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

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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.

Abstract
This article presents Pardinus, an extension of the popular Kodkod relational model finder with linear temporal logic (including past operators), to simplify the analysis of dynamic systems. Pardinus includes a SAT-based bounded-model checking engine and an SMV-based complete model checking engine, both allowing iteration through the different instances (or counter-examples) of a specification. It also supports a decomposed parallel analysis strategy that improves the efficiency of both analysis engines on commodity multi-core machines.

Abstract
Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent label noise on a binary classification task: quality assessment for photoplethysmography (PPG). PPG signal is used to detect physiological changes and its quality can have a significant impact on the subsequent tasks, which makes PPG quality assessment a particularly good target for examining the impact of label noise in the field of biomedicine. Random and class-dependent label noise was introduced separately into the training set to emulate the errors associated with fatigue and bias in labeling data samples. We also tested different representations of the PPG, including features defined by domain experts, 1D raw signal and 2D image. Three different classifiers are tested on the noisy training data, including support vector machine (SVM), XGBoost, 1D Resnet and 2D Resnet, which handle three representations, respectively. The results showed that the two deep learning models were more robust than the two traditional machine learning models for both the random and class-dependent label noise. From the representation perspective, the 2D image shows better robustness compared to the 1D raw signal. The logits from three classifiers are also analyzed, the predicted probabilities intend to be more dispersed when more label noise is introduced. From this work, we investigated various factors related to label noise, including representations, label noise type, and data imbalance, which can be a good guidebook for designing more robust methods for label noise in future work.