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This work was supported by grant from the Ministry of Economy, Knowledge, Business and University, of the Andalusian Government, within the framework of the FEDER Andalusia 2014–2020 operational program. Specific objective 1.2.3. «Promotion and generation of frontier knowledge and knowledge oriented to the challenges of society, development of emerging technologies») within the framework of the reference research project (UPO-1380624). FEDER co-financing percentage 80%., School bullying is a growing concern in almost all developed economies, bringing negative and serious consequences for those students involved in the role of victims. In this paper, we propose to analyze this topic for the case of Spain, considering the data compiled in the Programme for International Student Assessment (PISA) report in 2018. The sample size consists of 12,549 15-old-year students (51.84% females and 48.16% males). With the help of structural equation models (SEM), we aim to detect the relationship between the risk of being a victim of bullying and several self-appreciations expressed by the students. We have considered variables that try to measure individual perceptions in several aspects, such as the self-image, the help provided by parents and teachers and how the school environment’s safety is perceived. A multigroup analysis was also performed to see the impact of the socioeconomic level of the families and the students’ academic performances on the proposed model. We conclude that several of those aspects are directly related with the risk of being bullied and this risk is higher in those students who present school failure and have a lower socioeconomic status. In this regard, the results would permit pointing out some aspects in which the decision-makers can focus their proposals to establish prevention measures., Departamento de Economía, Métodos Cuantitativos e Historia Económica. Universidad Pablo de Olavide

Family capital is all social, human and financial capital a family has at their disposal in the family to advance the business. Family capital is the pool of resources unique to family business and has the potential for family businesses to gain competitive advantage over nonfamily businesses in today’s competitive landscape. In order to advance the knowledge about strategic management in family businesses, this chapter reviews quantitative empirical work on each dimension of family capital and concludes that the field is still in its infancy. Most studies concentrate on (a part of) social capital, a few on human capital and a very limited number on all three dimensions. The review of the literature enables to describe avenues for further research on family capital.

The exposure of metal sulfides to air or water, either produced naturally or due to mining activities, can result in environmentally damaging acid mine drainage (AMD). This needs to be accurately monitored and remediated. In this study, we apply high-resolution unmanned aerial system (UAS)-based hyperspectral mapping tools to provide a useful, fast, and non-invasive method for the monitoring aspect. Specifically, we propose a machine learning framework to integrate visible to near-infrared (VNIR) hyperspectral data with physicochemical field data from water and sediments, together with laboratory analyses to precisely map the extent of acid mine drainage in the Tintillo River (Spain). This river collects the drainage from the western part of the Rio Tinto massive sulfide deposit and discharges large quantities of acidic water with significant amounts of dissolved metals (Fe, Al, Cu, Zn, amongst others) into the Odiel River. At the confluence of these rivers, different geochemical and mineralogical processes occur due to the interaction of very acidic water (pH 2.5–3.0) with neutral water (pH 7.0–8.0). This complexity makes the area an ideal test site for the application of hyperspectral mapping to characterize both rivers and better evaluate contaminated water bodies with remote sensing imagery. Our approach makes use of a supervised random forest (RF) regression for the extended mapping of water properties, using the samples collected in the field as ground-truth and training data. The resulting maps successfully estimate the concentration of dissolved metals and related physicochemical properties in water, and trace associated iron species (e.g., jarosite, goethite) within sediments. These results highlight the capabilities of UAS-based hyperspectral data to monitor water bodies in mining environments, by mapping their hydrogeochemical properties, using few field samples. Hence, we have demonstrated that our workflow allows the rapid discrimination and mapping of AMD contamination in water, providing an essential basis for monitoring and subsequent remediation.