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Funding Information: Acknowledgments: This work was supported by the Portuguese Foundation for Science and Technology (FCT) and the Center of Technology and Systems (CTS). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. The notion of Industry 4.0 encompasses the adoption of new information technologies that enable an enormous amount of information to be digitally collected, analyzed, and exploited in organizations to make better decisions. Therefore, finding how organizations can adopt big data (BD) components to improve their performance becomes a relevant research area. This issue is becoming more pertinent for small and medium enterprises (SMEs), especially in developing countries that encounter limited resources and infrastructures. Due to the lack of empirical studies related to big data adoption (BDA) and BD’s business value, especially in SMEs, this study investigates the impact of BDA on SMEs’ performance by obtaining the required data from experts. The quantitative investigation followed a mixed approach, including survey data from 224 managers from Iranian SMEs, and a structural equation modeling (SEM) methodology for the data analysis. Results showed that 12 factors affected the BDA in SMEs. BDA can affect both operational performance and economic performance. There has been no support for the influence of BDA and economic performance on social performance. Finally, the study implications and findings are discussed alongside future research suggestions, as well as some limitations and unanswered questions. publishersversion published

In recent years, the selection of a robot for particular industrial purposes is one of the most challenging problems in the manufacturing environment based on automation and smartness for real-time decision-making. At present, several types of industrial robots with various capabilities, features, facilities, and specifications are available in the market. This makes the decision-making process more and more complicated due to the increase in complexity, advanced technologies, and features that are continually being incorporated into the robots by several manufacturers. The decision-maker needs to identify and select the best-suited robot to attain the desired output with precise application ability, and minimum cost. This paper tries to solve the robot selection problem using Fuzzy Best-Worst Method and PROMETHEE as the two most appropriate multi-criteria decision-making (MCDM) methods for weighting criteria and ranking of decision alternatives, respectively., Industrial engineering; Multidisciplinary design optimization; Manufacturing engineering; Technology management; Operations management; Industry management; Business management; Industrialization; Industrial robots; Fuzzy best-worst method; PROMETHEE; MCDM; Robot selection; Criteria.

UID/EEA/00066/2019 Evaluating organizational readiness for adopting new technologies always was an important issue for managers. This issue for complicated subjects such as Big Data is undeniable. Managers tend to adopt Big Data, with the best readiness. But this is not possible unless they can assess their readiness. In the present paper, we propose a model to evaluate the organizational readiness for Big Data adoption. To accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. In the next step using Principal Component Analysis (PCA) for criterion reduction and integration, twelve main criteria were identified. Then the hierarchical structure of criteria was developed. Further, Fuzzy Best-Worst Method (FBWM) has been used to identify the weight of the criteria. The finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for Big Data adoption. Statistics-based hierarchical model and MCDM based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for Big Data adoption. publishersversion published

UID/EEA/00066/2019 POCI-01-0247-FEDER-033926 The notion of digital transformation encompasses the adoption and integration of a variety of new information and communication technologies for the development of more efficient, flexible, agile, and sustainable solutions for industrial systems. Besides technology, this process also involves new organizational forms and leads to new business models. As such, this work addresses the contribution of collaborative networks to such a transformation. An analysis of the collaborative aspects required in the various dimensions of the 4th industrial revolution is conducted based on a literature survey and experiences gained from several research projects. A mapping between the identified collaboration needs and research results that can be adopted from the collaborative networks area is presented. Furthermore, several new research challenges are identified and briefly characterized. published

The increasing growth of information and communication technologies along with the extensive reliance of today's organizations and businesses on these technologies have rendered decisions on how to outsource the implementation of information system (IS) as well as information technology (IT) projects. Besides, the selection of competent contractors for IS/IT projects is one of the most crucial issues with which organizations must deal. Therefore, numerous researchers have conducted studies in this regard. However, because of the expanded and intensive inter-organizational interactions and inevitable disruptions, there is a need that evaluation criteria and methods of contractor selection be revised or completed. Resiliency, therefore, as the ability to minimize the adverse impact of disruptions and maximize the ability to recover from them, should be afforded further attention. To this end, by reviewing the existing literature on contractor evaluation and some researches on the subject of resiliency, as well as through interviewing a number of experts, the present study collected a set of relevant criteria. Afterward, with the idea of having the evaluation of contractors be more specifically indicative of their resilience, a revised categorization of the criteria was proposed, then screened and approved by experts. Finally, using the Best Worst Method (BWM), the weight and significance of the criteria were determined.

UIDB/00066/2020 Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production and products. Quality 4.0 is a branch of I4.0 with the aim of boosting quality by employing smart solutions and intelligent algorithms. There are many conceptual frameworks and models, while the main challenge is to have the experience of Quality 4.0 in action at the workshop level. In this paper, a hybrid model based on a neural network (NN) and expert system (ES) is proposed for dealing with control chart patterns (CCPs). The idea is to have, instead of a passive descriptive model, a smart predictive model to recommend corrective actions. A construction plaster-producing company was used to present and evaluate the advantages of this novel approach, while the result shows the competency and eligibility of Quality 4.0 in action. publishersversion published

Contemporary business ecosystems are continuously challenged by unexpected disruptive events, which are increasing in their frequency and effects. A critical question is why do some organizations collapse in face of extreme events, while others not? On the other hand, current engineering and socio-technical systems were designed to operate in “mostly stable” situations; sporadic instability and disturbances are at best captured by exception handling mechanisms, focusing on reliability and robustness. Recent and more ambitious design goals, however, aim at building systems that are expected to cope with severe disruptions, and survive or even thrive in a context of volatility and uncertainty. This led to an increasing attention to the concepts of resilience and antifragility. As such, this article introduces the findings of a comprehensive literature survey aimed at shedding light on emerging concepts and approaches to handle disruptions in business ecosystems. Main contributions include a clarification of related concepts, identification and classification of disruption sources and drivers, and extensive lists of strategies and underlying capabilities to cope with disruptions. Related perspectives and approaches developed in multiple knowledge areas are also analysed and synthesized. Finally, a collection of engineered systems implementing promising approaches to increase resilience and antifragility are presented.

Part 13: Electronics; International audience; The photosensor studied in this document, is an amorphous silicon structure deposited on transparent glass allowing illumination on both sides. It responds to wavelengths from near infrared range to the ultra-violet. The front illumination surface is used for inputting light signal. The dynamic characteristics of the photosensor are altered by using optical bias on either surface of the sensor, thus the same input results in different outputs. Experimental studies made with the photosensor evaluate its applicability as a multiplexer, demultiplexer and logical operations device. A memory effect was observed. A programmable pattern emission system, and a validation and recovery system were built to illuminate the sensor with input light signals and to analyze the resulting output.

Part 4: Smart Manufacturing Systems; International audience; With the advent of the Industry 4.0 movement, smart multiagent-based cyber-physical systems (CPS) are being more and more often proposed as a possible solution to tackle the requirements of intelligence, pluggability, scalability and connectivity of this paradigm. CPS have been suggested for a wide array of applications, including control, monitoring and optimization of manufacturing systems. However, there are several associated challenges in terms of validating and testing these systems due to their innate characteristics, emergent behavior, as well as the availability and cost of physical resources. Therefore, a dynamic simulation model constructed in V-REP is proposed as a way to test, validate and improve such systems, being applied to a data acquisition and pre-processing scenario as one of the key aspects of the interaction between a CPS and the shop-floor.

Part 5: Smart Sensorial Systems; International audience; In this paper a deep learning based dimension reduction, feature transformation and classification method is proposed for automatic sleep stage classification. In order to enhance the feature vector, before feeding it to the deep network, a discriminative feature selection method is applied for removing the features with minimum information. Two-layer Stacked Sparse Autoencoder together with Softmax classifier is selected as the deep network model. The performance of the proposed method is compared with Softmax and k-nearest neighbour classifiers. Simulation results show that proposed deep learning structure outperformed others in terms of classification accuracy.

Part 1: Collaborative Networks; International audience; A good amount of research within the last few decades has been focusing on computational models of emotion and the relationships they have with human emotional processes and how they affect the surrounding environments. The study of emotions is interdisciplinary and ranges from basic human emotion research, like in psychology, to the social sciences studies present in sociology. The interactions between those and the computational sciences are becoming a challenge. One particular challenge that is presented in this paper is the study of collaborative emotions within a Collaborative Network (CN) environment. A CN is composed of different participants with different interaction characteristics such as, expectations, will to cooperate and share, leadership, communication, and organizational abilities, among others. This paper presents an approach, based on system dynamics and agent-based modeling, to model the emotional state of an individual member of the network (via a non-intrusive way). Some simulation results illustrate the approach.

Part 1: Collaborative Networks; International audience; In diverse sectors companies collaborate to offer integrated user-centric services to obtain competitive advantage. However, regarding elderly care, services are typically isolated, and mostly provided by a single provider. Moreover, there is a need to personalize services in respect of the individuality of each senior and making these services evolve according to the lifestyle and necessities of the person. In this context, the concept of Elderly Care Ecosystem (ECE) emerges as a computer-based collaborative environment that promotes the integration of distinct services and providers. ECEs are complex systems that demand a clear description of their requirements, especially of those related to personalization of services. In this paper, we describe a set of core requirements and challenges for designing personalized services in an elderly care ecosystem. In particular, our contributions are: the identification of the main stakeholders of an ECE, the requirements to build a persona profile in ECE, and a reference requirements model based on i* for service personalization in ECE.

Part 3: Systems Analysis; International audience; This work is focused on the development of system able to keep tracking driver’s behavior without a black box device mounted inside the car. Firstly, we intend to explore the data from GPS (Global Positioning System), accelerometer, gyroscope and magnetometer for a full characterization of the vehicle dynamics. Secondly, we develop an event detector that determines and classifies distinct kind of maneuvers, like turns, lane change, U-turns, among others. Finally, we developed a simple aggressiveness classifier using fuzzy logic. Experiments have been conducted and the initial results of the system were found to be encouraging on the implementation of a non-intrusive system for driver analysis.

Part 3: Systems Analysis; International audience; The focus of the most recent theories of emotional state analysis is the Autonomic Nervous System. Those theories propose that sympathetic and parasympathetic nervous systems interact antagonistically accordingly to each emotional state implying variations of interbeat intervals of consecutive heart beats. Emotional arousal and attention can be inferred based on the electrocardiogram (ECG) specifically through Heart Rate Variability (HRV) analysis, including the Low Frequency (LF), High Frequency (HF), and ratio LF/HF. The aim of this study is to analyze the impact of classic background music, in students’ emotional arousal and attention, and performance in the context of e-Learning training courses. As a result, it is foreseen the development of a system integrating wearables to smoothly gather the mentioned biosignals, which will be able to sense user’s emotions to further automatically propose recommendations for better learning approaches and contents, aiming student’s attention improvement.

Part 7: Energy: Management; International audience; Nowadays, high penetration of Distributed Generations (DG)s in power systems caused some protection issues. One of these issues is unintentional islanding. As regards IEEE 1547 standard, this situation must be recognized immediately, and DG must be separated from the load in less than 2 s. In this paper, to detection of islanding in an inverter-based distributed generation, a new hybrid method with high performance is proposed. In the proposed method, a primary detection of islanding is conducted by measuring the voltage harmonic distortion at the Point of Common Coupling (PCC), as well as comparing the variations to a specified threshold level. After this primary detection, a temporary reactive current signal is injected to the PCC by the inverter of DG, and its terminal voltage and frequency are measured. In the case of deviation of voltage or frequency from permissible range, definitive detection of islanding is determined. Simulation results indicate the efficiency and accuracy of the proposed detection method in different circumstances, especially for loads with the different quality factors.

Part 7: Energy: Management; International audience; In this paper, the impacts of an incentive-based Demand Response, i.e., Ancillary Service DR (ASDR), and a price-based DR, i.e., Time of Use (ToU), are revealed in a restructured power system which has some wind farms. This network is designed based on the pre-emptive market which is a day-ahead market with a balancing market prognosis. It is a proper mechanism to deal with the stochastic nature of non-dispatchable and outage of all units of the network. With Monte Carlo Simulation (MCS) method, several scenarios are generated in order to tackle the variability and uncertainties of the wind farms generation. The impacts of merging ASDR and ToU are investigated through running a two-stage stochastic security constrained unit commitment (SCUC), separately.

Part 12: Power Electronics; International audience; The applications of wireless power transmission have become widely increasing over the last decade, mainly in the battery charging systems for electric vehicles. This paper focuses on the single-phase wireless power transfer prototype controlled by magnetic core reactors in either side of the system: that of the transmitter, and that of the receiver. The described wireless power transfer system prototype employs a strong magnetic coupling technology to improve the power transmission efficiency. In the same time, a magnetic core reactor is used to control the “tuning” between the transmitter and the receiver frequencies, allowing for that increase of the system efficiency. Finally, practical results of the implemented prototype are presented.

Part 8: Energy: Optimization; International audience; In this paper, an Integer Linear Programming (ILP) problem for a model of Multi-Objective Optimal PMU Placement (MOPP) is proposed. The proposed approach concurrently deals with two objectives. The first objective is the number of phasor measurement units (PMUs) which should be minimized. The second objective function is measurement redundancy which is the number of observable buses in the case of PMU outage. In fact, whatever the amount of second objective increases, the system would be more reliable. Furthermore, some linearized formulations are defined for each nonlinear formula. In fact, the nonlinear nature of formulation related to redundancy is substituted by linear inequality and so there is no nonlinear formula such that the calculation of the problem would be simplified. Finally, a modified 9-bus test system is implemented to show how the proposed method is effective.

Part 2: Computational Intelligence; International audience; In recent years, the level of variable Renewable Energy Sources (vRESs) integrated in power systems has been increasing steadily. This is driven by a multitude of global and local concerns related to energy security and dependence, climate change, etc. The integration of such energy sources is expected to continue growing in the coming years. Despite their multifaceted benefits, variable energy sources introduce technical challenges mainly because of their intermittent nature, particularly at distribution levels. The flexibility of existing distribution systems should be significantly enhanced to partially reduce the side effects of vRESs. One way to do this is using a dynamic network reconfiguration. Framed in this context, this work presents an optimization problem to investigate the impacts of grid reconfiguration on the level of integration and utilization of vRES power in the system. The developed combinatorial model is solved using a genetic algorithm. A standard IEEE 33-node distribution system is employed in the analysis. Simulation results show the capability of network switching in supporting large-scale integration of vRESs in the system while alleviating their side effects. Moreover, the simultaneous consideration of vRES integration and network reconfiguration lead to a better voltage profile, reduced costs and losses in the system.

[EN] The establishment of collaborative relationships with the network partners provides them important advantages, such as competitiveness and agility, when responding to the current rapid market evolutions. Nonetheless, the partic‐ ipation in collaborative networks becomes a complex process that starts with the alignment of all the enterprises objectives and strategies. Smart systems and approaches are needed in order support collaborative partners to deal with the strategies alignment challenge. The lack of alignment emerges because each enterprise defines its own objectives and strategies, to perform their business, and it could happen that non-compatible strategies are activated, involving the appear‐ ance of conflicts between strategies of different enterprises. To this regard, a decision support system is proposed, consisting of a mathematical model, a system dynamics method, a simulation tool and a guideline, with the main aim of supporting the process of identifying aligned strategies, among the enterprises of the collaborative network., This work has been funded in part by Programa Val i+d para investigadores en formación (ACIF 2012).

Part 10: Solar Energy; International audience; Tank fluid purging and night cooling are two overheating prevention techniques with potential to prevent photovoltaic-thermal collectors from experiencing temperatures capable of undermining their longevity and commercial appeal. Both techniques are readily available, inexpensive but inherently wasteful to use. Dynamic numerical simulations were conducted to determine the primary energy efficiency and the level of protection afforded by these techniques in active residential grid-connected solar domestic hot water systems. Also evaluated was the use of occupancy rate information, possible via so-called “smart systems”, to complement the techniques. The results revealed better performances for systems using stagnation control schemes relative to those not using them. Also, night cooling was shown to be unable to prevent overheating reliably while tank fluid purging proved to be more apt but resulted in substantial waste of water annually, which was slightly reduced by combining it with night cooling, which in turn proved to be the most energy efficient solution.

Part 10: Solar Energy; International audience; With the objective of having a solar thermoelectric system, running for 24 h a day along the different seasons of the year it is necessary to dimension the adequate storage and back-up systems. The choice of the back-up source of energy depends on how sustainable the power plant should be. In this study, the choice was the use of biomass in order to have a 100% renewable power plant. The selected site was the Alentejo region (Portugal). The local Direct Normal Irradiation (DNI) data was used to simulate with the System Advisor Model program (SAM) considering a solar system with north field and molten salt storage. The system needs no back-up during three months in a year. The use of biomass pellets is a viable alternative because it makes the power plant 100% renewable and dispatchable without loss of energy due to over-dimension of the expensive solar field and molten storage system.

Part 8: Energy: Optimization; International audience; Distributed Generation (DG) using renewable technologies is increasing due to their benefits including energy security and emission reduction. However, installing new DGs in distributed networks is limited due to network constraints such as feeder capacity and short circuit level, as well as higher investment costs. In this paper, network reconfiguration and reactive power planning are used to maximize DG penetration level and to minimize annual loss for DGs with biomass technologies. In order to model the problem uncertainties, 96 scenarios considering ten different network load levels are studied. A multi-objective method is applied for solving this optimization problem by using Pareto front. The numerical results indicate the positive impacts of the proposed approach on improving the network security.

Part 9: Distributed Infrastructure; International audience; This paper addresses the target tracking problem, by extracting received signal strength (RSS) and angle of arrival (AoA) information from the received radio signal, in the case where the target transmit power is considered unknown. By combining the radio observations with prior knowledge given by the target transition state model, we apply the maximum a posteriori (MAP) criterion to the marginal posterior distribution function (PDF). However, the derived MAP estimator cannot be solved directly, so we tightly approximate it for small noise power. The target state estimate is then easily obtained at any time step by employing a recursive approach, typical for Bayesian methods. Our simulations confirm the effectiveness of the proposed algorithm, offering good estimation accuracy in all considered scenarios.

Part 5: Smart Sensorial Systems; International audience; Otoliths are the fish bones that allow it to hear sounds and achieve balance. The otolith grows in size as fish grows; ring bands are formed in the otoliths’ surface registering periods of rapid and slow growth, opaque bands appear alternating with translucent bands. Age classification was made considering the number of translucent rings in the otolith; one translucent ring was equivalent to one year. The modeling of fish species abundance on the majority of fisheries assessment use age based models. The task of ring counting and ageing is time consuming and may introduce errors that can have a strong impact in stock assessment results. Thus, accurate and precise age estimates are crucial for the effective management and understanding of fisheries resources because recruitment dynamics, growth and mortality estimates relies on these data. The main goal of this study is to produce automatic reading procedures to help researchers, ageing blue whiting fish, minimize ring error count and improve accuracy and precision on age estimation.

Part 5: Smart Sensorial Systems; International audience; The common way for doctors to differentiate wrist injury in to a sprain or a fracture, is to take radiographs (X-ray), which expose patients to radiation. The purpose of this study is to explore a non-invasive method to screen for potential fractures. A small, computer run, hand-held system has been developed which consists of a vibration induction mechanism and a piezoelectric sensor for capturing the vibration signals. Two analyzing techniques were considered. The first involves extraction of wavelet coefficients from decomposition of data and the second applies Fast Fourier Transform to the data. Results of both techniques were then cluster analyzed to partition between fracture and sprain. The data were acquired from both the injured and uninjured wrists of six adult patients. This study is currently being evaluated on children’s wrists.

Part 2: Computational Intelligence; International audience; When dealing with events in moving vehicles, which can occur over widespread areas, it is difficult to identify sources that do not derive from material fatigue, but from situations that occur in specific spots. Considering a railway system, problems could occur in trains, not because of train’s equipment failure, but because the train is crossing a specific location. This paper presents a new smart system being developed that is able to generate geo-located sensor-data; transmit it for smart processing and fusing to the inference engine being built to correlate the data, and drill-down the information. Using a statistical approach within the inference engine, it is possible to combine results collected over long periods of time in a “heat-map” of frequent fault areas, mapping faulty events to detect hazardous locations using georeferenced sensor data, collected from several trains that will be integrated in these maps to infer high probability risk areas.

Part 3: Systems Analysis; International audience; The Internet-of-Things (IoT) is today a reality, and Smart Systems have taken advantage of this to improve its own sense, act and control capabilities. IoT is a highly heterogeneous environment composed by a vast number of “things” (sensors, smart objects, etc.). These “things” are based on hardware platforms which can differ widely since manufactures are being capable of develop new devices every day to tackle different application domains. Consequently, a problem emerges regarding which will be the suitable, proper hardware solution for an IoT deployment. Make a right decision is probably one of the toughest challenges for science and technology managers. This work proposes a novel methodology to analyze a set of hardware alternatives based on user’s multi-criteria requirements, and advice on the more suitable hardware solution for a specific situation. For proof-of-concept it is used different Arduino boards as hardware alternatives, in which user requirements are based on hardware features. This methodology foresees its use during the development of Smart Systems (e.g.: Transportation, Healthcare) to optimize the selection of hardware platforms.

Part 9: Distributed Infrastructure; International audience; Planning and developing the future Smart City is becoming mandatory due to the need of moving forward to a more sustainable society. To foster this transition an accurate simulation of energy production from renewable sources, such as Photovoltaic Panels (PV), is necessary to evaluate the impact on the grid. In this paper, we present a distributed infrastructure that simulates the PV production and evaluates the integration of such systems in the grid considering data provided by smart-meters. The proposed solution is able to model the behaviour of PV systems solution exploiting GIS representation of rooftops and real meteorological data. Finally, such information is used to feed a real-time distribution network simulator.

Part 11: Electrical Machines; International audience; The design of efficient and high power density electrical machines needs an accurate characterization of magnetic phenomena. Core losses estimation is usually addressed by empirical models, where its lack of accuracy is well known. Hysteresis models are able to take an insight into the magnetic physical mechanisms. Compared to the empirical models, they contribute to a higher accuracy in modeling electromagnetic systems, including core losses estimation. At a macroscopic level, two models are often used: the Preisach and the Jiles–Atherton (J–A) models. This paper presents their basic formulation, as well the main limitations and scope of application. This is a first step to investigate the possible application of hysteresis models, in order to reach accurate core losses estimation in switched reluctance machines.

Part 13: Electronics; International audience; The Internet of Thing (IoT) has given rise to the integration of smart systems in the industrial, healthcare, and social environments. These smart systems are often implemented by system-on-chip (SoC) solutions that require power management units, sensors, signal processors, and wireless interfaces. Hence, an independent voltage reference circuit is crucial for obtaining accurate measurements from the sensors and for the proper operation of the SoC. Making, bandgap circuits indispensable in these types of applications. Typically, bandgap circuits are implemented using bipolar transistors to generate two voltages with opposite temperature coefficients (Complementary To Absolute Temperature – CTAT; Proportional do Absolute Temperature - PTAT) which are added resulting in a temperature independent voltage reference. The disadvantage of using bipolar devices is that the power supply voltage must be larger than the ON base-emitter voltage, resulting in voltages larger than 0.7 V. The low power demands of IoT and of technology scaling, have forced lower values for the power supply voltage and thus new bandgap circuits using only CMOS transistors have gathered increased interest. In these, the MOS transistors operate in the weak inversion region where its current has an exponential relation with its gate-to-source voltage, as in the bipolar devices. Making it possible to generate both the PTAT and the CTAT voltages and thus produce a temperature independent voltage reference. This paper describes a design methodology of an all CMOS bandgap voltage reference circuit, in which one of the transistors works in the moderate region and the other in the weak inversion, to achieve the lowest possible voltage variation with temperature. The circuit produces a voltage reference of 0.45 V from a minimum power supply voltage of 0.6 V, with a total variation of 1.54 mV, over a temperature range of −40 to 100°C, resulting in a temperature coefficient of 24 ppm/°C, and a power supply rejection ratio (PSRR) of −40 dB.

Part 8: Energy: Optimization; International audience; In this paper, an optimal scheduling of thermal and wind power plants is presented by using a stochastic programming approach to cover the uncertainties of the forecasted generation of wind farms. Uncertainties related to wind forecast error, consequently wind generation outage power and also system load demand are modeled through scenario generation. Then, with regard to day-ahead and real-time energy markets and taking into account the relevant constraints, the thermal unit commitment problem is solved considering wind energy injection into the system. Besides, in order to assess impacts of Demand Response (DR) on the problem, a load reduction demand response model has been applied in the base model. In this approach, self and cross elasticity is used for modeling the customers’ behavior modeling. The results indicate that the DR Programs (DRPs) improves the market efficiency especially in peak hours when the thermal Gencos become critical suppliers and the combination of DRPs and wind farm can be so efficient.

Part 13: Electronics; International audience; A portable microelectromechanical system (MEMS) for mobile phones, or other portable devices, that measures body electrical signals, as well as, extracts transdermal biological fluid for invivo analysis is proposed. This system integrates two sensing methods: three points finger electrocardiography (ECG) and glucose monitoring, through one electrode with a microneedle-array. This work presents the: (1) device modeling and microneedle-array’ fabrication method, (2) signal processing and biasing circuitry’ design and simulation, (3) Analog Front End (AFE) for measured signals, and (4) Glucose sensor characterization. Design parameters and geometries are obtained by solving the capillarity model inside the microneedles and running optimization numeric methods. The AFE consists in a differential band pass filter that provides amplification, filtering, and noise rejection. This work presents clear technological innovation, for its miniaturization and integration of known biological signals’ measurement methods in a portable Smart System, which points in the direction of Internet of Things’ goals.