Tourism Guidance Tracking and Safety Platform

We propose a platform for tourism Guidance Tracking and Safety (GTS) based on beacons Bluetooth Low Energy (BLE), mobile device App and a central cloud server. Context information is provided to improve tourism trans portation options taking into account local offer and a collaborative gamified ap proach is applied to share trips and advice natural trails walk. Guidance, safety is provided in remote natural walks and a case application in Madeira, island Leva das, natural trails. This work is the results of a submitted project between ICSTE IUL and Madeira University used to create a tool to get tourism data, advice and orient tourism for ecological trails.info:eu-repo/semantics/publishedVersio

Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

Education can improve the negative perception of a threatened long-lived scavenging bird, the Andean condor

Human-wildlife conflicts currently represent one of the main conservation problems for wildlife species around the world. Vultures have serious conservation concerns, many of which are related to people's adverse perception about them due to the belief that they prey on livestock. Our aim was to assess local perception and the factors influencing people's perception of the largest scavenging bird in South America, the Andean condor. For this, we interviewed 112 people from Valle Fértil, San Juan province, a rural area of central west Argentina. Overall, people in the area mostly have an elementary education, and their most important activity is livestock rearing. The results showed that, in general, most people perceive the Andean condor as an injurious species and, in fact, some people recognize that they still kill condors. We identified two major factors that affect this perception, the education level of villagers and their relationship with livestock ranching. Our study suggests that conservation of condors and other similar scavengers depends on education programs designed to change the negative perception people have about them. Such programs should be particularly focused on ranchers since they are the ones who have the worst perception of these scavengers. We suggest that highlighting the central ecological role of scavengers and recovering their cultural value would be fundamental to reverse their persecution and their negative perception by people.Fil: Cailly Arnulphi, Verónica Beatríz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; ArgentinaFil: Lambertucci, Sergio Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Borghi, Carlos Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; Argentin

Collaborative prognostics in Social Asset Networks

With the spread of Internet of Things (IoT) technologies, assets have acquired communication, processing and sensing capabilities. In response, the fi eld of Asset Management has moved from fleet-wide failure models to individualised asset prognostics. Individualised models are seldom truly distributed, and often fail to capitalise the processing power of the asset fleet. This leads to hardly scalable machine learning centralised models that often must nd a compromise between accuracy and computational power. In order to overcome this, we present a novel theoretical approach to collaborative prognostics within the Social Internet of Things. We introduce the concept of Social Asset Networks, de ned as networks of cooperating assets with sensing, communicating and computing capabilities. In the proposed approach, the information obtained from the medium by means of sensors is synthesised into a Health Indicator, which determines the state of the asset. The Health Indicator of each asset evolves according to an equation determined by a triplet of parameters. Assets are given the form of the equation but they ignore their parametric values. To obtain these values, assets use the equation in order to perform a non-linear least squares t of their Health Indicator data. Using these estimated parameters, they are interconnected to a subset of collaborating assets by means of a similarity metric. We show how by simply interchanging their estimates, networked assets are able to precisely determine their Health Indicator dynamics and reduce maintenance costs. This is done in real time, with no centralised library, and without the need for extensive historical data. We compare Social Asset Networks with the typical self-learning and fleet-wide approaches, and show that Social Asset Networks have a faster convergence and lower cost. This study serves as a conceptual proof for the potential of collaborative prognostics for solving maintenance problems, and can be used to justify the implementation of such a system in a real industrial fleet.EU H202

A Feedback-Based Adaptive Service-Oriented Paradigm for the Internet of Things

© Springer International Publishing AG, part of Springer Nature 2018. With integrating physical devices into digital world, Internet of Things (IoT) have presented tremendous potential in various different application domains such as smart cities, intelligent transportation, smart home, healthcare and industrial automation. However, current IoT solutions and usage scenarios are still very limited because of the difficulty in sensing the context in continuously changing environments and adaptation to the changes accordingly. The complex dynamic interactions between system components and physical environments are a bit challenging especially when there are other concerns such as scalability and heterogeneity. To solve this problem, a novel adaptive service-oriented paradigm is proposed to support IoT from a low-level viewpoint. The paradigm can overcome some disadvantages of REST (Representational State Transfer) architecture style in the IoT. Two classical examples are illustrated using the proposed paradigm by adding an extra constraint based on REST to improve system states verification and enhance the functionality in modelling physical processes

Stretchable Dual-Capacitor Multi-Sensor for Touch-Curvature-Pressure-Strain Sensing

We introduce a new type of multi-functional capacitive sensor that can sense several different external stimuli. It is fabricated only with polydimethylsiloxane (PDMS) films and silver nanowire electrodes by using selective oxygen plasma treatment method without photolithography and etching processes. Differently from the conventional single-capacitor multi-functional sensors, our new multifunctional sensor is composed of two vertically-stacked capacitors (dual-capacitor). The unique dual-capacitor structure can detect the type and strength of external stimuli including curvature, pressure, strain, and touch with clear distinction, and it can also detect the surface-normal directionality of curvature, pressure, and touch. Meanwhile, the conventional single-capacitor sensor has ambiguity in distinguishing curvature and pressure and it can detect only the strength of external stimulus. The type, directionality, and strength of external stimulus can be determined based on the relative capacitance changes of the two stacked capacitors. Additionally, the logical flow reflected on a tree structure with its branches reaching the direction and strength of the corresponding external stimulus unambiguously is devised. This logical flow can be readily implemented in the sensor driving circuit if the dual-capacitor sensor is commercialized actually in the future

Policies to Regulate Distributed Data Exchange

This research is partially sponsored by the EPSRC grant EP/P011829/1, funded under the UK Engineering and Physical Sciences Council Human Dimensions of Cyber Security call (2016).Postprin

Determinants of Cross-Border M&As and Shareholder Wealth Effects in a Globalized World

We analyze theoretical insights and empirical regularities related to factors determining the cross-border mergers and acquisitions (M&As) and impact of M&As on shareholder value of acquires and targets. The analysis of cross-border M&As is a relatively new subject and only recently received rigorous attention in academic research. Within this nascent literature, the survey pays particular attention to the emerging markets, which, in line with their growing role of in the global economy, became an increasingly important arena for cross-border M&As. The existing evidence point out to prevailing challenges in studying cross-border M&As by emerging markets firms. The results are often contradictory and tend to focus on a single country falling short of formally testing existing theories or developing comprehensive theories for emerging economies. We show that the type of factors increasing the value enhancing effects of M&As tends to be similar to the factors affecting the likelihood of M&As transactions. The remaining methodological challenges for the existing studies are related to strong evidence with respect to nonrandom selection of acquisition targets, which, among other “selection issues,” has important implications for choosing counterfactual evidence in order to appropriately compare pre- and postacquisition performance of firms