Multi-Agents System Approach to Industry 4.0: Enabling Collaboration Considering a Blockchain

Dissertação de Mestrado em Engenharia InformáticaThe evolution of existing technologies and the creation of new ones paved the way for a new revolution in the industrial sector. With the introduction of the existing and new technologies in the manufacturing environment, the industry is moving towards the fourth industrial revolution, called Industry 4.0. The fourth industrial revolution introduces many new components like 3D printing, Internet of things, artificial intelligence, and augmented reality. The automation of the traditional manufacturing processes and the use of smart technology are transforming industries in a more interconnected environment, where there is more transparent information and decentralised decisions. The arrival of Industry 4.0 introduces industries to a new environment, where their manufacturing processes are more evolved, more agile, and with more efficiency. The principles of Industry 4.0 rely on the interconnection of machines, devices, sensors, and people to communicate and connect. The transparency of information guaranties that decision makers are provided with clear and correct information to make informed decisions and the decentralisation of decisions will create the ability for machines and systems to make decisions on their own and to perform tasks autonomously. Industry 4.0 is making manufacturing processes more agile and efficient, but due to the fast pace of trends and the shift from the traditional mass production philosophy towards the mass customisation, following the Industry 4.0 guidelines might not be enough. The mass customisation paradigm was created from the desire that customers have in owning custom made products and services, tailor made to their needs. The idea to perform small tweaks in a product to face the needs of a consumer group, keeping the production costs like the ones from the mass production, without losing efficiency in the production. This paradigm poses great challenges to the industries, since they must be able to always have the capability to answer the demands that may arise from the preparation and production of personalised products and services. In the meantime, organisations will try to increasingly mark its position in the market, with competition getting less relevant and with different organisations worrying less with their performance on an individual level and worrying more about their role in a supply chain. The need for an improved collaboration with Industry 4.0 is the motivation for the model proposed in this work. This model, that perceives a set of organisations as entities in a network that want to interact with each other, is divided into two parts, the knowledge representation and the reasoning and interactions. The first part relies on the Blockchain technology to securely store and manage all the organisation transactions and data, guaranteeing the decentralisation of information and the transparency of the transactions. Each organisation has a public and private profile were the data is stored to allow each organisation to evaluate the others and to allow each organisation to be evaluated by the remainder of the organisations present in the network. Furthermore, this part of the model works as a ledger of the transactions made between the organisations, since that every time two organisations negotiate or interact in any way, the interaction is getting recorded. The ledger is public, meaning that every organisation in the network can view the data stored. Nevertheless, an organisation will have the possibility, in some situations, to keep transactions private to the organisations involved. Despite the idea behind the model is to promote transparency and collaboration, in some selected occasions organisations might want to keep transactions private from the other participants to have some form of competitive advantage. The knowledge representation part also wants to provide security and trust to the organisation that their data will be safe and tamper proof. The second part, reasoning and interactions, uses a Multi-Agent System and has the objective to help improve decision-making. Imagining that one organisation needs a service that can be provided by two other organisations, also present in the network, this part of the model is going to work towards helping the organisations choose what is the best choice, given the scenario and data available. This part of the model is also responsible to represent every organisation present in the network and when organisations negotiate or interact, this component is also going to handle the transaction and communicate the data to the first part of the model.A constante evolução de tecnologias atuais e a criação de novas tecnologias criou as condições necessárias para a existência de uma nova revolução industrial. Com a evolução de dispositivos móveis e com a chegada de novas tecnologias e ferramentas que começaram a ser introduzidas em ambiente industrial, como a impressão 3D, internet das coisas, inteligência artificial, realidade aumentada, entre outros, a industria conseguiu começar a explorar novas tecnologias e automatizar os seus processos de fabrico tradicionais, movendo as industrias para a quarta revolução industrial, conhecida por Industria 4.0. A adoção dos princípios da Indústria 4.0 levam as indústrias a evoluir os seus processos e a ter uma maior e melhor capacidade de produção, uma vez que as mesmas se vão tornar mais ágeis e introduzir melhorias nos seus ambientes de produção. Uma dessas melhorias na questão da interoperabilidade, com máquinas, sensores, dispositivos e pessoas a comunicarem entre si. A transparência da informação vai levar a uma melhor interpretação dos dados para efetuar decisões informadas, com os sistemas a recolher cada vez mais dados e informação dos diferentes pontos do processo de manufatura. (...

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

In vivo assessment of accuracy of Propex II, Root ZX II, and radiographic measurements for location of the major foramen

Objectives The aim of this in vivo study was to assess the accuracy of 2 third-generation electronic apex locators (EALs), Propex II (Dentsply Maillefer) and Root ZX II (J. Morita), and radiographic technique for locating the major foramen (MF). Materials and Methods Thirty-two premolars with single canals that required extraction were included. Following anesthesia, access, and initial canal preparation with size 10 and 15 K-flex files and SX and S1 rotary ProTaper files, the canals were irrigated with 2.5% sodium hypochlorite. The length of the root canal was verified 3 times for each tooth using the 2 apex locators and once using the radiographic technique. Teeth were extracted and the actual WL was determined using size 15 K-files under a × 25 magnification. The Biostat 4.0 program (AnalystSoft Inc.) was used for comparing the direct measurements with those obtained using radiographic technique and the apex locators. Pearson's correlation analysis and analysis of variance (ANOVA) were used for statistical analyses. Results The measurements obtained using the visual method exhibited the strongest correlation with Root ZX II (r = 0.94), followed by Propex II (r = 0.90) and Ingle's technique (r = 0.81; p < 0.001). Descriptive statistics using ANOVA (Tukey's post hoc test) revealed significant differences between the radiographic measurements and both EALs measurements (p < 0.05). Conclusions Both EALs presented similar accuracy that was higher than that of the radiographic measurements obtained with Ingle's technique. Our results suggest that the use of these EALs for MF location is more accurate than the use of radiographic measurements

Redução da fração de ejeção em ecocardiografia seriada é preditor de eventos na doença arterial coronariana estável

1