Abstract
Industry 4.0 paradigm is a reality in the digitization of industrial processes and physical assets, as well as their integration into digital ecosystems with several suppliers of the value chain. In particular, Industry 4.0 is the technological evolution of embedded systems applied to Cyber-Physical Systems (CPSs). With this, a shift from the current paradigm of centralization to a more decentralized production, supported by Industrial Internet of Things (IIoT), is implied. The work reported in this paper focuses on the development of smart devices (SmartBoxes), based on low-cost hardware such as Raspberry Pi and also platforms certified for industrial applications, such as NI CompactRIO. Both platforms adopted the OPC-UA architecture to collect data from the shop-floor and convert it into OPC-UA Data Access standard for further integration in the proposed CPPS. Tests were also performed with the MQTT protocol for monitorization. Each SmartBox is capable of real-time applications that run on OPC-UA and MQTT, allowing easy interaction between supervisory systems and physical assets. © 2019 IEEE.

Abstract
We show a deterministic simulation (or lifting) theorem for composed problems f o Eq where the inner function (the gadget) is Equality on n bits. When f is a total function on p bits, it is easy to show via a rank argument that the communication complexity of f o Eq is Q(deg(f) " n). However, there is a surprising counter -example of a partial function f on p bits, such that any completion f' of f has deg(f)= Q(p), and yet f o Eq has communication complexity 0(n). Nonetheless, we are able to show that the communication complexity of f o Eq is at least D(f) " n for a complexity measure D(f) which is closely related to the AND -query complexity of f and is lower -bounded by the logarithm of the leaf complexity of f. As a corollary, we also obtain lifting theorems for the set-disjointness gadget, and a lifting theorem in the context of parity decision -trees, for the NOR gadget. As an application, we prove a tight lower -bound for the deterministic communication complexity of the communication problem, where Alice and Bob are each given p -many n -bit strings, with the promise that either all of the strings are distinct, or all -but -one of the strings are distinct, and they wish to know which is the case. We show that the complexity of this problem is e(p " n).

Abstract
Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price-elastic demand curve that relies on several factors such as estimation of a customer's elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price-elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer's disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.

Abstract
Transfer learning focuses on building better predictive models by exploiting knowledge gained in previous related tasks, being able to soften the traditional supervised learning assumption of having identical train-test distributions. Most efforts on transfer learning consider revisiting the data from the source tasks or rely on transferring knowledge for specific models. In this paper, a general framework is proposed for transferring knowledge by including a regularization factor based on the structural model similarity between related tasks. The proposed approach is instantiated to different models for regression, classification, ranking and recommender systems, obtaining competitive results in all of them. Also, we explore high-level concepts in transfer learning like sparse transfer, partially observable transfer and cross-model transfer.

Abstract
This paper estimates the profit of the joint operation of a wind farm and a li-ion battery energy storage system (BESS) in the Iberian electricity market (MIBEL). The day-ahead and first intraday energy markets, and the tertiary regulation market are considered to optimize the joint operation of both assets. A rolling window combined with a non-linear optimization model are used to design the operation strategy. The BESS lifetime (as a function of the depth of discharge) is considered in the optimization problem, and different BESS capacities and initial state of charge values are tested to determine their approximate optimum values. The model and strategy designed were tested using real data from the MIBEL market and predicted data from Sotavento wind farm. The resulting incomes were compared to the BESS investments costs to determine, for a given capacity, when the project becomes viable. © 2019 IEEE.

Abstract
This work discusses the solution of a Large-scale global optimization problem named Security Constrained Optimal Power Flow (SCOPF) using a method based on Cross Entropy (CE) and Evolutionary Particle Swarm Optimization (EPSO). The obtained solution is compared to the Entropy Enhanced Covariance Matrix Adaptation Evolution Strategy (EE-CMAES) and Shrinking Net Algorithm (SNA). Experiments show the approach reaches competitive results.