Abstract

Abstract
Small and medium-sized enterprises (SMEs) in Europe risk their competitiveness if they fail to embrace digitalization. Indeed, SMEs are aware of the need to digitalize-more than one in two SMEs are concerned that they may lose competitiveness if they do not adopt new digital technologies. However, a key obstacle is related with decision-makers' lack of awareness concerning digital technologies potential and implications. Some decision-makers renounce digital transition simply because they do not understand how it can be incorporated into the business. Take into account this common reality, especially among SMEs, this research project intends to identify the skills and subjects that need to be addressed and suggests the educational methodology and implementation strategy capable of maximizing its success. Therefore, and supported by a focused group research methodology, an innovative training program, oriented to decision-makers, was designed and implemented. The program was conceived based on a self-directed learning methodology, combining both asynchronous lecture/expositive and active training methodologies, strongly based on state-of-the-art knowledge and supported by reference cases and real applications. It is intended that the trainees/participants become familiar with a comprehensive set of concepts, principles, methodologies, and tools, capable of significantly enhancing decision-making capability at both strategic and tactical level. The proposed programme with a multidisciplinary scope explores different thematic chapters (self-contained) as well as cross-cutting thematic disciplines, oriented to the Industry 4.0 and digital transformation paradigm. Topics related with Digital Maturity Assessment, Smart Factories and Flexible Production Systems, Big Data, and Artificial Intelligence for Smarter Decision-Making in Industry and Smart Materials and Products, as well as new production processes for new business models. Each thematic chapter in turn is structured around a variable set of elementary modules and includes examples and case studies to illustrate the selected topics. A teaching-learning methodology centered on an online platform is proposed, having as a central element, a collection of videos complemented by a set of handouts that organize the set of key messages and take-ways associated with each module. In this paper, we present the design and practice of this training course specifically oriented to decision-makers in SME.

Abstract
Natural disasters in recent years have highlighted the need for enhancing the resilience of the power systems against these events. Dynamic microgrid (MG) formation using distributed energy resources (DERs) is the common approach in restoring the critical loads (CLs). On the other hand, vehicle-to-grid (V2G) and grid-tovehicle (G2V) capabilities in electric vehicles (EVs), as well as the presence of high-powered engine-generators (EGs) embedded in plug-in hybrid electric vehicles (PHEVs) provide a new capability for using electric and fossil energy stored in EVs simultaneously to restore the CLs during an outage. In this regard, the outage management system (OMS) cooperates with aggregators and uses EVs in the form of a public parking lot (PL) or residential parking (RP), besides other resources such as diesel generators and photovoltaic (PV) units. The approach presented in this paper shows the procedure of load restoration and energy management of available resources under a two-stage stochastic framework. Also, a new method is introduced for restoring CLs in the mesh network by using the load control and the master-slave control techniques. The problem is formulated as mixed-integer linear programming (MILP), and simulations are performed on IEEE 123-buses test system and a real distribution network.

Abstract
Lung cancer is the deadliest form of cancer, accounting for 20% of total cancer deaths. It represents a group of histologically and molecularly heterogeneous diseases even within the same histological subtype. Moreover, accurate histological subtype diagnosis influences the specific subtype's target genes, which will help define the treatment plan to target those genes in therapy. Deep learning (DL) models seem to set the benchmarks for the tasks of cancer prediction and subtype classification when using gene expression data; however, these methods do not provide interpretability, which is great concern from the perspective of cancer biology since the identification of the cancer driver genes in an individual provides essential information for treatment and prognosis. In this work, we identify some limitations of previous work that showed efforts to build algorithms to extract feature weights from DL models, and we propose using tree-based learning algorithms that address these limitations. Preliminary results show that our methods outperform those of related research while providing model interpretability.

Abstract
The increasing penetration of uncertain generation from renewable resources poses a challenge for keeping flexibility of power system. Therefore, in order to deal with the condition of insufficient flexibility, a day-ahead modified dispatching model considering the flexibility of power system is proposed. In this modified model, the flexibility is represented by up-regulated flexibility coefficient and down-regulated flexibility coefficient, and these parameters will be used to construct the wind power deviation cost in the objective function which is used to increase the regulated capacity of power system. In the case study, two wind power nodes in the IEEE 30-bus system are connected to two actual wind power farms in Hebei province respectively to verify the validity of the modified model. Finally, Simulation results show that, compared with conventional dispatching models, the modified dispatching model can not only reduce the economic cost, but also increase available regulated capacity to enhance the flexibility of power system.

Abstract
Gender affirmative action (AA) in management remains a controversial topic among scholars, practitioners, and employees. While some individuals may support the use of AA policies as a means of increasing representation of women, others are not supportive at all, further understanding gender AA as an unacceptable violation of merit-even when targeted by it. With the aim of analyzing how scholars have approached the subject, we systematically reviewed 76 published articles (SCOPUS database), covering the extant literature on gender AA and management. Findings indicate a consensus regarding the common antecedents of attitudes towards gender AA with prior experiences with AA and diversity management (DM) (as well as general perceptions of AA). Performance and satisfaction appear as the predominant outcomes. In addition, while investigating the differences among AA, equal employment opportunity (EEO) and diversity management (DM), scholars are mainly focused on the effectiveness of AA as a means of increasing the inclusion of minorities in general. We conclude that despite marginal studies on employees' attitudes toward gender AA, there is a gap in the literature, particularly an absence of research on the bivalent position of meritocracy (or merit violation) as both an antecedent and outcome of attitudes towards AA, which deserves further scrutiny.