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Semi-arid regions are known for erratic precipitation patterns with significant effects on the hydrological cycle and water resources availability. High temporal and spatial variation in precipitation causes large variability in runoff over short durations. Due to low soil water storage capacity, base flow is often missing and rivers fall dry for long periods. Because of its climatic characteristics, the semi-arid north-eastern region of Brazil is prone to droughts. To counter these, reservoirs were built to ensure water supply during dry months. This paper describes problems and solutions when calibrating and validating the eco-hydrological model SWIM for semi-arid regions on the example of the Pajeú watershed in north-eastern Brazil. The model was calibrated to river discharge data before the year 1983, with no or little effects of water management, applying a simple and an enhanced approach. Uncertainties result mainly from the meteorological data and observed river discharges. After model calibration water management was included in the simulations. Observed and simulated reservoir volumes and river discharges are compared. The calibrated and validated models were used to simulate the impacts of climate change on hydrological processes and water resources management using data of two representative concentration pathways (RCP) and five earth system models (ESM). The differences in changes in natural and managed mean discharges are negligible ( 5%) under RCP2.6 for the ESM ensemble mean. In semi-arid catchments, the enhanced approach should be preferred, because in addition to discharge, a second variable, here evapotranspiration, is considered for model validation., BMBF Germany, BMGF Germany

The development of 3-D applications with the use of augmented reality is proving increasingly efficient for the development of education. In this way, the objective is to develop an application for the study of molecular geometry that allows the union of the modeling process with augmented reality.

Atualmente, os profissionais, principalmente os gestores, deparam-se com problemas de naturezas diversas, num cenário de maior frequência e complexidade. Solucioná-los da maneira mais eficiente significa ganhos expressivos, fundamentais para o sucesso da empresa. Dentro deste escopo, a manutenção tem uma importante função na organização, haja vista que as falhas de operação podem gerar perdas irrecuperáveis para o negócio. No caso de uma empresa de transporte coletivo, a falha de um veículo reflete diretamente na qualidade do serviço. Este trabalho possui o objetivo de propor possíveis soluções para a falha de veículos de transporte rodoviário de passageiros, por meio do método de análise e solução de problemas. No estudo de caso, é feita a apresentação da empresa estudada, assim como seu processo de manutenção. As seguintes etapas do método de análise e solução de problemas são aplicadas: identificação do problema, observação, análise e plano de ação. Em última análise, o presente estudo viabilizou o mapeamento do problema de falhas de veículos durante a operação, possibilitando a visualização de todas as etapas da manutenção de um veículo. Palavras-chave: Análise e Solução de Problemas. Manutenção. Qualidade. Transporte Rodoviário. ABSTRACT Currently, professionals, mainly managers, faced with problems of various kinds, in a scenario where they have increased in frequency and complexity. Address them in the most efficient way means significant, major gains for the company's success. Within this scope, maintenance plays an important role in the organization, given that operating failures can cause irretrievable losses to the business. In the case of a public transport company, the failure of a vehicle directly reflects the quality of service. This work has the objective of proposing possible solutions to the failure of road transport passenger vehicles through the method of analysis and troubleshooting. In the case study is the presentation of the studied company made, as well as its maintenance process. The following steps of the method of analysis and troubleshooting are applied: problem identification, observation, analysis and action plan. Ultimately, the present study enabled the mapping of the problem of vehicle failures during the operation, allowing the visualization of all the stages of the maintenance of a vehicle. Keywords: Analysis and Troubleshooting. Maintenance. Quality. Road Transport.

O objetivo do presente trabalho é propor soluções dentro da segurança de processos baseadas em riscos para prevenir, minimizar ou mitigar os riscos existentes na área offshore em incidentes de descarga de óleo, substâncias nocivas ou perigosas, dentro da Bacia de Campos. O presente estudo empreende uma análise qualitativa de riscos por meio de dados de incidentes operacionais, considerando como recorte temporal o período compreendido entre 2012 e 2014. O estudo em tela contempla as searas de perigo, risco, vazamento e descarga. No plano metodológico, emprega o método de análise preliminar de risco, bem como a análise preliminar de perigo. Adicionalmente, é apresentado um panorama acerca da segurança de processo baseada em risco, bem como estratégias a serem observadas no gerenciamento de segurança.Palavras-chave: Gestão de riscos; Segurança de processos; Descargas de óleo.

Made available in DSpace on 2018-11-12T17:25:49Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-09-01. Added 1 bitstream(s) on 2018-11-12T17:34:02Z : No. of bitstreams: 1 S0100-204X2017000900806.pdf: 365487 bytes, checksum: e863331bcdd56412c21c41d0b09724f4 (MD5) Resumo: O objetivo deste trabalho foi avaliar o reuso do xarope de sacarose na desidratação osmótica de abacaxi (Ananas comosus) e o uso da solução final na formulação de drageados de frutas. Os ensaios de desidratação osmótica foram conduzidos em cinco ciclos (65 °Brix/45°C/3 horas), com reuso direto da solução osmótica, com apenas uma etapa intermediária de recondicionamento. Foram observadas variações nas propriedades da solução osmótica e nos parâmetros da desidratação, bem como baixa carga microbiana do sistema. A solução final apresentou-se rica em vitamina C (30 mg 100 g-1). Foram obtidos drageados de abacaxi cobertos com frutas vermelhas e açaí em pó com a solução final recondicionada como solução de aderência. O drageado apresentou níveis elevados de vitamina C (176 mg 100 g-1), polifenóis (154 mg GAE 100 g-1), carotenoides (220 μg 100 g-1) e potássio (330 mg100 g-1). O produto teve boa aceitação e intenção de compra. O reuso do xarope de sacarose é tecnicamente viável na desidratação osmótica de abacaxi, bem como a aplicação da solução final como ingrediente na produção de drageados de frutas. Abstract: The objective of this work was to evaluate the reuse of sucrose syrup in pineapple (Ananas comosus) osmotic dehydration and the application of the spent solution in fruit dragée formulation. Osmotic dehydration trials were performed in five cycles (65° Brix/45°C/3 hours), directly reusing the osmotic solution, with only one intermediate reconditioning step. Variations in osmotic solution properties and in dehydration parameters were observed, as well as a low microbial load in the system. The spent solution was rich in vitamin C (30 mg 100 g-1). Pineapple dragée covered with red fruits and acai powders were obtained with the reconditioned spent solution used as an adhesion solution. The dragée presented high levels of vitamin C (176 mg 100 g-1), polyphenols (154 mg GAE 100-1 g), carotenoids (220 μg 100 g-1), and potassium (330 mg 100 g-1). The product showed good sensory acceptance and purchase intention. Reusing sucrose syrup is technically feasible during pineapple osmotic dehydration, as is the application of the spent solution as an ingredient in fruit dragée production. Instituto de Tecnologia de Alimentos Centro de Tecnologia de Frutas e Hortaliças Universidade Estadual de São Paulo Escola Superior de Agricultura Luiz de Queiroz Departamento de Agroindústria Ital Centro de Tecnologia de Cereais e Chocolates Ital Centro de Ciência e Qualidade de Alimentos

The pollution originated from the petrochemical industry is currently one of the main environmental concerns. Most of this is due to the high volume of toxic hydrocarbons that are produced and transported around the globe. However, this industry is also associated with toxic metals that are minor components of petroleum and used during refining processes. Here we have evaluated the impact of nickel in the biodegradation of crude oil in natural soils. We have assessed bacterial community profiles in these samples using ion torrent 16S rRNA gene sequencing and Real-Time PCR quantification and shotgun metagenome sequencing. We have found that the contamination with oil and nickel reduced the bacterial abundance compared with only soil. There was also an increase in the abundance of Actinobacteria. This group substituted the Proteobacteria as the dominant hydrocarbonoclastic bacteria as shown by metagenome sequencing. The presence of both contaminants also increased the removal of hydrocarbons. Thus, this indicates that the shift between Proteobacteria and Actinobacteria was beneficial to the removal of the organic pollutant. Made available in DSpace on 2020-10-15T09:12:41Z (GMT). No. of bitstreams: 1 Melo-Effect-nickel-2020.pdf: 604375 bytes, checksum: c0e8b7484ede2bbf3f439f3872639a25 (MD5) Previous issue date: 2020

Irrigation is one of the most water-intensive agricultural activities in the world, which has been increasing over time. Choosing an optimal irrigation management plan depends on having available data in the monitoring field. A smart agriculture system gathers data from several sources; however, the data are not guaranteed to be free of discrepant values (i.e., outliers), which can damage the precision of irrigation management. Furthermore, data from different sources must fit into the same temporal window required for irrigation management and the data preprocessing must be dynamic and automatic to benefit users of the irrigation management plan. In this paper, we propose the Smart&Green framework to offer services for smart irrigation, such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management enriched by the prediction of soil moisture. Outlier removal techniques allow for more precise irrigation management. For fields without soil moisture sensors, the prediction model estimates the matric potential using weather, crop, and irrigation information. We apply the predicted matric potential approach to the Van Genutchen model to determine the moisture used in an irrigation management scheme. We can save, on average, between 56.4% and 90% of the irrigation water needed by applying the Zscore, MZscore and Chauvenet outlier removal techniques to the predicted data.

Made available in DSpace on 2018-05-22T00:54:37Z (GMT). No. of bitstreams: 1 ART18012.pdf: 780767 bytes, checksum: 941e53a2e238441a0a583edcda3e5b55 (MD5) Previous issue date: 2018-05-09

This paper presents the impact of a local oscillator phase noise on digital modulation schemes based on bit error rate and error vector magnitude. The adopted methodology enables base band metrics estimation which is dependent of a specific communication standard. A generic local oscillator phase noise model is presented to evaluate the impact of different phase noise values on error vector magnitude and bit error rate. For a phase noise of -65dBc @ 1kHz the error vector magnitude changes from 7% to 12.8%. Increasing the modulation order leads to bit error rate increment for the same phase noise value.

This study was conducted at the Psychology Research Centre ( PSI/01662 ), School of Psychology , the University of Minho , supported by the Foundation for Science and Technology (FCT) through the Portuguese State Budget ( UID/PSI/01662/2020 ). This research was also supported by FCT projects PTDC/MHC/PCN/1530/2014. We thank the anonymous reviewers for their insightful comments and recommendations, which has assisted us in improving the quality and presentation of this article. Publisher Copyright: © 2023 The Authors The caregiver's touch behavior during early infancy is linked to multiple developmental outcomes. However, social touch remains a challenging construct to operationalize, and although observational tools have been a gold standard for measuring touch in caregiver-infant interactions, no systematic review has been conducted before. We followed the PRISMA guidelines and reviewed the literature to describe and classify the main characteristics of the available observational instruments. Of the 3042 publications found, we selected 45 that included an observational measure, and from those we identified 12 instruments. Most of the studies were of infants younger than six months of age and assessed touch in two laboratory tasks: face-to-face interaction and still-face procedure. We identified three approaches for evaluating the caregiver's touch behavior: strictly behavioral (the observable touch behavior), functional (the functional role of the touch behavior), or mixed (a combination of the previous two). Half of the instruments were classified as functional, 25% as strictly observational, and 25% as mixed. The lack of conceptual and operational uniformity and consistency between instruments is discussed. publishersversion published

Funding Information: We thank Instituto Politécnico de Lisboa for providing funding for this study. Publisher Copyright: © 2023 by the authors. Licensee ESJ, Italy. Objective: This study examines the asymmetric impact of both internal (military, education, and health expenditures) and external (trade opening and foreign direct investment) factors that contribute to poverty reduction. Methodology: To find an asymmetric relationship between the proposed variables, we used a non-linear ARDL co-integration approach for the period ranging from 1981-2019. Findings: The findings of the study confirm the asymmetric impact of internal (education, military, health expenditures, quality of governance) and external (foreign direct investment, openness) factors on poverty. The finding confirms that ignoring nonlinear or asymmetric properties of macroeconomic variables may mislead inferences. This study has policy implications for government officials to reduce poverty. Novelty: the economic theory of poverty is studied from different perspectives by using internal and external factors that have direct and indirect effects on poverty. Furthermore, for in-depth analysis, a nonlinear approach is used to determine which factor has a strong contribution to eliminating poverty. publishersversion published

Publisher Copyright: © 2023 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. Low cost video sensors used for streaming video signals to help firefighters, require high bit rate due to uncompressed images. To increase spectral efficiency given a limited bandwidth, very high order constellations in high signal to noise ratio regimes can be used. However, noise is not the only factor effecting the high order constellations. These constellations are also sensitive to hardware impairments and system non-linearities. Therefore, in this paper, the effect of carrier frequency offset (CFO) on the performance of an orthogonal frequency division multiplexing (OFDM) system with high order quadrature amplitude modulation (QAM) is studied. A closed form expression is derived for the maximum normalized residual CFO that an OFDM system with M-QAM constellation can resist to have an error free symbol detection. Finally, the suitability of common previous CFO estimation techniques such as the cyclic prefix based technique and the Moose technique in these systems are investigate. The results show that the maximum residual CFO that an OFDM system with M-QAM constellation can resist is proportional to the inverse of (Formula presented.). The results also show that very large order QAM constellations such as 4096-QAM are very sensitive to even small residual CFO values and their performance degrades, significantly. However, the bit error rate analysis indicate that the Moose CFO estimation technique can be used in these systems to compensate the CFO effect, accurately. publishersversion published

This research was supported by Portuguese national funds provided by FCT—Fundação para a Ciência e Tecnologia, through grant SFRH/BD/09347/2021, and UIDB/EEA/00066/2020, UIDP/EEA/00066/2020, LA/P/0104/2020 and by Instituto Politecnico de Lisboa through project IPL/2022/Metalogic_ISEL. Publisher Copyright: © 2023 by the authors. The recent progresses in the research of plasmonic phenomena and materials paved the route toward the development of optical sensing platforms based on metal nanostructures with a great potential to be integrated into point-of-care (POC) devices for the next generation of sensing platforms, thus enabling real-time, highly sensitive and accurate diagnostics. In this review, firstly, the optical properties of plasmonic metal nanoparticles will be illustrated, whereafter the engineering of POC platforms, such as microfluidics and readout systems, will be considered with another critical point which is surface functionalization. Attention will also be given to their potential in multiplexed analysis. Finally, the limitations for effective implementation in real diagnostics will be illustrated with a special emphasis on the latest trend in developing cutting-edge sensing systems. publishersversion published

This Open Access book aims to present practical contributions to the ethics governance framework, the conceptualization and characteristics of ethics tools, as well as the experience gained from their application in different institutions. Its main objective is to provide a practical and useful guide that will help other institutions to start introducing Research Ethics effectively in their organizations. The European initiative ETHNA System has designed an ethics governance framework that can be implemented following specific guidelines and tools that can help different types of institutions to promote and generate responsible research and innovation. The papers included in this book were organized in topical sections as follows: foundations; experiences and lessons learned; ethics tools in practice; and looking into the future: main challenges. publishersversion published

Funding Information: This work was funded by Instituto Politécnico de Lisboa, reference code: IPL/2021/ATS2SPV_ISEL. Publisher Copyright: © 2023 by the authors. Solar photovoltaic simulators are valuable tools for the design and evaluation of several components of photovoltaic systems. They can also be used for several purposes, such as educational objectives regarding operation principles, control strategies, efficiency, maintenance, and other aspects. This paper presents an automated solar photovoltaic simulation system with the capability to generate automated tests considering different parameters of solar photovoltaic panels and different operation conditions. The proposed simulator is composed of three buck-boost DC–DC power converters controlled in such a way that will behave similarly to solar photovoltaic panels. It allows to introduce additional variable loads and maximum power point tracker algorithms similar to real systems. Some converters are controlled by a DSP microcontroller connected to a single programmable logic controller which generates the automated tests. Thus, using the presented solution, it is possible to implement the I-V and P-V characteristic curves of solar photovoltaic panels and evaluate different maximum power point tracker algorithms considering different meteorological conditions and load variations, being a useful tool to teach subjects related to renewable energy sources and related applications. Several simulation results using Matlab/Simulink and experimental results are presented to validate the operation of the proposed solution. Experimental results achieve a ripple between 2% and 5% of the desired average current in MPP conditions. publishersversion published

Funding Information: This study was supported by the Center of Technology and Systems (CTS-UNINOVA). Funding Information: European Commission ERASMUS + through grant number ° 2021-1-RO01-KA220-HED-000032176 LLSF. Publisher Copyright: © 2023 by the authors. Globally, educational institutes are trying to adapt modernized and effective approaches and tools to their education systems to improve the quality of their performance and achievements. However, identifying, designing, and/or developing promising mechanisms and tools that can impact class activities and the development of students’ outputs are critical success factors. Given that, the contribution of this work is to propose a methodology that can guide and usher educational institutes step by step through the implementation of a personalized package of training Toolkits in Smart Labs. In this study, the package of Toolkits refers to a set of needed tools, resources, and materials that, with integration into a Smart Lab can, on the one hand, empower teachers and instructors in designing and developing personalized training disciplines and module courses and, on the other hand, may support students (in different ways) in developing their skills. To demonstrate the applicability and usefulness of the proposed methodology, a model was first developed, representing the potential Toolkits for training and skill development. The model was then tested by instantiating a particular box that integrates some hardware to be able to connect sensors to actuators, with an eye toward implementing this system mainly in the health domain. In a real scenario, the box was used in an engineering program and its associated Smart Lab to develop students’ skills and capabilities in the areas of the Internet of Things (IoT) and Artificial Intelligence (AI). The main outcome of this work is a methodology supported by a model able to represent Smart Lab assets in order to facilitate training programs through training Toolkits. publishersversion published

© 2023 by the authors. Licensee MDPI, Basel, Switzerland The notions of a collaborative virtual power plant ecosystem (CVPP-E) and a cognitive household digital twin (CHDT) have been proposed as contributions to the efficient organization and management of households within renewable energy communities (RECs). CHDTs can be modeled as software agents that are designed to possess some cognitive capabilities, enabling them to make autonomous decisions on behalf of their human owners based on the value system of their physical twin. Due to their cognitive and decision-making capabilities, these agents can exhibit some behavioral attributes, such as engaging in diverse collaborative actions aimed at achieving some common goals. These behavioral attributes can be directed to the promotion of sustainable energy consumption in the ecosystem. Along this line, this work demonstrates various collaborative practices that include: (1) collaborative roles played by the CVPP manager such as (a) opportunity seeking and goal formulation, (b) goal proposition/invitation to form a coalition or virtual organization, and (c) formation and dissolution of coalitions; and (2) collaborative roles played by CHDTs which include (a) acceptance or decline of an invitation based on (i) delegation/non-delegation and (ii) value system compatibility/non-compatibility, and (b) the sharing of common resources. This study adopts a simulation technique that involves the integration of multiple simulation methods such as system dynamics, agent-based, and discrete event simulation techniques in a single simulation environment. The outcome of this study confirms the potential of adding cognitive capabilities to CHDTs and further shows that these agents could exhibit certain collaborative attributes, enabling them to become suitable as rational decision-making agents in households. publishersversion published

Copyright © 2023 Camarinha-Matos publishersversion published

Publisher Copyright: © 2013 IEEE. This work was supported in part by the European Union (EU) Project Advanced Manufacturing Solutions Tightly Aligned With Business Needs (AVANGARD), and in part by the European Union's Horizon 2020 Research and Innovation Program under Grant 869986. The scheduling applied to manufacturing represents a huge opportunity for companies to stand out in a world of fast and big changes. Having a reliable scheduling system will allow factories to deal with the significant demand for highly customized products. Although manufacturing scheduling has been deeply studied for decades, there is still a gap between academia and industry, namely because the lack of flexibility and homogeneity among scheduling solutions, which makes them very use case-oriented. Furthermore, the absence of standardization is also making it difficult to implement smart scheduling solutions in industrial scenarios. Thus, this work presents a set of requirements and design principles based on axiomatic design concept, to make the first steps to standardize the designing and development of manufacturing scheduling solutions in the context of Industry 4.0. At the end, is presented a scheduling generic framework targeting smart manufacturing and evaluated in a practical use case. publishersversion published

Funding Information: This work was partially supported by the SIMShore: SIMOcean Nearshore Bathymetry Based on Low Cost Approaches. This project received funding from the EEA Grants Portugal research and innovation program under grant agreement No PT-INNOVATION-0027. Publisher Copyright: © 2023 by the authors. The energy consumption of production processes is increasingly becoming a concern for the industry, driven by the high cost of electricity, the growing concern for the environment and the greenhouse emissions. It is necessary to develop and improve energy efficiency systems, to reduce the ecological footprint and production costs. Thus, in this work, a system is developed capable of extracting and evaluating useful data regarding production metrics and outputs. With the extracted data, machine learning-based models were created to predict the expected energy consumption of an automotive spot welding, proving a clear insight into how the input values can contribute to the energy consumption of each product or machine, but also correlate the real values to the ideal ones and use this information to determine if some process is not working as intended. The method is demonstrated in real-world scenarios with robotic cells that meet Volkswagen and Ford standards. The results are promising, as models can accurately predict the expected consumption from the cells and allow managers to infer problems or optimize schedule decisions based on the energy consumption. Additionally, by the nature of the conceived architecture, there is room to expand and build additional systems upon the currently existing software. publishersversion published

Funding Information: The first author was partially funded by national funds through the Foundation for Science and Technology of Portugal, under the projects UIDB/00066/2020. The second author was supported by the Autonomous University of Mexico City (UACM) under the project Ccyt-2021-11. Publisher Copyright: © 2023 by the authors. Fractionalisation and solution of the Ambartsumian equation is considered. The general approach to fractional calculus suitable for applications in physics and engineering is described. It is shown that Liouville-type derivatives are the necessary ones, because they fully preserve backward compatibility with classical results. Such derivatives are used to define and solve the fractional Ambartsumian equation. First, a solution in terms of a slowly convergent fractional Taylor series is obtained. Then, a simple solution expressed in terms of an infinite linear combination of Mittag–Leffler functions is deduced. A fast algorithm, based on a bilinear transformation and using the fast Fourier transform, is described and demonstrated for its approximate numerical realisation. publishersversion published

Funding Information: This work was financially supported by FCT (National Foundation of Science and Technology) within the Research Unit CTS—Centre of Technology and Systems, UIDB/00066/2020 and through Project foRESTER (PCIF/SSI/0102/2017, http://www.forester.pt, accessed on 19 October 2022) and project PEST (UIDB/00066/2020). Publisher Copyright: © 2023 by the authors. To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications. publishersversion published

Funding Information: We acknowledge the support from the European Commission through the ETHNA System project (Grant Nº 872360). Partial financial support was also provided by Portuguese FCT program UIDB/00066/2020 (CTS – Center of Technology and Systems). Publisher Copyright: © 2023, The Author(s). New organizational forms are emerging today at all levels of society, and more and more research is conducted in dynamic collaborative networks or ecosystems. Unlike traditional research centers, these new types of organization are very dynamic, with fluid boundaries, and volatile in terms of membership. This characteristic requires that more attention be paid to research ethics and RRI. This work reports on an implementation process carried out in a research and innovation ecosystem according to the principles and guidelines proposed by the ETHNA project. The process, its barriers and drivers are described, and finally, learned lessons and recommendations are presented. publishersversion published

Publisher Copyright: © 2023 Taylor & Francis Group, LLC. DC microgrids are now starting to attract huge attention from researches and engineers. Among the several DC microgrid architectures, bipolar DC microgrids are advantageous to accommodate a wider range of DC loads. However, due to mismatched loads connected to the two poles, bipolar DC microgrids may show voltage balancing issues. In this context, this article presents a new high input-to-output voltage gain, non-isolated DC–DC converter specifically designed for bipolar DC Microgrids. The proposed converter supports the balance of voltages at both bipolar DC microgrid poles, as the converter can transfer energy between the two poles in an unbalanced way. The description, theoretical support, and operation of the proposed converter will be presented. The theoretical assumptions will be complemented with several results obtained from computer simulation tests. The simulation tests will be compared to laboratory results from an experimental prototype. The obtained results confirm the theoretical properties of the proposed converter. publishersversion published

Funding Information: Financial support was provided by the Swiss National Science Foundation (Grant No. 200021_184940). Funding Information: Networking support provided by the European Cooperation in Science and Technology, COST Action CA19108 (Hi-SCALE) is acknowledged. Funding Information: Supported by Fundação para a Ciência e a Tecnologia, Portugal, with reference UIDB/00066/2020. Funding Information: A part of this work was supported by the Russian National Technology Initiative Foundation (Grant ID 0000000007418QR20002). Funding Information: The research was also supported by the European Synchrotron Radiation Facility (Grant No. MA-2767). Funding Information: This work was supported in part by the New Zealand Ministry of Business, Innovation and Employment (MBIE) by the Strategic Science Investment Fund ‘‘Advanced Energy Technology Platforms’’ under Contract RTVU2004. Funding Information: Y Wang acknowledges support from the National Science Foundation (NSF) Award DMR-2132338. R P Camata and C-C Chen are supported by the FTPP Program funded by NSF EPSCoR RII Track-1 Cooperative Agreement OIA-2148653. C-C Chen also acknowledges support from the NSF Award DMR-2142801. Publisher Copyright: © 2023 The Author(s). Published by IOP Publishing Ltd. This paper presents a roadmap to the application of AI techniques and big data (BD) for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and BD techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10-20 yr time-frame. publishersversion published

Publisher Copyright: © 2013 IEEE. This research is supported by the Digital Manufacturing and Design Training Network (DiManD) project funded by the European Union through the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) under grant agreement no. 814078 The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition. publishersversion published

Publisher Copyright: © 2013 IEEE. Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components -Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented. publishersversion published

Funding Information: The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement n°. ZDMP 825631. Publisher Copyright: © 2022 by the authors. With the spread of the Industry 4.0 concept, implementing Artificial Intelligence approaches on the shop floor that allow companies to increase their competitiveness in the market is starting to be prioritized. Due to the complexity of the processes used in the industry, the inclusion of a real-time Quality Prediction methodology avoids a considerable number of costs to companies. This paper exposes the whole process of introducing Artificial Intelligence in plastic injection molding processes in a company in Portugal. All the implementations and methodologies used are presented, from data collection to real-time classification, such as Data Augmentation and Human-in-the-Loop labeling, among others. This approach also allows predicting and alerting with regard to process quality loss. This leads to a reduction in the production of non-compliant parts, which increases productivity and reduces costs and environmental footprint. In order to understand the applicability of this system, it was tested in different injection molding processes (traditional and stretch and blow) and with different materials and products. The results of this document show that, with the approach developed and presented, it was possible to achieve an increase in Overall Equipment Effectiveness (OEE) of up to 12%, a reduction in the process downtime of up to 9% and a significant reduction in the number of non-conforming parts produced. This improvement in key performance indicators proves the potential of this solution. publishersversion published

SMART-E-PTDC/CTM-PAM/04012/2022, IDS-PAPER-PTDC/CTM-PAM/4241/2020 and PEST (CTS/UNINOVA)-UIDB/00066/2020. This work also received funding from the European Community’s H2020 program [Grant Agreement No. 716510 (ERC-2016-StG TREND) and 952169 (SYNERGY, H2020-WIDESPREAD-2020-5, CSA)]. Publisher Copyright: © 2022 by the authors. In this paper, the most suited analog-to-digital (A/D) converters (ADCs) for Internet of Things (IoT) applications are compared in terms of complexity, dynamic performance, and energy efficiency. Among them, an innovative hybrid topology, a digital–delta (Δ) modulator (ΔM) ADC employing noise shaping (NS), is proposed. To implement the active building blocks, several standard-cell-based synthesizable comparators and amplifiers are examined and compared in terms of their key performance parameters. The simulation results of a fully synthesizable Digital-ΔM with NS using passive and standard-cell-based circuitry show a peak of 72.5 dB in the signal-to-noise and distortion ratio (SNDR) for a 113 kHz input signal and 1 MHz bandwidth (BW). The estimated (Formula presented.) is close to 16.2 fJ/conv.-step. publishersversion published

Funding Information: This work was funded by the Fundação para a Ciência e Tecnologia (FCT, Portugal) and NMT, S.A in the scope of the PhD grant PD/BDE/150312/2019 and by FCT within the scope of the CTS Research Unit—Center of Technology and Systems—UNINOVA, under the project UIDB/00066/2020 (FCT). Publisher Copyright: © 2022 by the authors. Heart Rate Variability (HRV) is a biomarker that can be obtained non-invasively from the electrocardiogram (ECG) or the photoplethysmogram (PPG) fiducial points. However, the accuracy of HRV can be compromised by the presence of artifacts. In the herein presented work, a Simulink® model with a deep learning component was studied for overly noisy PPG signals. A subset with these noisy signals was selected for this study, with the purpose of testing a real-time machine learning based HRV estimation system in substandard artifact-ridden signals. Home-based and wearable HRV systems are prone to dealing with higher contaminated signals, given the less controlled environment where the acquisitions take place, namely daily activity movements. This was the motivation behind this work. The results for overly noisy signals show that the real-time PPG-based HRV estimation system produced RMSE and Pearson correlation coefficient mean and standard deviation of 0.178 ± 0.138 s and 0.401 ± 0.255, respectively. This RMSE value is roughly one order of magnitude above the closest comparative results for which the real-time system was also used. publishersversion published

Publisher Copyright: © 2022, The Author(s). In recent years, the manufacturing sector is going through a major transformation, as reflected in the concept of Industry 4.0 and digital transformation. The urge for such transformation is intensified when we consider the growing societal demands for sustainability. The notion of sustainable manufacturing has emerged as a result of this trend. Additionally, industries and the whole society face the challenges of an increasing number of disruptive events, either natural or human-caused, that can severely affect the normal operation of systems. Furthermore, the growing interconnectivity between organizations, people, and physical systems, supported by recent developments in information and communication technologies, highlights the important role that collaborative networks can play in the digital transformation processes. As such, this article analyses potential synergies between the areas of sustainable and resilient manufacturing and collaborative networks. The work also discusses how the responsibility for the various facets of sustainability can be distributed among the multiple entities involved in manufacturing. The study is based on a literature survey, complemented with the experience gained from various research projects and related initiatives in the area, and is organized according to various dimensions of Industry 4.0. A brief review of proposed approaches and indicators for measuring sustainability from the networked manufacturing perspective is also included. Finally, a set of key research challenges are identified to complement strategic research agendas in manufacturing. publishersversion published

The research leading to these results has received funding from the European Unions Horizon 2020 research and innovation program, mainly project ZDMP under grant agreement 825631, and Eur3ka, TALON, and RE4DY, EU H2020 projects under grant agreements 101016175, 101070181, and 101058384 accordingly. Zero-Defect Manufacturing (ZDM) is the next evolutionary step in quality management for manufacturing that makes use of Industry 4.0 technologies to support quality in manufacturing. These technologies help reduce the cost of inspection, allowing for more inspection points throughout the manufacturing process, reducing the size of quality feedback loops, and guaranteeing that no defective product is delivered to the customer. There are several ZDM-related initiatives, but still no harmonized terminology. This article describes the methodological approach to provide a common agreement on the ZDM concept and its associated terminology taking place within an open CENCENELEC Workshop. The methodology has the support of ISO standards for terminology work such as ISO 704, ISO 860, and ISO 10241–1/2. This work shows that the terminology for ZDM has a significant overlap with the terminology of quality management, metrology, dependability, statistics, non-destructive inspection, and condition monitoring. The proposed new terms and definitions can be used to further extend ISO’s and IEC’s already available terminologies and support present and future researchers in the field to conduct their research using a common vocabulary. publishersversion published

Funding Information: This work was supported by Project CESME (Collaborative & Evolvable Smart Manufacturing Ecosystem) and the Portuguese FCT program UIDB/00066/2020. Publisher Copyright: © 2022 The Author(s) The rapid proliferation of renewable energy communities/ecosystems is an indication of their potential contribution to the ongoing energy transition. A common characteristic of these ecosystems is their complex composition, which often involves the interaction of multiple actors. Currently, the notions of "networking", "collaboration", "coordination", and "cooperation", although having different meanings, are often loosely used to describe these interactions, which creates a sense of ambiguity and confusion. To better characterize the nature of interactions in current and emerging ecosystems, this article uses the systematic literature review method to analyse 34 emerging cases. The objective is threefold (a) to study the interactions and engagements between the involved actors, aiming at identifying elements of collaboration. (b) Identify the adopted technological enablers, and (c) ascertain how the composition and functions of these ecosystems compare to virtual power plants. The outcome revealed that the interactions between the members of these ecosystems can be described as cooperation and not necessarily as collaboration, except in a few cases. Regarding technological enablers, a vast panoply of technologies, such as IoT devices, smart meters, intelligent software agents, peer-to-peer networks, distributed ledger systems/blockchain technology (including smart contracts, blockchain as a platform service, and cryptocurrencies) were found. In comparison with virtual power plants, these ecosystems have similar composition, thus, having multiple actors, comprised of decentralized and heterogeneous technologies, and are formed by aggregating various distributed energy resources. They are also supported by ICT and are characterized by the simultaneous flow of information and energy. publishersversion published

info:eu-repo/grantAgreement/FCT/Investigador FCT/IF%2F00325%2F2015%2FCP1275%2FCT0001/PT Publisher Copyright: © 2022 by the authors. This paper focuses on the gaps that occur inside plantations; these gaps, although not having anything growing in them, still happen to be watered. This action ends up wasting tons of liters of water every year, which translates into financial and environmental losses. To avoid these losses, we suggest early detection. To this end, we analyzed the different available neural networks available with multispectral images. This entailed training each regional and regression-based network five times with five different datasets. Networks based on two possible solutions were chosen: unmanned aerial vehicle (UAV) depletion or post-processing with external software. The results show that the best network for UAV depletion is the Tiny-YOLO (You Only Look Once) version 4-type network, and the best starting weights for Mask-RCNN were from the Tiny-YOLO network version. Although no mean average precision (mAP) of over 70% was achieved, the final trained networks managed to detect mostly gaps, including low-vegetation areas and very small gaps, which had a tendency to be overlooked during the labeling stage. publishersversion published

Worldwide, many wheelchair users find it difficult to use or acquire a wheelchair that is appropriate for them, either because they do not have the necessary financial support or because they do not have access to trained healthcare professionals (HCPs), but they are essential for the correct provision of assistive products and user training. Consequently, although wheelchairs are designed to promote the well-being of many users, in many cases, they end up being abandoned or do not provide any benefit, with the chance of causing harm and potentially putting people in danger. This article proposes the creation and use of a Digital Twin (DT) of a Power Wheelchair (PWC) to promote the health of wheelchair users, by facilitating and improving the delivery of remote services by HCPs, as well as to include monitoring services to support timely maintenance. Specifically, a DT is a virtual counterpart that is seamlessly linked to a physical asset, both relying on data and information exchange for mirroring each other. Currently, DT is emerging and being applied to different areas as a promising approach to gather insightful data, which are shared between the physical and virtual worlds and facilitate the means to design, monitor, analyze, optimize, predict, and control physical entities. This article gives an overview of the Digital Twin concept, namely its definition, types, and properties, and seeks to synthesize the technologies and tools frequently used to enable Digital Twins; we also explain how a DT can be used in the technical phases of the PWC provision process and propose a conceptual model highlighting the use of an MDD approach benefiting from a Petri net formalism, which is presented to systematize the development of a PWC D publishersversion published

and also by the Polytechnic Institute of Lisbon through the Projects for Research, Development, Innovation and Artistic Creation (IDI&CA), within the framework of the project ReEdIA—Risk Assessment and Management in Open Innovation, IPL/2021/ReEdIA/ISEL. Publisher Copyright: © 2022 by the authors. This paper proposes a qualitative model to overcome the limitations of conventional failure mode and effects analysis (FMEA), which does not consider risk mitigation capabilities when prioritizing risks. Failure to consider these capabilities can lead to unrealistic risk estimates, especially when the level of uncertainty is high. In the proposed model, the original applicability of conventional FMEA was retained along with the three conventional risk variables, namely, severity, occurrence, and detectability. In addition, a fourth variable was added to account for risk mitigation capabilities. A case study in the project selection of aircraft repairs was used to demonstrate the implementation of the model and its applicability. The results show that the inclusion of mitigation options leads to more realistic risk scenarios, suggesting that the original FMEA approach may lead to non-conservative risk estimates. publishersversion published

By-products of industrial processes nutritionally rich as castor meal have been used as an energy source in feed for goats in order to increase the lipid characteristics of milk and dairy products. The present study was conducted to evaluate the physicochemical, microbiological and sensory properties of goat cheese "Coalho Type" made from milk of goats submitted to diets with increasing levels (33, 67 and 100%) of detoxified castor meal, replacing soybean meal. The cheeses produced showed no changes in sensory attributes (P>0.05). However, the diet with different levels of detoxified castor meal promoted an increase in the levels of unsaturated fatty acids in milk and cheese, especially the C6:0; C10:0; C14:0; C16:0; C18:1 and C18:3 profile, whereas reduced contents of saturated fatty acids were observed (P

Publisher Copyright: © 2022 by the authors. The world is once again facing massive energy- and environmental challenges, caused by global warming. This time, the situation is complicated by the increase in energy demand after the pandemic years, and the dramatic lack of basic energy supply. The purely “green” energy is still not ready to substitute the fossil energy, but this year the fossil supplies are heavily questioned. Consequently, engineering must take flexible, adaptive, unexpected directions. For example, even the natural gas power plants are currently considered “green” by the European Union Taxonomy, joining the “green” hydrogen. Through a tight integration of highly intermittent renewable, or other distributed energy resources, the microgrid is the technology of choice to guarantee the expected impacts, making clean energy affordable. The focus of this work lies in the techno-economic optimization analysis of Combined Heat and Power (CHP) Multi-Micro Grids (MMG), a novel distribution system architecture comprising two interconnected hybrid microgrids. High computational resources are needed to investigate the CHP-MMG. To this aim, a novel nature-inspired two-layer optimization-simulation algorithm is discussed. The proposed algorithm is used to execute a techno-economic analysis and find the best settings while the energy balance is achieved at minimum operational costs and highest revenues. At a lower level, inside the algorithm, a Sequential Least Squares Programming (SLSQP) method ensures that the stochastic generation and consumption of energy deriving from CHP-MMG trial settings are balanced at each time-step. At the upper level, a novel multi-objective self-adaptive evolutionary algorithm is discussed. This upper level is searching for the best design, sizing, siting, and setting, which guarantees the highest internal rate of return (IRR) and the lowest Levelized Cost of Energy (LCOE). The Artificial Immune Evolutionary (AIE) algorithm imitates how the immune system fights harmful viruses that enter the body. The optimization method is used for sensitivity analysis of hydrogen costs in off-grid and on-grid highly perturbed contexts. It has been observed that the best CHP-MMG settings are those that promote a tight thermal and electrical energy balance between interconnected microgrids. The results demonstrate that such mechanism of energy swarm can keep the LCOE lower than 15 c€/kWh and IRR of over 55%. publishersversion published

Funding: This research was partially supported by funds provided by the European Commission in the scope of FoF/H2020-723710 vf-OS, ICT/H2020-825631 ZDMP projects, and by the FCT— Fundação para a Ciência e a Tecnologia in the scope of UIDB/00066/2020 related to CTS—Centro de Tecnologia e Sistemas research unit. In the last decades, the increasing complexity of industrial information technology has led to the emergence of new trends in manufacturing. Factories are using multiple Internet of Things (IoT) platforms to harvest sensor information to improve production. Such a transformation contributes to efficiency growth and reduced production costs. To deal with the heterogeneity of the services within an IoT system, Service-Oriented Architecture (SOA) is referred to in the literature as being advantageous for the design and development of software to support IoT-based production processes.The aim of SOA-based design is to provide the leverage to use and reuse loosely coupled IoT services at the middleware layer to minimise system integration problems. We propose a system architecture that follows the SOA architectural pattern and enables developers and business process designers to dynamically add, query or use instances of existing modular software in the IoT context. Furthermore, an analysis of utilization of modular software that presents some challenges and limitations of this approach is also in the scope of this work publishersversion published

Publisher Copyright: Copyright: © 2023 Marques et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Neuron specification and maturation are essential for proper central nervous system development. However, the precise mechanisms that govern neuronal maturation, essential to shape and maintain neuronal circuitry, remain poorly understood. Here, we analyse early-born secondary neurons in the Drosophila larval brain, revealing that the early maturation of secondary neurons goes through 3 consecutive phases: (1) Immediately after birth, neurons express pan-neuronal markers but do not transcribe terminal differentiation genes; (2) Transcription of terminal differentiation genes, such as neurotransmitter-related genes VGlut, ChAT, or Gad1, starts shortly after neuron birth, but these transcripts are, however, not translated; (3) Translation of neurotransmitter-related genes only begins several hours later in mid-pupa stages in a coordinated manner with animal developmental stage, albeit in an ecdysone-independent manner. These results support a model where temporal regulation of transcription and translation of neurotransmitter-related genes is an important mechanism to coordinate neuron maturation with brain development. publishersversion published

UIDB/50025/2020-2023 In this article, characterization of fully patterned zinc-tin oxide (ZTO)-based memristive devices with feature sizes as small as 25 µm2 is presented. The devices are patterned via lift-off with a platinum bottom contact and a gold-titanium top contact. An on/off ratio of more than two orders of magnitude is obtained without the need for electroforming processes. Set operation is a current controlled process, whereas the reset is voltage dependent. The temperature dependency of the electrical characteristics reveals a bulk-dominated conduction mechanism for high resistance states. However, the charge transport at low resistance state is consistent with Schottky emission. Synaptic properties such as potentiation and depression cycles, with progressive increases and decreases in the conductance value under 50 successive pulses, are shown. This validates the potential use of ZTO memristive devices for a sustainable and energy-efficient brain-inspired deep neural network computation. publishersversion published

Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. Funding: This research received no external funding. The development of an enterprise under current conditions requires an integrated approach and an appropriate financing system. The purpose of this study is to justify the replication model of financing the integrated enterprise development. The research methodology suggests that each enterprise has its own development “genome”, which makes it possible to apply the replication of its directions based on a justified financing system of such an integration. The proposed replication model is augmented by regression analysis, which made it possible to carry out scenario forecasting of alternative options for the company’s development. The conduction of the study is based on 16 textile enterprises. The formed map of the integrated enterprise development enabled the determination of their points to replicate resources in four directions (environmental and economic, innovative, informational, and organizational). The interaction of companies on the basis of strengths diffusion (exchange of potential) with the application of financial netting is considered an alternative to replication. The research proved that an alternative option can solely be recommended for developed companies and requires the identification and minimization of risks. The strong link between the level of integrated and overall development of companies was acknowledged. Asymmetry of business development in the context of a replication model of its integration enables the achievement of high results while minimizing financial resources. Enterprises implementing the replication model of integration considerably improve their prospects and increase overall development performance. The application of the replication model of financing integration under the crisis and the COVID-19 pandemic fosters efficient use of financial resources and the overall enterprise development. publishersversion published

This paper addresses two complementary problems of spacecraft formation flying, namely spacecraft platooning and on-orbit spacecraft servicing, using Model Predictive Control (MPC). With the proposed solutions, these space formation scenarios can be regarded as a cooperative system composed of several spacecraft with a common goal, which may have clear advantages relative to other approaches. For each application scenario, a different optimization problem and MPC design is presented, including relevant constraints to deal with physical limitations, visibility problems, and also to guarantee a collision-free trajectory from other spacecraft or obstacles. The proposed methods are validated with realistic simulation results, showing that all vehicles demonstrate reliable performance following a given trajectory or goal in a formation, while satisfying all the considered constraints., This work was partially funded by FCT project REPLACE (PTDC/EEIAUT/32107/2017) which includes Lisboa 2020 and PIDDAC funds, project CAPTURE (PTDC/EEIAUT/1732/2020), and also projects CTS (UIDB/00066/2020) and LARSYS (UIDB/50009/2020).

Quality control is an area of utmost importance for fabric production companies. By not detecting the defects present in the fabrics, companies are at risk of losing money and reputation with a damaged product. In a traditional system, an inspection accuracy of 60-75% is observed. In order to reduce these costs, a fast and automatic defect detection system, which can be complemented with the operator decision, is proposed in this paper. To perform the task of defect detection, a custom Convolutional Neural Network (CNN) was used in this work. To obtain a well-generalized system, in the training process, more than 50 defect types were used. Additionally, as an undetected defect (False Negative - FN) usually has a higher cost to the company than a non-defective fabric being classified as a defective one (false positive), FN reduction methods were used in the proposed system. In testing, when the system was in automatic mode, an average accuracy of 75% was attained; however, if the FN reduction method was applied, with intervention of the operator, an average of 95% accuracy can be achieved. These results demonstrate the ability of the system to detect many different types of defects with good accuracy whilst being faster and computationally simple. publishersversion published