Prevalence of depression and health related quality of life among patients with diabetes mellitus and hypertension attending a secondary care hospital in district Faridabad, Haryana

Background:  Diabetes mellitus (DM) and hypertension (HT) have significant effect on the mental health of the patient. and. We aimed to estimate the prevalence of depression, and the quality of life among patients with diabetes mellitus and hypertension who attended a secondary care hospital. Methods: A cross-sectional study was carried out among 618 patients who had DM and/or HT.  PHQ-9 and WHO-BREF QOL questionnaire were administered to assess depression and health related quality of life respectively. Results: More than 2/3rd of patients had depression. Among those who had depression, nearly half (46%) had moderate depression and 2.1% had severe depression. The proportion of severely depressed patients was higher in diabetes mellitus group compared to the hypertension group.   Patients that were depressed had poorer quality of life compared to non-depressed, and the difference was statistically significant. Conclusion: Patients with diabetes mellitus and hypertension may be screened for depression and managed accordingly

EFAR-MMLA: An evaluation framework to assess and report generalizability of machine learning models in MMLA

Producción CientíficaMultimodal Learning Analytics (MMLA) researchers are progressively employing machine learning (ML) techniques to develop predictive models to improve learning and teaching practices. These predictive models are often evaluated for their generalizability using methods from the ML domain, which do not take into account MMLA’s educational nature. Furthermore, there is a lack of systematization in model evaluation in MMLA, which is also reflected in the heterogeneous reporting of the evaluation results. To overcome these issues, this paper proposes an evaluation framework to assess and report the generalizability of ML models in MMLA (EFAR-MMLA). To illustrate the usefulness of EFAR-MMLA, we present a case study with two datasets, each with audio and log data collected from a classroom during a collaborative learning session. In this case study, regression models are developed for collaboration quality and its sub-dimensions, and their generalizability is evaluated and reported. The framework helped us to systematically detect and report that the models achieved better performance when evaluated using hold-out or cross-validation but quickly degraded when evaluated across different student groups and learning contexts. The framework helps to open up a “wicked problem” in MMLA research that remains fuzzy (i.e., the generalizability of ML models), which is critical to both accumulating knowledge in the research community and demonstrating the practical relevance of these techniques.Fondo Europeo de Desarrollo Regional - Agencia Nacional de Investigación (grants TIN2017-85179-C3-2-R and TIN2014-53199-C3-2-R)Fondo Europeo de Desarrollo Regional - Junta de Castilla y León (grant VA257P18)Comisión Europea (grant 588438-EPP-1- 2017-1-EL-EPPKA2-KA

Multimodal Data Value Chain (M-DVC): A Conceptual Tool to Support the Development of Multimodal Learning Analytics Solutions

Producción CientíficaMultimodal Learning Analytics (MMLA) systems, understood as those that exploit multimodal evidence of learning to better model a learning situation, have not yet spread widely in educational practice. Their inherent technical complexity, and the lack of educational stakeholder involvement in their design, are among the hypothesized reasons for the slow uptake of this emergent field. To aid in the process of stakeholder communication and systematization leading to the specification of MMLA systems, this paper proposes a Multimodal Data Value Chain (M-DVC). This conceptual tool, derived from both the field of Big Data and the needs of MMLA scenarios, has been evaluated in terms of its usefulness for stakeholders, in three authentic case studies of MMLA systems currently under development. The results of our mixed-methods evaluation highlight the usefulness of the M-DVC to elicit unspoken assumptions or unclear data processing steps in the initial stages of development. The evaluation also revealed limitations of the M-DVC in terms of the technical terminology employed, and the need for more detailed contextual information to be included. These limitations also prompt potential improvements for the M-DVC, on the path towards clearer specification and communication within the multi-disciplinary teams needed to build educationally-meaningful MMLA solutions.Junta de Castilla y León (Project VA257P18)Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R)CEITER (grant agreements no. 669074

CIMLA: A Modular and Modifiable Data Preparation, Organization, and Fusion Infrastructure to Partially Support the Development of Context-aware MMLA Solutions

Multimodal Learning Analytics (MMLA) solutions aim to provide a more holistic picture of a learning situation by processing multimodal educational data. Considering contextual information of a learning situation is known to help in providing more relevant outputs to educational stakeholders. However, most of the MMLA solutions are still in prototyping phase and dealing with different dimensions of an authentic MMLA situation that involve multiple cross-disciplinary stakeholders like teachers, researchers, and developers. One of the reasons behind still being in prototyping phase of the development lifecycle is related to the challenges that software developers face at different levels in developing context-aware MMLA solutions. In this paper, we identify the requirements and propose a data infrastructure called CIMLA. It includes different data processing components following a standard data processing pipeline and considers contextual information following a data structure. It has been evaluated in three authentic MMLA scenarios involving different cross-disciplinary stakeholders following the Software Architecture Analysis Method. Its fitness was analyzed in each of the three scenarios and developers were interviewed to assess whether it meets functional and non-functional requirements. Results showed that CIMLA supports modularity in developing context-aware MMLA solutions and each of its modules can be reused with required modifications in the development of other solutions. In the future, the current involvement of a developer in customizing the configuration file to consider contextual information can be investigated

Learning Analytics Summer Institute (LASI Spain 2019)

Producción CientíficaMultimodal Learning Analytics (MMLA) uncovers the possibility to get a more holistic picture of a learning situation than traditional Learning Analytics, by triangulating learning evidence collected from multiple modalities. However, current MMLA solutions are complex and typically tailored to specific learning situations. In order to overcome this problem we are working towards an infrastructure that supports MMLA and can be adapted to different learning situations. As a first step in this direction, this paper analyzes four MMLA scenarios, abstracts their data processing activities and extracts a Data Value Chain to model the processing of multimodal evidence of learning. This helps us to reflect on the requirements needed for an infrastructure to support MMLA.European Union’s Horizon 2020 research and innovation programme (grant 669074)Ministerio de Ciencia, Innovación y Universidades - Fondo Europeo de Desarrollo Regional (projects TIN2017-85179-C3-2-R / TIN2014-53199- C3-2-R)Junta de Castilla y León - Fondo Europeo de Desarrollo Regional (project VA257P18)Comisión Europea (project 588438-EPP-1-2017-1-EL-EPPKA2- KA

Recent Advancements in Microalgal Mediated Valorisation of Wastewater from Hydrothermal Liquefaction of Biomass

Hydrothermal liquefaction (HTL) is an evolving technology that can convert waste with high moisture and low energy content to electricity, heat, hydrogen and other synthetic fuels more efficiently. The lee side is that the HTL process produces enormous amounts of wastewaters (HTWW), having high organic and nutrient load. The discharge of the HTWW would contaminate the environment and result in the loss of valuable bioenergy sources. The valorisation of HTWW has drawn considerable interest. Therefore, this review highlights the valorisation of wastewater during the HTL of biomass. The review paper begins with the discussion of the role of microalgae in valorizing the HTWW. The survey illustrates that the selection of appropriate technology is dependent on biomass characteristics of the microalgae. Finally, potential research opportunities are recommended to improve the viability of the HTL wastewater valorisation for bioenergy production. Overall, this review concludes that combining various processes, such as microalgae-anaerobic digestion, and bio-electrochemical system - microalgae-anaerobic digestion would be beneficial in maximizing HTWW valorisation

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

International audienceA seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in)