Abstract
Diabetes Mellitus (DM) is one of the most predominant diseases in the world, causing a high number of deaths. Diabetic foot is one of the main complications observed in diabetic patients, which can lead to the development of ulcers. As the risk of ulceration is directly linked to an increase of the temperature in the plantar region, several studies use thermography as a method for automatic identification of problems in diabetic foot. As the distribution of plantar temperature of diabetic patients do not follow a specific pattern, it is difficult to measure temperature changes and, therefore, there is an interest in the development of methods that allow the detection of these abnormal changes. The objective of this work is to develop a methodology that uses thermograms of the feet of diabetic and healthy individuals and analyzes the thermal changes diversity in the plantar region, classifying each foot as belonging to a DM or a healthy individual. Based on the concept of clustering, a binary classifier to predict diabetic foot is presented; both a quantitative indicator and a classification thresholder (evaluated and validated by several performance metrics) are presented. To measure the binary classifier performance, experiments were conducted on a public dataset (with 122 images of DM individuals and 45 of healthy ones), being obtained the following metrics: Sensitivity = 0.73, Fmeasure = 0.81 and AUC = 0.84.

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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.