Abstract
The increased use of smartphones and the COVID-19 pandemic directly influenced the development of remote tools in several areas. In the context of oncology, it was no different, as several studies address health care or services related to mobile devices. Apps aimed at the medical field (m-health) focus directly on monitoring symptoms and improving interaction between health professionals and patients, combined with the convenience of smartphones. In this context, this work aims to address recent studies on the use of m-health in the clinical practice of oncological diseases and report the characteristics of the apps involved. For this, a review of m-health focused on oncology was conducted using the PubMed and Science Direct databases. The investigation was carried out using tools inherent in international databases and was limited to articles published between 2015 and 2022. In total, 34 articles were analyzed, with a higher frequency of publications between 2019 and 2022. The resources observed were patient follow-up, prevention of signs and symptoms, monitoring of treatment and aid in prognosis and diagnosis of patients. It is concluded that a close collaboration among patients, health professionals, and information technology professionals is necessary to optimize symptom recognition and improve patient-professional communication. Although the pandemic has intensified the increase in the use of m-health, its use is expected to increase in the post-pandemic scenario, bearing in mind the changes in social dynamics and the growing dissemination of technologies. © 2023 ITMA.

Abstract

Abstract
Solid-phasetemplate-assisted mechanosynthesis of Fe-N-C,featuring low-cost and sustainable FeCl3, 2,4,6-tri(2-pyridyl)-1,3,5-triazine(TPTZ), and NaCl is reported. Efficient and sustainable synthesis of performant metal/nitrogen-dopedcarbon (M-N-C) catalysts for oxygen reduction and evolutionreactions (ORR/OER) is vital for the global switch to green energytechnologies-fuel cells and metal-air batteries. Thisstudy reports a solid-phase template-assisted mechanosynthesis ofFe-N-C, featuring low-cost and sustainable FeCl3, 2,4,6-tri(2-pyridyl)-1,3,5-triazine (TPTZ), and NaCl. ANaCl-templated Fe-TPTZ metal-organic material was formed usingfacile liquid-assisted grinding/compression. With NaCl, the Fe-TPTZtemplate-induced stability allows for a rapid, thus, energy-efficientpyrolysis. Among the produced materials, 3D-FeNC-LAG exhibits remarkableperformance in ORR (E (1/2) = 0.85 V and E (onset) = 1.00 V), OER (E ( j=10) = 1.73 V), and in the zinc-airbattery test (power density of 139 mW cm(-2)). Themultilayer stream mapping (MSM) framework is presented as a tool forcreating a sustainability assessment protocol for the catalyst productionprocess. MSM employs time, cost, resource, and energy efficiency astechnoeconomic sustainability metrics to assess the potential upstreamimpact. MSM analysis shows that the 3D-FeNC-LAG synthesis exhibits90% overall process efficiency and 97.67% cost efficiency. The proposedsynthetic protocol requires 2 times less processing time and 3 timesless energy without compromising the catalyst efficiency, superiorto the most advanced methods.

Abstract
Machine vision systems are widely used in assembly lines for providing sensing abilities to robots to allow them to handle dynamic environments. This paper presents a comparison of 3D sensors for evaluating which one is best suited for usage in a machine vision system for robotic fastening operations within an automotive assembly line. The perception system is necessary for taking into account the position uncertainty that arises from the vehicles being transported in an aerial conveyor. Three sensors with different working principles were compared, namely laser triangulation (SICK TriSpector1030), structured light with sequential stripe patterns (Photoneo PhoXi S) and structured light with infrared speckle pattern (Asus Xtion Pro Live). The accuracy of the sensors was measured by computing the root mean square error (RMSE) of the point cloud registrations between their scans and two types of reference point clouds, namely, CAD files and 3D sensor scans. Overall, the RMSE was lower when using sensor scans, with the SICK TriSpector1030 achieving the best results (0.25 mm +/- 0.03 mm), the Photoneo PhoXi S having the intermediate performance (0.49 mm +/- 0.14 mm) and the Asus Xtion Pro Live obtaining the higher RMSE (1.01 mm +/- 0.11 mm). Considering the use case requirements, the final machine vision system relied on the SICK TriSpector1030 sensor and was integrated with a collaborative robot, which was successfully deployed in an vehicle assembly line, achieving 94% success in 53,400 screwing operations.

Abstract
Narratives are present in many forms of human expression and can be understood as a fundamental way of communication between people. Computational understanding of the underlying story of a narrative, however, may be a rather complex task for both linguists and computational linguistics. Such task can be approached using natural language processing techniques to automatically extract narratives from texts. In this paper, we present an in depth survey of narrative extraction from text, providing a establishing a basis/framework for the study roadmap to the study of this area as a whole as a means to consolidate a view on this line of research. We aim to fulfill the current gap by identifying important research efforts at the crossroad between linguists and computer scientists. In particular, we highlight the importance and complexity of the annotation process, as a crucial step for the training stage. Next, we detail methods and approaches regarding the identification and extraction of narrative components, their linkage and understanding of likely inherent relationships, before detailing formal narrative representation structures as an intermediate step for visualization and data exploration purposes. We then move into the narrative evaluation task aspects, and conclude this survey by highlighting important open issues under the domain of narratives extraction from texts that are yet to be explored.

Abstract
Secure Multiparty Computation (SMC) enables multiple parties to cooperate securely without compromising their privacy. SMC has the potential to offer solutions for privacy obstacles in vehicular networks. However, classical SMC implementations suffer from efficiency and security challenges. To address this problem, two quantum communication technologies, Quantum Key Distribution (QKD) and Quantum Oblivious Key Distribution were utilised. These technologies supply symmetric and oblivious keys respectively, allowing fast and secure inter-vehicular communications. These quantum technologies are integrated with the Faster Malicious Arithmetic Secure Computation with Oblivious Transfer (MASCOT) protocol to form a Quantum Secure Multiparty Computation (QSMC) platform. A lane change service is implemented in which vehicles broadcast private information about their intention to exit the highway. The proposed QSMC approach provides unconditional security even against quantum computer attacks. Moreover, the communication cost of the quantum approach for the lane change use case has decreased by 97% when compared to the classical implementation. However, the computation cost has increased by 42%. For open space scenarios, the reduction in communication cost is especially important, because it conserves bandwidth in the free-space radio channel, outweighing the increase in computation cost. A Quantum Secure Multiparty Computation (QSMC) solution for lane change service in vehicular networks that uses two quantum technologies, Quantum Key Distribution (QKD) and Quantum Oblivious Key Distribution (QOKD) is proposed. This quantum-based approach is resistant to quantum computer attacks and requires less communication resources compared to classical methods.image