Abstract
The European Structural and Investment Funds (ESIF) represent the main instrument of European Cohesion Policy to sustain territorial development and to eliminate regional disparities between the EU member states. The study of the impacts of ESIF on the economic growth and development of EU members has gained increasing attention in the academic community, and there has been significant scientific production on this topic. This study consists of a bibliometric analysis of articles published in the period from 2011 to 2020 and indexed in two of the main scientific databases: Web of Science (WoS) and Scopus. The results of the bibliometric analysis point to relevant trends on the subject, such as the growing number of publications over the years; the great concentration of studies applied in Central and Eastern European (CEE) countries, namely Romania; and the current interest of researchers in important topics associated with European funds, such as the competitiveness and innovation of SMEs, climate change, economic impact of global crises, sustainable development, institutional capacity of organisations, and cross-border cooperation.

Abstract
This work reports the first use of cork as substrate to produce 3-electrode electrochemical devices, which may be very important to conduct sustainable worldwide biochemical testing in point-of-care. It consists of laminated cork covered by a thin-film of an insulating resin and printed in a 3-electrode system format. Silver ink was used to print electrical tracks and the reference electrode, while carbon ink was used to print working and auxiliary electrodes. The analytical performance of the cork-based devices was and compared to other common supports, as PET and ceramics in the form of screen-printed electrodes (SPEs). The cork-based devices displayed higher current values and better reversibility features and were able to undergo stable modification with conductive nanomaterials. They were further applied to detect sulfadiazine (SDZ), an antibiotic of human use that is also an environmental contaminant, by modifying the working electrode with a molecularly imprinted polymer (MIP) layer obtained by electropolymerization of pyrrol. The results confirmed the ability of the MIP film to detect SDZ selectively and showed reproducible increasing current signals for increasing concentrations of SDZ, from 8.0 to 186.0 ?M. Direct comparison with commercial carbon SPEs showed greater sensitivity for the cork-based SPEs, with 10? lower limits of detection. Overall, cork-based devices are a valuable alternative to currently available SPEs systems, considering environment and cost features and also the analytical gains of this approach. These are especially important in times where a global biochemical testing became necessary for improved public health management.

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Abstract
In this paper we propose and analyze a game theoretical model regarding the dynamical interaction between government fiscal policy choices toward innovation and training (I&T), firm's innovation, and worker's levels of training and education. We discuss four economic scenarios corresponding to strict pure Nash equilibria: the government and I&T poverty trap, the I&T poverty trap, the I&T high premium niche, and the I&T ideal growth. The main novelty of this model is to consider the government as one of the three interacting players in the game that also allow us to analyse the I&T mixed economic scenarios with a unique strictly mixed Nash equilibrium and with I&T evolutionary dynamical cycles.

Abstract
Photovoltaic (PV) as one of the most promising energy alternatives brings a set of serious challenges in the operation of the power systems including PV system protection. Accordingly, it has become even more vital to provide reliable protection for the PV generations. To this end, this paper proposes two-stage data-driven methods. In the first stage, a feature selection method, namely t-distributed stochastic neighbor embedding (t-SNE) is implemented to select the optimal features. Then, the output of t-SNE is directly fed into the strong data-driven classification algorithm, namely robust soft learning vector quantization (RSLVQ) to detect PV array fault and identify the fault types in the second stage. The proposed method is able to detect the two different line-to-line faults (in strings and out of strings) and open circuit fault and fault type considering partial shedding effects. The results have been discussed based on simulation results and have been demonstrated the high accuracy and reliability of the proposed two-stage method in detection and fault type identification based on confusion matrix values.

Abstract
Congestive heart failure (CHF) is a severe condition that affects the pumping power of your cardiac muscle. In this work, long-term memory (LSTM) and Bidirectional LSTM (BiLSTM) networks were used to identify congestive heart failure human beings using datasets from the PhysioNET. Two approaches were adopted, the first considers beating signals directly to feed the LSTM networks, and the second one used features signals extracted from the beating signals. The BiLSTM considering features signals obtain the best results reaching an accuracy of 90%.