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Group sexual offending is a phenomenon that occurs in our society, but little research has actually been carried out to unravel the complexity of this type of crime. The present study explores the ...

Doll and robot are effective and beneficial non-pharmacological therapies applied in different clinical settings. Doll therapy (DT), principally based in Bowlby's attachment theory, uses an empathy or lifelike baby doll to conduct caring behaviors of it. Robot therapies (RT) use care robots with a friendly attitude and appearance that create emotions and movements that lead to different verbal, motor and emotional reactions. Both DT and RT are person-centred therapies that aim to improve wellbeing of people that suffer from different neurological, psychological and mental health disorders, such as Alzheimer's Disease, autism spectrum disorder, stress or depression, by providing a realistic experience. In this paper, the characteristics of both therapies, their benefits and the possibilities of innovation in the therapeutic field are presented.

Conservation of cultural heritage is crucial to safeguarding society’s history and memory. This research proposal aims to develop a smart platform that manages cultural heritage in cities, contributing to the generation and transfer of knowledge, working as a supporting tool for decision-making.

The industrial sector is the key driver of the society’s economic and social development. However, it is necessary for workers in this sector to have knowledge of and comply with the safety standards of the industry, designed to ensure their safety at work. Companies take different measures to reduce the rate of accidents; they use Internet of Things and Industry 4.0 technologies to detect and give notifications of anomalies detected in the work environment. This article proposes the design of an architecture using Personal Protective Equipment (PPE), where the collected information is processed by Artificial Intelligence (AI) techniques through Edge Computing and the implementation of Multi Agent System and ROS technology. The proposed system is to be embedded in the PPE worn by workers, guaranteeing their safety and integrity through the prediction and notification of anomalies detected in their environment with no need for internet give that in some cases there is internet connection is not possible.

Lithopia is a prototype of a blockchain-managed fictional village that uses satellite and drone data to trigger smart contracts on the open source blockchain platform, Hyperledger. The project is testing the possibility of anticipatory governance of emerging blockchain and distributed ledger technologies (DLTs) by involving stakeholders in the design process over templates. The goal is to question the promises of blockchain governance happening over automation and smart contracts and to offer an alternative to the misuses of emerging technologies in the so-called predictive and anticipatory design. The prototype consists of a functional Node-RED dashboard used as an interface for the Hyperledger smart contracts and a design fiction movie about the lives of the Lithopians.

This paper presents an experimental study of a R407C liquid-to-water heat pump for combined space heating (SH) and domestic hot water (DHW) production. The main components of the heat pump are a scroll compressor, a desuperheater for DHW production, two brazed plate heat exchangers (condenser and evaporator) and a thermostatic expansion valve. A liquid–vapor heat exchanger (LVHEx) has been included in order to study its effect on the heat pump performance. Experimental results for typical operating conditions in the heating mode of operation (low and medium temperature applications) are presented and discussed. These operating conditions are combined with simultaneous DHW production at different flow rates and temperature values. Results show that, in general, the COP is higher with simultaneous SH and DHW production than with SH alone. Also, using the LVHEx yields lower heating and compression powers and, in general, slightly lower COP values. Ministerio de Economía y Competitividad | Ref. ENE2016-79771-R

[EN] With bore fields for energy extraction and injection, it is often necessary to predict the temperature response to heat loads for many years ahead. Mathematical methods, both analytical and numerical, with different degrees of sophistication, are employed. Often the g-function concept is used, in which the borehole wall is assumed to have a uniform temperature and the heat injected is constant over time. Due to the unavoidable thermal resistance between the borehole wall and the circulating fluid and with varying heat flux along the boreholes, the concept of uniform borehole wall temperature is violated, which distorts heat flow distribution between boreholes. This aspect has often been disregarded. This paper describes improvements applied to a previous numerical model approach. Improvements aim at taking into account the effect of thermal resistance between the fluid and the borehole wall. The model employs a highly conductive material (HCM) embedded in the boreholes and connected to an HCM bar above the ground surface. The small temperature difference occurring within the HCM allows the ground to naturally control the conditions at the wall of all boreholes and the heat flow distribution to the boreholes. The thermal resistance between the fluid and the borehole wall is taken into account in the model by inserting a thermally resistive layer at the borehole wall. Also, the borehole ends are given a hemispherical shape to reduce the fluctuations in the temperature gradients there. The improvements to the HCM model are reflected in a changed distribution of the heat flow to the different boreholes. Changes increase with the number of boreholes. The improvements to the HCM model are further illustrated by predicting fluid temperatures for measured variable daily loads of two monitored GCHP installations. Predictions deviate from measured values with a mean absolute error within 1.1 and 1.6¿K., The Swedish Energy Agency is acknowledged for financing this project. The authors thank Massimo Cimmino (Ecole Polytechnique de Montreal, Canada) for contributing to the generation of a particular solution with his approach.

The recent increase in smart meters installations in households and small bussiness by electric companies has led to interest in monitoring load techniques in order to provide better quality service and get useful information about appliance usage and user consumption behavior. This works summarizes the current state of the art in Non Intrusive Load Monitoring from its beginning, describes the main process followed in the literature to perform this technique and shows current methods and techniques followed nowadays. The possible application of this techniques in the context of ambient intelligence, energy eciency, occupancy detection are described. This work also points the current challenges in the eld and the future lines of research in this broad topic.

Indoor positioning technology is commercially available now, however, the positioning accuracy is not sufficient in the current technologies. Currently available indoor positioning technologies differ in terms of accuracy, costs and effort, but have improved quickly in the last couple of years. It has been actively conducted research for estimating indoor location using RSSI (Received Signal Strength Indicator) level of Wi-Fi access points or BLE (Bluetooth Low Energy) tags. WiFi signal is commonly used for the indoor positioning technology. However, It requires an external power source, more setup costs and expensive. BLE is inexpensive, small, have a long battery life and do not require an external energy source. Therefore, using BLE tags we might be able to make the positioning system practical and inexpensive way. In this paper, we investigate such practical type of indoor positioning method based on BLE. BLE RSSI are processed by Multilayer Perceptron(MLP). Also, compass data and walking speed estimation with an extended Kalman filter is used to improve the accuracy. Our preliminary experimental result shows 2.21m error in case of the MLP output. In preliminary experimental results, the proposed approach improved the accuracy of indoor positioning by 21.2%.

The ACM SAC 2017 Bioinformatics Track aimed at promoting current advances in biological sciences relying on analytical methods that integrate mathematical, physical and computer sciences. The track is primary devoted to publish papers very focused on timely well-defined biological issues whose solution have benefited from the use of computational techniques or the implementation of new ones. The track solicited the submission of papers presenting a biological problem in a comprehensive way and (part of) its solution obtained through the application of computational methods including analysis, modeling and simulation.

Nowadays, in many urban areas we can find vehicle renting services, whose members can rent and deposit a vehicle. These services operate both nationally and internationally and the most commonly rented vehicle is the bicycle, this is because it is more personal, the hiring cost is low and it has no negative impact on the environment. This work presents an optimization method that seeks to minimize the time a bicycle user has to wait until they can travel to their desired destination; this will be done by adapting the stations at which the users deposit their bikes at the end of their trips.

The objective of this paper is to define a decision support system over SOX (Sarbanes-Oxley Act) compatibility and quality of the Suppliers Selection Process based on Artificial Intelligence and Argumentation Theory knowledge and techniques. The present SOX Law, in effect nowadays, was created to improve financial government control over US companies. This law is a factor standard out United States due to several factors like present globalization, expansion of US companies, or key influence of US stock exchange markets worldwide. This paper constitutes a novel approach to this kind of problems due to following elements: (1) it has an optimized structure to look for the solution, (2) it has a dynamic learning method to handle court and control gonvernment bodies decisions, (3) it uses fuzzy knowledge to improve its performance, and (4) it uses its past accumulated experience to let the system evolve far beyond its initial state.

Today, there is a common trend to use tools and methodologies that allow the development of Multi- Agent Systems (MAS) with capabilities of reorganization and adaptation to determine changes in their environments. This work presents an architecture based on different levels and whose key level is the one corresponding to the semi-open type of MAS, structured in such a way that it is able to solve conflicts. In addition, a case study is introduced with the objective of showing the possibilities on conflict resolution basis, where a specifically designed architecture is utilized for that purpose. In particular, the system is applied to the resolution of the conflict raised by the decision of the technology to be used in order to obtain or to measure information in smart cities.

More and more our society is linked to the stability of financial markets and this stability depends on key players like private companies, financial markets, investors, analysts, government control agencies and so on. Sarbanes-Oxley Act is a mandatory law in EEUU market and a facto standard in rest of the world and has as main objective to keep the desire financial stability. Within this chapter it will be shown a new decision support intelligent financial model over SOX compatibility based on Artificial Intelligent technology together with Theory of Argumentation . The main aim of this model is to help and support private companies, auditors, executive boards and regulatory bodies to take a SOX compliant decision over an specific process of a typical purchasing financial cycle: The Contracting Process. The decision will be supported by the whole argumentation process drive by this model and will be reinforce with quality measures with the final objective to create a very clear argumentative background about the suggested decision. This model directly contributes to both scientific research artificial intelligence area and business sector. From business perspective it empowers the use of intelligent models and techniques to drive decision making over financial statements. From scientific and research area the impact is based on the combination of the following innovative elements: (1) an specific Information Seeking Dialog Protocol, (2) a Facts Valuation based Protocol in which previous gathered facts are analyzed, (3) the already incorporated initial knowledge coming from human expert knowledge, (4) the Intra-Agent Decision Making Protocol based on deductive argumentation and (5) the Semi Automated Fuzzy Dynamic Knowledge Learning Protocol giving as a result a novel approach to this kind of problems.