvements have already been implemented and commissioned by the end of 2021, making them accessible to the community. The augmentation of sky coverage involves increasing the maximum angular separation between the celestial science object and the fringe tracking object from the previous 2 arcseconds (limited by the field of view of the VLTI) to 20 - 30 arcseconds (constrained by atmospheric conditions during observation). Phase 1 of GRAVITY+ Wide utilizes the earlier PRIMA Differential Delay Lines to compensate for the optical path length variation between the science and fringe tracking beams throughout an observation. In phase 2, we are upgrading the existing beam compressors (BC) to integrate optical path length difference compensation directly into the BC. This modification eliminates five optical reflections per beam, thereby enhancing the optical throughput of the VLTI-GRAVITY [GRAPHICS] system and the bandwidth of the vibrational control. We will present the implementation of phase 2 and share preliminary results from our testing activities for GRAVITY+ Wide.

Abstract