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Cultivada em todo o território brasileiro, a batata-doce (Ipomoea batatas (L.) Lam.) é uma hortaliça que apresenta ampla adaptação e fácil cultivo, além de ser fonte de antioxidantes e nutrientes importantes para a saúde humana. Os produtos desenvolvidos a partir dessa raiz, como as farinhas e amidos, agregam valor à produção e são formas alternativas de comercialização. Este estudo teve por objetivo testar a utilização do amido da batata-doce de polpa roxa na fabricação de produtos alimentícios, bem como avaliar o aproveitamento da água residual na produção de corantes. Após a extração e secagem, o amido obtido da batata-doce foi macerado e enviado para análise e para a produção de sequilhos. O amido obtido apresentou 6,11% de umidade e teor de antocianinas de 0,45 mg 100 g-1. Na produção dos sequilhos, observou-se que eles apresentaram um fator térmico abaixo de 1 e redução de umidade durante o cozimento. As análises realizadas demonstraram que o uso do amido da batata doce na produção de sequilhos resultou em um produto com baixa umidade (3,26%) e bom rendimento de produção (87%). O corante produzido com a água residual do amido apresentou bons índices de solubilidade (10,93) e absorção em água (0,33), mas elevado teor de açúcares redutores (81,46%). Os resultados deste estudo revelam que o amido da batata-doce de polpa roxa é promissor na fabricação de produtos alimentícios, com boas propriedades tecnológicas e funcionais na produção de sequilhos. Em relação ao corante da água residual, estudos futuros são necessários para alcançar maior estabilidade de conservação durante seu armazenamento e utilização.

Given the growth of domotics and home automation, there is a need to use smart devices that integrate energy management systems and enable the automation of the environment. Considering the need to study the relationship between the environmental parameters in which the equipment is located and the energy parameters, an Environmental Awareness smart Plug (EnAPlug) is proposed with the application of machine learning (Tiny ML).This article presents a demonstration of EnAPlug applied to a refrigerator for predictions on internal humidity and activation motor for 5 min-ahead prediction on its operation, i.e., turning on or off. The two models for forecasting humidity presented Root Mean Squared Error (RMSE) results of 0.055 and 0.058 and a Coefficient of determination (r2 score) of 0.97 and 0.99, respectively. For the motor activation prediction, the results obtained were an accuracy of 94.74% and 94.84%, an F1 score of 0.97 for OFF, 0.94 for ON for Forecast 1 and 0.97 for OFF and 0.93 for ON for Forecast 2. Although the prototype does not have commercial purposes, what differs from existing smart plugs is the option to store data locally. The results are promising, as it allows for better energy management with implementation of machine learning.

O estímulo do consumo de pescado com base nas vantagens nutricionais e no efeito benéfico à saúde é limitado pela dificuldade em se encontrar no mercado um pescado de qualidade e de rápida manipulação/preparo. O objetivo deste trabalho foi aplicar a liofilização no beneficiamento da carne de raia. O estudo foi realizado em duas etapas, em função do tempo de congelamento e de liofilização, definindo-se, assim, a condição ótima do processo para obtenção de raia liofilizada. Os resultados obtidos mostraram que a variação do tempo de congelamento não teve grande importância nas variáveis respostas (umidade, atividade de água e rendimento). Já o planejamento com a variação do tempo de liofilização teve efeito significativo nas caraterísticas do produto. Um tempo de congelamento de 2 horas conferiu à amostra o congelamento necessário para o processo de liofilização, e, com 16 horas de secagem, mais de 95% da umidade da amostra foi retirada, o que indica que esse período é suficiente para que o produto atinja uma umidade e atividade de água necessárias à sua estabilidade, segurança microbiológica e vida comercial prolongada.

Controllers for discrete-event systems are commonly designed using state-based formalisms, like state diagrams and Petri nets. These formalisms are strongly supported by the concept of events, which, from an automation system perspective, can be associated with a simple change in the value of a signal or more complex behavioral evolutions of the signals. In this paper, the characterization of several types of events is proposed, associated with different types of signals, such as Boolean and multivalued signals. The major goal of this characterization is to improve the compactness of the model, benefiting the editing and visual interpretation of the graphical model but keeping precise execution semantics, which in turn allows for the use of computational tools covering the different stages of system development. The behavioral model of the controller is produced using a non-autonomous class of Petri nets, the IOPT nets, and the associated IOPT-Tools, which supports the specification, simulation, property verification, and automatic code generation ready to be deployed into implementation platforms. All the types of proposed events have a behavioral sub-model executed concurrently with the main model of the controller. An application example is provided to illustrate some of the advantages of the adoption of the proposed approach, encapsulating the behavioral dependencies on the evolution of input signals into events.

This paper presents a Graphical User Interface (GUI) Builder for embedded systems and Cyber-Physical Systems (CPSs). The hardware platforms used in CPSs may employ physical devices without graphical and user-input capabilities, or devices that may be placed on multiple remote locations, often with difficult physical access, that brings new challenges to GUI design. The proposed tool, integrated within the IOPT-Flow framework, enables a new approach to answer these challenges. This framework supports the development of embedded systems and distributed CPSs, offering a set of web-based tools for model edition, simulation, and implementation on physical devices. Systems are designed using graphical models combining Data-flows, Signals, and Petri nets, that permit the specification of remote communication channels just by drawing arcs. The new GUI Builder tool uses this infrastructure to automate the creation of graphical user interfaces for embedded systems and distributed CPSs, benefiting from a development with combined design, validation and automatic code generation methods. Finally, an application example of a power wheelchair controller integrating a GUI, is presented.

Abstract In the past decade, the global distribution of energy resources has expanded significantly. The increasing number of prosumers creates the prospect for a more decentralized and accessible energy market, where the peer-to-peer energy trading paradigm emerges. This paper proposes a methodology to optimize the participation in peer-to-peer markets based on the double-auction trading mechanism. This novel methodology is based on two reinforcement learning algorithms, used separately, to optimize the amount of energy to be transacted and the price to pay/charge for the purchase/sale of energy. The proposed methodology uses a competitive approach, and that is why all agents seek the best result for themselves, which in this case means reducing as much as possible the costs related to the purchase of energy, or if we are talking about sellers, maximizing profits. The proposed methodology was integrated into an agent-based ecosystem where there is a direct connection with agents, thus allowing application to real contexts in a more efficient way. To test the methodology, a case study was carried out in an energy community of 50 players, where each of the proposed models were used in 20 different players, and 10 were left without training. The players with training managed, over the course of a week, to save 44.65 EUR when compared to a week of peer-to-peer without training, a positive result, while the players who were left without training increased costs by 17.07 EUR.

A challenge that consistently arises when reviewing and justifying novel energy models and theorems is the accuracy of the electrical data used. Therefore, this paper presents a dataset representing a complete European residential community based on real-life data. In this case, a community of 250 residential households was constructed with actual energy consumption and photovoltaic generation profiles collected by smart meters in households in different European locations. Additionally, 200 members of the community were ascribed with their photovoltaic generation, while 150 were owners of a battery storage system. New profiles were generated from the sample collected and were randomly given to each end-user according to their previously defined characteristics. Furthermore, one regular and one premium vehicle were allocated to each household – a total of 500 electric vehicles – with information on their capacity, state of charge, and usage. Moreover, data on the location, type, and prices of public electric vehicle chargers were specified.

A Cyber-Physical-Social System (CPSS) is a novel paradigm of cyber-physical and cyber-social systems with a highly inhomogeneous and distributed nature integrating dynamic stochastic hybrid systems including computation, communication, sensing and actuation, and social systems.Their usage can be traced to homes such as smart homes, manufacturers in Industry 5.0, critical infrastructures, smart cities, medicines, healthcare systems, and many other examples. They can simplify and speed up tasks and provide a higher level of control and accessibility. In this study, we provide a systematic review of the definition of CPSS,and an uniform definition of CPSS. We propose a novel taxonomy to define CPSS to help future researches and system designing. The CPSS taxonomy aims to provide a comprehensive and understating of CPSS characteristics and aspects from system-to-system point of view. Furthermore, we discuss about social integration in CPSS and their relationships with CPS. We mention issues and opportunities in CPSS in the designing and implementing phases.

The energy management of buildings plays a vital role in the energy sector. With that in mind, and targeting an accurate forecast of electricity consumption, in the present paper is aimed to provide decision on the best prediction algorithm for each context. It may also increase energy usage related with renewables. In this way, the identification of different contexts is an advantage that may improve prediction accuracy. This paper proposes an innovative approach where a decision tree is used to identify different contexts in energy patterns. One week of five-minutes data sampling is used to test the proposed methodology. Each context is evaluated with a decision criterion based on reinforcement learning to find the best suitable forecasting algorithm. Two forecasting models are approached in this paper, based on K-Nearest Neighbor and Artificial Neural Networks, to illustrate the application of the proposed methodology. The reinforcement learning criterion consists of using the Multiarmed Bandit algorithm. The obtained results validate the adequacy of the proposed methodology in two case-studies: building; and industry.

Energy data measured on-site from buildings can help describe the consumption behavior of end-users and can be used to examine and prove certain theorems and models, that require a large volume of data to be gathered. However, the direct extraction of this data can often be a lengthily and costly process. As a result, a dataset of a residential community was constructed based on real data, where sample consumption and photovoltaic generation profiles were attributed to 50 residential households and a public building (municipal library), a total of 51 buildings. In addition, the overall power consumption of these houses was desegregated into the consumption of 10 commonly used appliances using real energy profiles. First, several consumption and photovoltaic generation profiles, as well as a vast collection of appliance profiles, were gathered. These profiles were obtained from household readings in different locations, while the public building's profile was based on the consumption and photovoltaic production profiles of the research building GECAD. The profiles went through the process of normalization and new profiles were generated to complete the number of end-users needed. Moreover, these profiles were given a maximum consumption and production level at random before being accepted by one of the end-users. Therefore, fourteen of these households and the public building were randomly attributed with renewable solar energy. Finally, if possible, the tool created allocated, at random in previously determined intervals, the appliances' load profiles into each end-user's available consumption areas.

Abstract With the rise in popularity of artificial intelligence, coupled with the growing concern over the environment, there has been a surge in the use of intelligent energy management systems. Additionally, as more buildings transition into the smart grid and, consequently, more energy and environmental data is gathered, there has been a significant increase in the number of data-driven approaches for building management systems. This paper proposes a methodology that aims to optimize the climatization and luminosity inside a building, using a genetic algorithm, a random forest, and two polynomial models. The proposed methodology enables the real-time management of the building taking into account the user needs and preferences. Air conditioner units and light systems are optimized to minimize energy costs, while also improving the air quality and considering the users’ temperature and luminosity preferences. This paper shows the results achieved, by the proposed solution, in an office building case study. The promising results demonstrate the possibility of minimizing energy costs while maximizing the users’ comfort.

Abstract The appearance of citizen energy communities demands the conception, development, and testing of new management models for the community and its end-users. Citizen energy communities promote the active participation of end-users, including them in the management of the community. End-users are incentivized to participate in demand response programs and share their energy among peers, enabling a decrease in their energy costs. In this paper, it is proposed a platform for the management of citizen energy communities. The paper focuses and presents four services related to energy tariffs, end-users’ aggregation, price elasticity, and load response. The services are based on historical data and enable deep analysis of end-users’ energy profiles. As the platform allows the upload of different scenarios, it is possible to test and validate management models in multiple energy communities and scenarios and study their impact in different conditions. The paper presents a case study, where all the services are applied to a community with 996 end-users.

The description of structure and complex interactions in Multi-agent-based Industrial Cyber-physical (MAS-ICPS) systems has been elusively addressed in the literature. Existing works, grounded on model-based engineering, have been successful at characterizing and solving system integration problems. However, they fail to describe accurately the collective and dynamic execution behaviour of large and complex industrial systems, particularly in more discrete production domains, such as: automotive, home appliances, aerospace, food and beverages, etc. In these domains, the execution flow diverts dynamically due to production disturbances, custom orders, fluctuations in demand in mixed model production, faults, quality-control and product rework, etc. These dynamic conditions require re-allocation and reconfiguration of production resources, redirection of production flows, re-scheduling of orders, etc. A meta-model for describing the structure and complex interactions in MAS-ICPS is defined in this paper. This contribution goes beyond the State-Of-The-Art (SOTA) as the proposed meta-model describes structure, as many other literature contributions, but also describes the execution behaviour of arbitrarily complex interactions. The previous is achieved with the introduction of general execution flow control operators in the meta-model. These operators cover, among other aspects, delegation of the execution flow and dynamic decision making. Additionally, the contribution also goes beyond the SOTA by including validation mechanisms for the models generated by the meta-model. Finally, the contribution adds to the current literature by providing a meta-model focusing on production execution and not just on describing the structural connectivity aspects of ICPSs.

The current symbiotic view of the organisms also calls for new approaches in the way we perceive and manage our pest species. The olive fruit fly, the most important olive tree pest, is dependent on an obligate bacterial symbiont to its larvae development in the immature fruit. This symbiont, Candidatus (Ca.) Erwinia dacicola, is prevalent throughout the host life stages, and we have shown significant changes in its numbers due to olive fruit fly metamorphosis. The olive fruit fly microbiota was analyzed through 16S metabarcoding, at three development stages: last instar larvae, pupae, and adult. Besides Ca. E. dacicola, the olive fruit flies harbor a diverse bacterial flora of which 13 operational taxonomic units (grouped in 9 genera/species) were now determined to persist excluding at metamorphosis (Corynebacterium sp., Delftia sp., Enhydrobacter sp., Kocuria sp., Micrococcus sp., Propionibacterium sp., Pseudomonas sp., Raoultella sp., and Staphylococcus sp.). These findings open a new window of opportunities in symbiosis-based pest management.

The increasing penetration of renewable energy sources led to the development of several energy management approaches. One of the main topics in this field is related to the load forecast in buildings, which can contribute to more intelligent and sustainable energy consumption. However, it is necessary to build a proper forecast model, capable of detecting an accurate consumption profile. The minimum effort to achieve this is to extract a historic with energy consumptions to use as input. Additional information should be considered in order to achieve improvements in forecasting results. This way, information regarding the day of the week is discussed as a reliable source of information that may enhance the load forecast. In this paper, two forecasting techniques, namely neural networks and support vector machine, are used to predict the energy consumption of a building for all 5 min from a period. The proposed model finds the best forecasting technique and determines if the additional information regarding the day of the week enhances the load forecast. In this case study, a period of two years and a half data with a 5-minute time interval is used. Moreover, several tests are performed for varied inputs to understand if the insights are consistent for these tests. This data has been adapted from an office building to illustrate the advantages of the proposed methodology.

Diversas técnicas de secagem podem ser aplicadas, dependendo da matéria-prima que se deseja desidratar e do que se deseja obter. Uma forma de avaliar se o método de desidratação foi eficiente é por meio das isotermas de sorção. O objetivo deste trabalho foi analisar e comparar as isotermas de adsorção da polpa de cajá em pó obtida por atomização, com a utilização de maltodextrina (Ensaio A) e maltodextrina e lecitina de soja (Ensaio B). Para representação das isotermas, foram utilizados os modelos matemáticos de GAB, BET, Henderson e Oswin, nas temperaturas de 25 ºC, 30 ºC, 35 ºC e 40 ºC. Os resultados mostraram que a adição da lecitina confere um pó estatisticamente diferente (p < 0,05) de um pó sem adição em relação aos sólidos solúveis, aos açúcares totais e à sua coloração. O modelo de BET foi o que melhor se ajustou ao Ensaio A, e o modelo de Henderson ao Ensaio B, que se mostraram do Tipo III em formato de J. Foi observado também que a adição da lecitina de soja proporcionou uma maior adsorção de água, demonstrando que o produto necessita ser armazenado em local com baixos teores de umidade relativa, ou ser armazenado em embalagens com baixa permeabilidade.

Center pivot systems are widely used to overcome the irrigation needs of agricultural fields. In this paper, an autonomous approach is proposed in order to improve the low efficiency of irrigation by developing a system based on the water requirement of the plantations, through field data. The data are local temperature, local wind, soil moisture, precipitation forecast, and soil evapotranspiration calculation. This information enables the system to calculate the real evapotranspiration for not being necessary to restrict to lysimetric measures. By this way, the system schedules the irrigation for the lower cost periods, considering the produced energy by the local resources, and the price of energy purchased from the utility grid. Also, it is considered that the irrigation must be carried out within the time interval in which the plantations do not reach the wilding point, so it will be carried out at the periods with the lowest cost. This will optimize the overall operational costs of the irrigation. Keywords: Smart farming, Water resource scheduling, Agricultural irrigation, Energy optimization

Smart grids concept benefits and leverage distributed management systems while allowing its players to actively participate in the smart grid. This paper merges the concepts of microgrid and transactive energy. The proposed model is tested in an office building with multiple tenants. An agent-based platform, running in single-board computers, for microgrid intelligent management with a peer-to-peer energy transaction model is proposed in this paper. This paper describes the peer-to-peer transaction auction model and the deployment of the platform in an office building. The results regard a one-week period where the use of peer-to-peer transactions is compared with a scenario where no transactions among agents are performed. The results are promising, showing the energy price inside the microgrid dropping for the majority of players/agents. The presented work demonstrates how smart grid players can decrease their energy costs using simple approaches that do not require load shifting, consumption optimization nor the acquisition of new equipment.

The adoption of smart grids is becoming a common reality worldwide. This new reality is starting to impact energy customers as they face a dynamic grid in which they can actively participate. However, if energy customers are not prepared to participate actively, they can have their energy costs increased. This paper provides a review of acceptance models and customer surveys around the world made to assess the customers’ perception and willingness to participate in smart grids. Contributing to this assessment, this paper presents a survey undertaken in Portugal. The survey results demonstrate a willingness, from the customer’s end, to actively participate in smart grid initiatives. It was found that 92.9% of participants are willing to plan their energy usage to face hourly energy prices and that 95.0% of participants are willing to accept an external control of at least one appliance, enabling direct load control demand response programs. Also, the results identified two cognitive tendencies, negativity bias, and loss aversion, which can impact how customers participate in smart grids. These cognitive tendencies and the literature acceptance models demonstrate the importance of conducting social science studies targeting smart grids to fully achieve the efficient participation of end customers.

In recent years, there is an increasing synergy between computational technologies and physical components. So-called Cyber-Physical Systems (CPS) are composed of a collection of devices interacting with each other and communicating with the physical world. They integrate computation and communication aspects together with control and monitoring techniques. Various CPS applications can be found in almost all areas of human life, such as manufacturing systems, smart grids, robotics, transportation systems, medical devices, military, home area networks and smart buildings.

Access to finance is a key element of sustainable and inclusive landscapes. We conducted a literature review to identify the factors that contribute to or hinder inclusive financing for micro/small/medium-sized enterprises and projects across sectors in ways that collectively contribute to more sustainable landscapes in the tropics. The key factors in the design of inclusive landscape finance are landscape governance, the financial literacy of local stakeholders, access to finance technology and services, and inclusive finance facilities and associated mechanisms for integrated (i.e., multi-project, multi-sector, spatially coordinated) landscape finance. The most frequent challenges are the types of existing financial products, the lack of livelihood assets among recipients (such as capital and income), the lack of transparency in finance mechanisms, the small scale of potential business cases, and the high risks perceived by finance providers and their customers. From this review, we propose components specifically focused on financial inclusion that complement the framework for integrated landscape finance developed by the Finance Solutions Design Team for the 1000 Landscapes for 1 Billion People Initiative. We suggest how the revised framework can be applied in designing and assessing the inclusiveness of finance mechanisms for integrated landscape management and to guide further research.

The use of energy sharing models in smart grids has been widely addressed in the literature. However, feasible technical solutions that can deploy these models into reality, as well as the correct use of energy forecasts are not properly addressed. This paper proposes a simple, yet viable and feasible, solution to deploy energy management systems on the end-user-side in order to enable not only energy forecasting but also a distributed discriminatory-price auction peer-to-peer energy transaction market. This work also analyses the impact of four energy forecasting models on energy transactions: a mathematical model, a support-vector machine model, an eXtreme Gradient Boosting model, and a TabNet model. To test the proposed solution and models, the system was deployed in five small offices and three residential households, achieving a maximum of energy costs reduction of 10.89% within the community, ranging from 0.24% to 57.43% for each individual agent. The results demonstrated the potential of peer-to-peer energy transactions to promote energy cost reductions and enable the validation of auction-based energy transactions and the use of energy forecasting models in today’s buildings and end-users.

In the energy sector, prosumers are becoming relevant entities for energy management systems since they can share energy with their citizen energy community (CEC). Thus, this paper proposes a novel methodology based on demand response (DR) participation in a CEC context, where unsupervised learning algorithms such as convolutional neural networks and k-means are used. This novel methodology can analyze future events on the grid and balance the consumption and generation using end-user flexibility. The end-users’ invitations to the DR event were according to their ranking obtained through three metrics. These metrics were energy flexibility, participation ratio, and flexibility history of the end-users. During the DR event, a continuous balancing assessment is performed to allow the invitation of additional end-users. Real data from a CEC with 50 buildings were used, where the results demonstrated that the end-users’ participation in two DR events allows reduction of energy costs by EUR 1.31, balancing the CEC energy resources.

Coconut water is a natural,tasty, low calorie drink,. Its consumption is continuously increasing , mainly because of its properties of electrolytes replacement. The extraction and packaging stages are quite susceptible to microbial attack, resulting in loss of quality and, consequently, a decrease in its nutritional value. (PH, total soluble solids, total titratable acidity, reducing sugars, ashes), microbiological quality (total coliforms, thermotolerant to 45ºC, Salmonella, molds and yeasts, Global impression and purchase attitude) of coconut water refrigerated, bottled and commercialized in the Sertão do Pajeú, Pernambuco. Two brands (A and B) were identified and had samples collected in the companies so as to reduce variations during analyzes. The two samples were within the standards established by current legislation, but sample B presented aspects that had greater acceptability among consumers and were also safer to them .

Heating, ventilating, and air conditioning (HVAC) units account for a significant consumption share in buildings, namely office buildings. Therefore, this paper addresses the possibility of having an intelligent and more cost-effective solution for the management of HVAC units in office buildings. The method applied in this paper divides the addressed problem into three steps: (i) the continuous acquisition of data provided by an open-source building energy management systems, (ii) the proposed learning and predictive model able to predict if users will be working in a given location, and (iii) the proposed decision model to manage the HVAC units according to the prediction of users, current environmental context, and current energy prices. The results show that the proposed predictive model was able to achieve a 93.8% accuracy and that the proposed decision tree enabled the maintenance of users’ comfort. The results demonstrate that the proposed solution is able to run in real-time in a real office building, making it a possible solution for smart buildings.

This paper presents MARTINE (Multi-Agent based Real-Time INfrastruture for Energy), a simulation, emulation and energy management platform for the study of problems related to buildings and smart grids. Relevant advances related to buildings and smart grid management and operation have been proposed, focusing either on software models for decision support or on physical infrastructure and control approaches. These two perspectives are, however, complementary, and no practical assessment can be achieved without a suitable interaction and analysis of the impact that decision-making models have on physical resources, and vice-versa. MARTINE overcomes this limitation by integrating, in a single platform: real buildings with the associated devices and resources; emulated components that complement the ones present in the buildings; simulated resources, players and buildings using multi-agent systems, real-time simulation with hardware in the loop capabilities, which enables integrating virtual and physical components; and a knowledge layer that incorporates all the required decision support and energy management models. MARTINE thus provides a comprehensive platform for the study and management of energy resources. The advantages of this platform are demonstrated in this paper through three use cases, related to agriculture irrigation, practical implementation of demand response and load modeling using various network configurations.

The scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of production lines. A case study using real production data that represents a textile industry is presented, where the tasks for six days are scheduled. During the week, a demand response event is launched, and the proposed algorithm shifts the consumption by changing task orders and machine usage.

This article presents a 10-year experience of soft and transferable skills acquisition through the involvement of PhD students in the organization of an international conference. Soft and transferable skills acquisition is currently perceived as a core component of doctoral studies. Examples include writing and communication, teamwork, time management, leadership, resource management, negotiation, problem solving, listening, planning, entrepreneurial spirit, mastering ethics awareness, etc. The need for such skills is due to the leading role that doctoral students are expected to play in society. As such, various organizations have issued recommendations for doctoral programs to include a formal component of soft skills training. In this article, an effective way of introducing soft and transferable skills acquisition in doctoral engineering education is introduced. Namely, a form of collaborative project-based learning is designed as a compulsory course. This includes a set of base lectures, a long period of parallel working groups focusing on the various aspects of organizing an international conference, running the actual conference, and performing a post-conference assessment. Results and lessons learned demonstrate the validity and effectiveness of the proposed approach.

Recommender systems are able to suggest the most suitable items to a given user, taking into account the user’s and item`s data. Currently, these systems are offered almost everywhere in the online world, such as in e-commerce websites, newsletters, or video platforms. To improve recommendations, the user’s context should be considered to provide more accurate algorithms able to achieve higher payoffs. In this paper, we propose a pre-filtering recommendation system that considers the context of a coworking building and suggests the best workplaces to a user. A cyber-physical context-aware multi-agent system is used to monitor the building and feed the pre-filtering process using fuzzy logic. Recommendations are made by a multi-armed bandit algorithm, using ϵ -greedy and upper confidence bound methods. The paper presents the main results of simulations for one, two, three, and five years to illustrate the use of the proposed system.

The increase in sensors in buildings and home automation bring potential information to improve buildings’ energy management. One promissory field is load forecasting, where the inclusion of other sensors’ data in addition to load consumption may improve the forecasting results. However, an adequate selection of sensor parameters to use as input to the load forecasting should be done. In this paper, a methodology is proposed that includes a two-stage approach to improve the use of sensor data for a specific building. As an innovation, in the first stage, the relevant sensor data is selected for each specific building, while in the second stage, the load forecast is updated according to the actual forecast error. When a certain error is reached, the forecasting algorithm (Artificial Neural Network or K-Nearest Neighbors) is trained with the most recent data instead of training the algorithm every time. Data collection is provided by a prototype of agent-based sensors developed by the authors in order to support the proposed methodology. In this case study, data over a period of six months with five-minute time intervals regarding eight types of sensors are used. These data have been adapted from an office building to illustrate the advantages of the proposed methodology.

The olive moth -Prays oleae Bern.- remains a significant pest of olive trees showing situation dependent changes in population densities and in severity of damages. The genetic variability of olive moth was assessed on three main olive orchards regions in Portugal by three different markers (COI, nad5 and RpS5), suggesting high species diversity albeit with no obvious relation with a regional pattern nor to an identified ecological niche. Selected COI sequences obtained in this study were combined with those available in the databases for Prays genus to generate a global dataset. The reconstruction of the Prays phylogeny based on this marker revealed the need to revise Prays oleae to confirm its status of single species: COI data suggests the co-existence of two sympatric evolutionary lineages of morphologically cryptic olive moth. We show, however, that the distinct mitochondrial subdivision observed in the partial COI gene fragment is not corroborated by the other DNA sequences. There is the need of understanding this paradigm and the extent of Prays variability, as the disclosure of lineage-specific differences in biological traits between the identified lineages is fundamental for the development of appropriate pest management practices.

Objective : To evaluate the accuracy of free-hand pedicle screws placement at the seventh cervical vertebra. Methods : The authors have exposed the cervicothoracic junction of 9 adult cadavers (7 male and 2 female) preserved in formalin from the Faculty of Medicine of the Universidad Andina Néstor Cáceres Velásquez, city of Juliaca, Puno - Peru, locating the C7 vertebra based on anatomical parameters. According to previous publications, the entry point for the C7 pedicle was determined as 3-4mm lateral and 5-6mm superior to the center of the lateral mass, and the pedicle was drilled manually and instrumented with 3.5mm screws. After the screws placement, the C7 vertebrae were removed for radiographic analysis. Results : The authors were able to adequately locate the C7 entry point in 12 pedicles (66.6% accuracy), finding a great variability both laterally (2-5mm) and cranially (3-10mm). The angulation in the coronal plane was correct in 13 pedicles (72.3%), despite the incorrect location of the entry point. Angle values in the coronal plane ranged from 38 to 62 degrees. In the sagittal plane angulation, 2 screws were placed in the C6-C7 disc. The midtransversal diameter of the 18 pedicles ranged from 4 to 7mm. Conclusions : The location of the entry point for placement of C7 pedicle screws with pure free-hand technique is very variable due to anatomical differences and the authors recommend some type of guidance for increased safety and accuracy.

A MATLAB/SIMULINK software simulation model (structure and component blocks) has been constructed in order to view and analyze the potential of the PSD (Position Sensitive Detector) array concept technology before it is further expanded or developed. This simulation allows changing most of its parameters, such as the number of elements in the PSD array, the direction of vision, the viewing/scanning angle, the object rotation, translation, sample/scan/simulation time, etc. In addition, results show for the first time the possibility of scanning an object in 3D when using an a-Si:H thin film 128 PSD array sensor and hardware/software system. Moreover, this sensor technology is able to perform these scans and render 3D objects at high speeds and high resolutions when using a sheet-of-light laser within a triangulation platform. As shown by the simulation, a substantial enhancement in 3D object profile image quality and realism can be achieved by increasing the number of elements of the PSD array sensor as well as by achieving an optimal position response from the sensor since clearly the definition of the 3D object profile depends on the correct and accurate position response of each detector as well as on the size of the PSD array.

Os dutos ou túneis são formados a partir do carreamento de pequenos grãos do solo, partículas de argila e outros colóides, pelo processo de piping. O transporte destes materiais é realizado através do transporte de sólidos e dos componentes dissolvidos, tanto com as forças físicas, quanto químicas. Os Dutos formados por piping constituem feições erosivas sub-superficiais, a literatura atribui a formação de voçorocas neste processo. Este artigo aborda a ocorrência de três tipos de dutos encontrados na área mapeada localizada em Barreiras - BA, na região Oeste do estado, em latossolos vermelho-amarelos no cerrado, sobre sedimentos do Grupo Urucuia, da Formação Chapadão e representam as coberturas cenozoicas incosolidadas dominantemente arenosas da Bacia Sanfranciscana, sendo classificadas como aluvionares, coluvionares e eluvionares, com uma sedimentação predominantemente areno-argilosos com níveis lateritizados. O piping foi identificado e classificado com base na existência ou não de colapso do solo e sua associação com a presença ou não de voçoroca. O colapso do terreno gera o aparecimento de um canal superficial, causando o desenvolvimento de voçoroca e uma variedade de canais ramificados, com e sem colapso do teto. As observações em campo permitem associar uma correlação entre o piping e o início do desenvolvimento de voçorocas.

In this study, the presence and variability of Colletotrichum spp. was evaluated by comparing fungal isolates obtained from olive trees under long-time phytosanitary treatments with trees without any phytosanitary treatments (treated and untreated, respectively). Olive fruits of trees of the highly susceptible ‘Galega vulgar’ cultivar growing in the Alentejo region were used as samples. From the 210 olive trees sampled (half from treated and half from untreated orchards), 125 (59.5%) presented Colletotrichum spp., with a significant lower number of infected trees in treated (39) when compared to untreated orchards (86). The alignment and analysis of beta-tubulin (tub2), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), actin (ACT), chitin synthase (CHS-1) and histone H3 (HIS-3) gene sequences allowed the identification of all 125 isolates as belonging to the C. acutatum complex. The vast majority of the isolates (124) were identified as C. nymphaeae and one isolate, from an untreated tree, was identified as C. godetiae. Isolates were divided into five different groups: Group A: 39 isolates from treated trees matched in 100% with C. nymphaeae sequences from the database; Group B: 76 isolates from untreated trees matched in 100% with C. nymphaeae sequences from the database; Group C: one isolate from untreated trees presenting a single nucleotidic difference in the HIS-3 sequence; Group D: eight isolates from untreated trees presenting differences in two nucleotides in the tub2 sequences that changed the protein structure, together with differences in two specific nucleotides of the GAPDH sequences; Group E: one isolate, from untreated olive trees, matched 100% with C. godetiae sequences from the database in all genes. Considering the similarities of the sampled areas, our results show that the long-time application of fungicides may have caused a reduction in the number of olive trees infected with Colletotrichum spp. but an increase in the number of fruits positive to Colletotrichum spp. within each tree, which may suggest different degrees of virulence of Colletotrichum isolates from trees growing different management regimes. It is imperative that the fungicides described as causing resistance are applied at appropriate times and intervals, since their efficiency decreases when applied incorrectly and new and more virulent species may arise.

The management of the olive fruit fly (Bactrocera oleae) is traditionally based upon the use of organophosphate insecticides, mainly dimethoate. In this evolutionary arms race between man and pest, the flies have adapted a pesticide resistance, implying two point-mutations of the Ace gene -I214V and G488S- and a 9bp deletion -Δ3Q. We revisited 11 Iberian locations to evaluate this adaptation of organophosphate (OP)-resistant alleles through amplicon sequencing. Screening for populations where the wild type is prevalent allows an identification of hotspots for targeted mitigation measures; we have hence refined the scale to the region with the lowest OP-resistant alleles frequency 71 locations were sampled and individuals checked using a fast and low-cost allele-specific-primer polymerase chain reaction (ASP-PCR) method]. An increase in Ace gene point-mutations was observed, and the Δ3Q mutation remains undetected. The lowest frequencies of the OP-resistant alleles remain in the west, underlining the hypothesis of an introduction of resistance from eastern Mediterranean areas. A field test was performed by sampling the fly population before and after in-practice dimethoate application. A clear reduction in olive fruit fly numbers was observed, with no relevant changes in the genotypic frequencies of the resistance alleles. The findings are discussed in frame of the type and intensity of the selection pressure that has led to the adaptation to resistance and its consequences from the producer perspective.

Renewable forms of energy are increasingly penetrating the electricity market, particularly, geothermal energy. A wide range of resource temperatures and fluid quality are converted mostly using traditional binary power plants and, recently, using Climeon modular units. Portuguese natural geothermal resources are far from precise estimations. Despite the parameter uncertainties, electric power resource estimations of two natural geothermal reservoirs are presented: a volcanic sourced heated high-enthalpy geothermal reservoir in Sete Cidades, São Miguel Island, Azores; and a low-enthalpy geothermal reservoir linked to a fractured zone in a granitic setting in Longroiva, in the northern part of the Portuguese mainland. Based on the volumetric method, we assessed the power potential of geothermal resources in Sete Cidades and Longroiva using a probabilistic methodology—Monte Carlo simulation. The average reserve estimations for Climeon module were 5.66 MWe and 0.64 MWe for Sete Cidades and Longroiva, respectively. This figure was by far higher when compared to traditional binary technology; those differences were mostly attributed to distinct conversions efficiency factors.

Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that allow the control of electrical resources while providing a retrofitting solution, hence avoiding the need for replacing the electrical devices. However, current so-called smart plugs lack the ability to understand the environment they are in, or the electrical appliance/resource they are controlling. This paper applies environment awareness smart plugs (EnAPlugs) able to provide enough data for energy management systems or act on its own, via a multi-agent approach. A case study is presented, which shows the application of the proposed approach in a house where 17 EnAPlugs are deployed. Results show the ability to shared knowledge and perform individual resource optimizations. This paper evidences that by integrating artificial intelligence on devices, energy advantages can be observed and used in favor of users, providing comfort and savings.

This paper presents IoH (Intelligence of Home), a platform developed to test some basic intelligent behaviors in Home context. Internet of Things, ambient intelligence and context awareness approaches motivated the development of IoH. The platform involves six layers, responsible by connectivity, persistency, unification, Internet of Things integration, subsystems integration and user interface. The integrated subsystems involve intelligent systems for light control, television brightness control, desk light control, persons counting and air conditioner control. The IoH platform is then tested for a real building, and results and conclusions are obtained. Different intelligent methods and technologies are used, form the use of a diversity of sensors, actuators, and controllers and processing units to a set of artificial intelligence approaches varying from machine learning and optimization algorithms to the use of sensor fusion and computer vision. The use of IoH day-by-day demonstrated an intelligent performance for the real building occupants.

The massive dissemination of smart devices in current markets provides innovative technologies that can be used in energy management systems. Particularly, smart plugs enable efficient remote monitoring and control capabilities of electrical resources at a low cost. However, smart plugs, besides their enabling capabilities, are not able to acquire and communicate information regarding the resource’s context. This paper proposes the EnAPlug, a new environmental awareness smart plug with knowledge capabilities concerning the context of where and how users utilize a controllable resource. This paper will focus on the abilities to learn and to share knowledge between different EnAPlugs. The EnAPlug is tested in two different case studies where user habits and consumption profiles are learned. A case study for distributed resource optimization is also shown, where a central heater is optimized according to the shared knowledge of five EnAPlugs.

Demand response and distributed generation are key components of power systems. Several challenges are raised at both technical and business model levels for integration of those resources in smart grids and microgrids. The implementation of a distribution network as a test bed can be difficult and not cost-effective; using computational modeling is not sufficient for producing realistic results. Real-time simulation allows us to validate the business model’s impact at the technical level. This paper comprises a platform supporting the real-time simulation of a microgrid connected to a larger distribution network. The implemented platform allows us to use both centralized and distributed energy resource management. Using an optimization model for the energy resource operation, a virtual power player manages all the available resources. Then, the simulation platform allows us to technically validate the actual implementation of the requested demand reduction in the scope of demand response programs. The case study has 33 buses, 220 consumers, and 68 distributed generators. It demonstrates the impact of demand response events, also performing resource management in the presence of an energy shortage.

O leite de cabra é definido como, produto oriundo da ordenha completa, ininterrupta, em condições de higiene, de animais da espécie caprina sadios, bem alimentados e descansados. É considerado um alimento de alto valor nutritivo, diferenciado do leite de vaca pela alta digestibilidade, alcalinidade e capacidade tamponante, se tornou ótima opção de alimentação para pessoas alérgicas ao leite de vaca e para pessoas que buscam uma alimentação nutricional saudável e com alto valor agregado. Este trabalho foi realizado com o intuito de avaliar as tendências de consumo do leite de cabra, descrevendo as características do produto, as perspectivas de mercado e analisar como as medidas para melhorar a qualidade de leite de cabra têm levado a um aumento de consumo do produto.

O Serviço Geológico / Departamento de Recursos Minerais do Estado do Rio de Janeiro - DRM-RJ, através de sua Coordenadoria de Hidrogeologia, vem conduzindo diversos estudos e atendendo as demandas das Prefeituras Municipais e órgãos governamentais em geral no campo das águas subterrâneas. A crescente utilização das águas subterrâneas, realizada sem a gestão desejável, a desordenada ocupação e uso do solo, a ausência de infra-estrutura de saneamento, entre outras mazelas, trouxeram a ameaça da poluição e depleção da água subterrânea, já tornada um fato real em muitos casos. O presente trabalho tem como objetivo a produção de um mapa preliminar de vulnerabilidade à contaminação das águas subterrâneas, em cima dos dados disponíveis, utilizando o método DRASTIC (Aller et al, 1987) combinado com a utilização do Sistema de Informações Geográficas (SIG) ArcView 3.3 (ESRI, 1999). Localizada no município de Campos de Goytacazes, a área estudada situa-se sobre aqüíferos livres da Baixada Campista, onde se encontram os aqüíferos mais importantes do Estado. De acordo com a classificação da escala DRASTIC, os resultados indicaram que a área em estudo, cujos índices variaram entre 155 e 35, pode ser classificada como de vulnerabilidade intermediária a baixa.

O município de São José de Ubá ocupa cerca de 90% na Bacia Hidrográfica do Rio São Domingos – BHRSD, na região denominada Noroeste do Estado do Rio de Janeiro, castigada freqüentemente por severas e prolongadas estiagens, que secam a maioria dos tributários da bacia. Nessa ocasiões, a água subterrânea ganha importância crucial, tornando-se a alternativa para os diversos usos. Por este motivo, a BHRSD foi escolhida como área de abrangência do Projeto “ Planejamento Conservacionista das Terras e Modelagem Preditiva de Sistemas Aqüíferos do Cristalino para a Recarga Hídrica em Bacias Hidrográficas de Relevo Acidentado” , concebido para subsidiar tecnicamente a implantação de práticas conservacionistas em áreas de agricultura de base familiar, tendo como uma de suas diretrizes a racionalização do manejo e uso da água, através da maximização da infiltração e da retenção da água na bacia. O projeto, financiado pelo Banco Mundial/PRODETAB, tem a participação da EMBRAPA Solos, DRM-RJ, UFRJ, UERJ, Observatório Nacional, PUC-Rio, EMATER-RJ, INT, FEEMA, Prefeitura de S. José de Ubá, entre outras instituições. São apresentados e comentados neste trabalho os resultados preliminares de nove meses de monitoramento piezométrico do sub projeto “ Monitoramento Integrado dos Recursos Hídricos e Definição das Zonas de Recarga dos Aqüíferos” , coordenado pelo DRM-RJ.

Resultados do "Projeto Eletroresistividade" executado pelo Departamento de Geociências do Instituto de Agronomia da Universidade Federal Rural do Rio de Janeiro, obtidos na região de Conservatória, Município de Valença, região serrana do sudoeste do Estado do Rio de Janeiro, são apresentados e consistem de um mapa em profundidade do tôpo do cristalino e de um mapa isoresistivimétrico mostrando as condições geoelétricas do cristalino granítico/gnáissico na região estudada. Valores resistivimétricos acima de 2.100 ohm.m indicam rocha sã; valores entre 2.100 - 1700 ohm.m sugerem rocha fraturada não saturada e valores resistivimétricos variando entre 600 - 1600 ohm.m identificaram uma zona de fraturamento saturada, onde 2 poços tubulares profundos com 50m e 70m foram perfurados, fornecendo 6.000 e 13.000 l/h, respectivamente.