Abstract
Fashion influencers are a new phenomenon and profession to which many young individuals may currently aspire; such is its impact in the digital and online world. Hence, the article serves an upcoming group of fashion-influencers-to-be, as well as firms that seek the help of such professionals. This study aimed to test the mediating role of the attitude toward influencers in the relation between, on the one hand, perceived credibility, trustworthiness, perceived expertise, likeability, similarity, familiarity, and attractiveness, and, on the other hand, purchase intention. Path analysis was used to test a conceptual model in which attitude toward influencers mediates the relation between perceived credibility, trustworthiness, perceived expertise, likeability, similarity, familiarity, attractiveness, and purchase intention. Among the seven components, the association between perceived credibility, trustworthiness, perceived expertise, similarity, and familiarity, on the one hand, and purchase intention, on the other, was completely and significantly mediated through attitudes toward influencers. It was found that the attitude toward the influencer determines the purchase intent; this attitude is, in turn, conditioned by the competence, the resemblance, and the proximity that the consumer perceives in the influencer. Thus, to lead the consumer to buy a certain product, influencers must pay attention to perceived credibility, trustworthiness, perceived expertise, similarity, and familiarity with the product (or service).

Abstract
In the last decade, gamification has been a widely used mechanism to influence behavioral habits in users so that they are more positively involved in learning and business processes. There are many articles or applications that materialize this adoption by showing success stories. However, few are those who mention the dangers of its use. This article shares the main precautions to be taken when creating a gamified strategy, promoting the use of gamification design and evaluation frameworks, in order to create a balanced approach that meets the profiles of its users.

Abstract
The spatial monitoring of plant diversity in the endangered species-rich grasslands of European mountain pastoral systems is an important step for fairer and more efficient Agri-Environmental policy schemes supporting conservation. This study assessed the underlying support for a spatially explicit monitoring of plant species richness at parcel level (policy making scale) in Southern European mountain grasslands, with statistical models informed by Sentinel-2 satellite and environmental factors. Twenty-four grassland parcels were surveyed for species richness in the Peneda-Geres National Park, northern Portugal. Using a multi-model inference approach, three competing hypotheses guided by the species-scaling theoretical framework were established: species-area (P1), species-energy (P2) and species-spectral heterogeneity (P3), each representing a candidate spatial pathway to predict species richness. To evaluate the statistical support of each spatial pathway, generalized linear models were fitted and model selection based on Akaike information criterion (AIC) was conducted. Later, the performance of the most supported spatial pathway(s) was assessed using a leave-one-out cross validation. A model guided by the species-energy hypothesis (P2) was the most parsimonious spatial pathway to monitor plant species richness in mountain grassland parcels (P2, AICc = 137.6, increment AIC = 0.0, wi = 0.97). Species-area and species-spectral heterogeneity pathways (P1 and P3) were less statistically supported (Delta AICc values in the range 5.7-10.0). The underlying support of the species-energy spatial pathway was based on Sentinel-2 satellite data, namely on the near-infrared (NIR) green ratio in the spring season (NIR/Green(spring)) and on its ratio of change between spring and summer (NIR/Green(change)). Both predictor variables related negatively to species richness. Grassland parcels with lower values of near-infrared (NIR) green ratio and lower seasonal amplitude presented higher species richness records. The leave-one-out cross validation indicated a moderate performance of the species-energy spatial pathway in predicting species richness in the grassland parcels covered by the dataset (R-2 = 0.44, RMSE = 4.3 species, MAE = 3.5 species). Overall, a species-energy framework based on Sentinel 2 data resulted in a promising spatial pathway for the monitoring of species richness in mountain grassland parcels and for informing decision making on Agri-Environmental policy schemes. The near-infrared (NIR) green ratio and its change in time seems a relevant variable to deliver predictions for plant species richness and further research should be conducted on that.

Abstract
The introduction of Cyber-Physical Systems (CPS) in the industry through the digitalization of equipment, also known as Digital Twins, allows for a more customized production. Due to high market fluctuation, the implementation of a CPS should guarantee a high flexibility in both hardware and software levels to achieve a high responsiveness of the system. The software reconfiguration, specifically, introduces a question: With heterogeneous equipment with different capabilities namely processing and memory capabilities - where a certain software module should execute? ; that question fits on the task/resource allocation area applied to CPS software reconfiguration. Although in task allocation issue several approaches address such a problem, only a few of them focus on CPS resources optimization. Given that, an approach based on the Dutch Auction algorithm is proposed, implemented at the CPS level enables the software reconfiguration of the CPS according to the existing equipment resources. This approach, besides the optimization of the CPS resources and the energy consumption, transforms the CPS in more reliable and fault-tolerant systems. As shown by the results, despite the demonstration of its suitability in task/resource allocation problems in decentralized architectures, the proposed approach also as a major advantage of quickly finding a near-optimal solution.

Abstract
Software engineering companies have progressively incorporated agile project management methodologies. Initially, this migration occurred mostly in the context of startups, but in recent years it has also sparked interest from other companies with larger and more geographically dispersed teams. One of the frameworks used for large-scale agile implementation is the LeSS framework. This study seeks to explore how Management 3.0 principles can be applied in the context of the ten practices proposed in the LeSS framework. To this end, a qualitative research methodology based on four case studies is used to identify and explore the role of Management 3.0 in software management and development processes that adopt this agile paradigm. The findings show that the principles of Management 3.0 are relevant to the implementation of the LeSS framework practices, especially in fostering team values and personal values; however, distinct principles between the two paradigms are also identified, namely the greater rigidity of processes advocated in the LeSS framework and a greater focus on process automation.

Abstract
Lower limb amputation is a condition affecting millions of people worldwide. Patients are often prescribed with lower limb prostheses to aid their mobility, but these prostheses require frequent adjustments through an iterative and manual process, which heavily depends on patient feedback and on the prosthetist's experience. New computer-aided design and manufacturing technologies have been emerging as ways to improve the fitting process by creating virtual models of the prosthesis' interface component with the limb, the socket. Using Adversarial Autoencoders, a generative model describing both transtibial and transfemoral sockets was created. Two strategies were tested to counteract the small size of the dataset: transfer learning using the ModelNet dataset and data augmentation through a previously validated socket statistical shape model. The minimum reconstruction error was 0.00124 mm and was obtained for the model which combined the two approaches. A single-blind assessment conducted with prosthetists showed that, while generated and real shapes are distinguishable, most generated ones assume plausible shapes. Our results show that the use of transfer learning allowed for a correct training and regularization of the latent space, inducing in the model generative abilities with potential clinical applications.