Abstract
In this paper, we investigated the evolution of the dispersion curves of long-period fiber gratings (LPFGs) from room temperature down to 0 K. We considered gratings arc-induced in the SMF28 fiber and in two B/Ge co-doped fibers. Computer simulations were performed based on previously published experimental data. We found that the dispersion curves belonging to the lowest-order cladding modes are the most affected by the temperature changes, but those changes are minute when considering cladding modes with dispersion turning points (DTP) in the telecommunication windows. The temperature sensitivity is higher for gratings inscribed in the B/Ge co-doped fibers near DTP and the optimum grating period can be chosen at room temperature. A temperature sensitivity as high as -850 pm/K can be obtained in the 100-200 K temperature range, while a value of -170 pm/K is reachable at 20 K.

Abstract
By carefully recommending selected items to users, recommender systems ought to increase profit from product sales. To achieve this, recommendations need to be relevant, novel and diverse. Many approaches to this problem exist, each with its own advantages and shortcomings. This paper proposes a novel way to combine model, memory and content-based approaches in a cascade-hybrid system, where each approach refines the previous one, sequentially. It is also proposed a straight-forward way to easily incorporate time-awareness into rating matrices. This approach focuses on being intuitive, flexible, robust, auditable and avoid heavy performance costs, as opposed to black-box fashion approaches. Evaluation metrics such as Novelty Score are also for-malized and computed, in conjunction with Catalog Coverage and mean recommendation price to better capture the recommender's performance.

Abstract

Abstract
Tackling uncertainty is becoming increasingly relevant for decision-support across fields due to its critical impact on real-world problems. Uncertainty is often modelled using scenarios, which are combinations of possible outcomes of the uncertain parameters in a problem. Alongside expected value methods, decisions under uncertainty may also be tackled using methods that do not rely on probability distributions and model different decision-maker risk profiles. Scenarios are at the core of these approaches. Therefore, we propose a scenario generation methodology that seizes the structure and concepts of genetic algorithms. This methodology aims to obtain a diverse set of scenarios, evolving a scenario population with a diversity goal. Diversity is here expressed as the difference in the impact that scenarios have on the value of potential solutions to the problem. Moreover, this method does not require a priori knowledge of probability distributions or statistical moments of uncertain parameters, as it is based on their range. We adapt the available code for Biased-Random Key Genetic Algorithms to apply the methodology to a packing problem under demand uncertainty as a proof of concept, also extending its use to a multiobjective setting. We make available these code adaptations to allow the straightforward application of this scenario generation method to other problems. With this, the decision-maker obtains scenarios with a distinct impact on potential solutions, enabling the use of different criteria based on their profile and preferences.

Abstract
Digitalization has spread across business and supply chains, becoming irreversible and affecting how companies run their businesses and fulfill their demand. This paper discusses the main aspects that propel and hinder digitalization in supply chains are. One could divide the boosters into two groups: the application of technological advances and circular boosters. On the other hand, the barriers are either sporadic or persistent. Despite the perceived barriers, if correctly applied, digitalization brings more benefits than problems to supply chains. Furthermore, recognizing this might help practitioners who are still reluctant about digitalization.

Abstract
In highly regulated areas such as healthcare there is a demand for explainable and trustworthy systems that are capable of providing some sort of foundation or logical reasoning to their functionality. Therefore, deep learning applications associated with such industry are increasingly required by this sense of accountability regarding their production value. Additionally, it is of utter importance to take advantage of all possible data resources, in order to achieve a greater amount of efficiency respecting such intelligent frameworks, while maintaining a realistic medical scenario. As a way to explore this issue, we propose two models trained with information retained in chest radiographs and regularized by the associated medical reports. We argue that the knowledge extracted from the free-radiology text, in a multimodal training context, promotes more coherence, leading to better decisions and interpretability saliency maps. Our proposed approach demonstrated to be more robust than their baseline counterparts, showing better classification performances, and also ensuring more concise, consistent and less dispersed saliency maps. Our proof-of-concept experiments were done using the publicly available multimodal radiology dataset MIMIC-CXR that contains a myriad of chest X-rays and its correspondent free-text reports.