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With the current utilization of Floating, Production, Storage, and Offloading (FPSO) units by the petroleum industry, and the trend towards optimizing the energy efficiency of electrical systems, Variable Frequency Drives (VFDs) play a crucial role in enhancing the performance and power quality (PQ) of the power system on these offshore platforms. The FPSO unit operates in various modes, depending on its regular activities, such as contingency, production and offloading. Each operating mode has multiple grid configurations with different generation and load connections, including the contingency mode that is used for planned maintenance or failure in one of the aeroderivative gas turbine generators (GTGs). During the contingency mode, the FPSO unit runs with two GTGs and implements load shedding. Therefore, this paper investigates a specific case of the contingency operating mode, considering scenarios with zero, one, two, and three VFDs connected to the largest motors. Additionally, it analyzes VFDs with both passive and active front-end rectifiers, aiming to minimize the number of loads to be shed during contingency. The main result shows that by using the active front-end VFDs, 14.3 MW of loads that would be shut down during contingency mode can continue to operate.

There is a growing tendency for people to spend more and more hours of their day sitting. This position leads to an increase in the postural problems in the population. For workers who have to spend long hours at work, in a seated position, investment should be directed towards the development of equipment that improves their working conditions, such as smart instrumented chairs with warnings of changes in position. With this work we propose a standard office chair instrumentation approach, designed to allow monitoring physiological parameters such as: posture of the seated person (user); body temperature; and respiratory frequency. The system also allows monitoring environmental parameters such as: temperature; relative humidity; atmospheric concentration of carbon dioxide (CO2); noise; and light levels. The chair enables the interaction with control equipment to adjust the comfort level, advising the need for rest time, repositioning notifications, and the real time visualization of data, using applications for Windows and Android. The system was tested by six users and evaluated in the detection of six different postures for each user, while sitting on the chair, presenting an 100% accuracy on the posture detection and a maximum of 18% error on the physiological parameters sensing. Experimental results show the adequate functionality of the instrumented chair, which could contribute to the prevention of pathologies associated with improper posture and the improvement of work productivity.

Virtualization enables cloud computing, allowing for server consolidation with cost reduction. It also introduces new challenges in terms of security and isolation, which are deterrents for the adoption of virtualization in critical systems. Virtualized systems tend to be very complex, and multi-tenancy is the norm, as the hypervisor manages the resources shared among virtual machines. This paper proposes a methodology that uses performance modeling for the detection of anomalies in virtualized environments that can be caused, for instance, by cyberattacks. Experiments are conducted to profile the system operation under normal conditions for its business transactions. The results are used to calibrate a performance model and to understand the impact of its parameters on the false positive probability. During operation, the system is monitored, and deviations are detected by applying a sequential analysis algorithm (the bucket algorithm). The methodology is evaluated using a representative cloud workload (TPCx-V), which was profiled during a set of controlled executions. We consider resource exhaustion anomalies to emulate the effects of attacks affecting the performance of the system. Our results show that the proposed approach is able to successfully detect anomalies, with a low number of false positives, and spot possible residual effects of anomalies on the system.

All over the world, there is a lot of patient health data in different locations such as hospitals, clinics, insurance companies, and other organizations. In this sense, global identification of the patient has emerged as an everyday healthcare challenge. Governments and institutions have to prioritize satisfactory, quick, and integrated decision-making in a wide, dispersed, and global environment because of unexpected challenges like pandemics or threats. In the current scientific literature, some of the existing challenges include support for a standard global unique identification that considers privacy issues, the combination of multiple technological biometry implementations, and personal documents. Thus, we propose a decentralized software model based on blockchain and smart contracts that includes privacy, global unique person identification supporting multiple combinations of documents, and biometric data using the Global Standards 1 - GS1 healthcare industry standard. Furthermore, we defined a methodology to evaluate a hypothetical use case of this model where an integrated and standard global health data sharing personal identification is crucial. For this, we implemented the proposed model in a global-wide continent location through cloud machines, fog computing, and blockchain considering the unique patient data identification and evaluate a use case scenario based on the top 5 most globally visited tourist destinations (France, Spain, the United States of America, China, and Italy), with an approach based on this model. The results show that using a model for a global id for healthcare can help reduce costs, time, and efforts, especially in the context of health threats, where agility and financial support must be prioritized.

5G has been heralded as a critical, decisive technology for verticals’ digital transformation. It targets innovative technical capabilities, allowing dedicated and improved communication mechanisms, going beyond the mere performance increase experienced by past generations’ evolution. Despite the literature already envisaging the application of 5G-enabled solutions in vertical-based deployments, there is still a lack of experimental insights shedding light on the actual feasibility of 5G capabilities in specific vertical deployments. This gap is particularly evident in verticals that did not traditionally rely on mobile network solutions for their communication needs, such as safety-related control signals in railway operations. This paper aims to fill this gap by delivering an extensive empirical analysis performed through a 5G-enabled railway deployment. We designed and implemented a joint testbed, which allows us to faithfully reproduce two important transportation railway use cases that leverage 5G communications involving the vertical, communication service solutions operator, and integration actors. There, we expose the necessary integration engineering behind the enablement of 5G capabilities in such scenarios, considering the highly safety-certified and standardized environment of railway vertical sector regulations (which never considered the utilization of mobile networks in their development). We empirically demonstrate the performance capability achieved by the 5G-enabled network using two different systems, a carrier-grade one and an open-development one, in light of the required key performance indicators. To the best of our knowledge, this is the very first field trial and empirical evaluation of the suitability of 5G with the implementation of a 5G-enabled railway cross-level scenario within the transportation context.

Microservice-based applications are increasingly being adopted along with cloud service models, and nowadays serve millions of customers daily. They are supported by container-based architectures which are managed by orchestration platforms, such as Kubernetes, that monitor, manage, and automate most of the tasks. Although these tools provide failover capabilities, it is not yet studied how effective they are in dealing with diverse types of faults. Fault injection is an effective methodology for validating components that are supposed to detect the malfunctions and report/correct them. This paper studies the effectiveness of Kubernetes in dealing with faults and aging in microservices, and on the possibility of using faults to accelerate aging effects for testing purposes. For this, we conducted an analysis of the implementation and tuning of Kubernetes probes, followed by experiments with varying load and fault injection into two distinct and representative microservice testbeds to analyze the capacity of probes in detecting issues in applications. The goal is to improve the knowledge of researchers and developers on whether Kubernetes can detect different faults and aging issues. Also, even though some services tend to accumulate aging effects, with increasing resource consumption, Kubernetes does not detect them nor acts on them, indicating that probes may be insufficient for aging scenarios. Results also showed that fault injection is useful to accelerate aging effects for the testing and evaluation purposes.

People spend more and more time sitting and this habit has been shown to cause spine pathologies. Thus, the scientific community and the industry have become interested in instrumented chairs development to detect incorrect user postures. In this work we present the development and implementation of invisibles and non-intrusive plastic optical fiber sensor cells to monitor the posture and evaluate the ergonomic behavior of a seated person. The low-cost plastic optical fiber (POF) based sensing devices developed in this work, were implemented in an office chair, to evaluate the workers posture throughout their work day. In addition to the sensors, Android and PC software applications were developed, to provide real time feedback and alerts to the user whenever an inadequate posture is detected, or the seating position is the same for a long time. The proposed approach was evaluated in a study involving six users, and results show that it can detect the user’s position with 96.6% accuracy.