Abstract
The digital society’s portrait involves being daily connected to the Internet, at home, at work and in the social life. But seniors do not feel this need, despite this need is increasing as everything around them is online. So, seniors take a change on web browsing, without being aware of the it is dangers, from the theft of personal data, fake news, or online frauds. Therefore, the investigation promotes a Serious Game that exposes these insecure digital situations by challenges to a group of seniors from a network of senior universities. Web Segura is an online educational game developed on the WordPress platform and with challenges of the H5P plugin. © 2023, University of Algarve Research Centre for Arts and Communication. All rights reserved.

Abstract
The most malignant tumors of the central nervous system are adult-type diffuse gliomas. Historically, glioma subtype classification has been based on morphological features. However, since 2016, WHO recognizes that molecular evaluation is critical for subtyping. Among molecular markers, the mutation status of IDH1 and the codeletion of 1p/19q are crucial for the precise diagnosis of these malignancies. In pathology laboratories, however, manual screening for those markers is time-consuming and susceptible to error. To overcome these limitations, we propose a novel multimodal biomarker classification method that integrates image features derived from brain magnetic resonance imaging and histopathological exams. The proposed model consists of two branches, the first branch takes as input a multi-scale Hematoxylin and Eosin whole slide image, and the second branch uses the pre-segmented region of interest from the magnetic resonance imaging. Both branches are based on convolutional neural networks. After passing the exams by the two embedding branches, the output feature vectors are concatenated, and a multi-layer perceptron is used to classify the glioma biomarkers as a multi-class problem. In this work, several fusion strategies were studied, including a cascade model with mid-fusion; a mid-fusion model, a late fusion model, and a mid-context fusion model. The models were tested using a publicly available data set from The Cancer Genome Atlas. Our cross-validated classification models achieved an area under the curve of 0.874, 0.863, and 0.815 for the proposed multimodal, magnetic resonance imaging, and Hematoxylin and Eosin stain slide images respectively, indicating our multimodal model outperforms its unimodal counterparts and the state-of-the-art glioma biomarker classification methods.

Abstract
The increasing demand for precise chemical and biological sensing has led to the development of highly efficient plasmonic optical fiber sensors. Therefore, it is essential to optimize and match the operating wavelength region of both the optical fiber configuration and localized surface plasmon resonance of nanoparticles (NPs). This can be achieved by developing NPs that can reach resonance at near-infrared wavelengths, where refractive index sensitivity is enhanced, and silica optical fibers have lower losses. High aspect-ratio bimetallic Au@Ag nanorods and different side-polished fiber structures are tested using numerical simulations. The selected optical fiber configuration was based on a side-polished fiber with a 1 mm polished section. It is compared power losses and power at the NP interface for two configurations: a step-index single-mode fiber (SMF) with core/cladding diameters of 8.2/125 µm and a multimode graded-index fiber (GIF) with 62.5/125 µm at various polishing depths. The results showed that the best performance for both configurations was achieved at similar polishing depths, namely 59.5 and 55.2 µm for the SMF and GIF, respectively. The optical impact of retardation effects due to the proximity with the fiber structure were also observed, which caused a reduction in sensitivity from 1750 nm/RIU to 1500 nm/RIU and a red-shift of around 70 nm. © 2023 SPIE.

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
Influential spreaders contribute substantially to managing and optimizing any spreading process in a network. Influential spreaders are nodes that hold importance within the network. Identifying them is a challenging task. Some encysting methods for such identification include local-structure-based, global-structure-based, semi-global-structure-based, and hybrid-structure-based methods. Semi-global structure-based methods show significant potential in identifying influential nodes in different network structures. However, existing semi-global structure-based methods often identify nodes from the network's periphery, where nodes are loosely connected, and their collective influence in spreading processes is minimal. This paper presents a novel method called Semi-global triangular and degree centrality (STC + K) to overcome this limitation by considering a node's degree, the number of triangles, and the third hop of neighbourhood connectivity information. The proposed novel method outperforms the existing noteworthy indexing methods regarding ranking performance. The experimental results show better performance, as indicated by two performance metrics: recognition rate and improvement percentage. By virtue of the fact that the empirically set free parameters are absent, our method eliminates the need for time-consuming preprocessing to select optimal parameter values for ranking nodes in large networks.

Abstract
Type systems and type inference systems can be used to help text and code generation models like GPT-3 produce more accurate and appropriate results. These systems provide information about the types of variables, functions, and other elements in a program or codebase, which can be used to guide the generation of new code or text. For example, a code generation model that is aware of the types of variables and functions being used in a program can generate code that is more likely to be syntactically correct and semantically meaningful. We argue for the specialization of language models such as GPT-3 for automatic program repair tasks, incorporating type information in the model's learning process. A trained language model is expected to perform better by understanding the nuances of type systems and using them for program repair, instead of just relying on the general structure of programs.