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

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

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

Chromium picolinate, biotin, and sodium bicarbonate combination as a dietary supplement in the treatment of type 2 diabetes

Background: Type 2 diabetes mellitus (T2DM) is characterized by hyperglycemia due to insulin resistance, which can lead to micro and macrovascular complications. The importance of glycemic control for prevention demands the need to promote accessible and safe treatments among others in the form of scientifically proven nutritional supplements. Previous studies have suggested that the consumption of bicarbonate-rich mineral water altered blood metabolites and gut microbiome which has beneficial effects on patients with T2DM. Likewise, chromium picolinate and biotin have shown usefulness in glycemic control. The objective of our study was to evaluate the supplementation with chromium picolinate, biotin, and sodium bicarbonate in patients with T2DM. Methods: We planned and supervised the execution of a crossover, randomized, double-blind, placebo-controlled study of patients with the diagnosis of T2DM that was conducted in Diabetes Clinics of the Endocrinology Service of the University Hospital “Dr. José E. González” of the Autonomous University of Nuevo Leon in Monterrey, Mexico from June 2011 to July 2012. Patients’ follow-ups during the study included a day-0 baseline visit and six more visits over the next six months. Efficacy of treatment was assessed by expressing changes in hemoglobin A1c (HbA1c), body mass index (BMI), and blood pressure (BP). Results: Forty-seven (62.6%) of the original 75 patients completed the trial. Regarding the baseline characteristics, 25 (53.1%) of the participants were male and the mean age was 55.23 ± 9.88. The mean HbA1c was 8.38 ± 1.08%, the mean BMI was 29.34 ± 4.64, the mean systolic BP of 143.84 ± 23.6 mm Hg, and the mean diastolic BP of 84.5 ± 12.13 mm Hg. When comparing the changes that occurred after both interventions, we observed that the HbA1c in the active ingredient group decreased (-0.15%) and in the placebo increased (+0.12%) (p=0.148). When we subdivided both groups according to their HbA1c level before the intervention and compared the participants with HbA1c ≥9, the placebo group had an increase of 0.15 ± 1.32 % and the reduction in the active ingredients was -0.68 ± 1.58 % (p=0.158). Conclusions: In our study, we observed that the supplementation with chromium picolinate, biotin, and sodium bicarbonate decreased HbA1c in 3 months compared to the placebo group in which there was an increase, but with a tendency in the statistical analyses. We believe that this could be due to two reasons: the size of our sample, due to the large percentage of participants who dropped out of the study, or because the treatment period to observe a greater difference should have been longer

Antimatter cosmic rays from dark matter annihilation: First results from an N-body experiment

[Abridged]. We take advantage of the galaxy-like 3D dark matter map extracted from the HORIZON Project results to calculate the positron and antiproton fluxes from dark matter annihilation, in a model-independent approach as well as for dark matter particle benchmarks relevant at the LHC scale (from supersymmetric and extra-dimensional theories). Such a study is dedicated to a better estimate of the theoretical uncertainties affecting predictions, while the PAMELA and GLAST satellites are currently taking data which will soon provide better observational constraints. We discuss the predictions of the antiproton and positron fluxes, and of the positron fraction as well, as compared to the current data. We finally discuss the limits of the Nbody framework in describing the dark matter halo of our Galaxy.Comment: 19 pages, 9 figures. Backgrounds included and additional comments and figures on the positron fraction. Accepted for publication in PR