Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Spatial surface soil moisture can be an important indicator of crop conditions on farmland, but its continuous estimation remains challenging due to coarse spatial and temporal resolution of existing remotely-sensed products. Furthermore, while preceding research on soil moisture using remote sensing (surface energy balance, weather parameters, and vegetation indices) has demonstrated a relationship between these factors and soil moisture, practical continuous spatial quantification of the latter is still unavailable for use in water and agricultural management. In this study, a methodology is presented to estimate volumetric surface soil moisture by statistical selection from potential predictors that include vegetation indices and energy balance products derived from satellite (Landsat) imagery and weather data as identified in scientific literature. This methodology employs a statistical learning machine called a Relevance Vector Machine (RVM) to identify and relate the potential predictors to soil moisture by means of stratified cross-validation and forward variable selection. Surface soil moisture measurements from irrigated agricultural fields in Central Utah in the 2012 irrigation season were used, along with weather data, Landsat vegetation indices, and energy balance products. The methodology, data collection, processing, and estimation accuracy are presented and discussed. © 2016 by the authors

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education

AI ethics and higher education : good practice and guidance for educators, learners, and institutions

Artificial intelligence (AI) is exerting unprecedented pressure on the global higher educational landscape in transforming recruitment processes, subverting traditional pedagogy, and creating new research and institutional opportunities. These technologies require contextual and global ethical analysis so that they may be developed and deployed in higher education in just and responsible ways. To-date, these efforts have been largely focused on small parts of the educational environments leaving most of the world out of an essential contribution. This volume acts as a corrective to this and contributes to the building of competencies in ethics education and to broader, global debates about how AI will transform various facets of our lives, not the least of which is higher education

Sustenabilitatea educației doctorale în economie și afaceri

Volumul ”Sustenabilitatea educației doctorale în economie și afaceri” valorifică ideile și cercetările doctoranzilor de la Universitatea “Alexandru Ioan Cuza” din Iași, școala doctorală de economie și administrarea afacerilor. Lucrările au fost prezentate, prin postere sau în plen, în conferința finală a proiectului SESYR, finanțat prin programul european Jean Monnet. Structurarea volumului în patru subcapitole generice are ca scop valorificarea domeniilor considerate prin filosofia proiectului:managementul proiectelor, antreprenoriat si angajabilitate pentru tinerii cercetători. O colecție de 24 de articole având 35 de autori, oferă un mediu de dezbatere științifică provocatoare pentru publicul cititor din domeniul economic. Focalizarea subiectelor din articolele prezente pe motivațiile de cercetare ale doctoranzilor și postdoctoranzilor face ca acest volum să reprezinte un debut publicistic pentru unii autori iar pentru alții, o consolidare a vocației. Diseminarea pasiunilor în astfel de contexte consolidează colaborarea și deschiderea spre noi subiecte investigative. Volumul este destinat studenților, cercetătorilor și profesorilor și îl propunem ca reper bibliografic pentru dezvoltarea altor idei de cercetare și inovare în arealul nostru tematic