Intermediate Mass Black Holes and Nearby Dark Matter Point Sources: A Critical Reassessment

The proposal of a galactic population of intermediate mass black holes (IMBHs), forming dark matter (DM) ``mini-spikes'' around them, has received considerable attention in recent years. In fact, leading in some scenarios to large annihilation fluxes in gamma rays, neutrinos and charged cosmic rays, these objects are sometimes quoted as one of the most promising targets for indirect DM searches. In this letter, we apply a detailed statistical analysis to point out that the existing EGRET data already place very stringent limits on those scenarios, making it rather unlikely that any of these objects will be observed with, e.g., the Fermi/GLAST satellite or upcoming Air Cherenkov telescopes. We also demonstrate that prospects for observing signals in neutrinos or charged cosmic rays seem even worse. Finally, we address the question of whether the excess in the cosmic ray positron/electron flux recently reported by PAMELA/ATIC could be due to a nearby DM point source like a DM clump or mini-spike; gamma-ray bounds, as well as the recently released Fermi cosmic ray electron and positron data, again exclude such a possibility for conventional DM candidates, and strongly constrain it for DM purely annihilating into light leptons.Comment: 4 pages revtex4, 4 figures. Improved analysis and discussion, added constraints from Fermi data, corrected figures and updated reference

Clinician experiences on training and awareness of sexual orientation in NHS Talking Therapies Services for Anxiety and Depression

Previous research that explored sexual minority service users’ experiences of accessing NHS Talking Therapies for Anxiety and Depression Services highlighted the need for specific sexual orientation training. Inconsistent or lack of training may contribute to disparities in treatment outcomes between sexual minority service users and heterosexual service users. The aim of the study was to explore clinicians’ competencies working with sexual minority service users, their experiences of sexual orientation training, their view of current gaps intraining provision, and ways to improve training. Self-reported sexual orientation competency scales and open ended questions were used to address the aims of the study. Participants (n=83) included Psychological Wellbeing Practitioners (PWPs) and high-intensity CBT therapists (HITs). Responses on competency scales were analysed using Kruskal–Wallis tests and thematic analysis was used to analyse qualitative responses. Participants who identified as 25–29 years old had higher scores on the knowledge scale than 45+-year-olds.Bisexual participants also had higher scores on the knowledge subscale than heterosexual participants. Threeover-arching themes were identified: (a) training received on sexual minority issues by Talking Therapies clinicians, (b) clinicians’ experiences of accessing and receiving sexual minority training, and (c) perceived gaps in current sexual minority training and ways to improve training. Findings were linked to previous literature and recommendations to stakeholders are made throughout the Discussion section with the view of improving sexual orientation training

Cosmic-ray antiproton constraints on light dark matter candidates

Some direct detection experiments have recently collected excess events that could be interpreted as a dark matter (DM) signal, pointing to particles in the $\sim$10 GeV mass range. We show that scenarios in which DM can self-annihilate with significant couplings to quarks are likely excluded by the cosmic-ray (CR) antiproton data, provided the annihilation is S-wave dominated when DM decouples in the early universe. These limits apply to most of supersymmetric candidates, eg in the minimal supersymmetric standard model (MSSM) and in the next-to-MSSM (NMSSM), and more generally to any thermal DM particle with hadronizing annihilation final states.Comment: Contribution to the proceedings of TAUP-2011 (Munich, 5-9 IX 2011). 4 page

Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.This work has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project funded by Spanish Ministry of Science, Innovation, and Universities and the DQIoT (INNO-20171060) project funded by the Spanish Center for Industrial Technological Development, approved with an EUREKA quality seal (E!11737DQIOT). Ana Lavalle holds an Industrial PhD Grant (I-PI 03-18) co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production

Improving sustainability is a key concern for industrial development. Industry has recently been benefiting from the rise of IoT technologies, leading to improvements in the monitoring and breakdown prevention of industrial equipment. In order to properly achieve this monitoring and prevention, visualization techniques are of paramount importance. However, the visualization of real-time IoT sensor data has always been challenging, especially when such data are originated by sensors of different natures. In order to tackle this issue, we propose a methodology that aims to help users to visually locate and understand the failures that could arise in a production process.This methodology collects, in a guided manner, user goals and the requirements of the production process, analyzes the incoming data from IoT sensors and automatically derives the most suitable visualization type for each context. This approach will help users to identify if the production process is running as well as expected; thus, it will enable them to make the most sustainable decision in each situation. Finally, in order to assess the suitability of our proposal, a case study based on gas turbines for electricity generation is presented.This work has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project funded by Spanish Ministry of Science, Innovation, and Universities and the DQIoT (INNO-20171060) project funded by the Spanish Center for Industrial Technological Development, approved with an EUREKA quality seal (E!11737DQIOT). Ana Lavalle holds an Industrial PhD Grant (I-PI 03-18) co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

Antimatter signals of singlet scalar dark matter

We consider the singlet scalar model of dark matter and study the expected antiproton and positron signals from dark matter annihilations. The regions of the viable parameter space of the model that are excluded by present data are determined, as well as those regions that will be probed by the forthcoming experiment AMS-02. In all cases, different propagation models are investigated, and the possible enhancement due to dark matter substructures is analyzed. We find that the antiproton signal is more easily detectable than the positron one over the whole parameter space. For a typical propagation model and without any boost factor, AMS-02 will be able to probe --via antiprotons-- the singlet model of dark matter up to masses of 600 GeV. Antiprotons constitute, therefore, a promising signal to constraint or detect the singlet scalar model.Comment: 24 pages, 8 figures. v2: minor improvements. Accepted for publication in JCA

Supervised fully polarimetric classification of the Black Forest test site: From MAESTROI to MAC Europe

A study on the performance of a supervised fully polarimetric maximum likelihood classifier for synthetic aperture radar (SAR) data when applied to a specific classification context: forest classification based on age classes and in the presence of a sloping terrain is presented. For the experimental part, the polarimetric AIRSAR data at P, L, and C-band, acquired over the German Black Forest near Freiburg in the frame of the 1989 MAESTRO-1 campaign and the 1991 MAC Europe campaign was used, MAESTRO-1 with an ESA/JRC sponsored campaign, and MAC Europe (Multi-sensor Aircraft Campaign); in both cases the multi-frequency polarimetric JPL Airborne Synthetic Aperture Radar (AIRSAR) radar was flown over a number of European test sites. The study is structured as follows. At first, the general characteristics of the classifier and the dependencies from some parameters, like frequency bands, feature vector, calibration, using test areas lying on a flat terrain are investigated. Once it is determined the optimal conditions for the classifier performance, we then move on to the study of the slope effect. The bulk of this work is performed using the Maestrol data set. Next the classifier performance with the MAC Europe data is considered. The study is divided into two stages: first some of the tests done on the Maestro data are repeated, to highlight the improvements due to the new processing scheme that delivers 16 look data. Second we experiment with multi images classification with two goals: to assess the possibility of using a training set measured from one image to classify areas in different images; and to classify areas on critical slopes using different viewing angles. The main points of the study are listed and some of the results obtained so far are highlighted

A smart data holistic approach for context-aware data analytics (AETHER-UA)

A smart data holistic approach for context-aware data analytics (AETHER-UA) is one of the four subprojects, developed in the University of Alicante, being part of the whole project AETHER. This project is being developed by four partners: (i) University of Malaga - Coordinator; (ii) University of Alicante, (iii) University of Castilla La-Mancha, and (iv) University of Seville. The project is funded by the Ministry of Science and Innovation. The main goal of this project is to advance towards a knowledge-based framework integrating novel solutions for data, process and business analytics. The research activities for designing and developing Aether will mainly focus on three main challenges: the characterization of the datasets, the improvement and automation of the algorithms, and the generation of mechanisms to enhance model explainability and interpretation of the results. The project is highly related to data processing, integration, analysis and modeling. More concretely, within the AETHER-UA project, several proposals are being developed for the modeling of user’s requirements for Machine Learning applications, the developing of a framework based on Model Driven Development (MDD) for eXplanable Artificial Intelligence and several approaches for the data bias analysis