Mobile Indoor Augmented Reality. Exploring applications in hospitality environments.

Augmented reality (AR) is been increasingly used in mobile devices. Most of the available applications are set to work outdoors, mainly due to the availability of a reliable positioning system. Nevertheless, indoor (smart) spaces offer a lot of opportunities of creating new service concepts. In particular, in this paper we explore the applicability of mobile AR to hospitality environments (hotels and similar establishments). From the state-of-the-art of technologies and applications, a portfolio of services has been identified and a prototype using off-the-shelf technologies has been designed. Our objective is to identify the next technological challenges to overcome in order to have suitable underlying infrastructures and innovative services which enhance the traveller?s experience

Enhancing Interaction With Smart Objects Throught Mobile Devices

Interaction with smart objects can be accomplished with different technologies, such as tangible interfaces or touch computing, among others. Some of them require the object to be especially designed to be 'smart', and some other are limited in the variety and complexity of the possible actions. This paper describes a user-smart object interaction model and prototype based on the well known event-condition-action (ECA) reasoning, which can work, to a degree, independently of the intelligence embedded into the smart object. It has been designed for mobile devices to act as mediators between users and smart objects and provides an intuitive means for personalization of object's behavior. When the user is close to an object, this one publishes its 'event & action' capabilities to the user's device. The user may accept the object's module offering, which will enable him to configure and control that object, but also its actions with respect to other elements of the environment or the virtual world. The modular ECA interaction model facilitates the integration of different types of objects in a smart space, giving the user full control of their capabilities and facilitating creative mash-uping to build customized functionalities that combine physical and virtual action

Dynamic Analysis, Stability and Design of Grid Forming Converters With PI-Based Voltage Control in DC and 3-Phase AC Microgrids

This paper analyzes the dynamic behavior of the voltage control loop based on proportional-integral regulators, commonly used for grid-forming converters in 3-phase AC and DC Microgrids and applications that involve a DC-link voltage control. The paper proposes a simple and accurate generalized analysis useful both for the system characterization and design. Two different control schemes, based on linear (Direct Voltage Control, DVC) and quadratic voltage feedback (Quadratic Voltage Control, QVC), are analytically studied, simulated and experimentally tested, demonstrating a superior performance of the QVC under the presence of constant power loads. The operation limits, the system stability and the disturbance rejection capability are analyzed considering the effect of control and plant parameters and the effect of the different types of disturbances and the operating point, taking into account the non-linearities of the system. The analysis is mainly focused on the effect of constant power loads given their negative impact on the system performance. The study provides a generic procedure for the analysis and design of proportional-integral voltage controllers, including the selection of the system capacitance for meeting specific dynamic specifications while considering system characteristics as the load level, the stability margins and the maximum voltage deviation under disturbances

Towards a fuzzy-based multi-classifier selection module for activity recognition applications

Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements

Modeling of Current Consumption in 802.15.4/ZigBee Sensor Motes

Battery consumption is a key aspect in the performance of wireless sensor networks. One of the most promising technologies for this type of networks is 802.15.4/ZigBee. This paper presents an empirical characterization of battery consumption in commercial 802.15.4/ZigBee motes. This characterization is based on the measurement of the current that is drained from the power source under different 802.15.4 communication operations. The measurements permit the definition of an analytical model to predict the maximum, minimum and mean expected battery lifetime of a sensor networking application as a function of the sensor duty cycle and the size of the sensed data

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.This work has been jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología de la Región de Murcia) under grant 15290/PI/2010, by the Spanish MINECO and the European Commission FEDER funds under grants TIN2009-14475-C04 and TIN2012-31345, and by the Catholic University of Murcia (UCAM) under grant PMAFI/26/12. This work was partially supported by the computing facilities of Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT), funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain

Inhibition of inflammatory signaling in Pax5 mutant cells mitigates B-cell leukemogenesis

Altres ajuts: We would like to thank the "Fundación Ramón Areces," a Research Contract with the "Fundación Síndrome de Wolf-Hirschhorn o 4p-", and institutional grants from the "Fundación Ramón Areces" and "Banco de Santander" to the CBMSO. Research in the ISG group is partially supported by by Junta de Castilla y León (UIC-017, CSI001U16, and CSI234P18), and by the German Jose Carreras Foundation (DJCLS R13/26; DJCLS 07R/2019). AC-G and M.I.-H. are supported by FSE-Conserjería de Educación de la Junta de Castilla y León 2019 and 2020 (ESF- European Social Fund) fellowship, respectively. J.R.-G. is supported by a scholarship from University of Salamanca co-financed by Banco Santander and ESF.PAX5 is one of the most frequently mutated genes in B-cell acute lymphoblastic leukemia (B-ALL), and children with inherited preleukemic PAX5 mutations are at a higher risk of developing the disease. Abnormal profiles of inflammatory markers have been detected in neonatal blood spot samples of children who later developed B-ALL. However, how inflammatory signals contribute to B-ALL development is unclear. Here, we demonstrate that Pax5 heterozygosis, in the presence of infections, results in the enhanced production of the inflammatory cytokine interleukin-6 (IL-6), which appears to act in an autocrine fashion to promote leukemia growth. Furthermore, in vivo genetic downregulation of IL-6 in these Pax5 heterozygous mice retards B-cell leukemogenesis, and in vivo pharmacologic inhibition of IL-6 with a neutralizing antibody in Pax5 mutant mice with B-ALL clears leukemic cells. Additionally, this novel IL-6 signaling paradigm identified in mice was also substantiated in humans. Altogether, our studies establish aberrant IL6 expression caused by Pax5 loss as a hallmark of Pax5-dependent B-ALL and the IL6 as a therapeutic vulnerability for B-ALL characterized by PAX5 loss

Tissue Compatibility of SN-38-Loaded Anticancer Nanofiber Matrices

Delivery of chemotherapy in the surgical bed has shown preclinical activity to control cancer progression upon subtotal resection of pediatric solid tumors, but whether this new treatment is safe for tumor‐adjacent healthy tissues remains unknown. Here, Wistar rats are used to study the anatomic and functional impact of electrospun nanofiber matrices eluting SN‐38 a potent chemotherapeutic agent on several body sites where pediatric tumors such as neuroblastoma, Ewing sarcoma, and rhabdomyosarcoma arise. Blank and SN‐38‐loaded matrices embracing the femoral neurovascular bundle or in direct contact with abdominal viscera (liver, kidney, urinary bladder, intestine, and uterus) are placed. Foreign body tissue reaction to the implants is observed though no histologic damage in any tissue/organ. Skin healing is normal. Tissue reaction is similar for SN‐38‐loaded and blank matrices, with the exception of the hepatic capsule that is thicker for the former although within the limits consistent with mild foreign body reaction. Tissue and organ function is completely conserved after local treatments, as assessed by the rotarod test (forelimb function), hematologic tests (liver and renal function), and control of clinical signs. Overall, these findings support the clinical translation of SN‐38‐loaded nanofiber matrices to improve local control strategies of surgically resected tumors

School-level factors associated with teacher connectedness: : A multilevel analysis of the structural and relational school determinants of young people’s health

© The Author 2017. Published by Oxford University Press on behalf of Faculty of Public Health. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.ABSTRACT Background Conducting research on the antecedents of teacher connectedness (TC) is key to inform intervention and policy that can leverage the public health potential of teachers for young people’s well-being. As part of the EU-funded Teacher Connectedness Project, this study aims to examine the contribution of a variety of school-level factors (including type of school, school size, student–teacher ratio, students per class and teacher gender). Methods Sample consisted of 5335 adolescents aged 11, 13 and 15 years that had participated in the HBSC study in England. Multilevel multinomial regression was used to examine the contributions of sociodemographic and school-level factors to TC. Results TC was lower in older adolescents and those from less affluent families, but similar in boys and girls. Regarding school-level factors, it was not the size of the school but the ratio of students per teacher which was significantly associated to TC, with higher student–teacher ratio being significantly associated with lower odds of medium-to-high TC. Some differences between mixed and all-girls schools were also found. Conclusions Health promotion strategies targeting student–teacher relationships need to consider how TC changes by age and SES and give attention to school-level factors, in particular the student–teacher ratio. Keywords educational settings, social determinants, young peoplePeer reviewedFinal Published versio