Abstract
Purpose This paper aims to investigate supply chain (SC) strategies, analyzing the adoption of lean, agile, leagile and traditional SC strategies with respect to product characteristics, environmental uncertainty, business performance and innovation performance. Design/methodology/approach The paper presents an empirical analysis carried out on a sample of 329 companies. Cluster analysis was applied, based on lean and agile SC characteristics, to identify patterns among different SC strategies. One-way analysis of variance of different constructs by types of SC clusters was conducted to test the research hypotheses. Findings Cluster analysis indicates that the companies studied adopt four types of SC strategies - lean, agile, leagile and traditional. The differences between the clusters are identified and discussed, highlighting that companies adopting a leagile SC strategy present the highest performance, while those that adopt a traditional SC present the lowest; companies adopting an agile SC compete in the most complex and dynamic environments, while companies with a lean SC present a clear predominance of functional rather than innovative products. Originality/value Based on the analysis of the relationship between constructs that have not been addressed previously, the paper adds to the knowledge regarding the role of SC strategies, as well as the antecedents and consequences of their adoption. The results may support managers in the difficult task of choosing the "right" SC strategy.

Abstract
This paper presents theory, a new approach and validation results for fault detection and isolation (FDI) in DC-DC power converters, based on inversion method. The developed method consists on the inversion-based estimation of faults and change detection mechanisms adapted to the power converters context. With the inverse model of a switched linear system, we have designed a real-time FDI algorithm with an integrated fuzzy logic scheme which detects and isolates abrupt changes (faults) at unknown time instants. A smoothing strategy is used to attenuate the effect of unknown disturbances and noise that are present at the outputs of this inverse model. Once the fault event is detected, a dedicated fuzzy-logic-based scheme is proposed to isolate the four types of faults: switch, voltage and current sensor, and capacitor. The performance of the proposed method is verified experimentally to detect and isolate the mentioned faults in the DC-DC boost power converter.

Abstract
Most business enterprises are small and medium-sized enterprises (SMEs), and many of them are without a human resource and recruitment department. Thus, one of the challenges that organizations currently face is to find a strategy to retain and attract talent. To overcome this difficulty, enterprises must invest in employer branding policies and be aware of the factors that differentiate them from others when attracting employees. This study aims to develop an employer branding model applicable to SMEs, to increase and enhance the attraction and retention of talents. An exploratory approach based on a quantitative perspective was adopted to develop an employer branding model applied to SMEs, with two major reference employer branding models and frameworks used as the main support. The model of employer branding was applied to SMEs regarding four dimensions, whereby essential questions are asked, namely (1) organizational culture (e.g., do employees have a job description aligned with the corporate culture?), (2) company strategy (e.g., what is the strategy if business volume decreases?), (3) company reputation (e.g., how do you perceive and treat negative feedback?), and (4) reward systems (e.g., do you feel that your employees are motivated intrinsically or extrinsically or both?), ordered by layers based on a logical sequence. The new proposed model is expected to serve as a useful strategic tool and as a basis for attracting, retaining and managing talent, specifically in the SMEs context. This new model provides a set of strategic and competitiveness benefits for SMEs, while contributing to making enterprises more profitable. The model also contributes to SMEs having a better image and reputation, enabling them to stand out from others in the war for talent.

Abstract
The acquisition of competences for the development of computer programs is one of the main challenges faced by computer science students. As a result of not being able to develop the abilities needed (for example, abstraction), students drop out the subjects and sometimes even the course. There is a need to study the causes of student success (or failure) in introductory curricular units to check for behaviours or characteristics that may be determinant and thus try to prevent and change said causes. The students of one programming curricular unit were invited to answer four surveys. We use machine learning techniques to try to predict the students' grades based on the answers obtained on the surveys. The results obtained enable us to plan the semester accordingly, by anticipating how many students might need extra support. We hope to increase the students' motivation and, with this, increase their interest on the subject. This way we aim to accomplish our ultimate goal: reducing the drop out and increasing the overall average student performance.

Abstract
This paper investigates how Information Technology (IT) leveraging capability supports buyer-supplier collaboration in New Product Development (NPD). IT-leveraging capability is defined as the ability to effectively use IT functionalities to support IT-enabled NPD activities. We consider three dimensions of this capability: effective use of Project and Resource Management Systems (PRMS), effective use of Knowledge Management Systems (KMS) and effective use of Cooperative Work Systems (CWS). We consider the dynamics between these three dimensions, which have usually been treated as equal. Using an in-depth case study approach, we show that effective use of KMS and CWS are key dimensions to support collaboration, creating a unique source of competitive advantage. On the other hand, while effective use of PRMS does not help to create differentiation it is important to support the coordination of KMS and CWS. Furthermore, the three dimensions have different intensities of contribution depending on the NPD stage and supplier involvement configuration.

Abstract
Energy hub system (EHS) incorporating multiple energy carriers, storage, and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation. This article proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This article proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where 1) the first stage optimizes EHS operation cost, and 2) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modeled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this article justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multienergy carriers.