Study of the doubly charmed tetraquark T+cc

Quantum chromodynamics, the theory of the strong force, describes interactions of coloured quarks and gluons and the formation of hadronic matter. Conventional hadronic matter consists of baryons and mesons made of three quarks and quark-antiquark pairs, respectively. Particles with an alternative quark content are known as exotic states. Here a study is reported of an exotic narrow state in the D0D0π+ mass spectrum just below the D*+D0 mass threshold produced in proton-proton collisions collected with the LHCb detector at the Large Hadron Collider. The state is consistent with the ground isoscalar T+cc tetraquark with a quark content of ccu⎯⎯⎯d⎯⎯⎯ and spin-parity quantum numbers JP = 1+. Study of the DD mass spectra disfavours interpretation of the resonance as the isovector state. The decay structure via intermediate off-shell D*+ mesons is consistent with the observed D0π+ mass distribution. To analyse the mass of the resonance and its coupling to the D*D system, a dedicated model is developed under the assumption of an isoscalar axial-vector T+cc state decaying to the D*D channel. Using this model, resonance parameters including the pole position, scattering length, effective range and compositeness are determined to reveal important information about the nature of the T+cc state. In addition, an unexpected dependence of the production rate on track multiplicity is observed

EPSRC HEED Data Repository: Surveys

The HEED project aims at understanding energy needs of refugees and displaced populations to improve access to clean energy. The focus of HEED is on the lived experiences of refugees living for protracted periods of time in three refugee camps in Rwanda (Nyabiheke, Gihembe and Kigeme) and internally displaced persons (IDPs) forced to leave their homes as a result of the 2015 earthquake in Nepal. As part of the project, an energy assessment survey of households in both countries was undertaken using quantitative and qualitative research methods with households living in different parts of the camps/settlements, entrepreneurs running small businesses, and those responsible for community facilities, such as schools and health clinics. In the first phase, a questionnaire-based survey targeting displaced populations was conducted with households living in three refugee camps in Rwanda and four displaced sites in Nepal (see tables 2.1 and 2.2 respectively). The second phase of the field research involved a series of interviews and focus group discussions with various stakeholders in Nepal and Rwanda. The surveys were designed and delivered between March and April 2018 by the project partner, Practical Action. In both countries, the enumerators for the survey received a two-day training on research methods, data collection and ethics.  With regards to the household survey, the sample size was derived using Cochran’s formula as described by Bartlett et. al. in Organizational Research: Determining Appropriate Sample Size in Survey Research. A minimum sample size of 119 households was derived by applying a margin of error of 0.03 and an alpha of 0.5. A breakdown of the focal group and specific sites where the surveys were delivered in Rwanda and Nepal is shown in tables 2.1 and 2.2 respectively. In Rwanda, a total of 814 surveys including 622 households, 155 enterprises and 37 community facilities from across three sites were conducted. The sample distribution across camp shows 211 for Gihembe, 202 for Kigeme and 209 for Nyabiheke. In Gihembe more than half of the respondents (118, 55.9%) sampled were females with the remaining 93 (44.1%) being males. This is in contrast with Kigeme where almost equal numbers of both male (100, 49.5%) and females (102, 50.5%) were sampled. In Nyabiheke the sample covered more females (123, 58.9%) than males (86, 41.1%). In Nepal, the sample covered 181 households, 18 enterprises and 3 community facilities (see table 2.2). The household sample in Nepal covered more males (126, 69.6%) than females (55, 30.4%).  Folder Structure: Surveys: Gihembe Community Facility Survey – Gihembe_CF.csv Gihembe Enterprise Survey – Gihembe_EN.csv Gihembe Household Survey – Gihembe_HH.csv Kigeme Community Facility Survey – Kigeme_CF.csv Kigeme Enterprise Survey – Kigeme_EN.csv Kigeme Household Survey – Kigeme_HH.csv Nepal Community Facility Survey – Nepal_CF.csv Nepal Enterprise Survey – Nepal_EN.csv Nepal Household Survey – Nepal_HH.csv Nyabiheke Community Facility Survey - Nyabiheke_CF.csv Nyabiheke Enterprise Survey – Nyabiheke_EN.csv Nyabiheke Household Survey – Nyabiheke_HH.csv Location Maps: Gihembe Community Facility Survey Map – CF_GIS_gihembe.csv Gihembe Enterprise Survey Map – EN_GIS_gihembe.csv Gihembe Household Survey Map – HH_GIS_gihembe.csv Kigeme Community Facility Survey Map – CF_GIS_kigeme.csv Kigeme Enterprise Survey Map – EN_GIS_kigeme.csv Kigeme Household Survey Map – HH_GIS_kigeme.csv Nepal Community Facility Survey Map – CF_GIS_nepal.csv Nepal Enterprise Survey Map – EN_GIS_nepal.csv Nepal Household Survey Map – HH_GIS_nepal.csv Nyabiheke Community Facility Survey Map - CF_GIS_nyabiheke.csv Nyabiheke Enterprise Survey Map – EN_GIS_nyabiheke.csv Nyabiheke Household Survey Map – HH_GIS_nyabiheke.csv The following information was gathered from each of the surveys: Households: The datasets contain information about household demographics, access to and use of electricity and lighting technologies, access to and use of cooking technologies and fuels, self-reported needs and priorities by the household, and ownership of energy products. Several key areas, such as solar lighting products and issues around fuel usage, are covered in more detail. Enterprises: The datasets contain information about the enterprise, their electrical and non-electrical lighting needs and supply, the usage of energy for ICT and entertainment, motive power, heating, and cooling applications, and their ownership of electrical appliances. Community facility: The datasets contain information about the community facility or institution, their electrical and non-electrical lighting needs and supply, the usage of energy for ICT and entertainment, motive power, heating, and cooling applications, and their ownership of electrical appliances. Community facilities offered healthcare services were presented additional questions about specific medical devices.  The survey results together with other methodological tools including field visits, workshops - ‘Design for Displacement (D4D)’ and ‘Energy for End-Users’ (E4E) workshops have provided relevant data and contextual knowledge to inform the design of the various interventions associated with the HEED. The data sets and results have been compiled, organised and uploaded in the data portal for use by researchers, students and all both within and outside of the project consortium, during and beyond the project lifetime

EPSRC HEED Data Repository: Nepal Household Appliance Survey

The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energy for Displacement research project (EP/P029531/1). The project aimed to understand the energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people.  As part of the project, three Appliance surveys were conducted in the Uttargaya settlement in Nepal. Appliance surveys are designed to assess the energy needs of a community based on the devices they use. The surveys span three instances across 18 months, starting in October 2018 and ending in April 2020. The survey splits the participants into four categories, organised into sheets, based on the type of metering participants have: 'bulk meter'; 'sub meter'; 'do not possess meter' and 'do not have electrical connection'.  The survey anonymises the name of participants and assigns them a unique id as a household number. Information is recorded on the gender of the household owner, the number of people in the household, the type of their electricity connection and the payment type for the electricity connection. The survey collects information on how many of the following appliances have: Electric Bulb; Mobile charger; Refrigerator; Television; Electric Radio; Table Fan; Electric Iron

European Elections Survey, 1979

Abstract copyright UK Data Service and data collection copyright owner.The survey was designed to record reactions to the first direct elections to the European Parliament, across the European Community. It forms part of an international study conducted through the International Institute of Communications on the role of broadcasting in those elections.Main Topics:Variables The survey focused on 6 areas of questioning. (I)To what extent were people interested and involved? Did they follow the campaign? Did they recognise issues? Vote? (II)Through which channels of communication did they follow the campaign? (III)How was campaign coverage evaluated? (IV)Did an issue agenda emerge? Was it common to all EC member states? (V)How did people vote? What influenced their choices? Were the influences 'European' or 'Domestic?' (VI)What was the relationship between responses to the election and opinions on the EC? How was the importance and relevance of the E. Parliament evaluated

European Elections Survey, 1979

Abstract copyright UK Data Service and data collection copyright owner.The survey was designed to record reactions to the first direct elections to the European Parliament, across the European Community. It forms part of an international study conducted through the International Institute of Communications on the role of broadcasting in those elections.Main Topics:Variables The survey focused on 6 areas of questioning. (I)To what extent were people interested and involved? Did they follow the campaign? Did they recognise issues? Vote? (II)Through which channels of communication did they follow the campaign? (III)How was campaign coverage evaluated? (IV)Did an issue agenda emerge? Was it common to all EC member states? (V)How did people vote? What influenced their choices? Were the influences 'European' or 'Domestic?' (VI)What was the relationship between responses to the election and opinions on the EC? How was the importance and relevance of the E. Parliament evaluated

EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) datasets

This repository contains raw datasets gathered under the EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities by creating an evidence base on the usage of seven different energy interventions, and provide recommendations for improved design of future energy interventions to better meet the needs of people. Below is a brief description of the interventions. Stove-use monitoring systems (July 2019 to October 2019) - Stove-use monitoring systems (SUMs) were deployed on clay stoves in Kigeme camp, Rwanda in July 2019. The aim of the study was to evaluate stove usage patterns by measuring temperature profiles within stove enclosure and on the surface of stoves. The SUMs consisted of 2 sensors - a thermocouple to measure temperature within the stove and a Si7021 sensor to measure temperature outside the stove, connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in values exceeded a set threshold for either of the readings. The SUMs were powered by a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh. The study was conducted in 2 phases. In phase 1 (02 July 2019 to 30 September 2019), data was collected from 15 SUMs and stored locally on SD cards as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (02 October 2019 to 17 October 2019), data was collected from 9 SUMs and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. The data from both phases of study is deposited here in SUM.zip. The cleaned dataset is available at 10.5281/zenodo.3946999.  Mobile Lantern monitoring systems (July 2019 to December 2019) - Mobile lantern monitoring systems (LMSs) were deployed in Nyabiheke camp, Rwanda, in July 2019. The aim of the study was to evaluate lantern usage (static or mobile) and consumption (charge and discharge) patterns. The monitors consisted of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device was the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL was used to calculate step count of a user based on activity and freefall interrupts. Additionally, the voltage of lantern battery was measured using an in-house voltage monitor to understand the discharging and charging patterns. The step count and battery voltage data were stored only if a significant change in the step count was detected. The LMSs were powered through a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh. The study was conducted in 2 phases. In phase 1 (03 July 2019 to 30 September 2019), data was collected from 60 lanterns and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (09 October 2019 to 18 December 2019), the design of lantern monitors was modified to circumvent these issues. The data was collected from 54 lanterns data and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. Additionally, an internal watchdog timer was used to reset the device in case of failures. While certain failures persisted, the data yield was considerably higher than phase 1 of the study. The data from both phases of study is deposited here in LMS.zip. The cleaned dataset is available at 10.5281/zenodo.4269809.  Individual appliance monitors (December 2018 to January 2020) - Individual appliance monitors (IAMs) were deployed in Uttargaya settlement, Nepal, in December 2018. The IAMs were simple, cost-effective and unobtrusive devices to collect data on the energy usage of connected appliances. The aim of the study was to understand energy consumption and usage patterns of different household appliances in grid-connected sub-metered displaced communities. The monitoring system consisted of 2 types of devices – Energenie MiHome Smart Plugs MIHO005 (referred to as the IAM) to sense data relating to power and voltage drawn by the connected appliance, and gateway nodes to collect data from IAM. The main component of the gateway node was a Raspberry Pi fitted with an Energenie ENER314-RT (receiver-transmitter) add-on board to allow the Pi to communicate with the smart plugs. The data collected by the RPi gateway was stored locally in an SD card as well as sent to a remote server hosted at Coventry University. The study was conducted until January 2020. The raw data from the study is deposited here in IAM.zip. The cleaned dataset is available at 10.5281/zenodo.4271714.  Footfall monitoring systems (December 2018 to January 2020) - Seven footfall monitoring systems (FMSs)  were deployed alongside seven solar streetlights to measure step count of passers-by in the Uttargaya settlement, Nepal, in December 2018. The aim of the study was to understand the level of pedestrian movement in the area and evaluate the effect of streetlights on the level of activity. Therefore, the footfall monitors were deployed prior to commissioning of streetlights to gather baseline data. The footfall monitors consisted of a Raspberry Pi 3B, PiFace Real Time Clock and CAM008 70º night vision IR sensor to detect footfall. Upon detection, footfall count along with the direction of movement and the timestamp (measured from PiFace RTC) was stored onto an SD card and communicated to a remote server hosted at Coventry University.  The study was conducted until January 2020. The raw data from the study is deposited here in FMS.zip. The cleaned dataset is available at 10.5281/zenodo.4271730.  Standalone Solar System for a Community Hall (June 2019 to March 2021) - A standalone solar system was deployed in a Community Hall in Nyabiheke camp, Rwanda in June 2019. The aim of the study was to understand the energy consumption behavior within a set location, and create an evidence base on the value of energy and its benefits for growing cooperatives and learning communities. The standalone system comprised of 2kW of solar panels and 12.2 kWh GEL battery storage capacity. Additional components included a Victron 150/35 charge controller and a Venus GX and 48/3000 MultiPlus Inverter. The system powered four AC 2-pin sockets, a 30 W entrance light, and six 30 W indoor lights. Each light and socket were individually metered and controlled via a remote monitoring unit. This allowed for quotas, maximum draws and periods of use to be remotely controlled. The study was conducted until March 2021. The raw data from the study is deposited here in Hall.zip. The cleaned dataset until March 2020 is available at 10.5281/zenodo.3949776.  PV-battery Microgrid (July 2019 to March 2021) - A PV-battery Microgrid was deployed in Kigeme camp, Rwanda in July 2019. The microgrid powered a playground and two nursery buildings.  The aim of the study was to identify best practice in the construction, control and operation of a micro-grid as a shared resource, understand optimal design features for user interfaces that allow negotiation over energy priorities and needs and understand community priorities for energy in the context of early years education and the rate of growth in energy utilization. The micro-grid system comprised of a 2.5 kW of solar panels and 21.1 kWh GEL battery storage capacity. Additional components included a Victron 250/60 charge controller, Venus GX 48/1200 MultiPlus Inverter and BMV-700 series battery monitor. Each Nursery building had three classrooms (A, B and C) with separate entrances. Each classroom was fitted with an AC socket, five 10 Watt indoor lights and a 10 Watt outdoor entrance light. A spare socket was located in the first classroom of each nursery building (Classroom A). Two outdoor double sockets were installed at the playground, and fifteen 10 Watt lights were located in the roof structure. Three transmission line poles were fitted with three 10 Watt lights for safety and security purposes, which also enabled them to act as streetlights. Each light and socket was individually monitored and controlled via a programmable remote monitoring unit (RMU). Wireless AC smart meters were used to control and measure power consumption at the socket loads. These meters communicated with the RMU to receive commands and notified the RMU when a command had been received and to transmit usage data. Each light was connected to a CPE (customer-premises equipment) unit, with three lights per CPE, which communicated wirelessly with the RMU. The CPEs received information from the RMU on when to turn the lights on/off and set the brightness. The CPE also monitored the power consumption of the three lights.  The study was conducted until March 2021. The raw data from the study is deposited here in Microgrid.zip. The cleaned dataset until March 2020 is available at 10.5281/zenodo.3949776.  Standalone solar streetlights (Nepal - June 2019 to October 2020; Rwanda - July 2019 to March 2021) - Seven advanced streetlights were installed by HEED in Khalte, Nepal, in July 2019. Four advanced streetlights and eight normal solar streetlights have been installed in Gihembe, Rwanda, in July 2019. Each advanced streetlight consisted of a solar streetlight and an electrical socket box for excess energy use. The aim of the study was to pilot community co-designed solar streetlights with ground-level sockets to demonstrate alternative energy governance models using new technologies to build community resilience and capacity. The solar light comprised a 300 Watt solar panel, Victron charge controller, 2 kWh li-ion batteries, reprogrammable 60 W LED light, Victron Venus GX for data logging, Victron BMV 700 series battery monitor, ground-level sockets/USB ports and a footfall sensor (only in Nepal). A Victron Battery Protect and remote relay on the Venus GX is used to control access to the secondary load to ensure that there is always sufficient energy to power the light.  The study was conducted until October 2020 in Nepal and March 2021 in Rwanda. The raw data from the study is deposited here in SL.zip. The cleaned dataset until March 2020 is available at 10.5281/zenodo.3947992

Metrics for quality control of results from super-resolution machine-learning algorithms – Data extracted from publications in the period 2017- May 2021

The quality enhancement and restoration of poor-quality low-resolution magnetic resonance (MR) data are paramount for improving patient care, accuracy in diagnosis and quality in clinical research. Since 2017, with the popularity of deep-learning machine-learning algorithms, there have been an increase in interest, and consequently funding, in applying these algorithms to enhance the spatial resolution of MR data. Deep-learning schemes have demonstrated superiority over the more conventional machine-learning algorithms, as they have produced very accurate results in different medical image processing tasks. The increase in the attempts of applying these algorithms to increase the spatial resolution of MRI data parallels an increase in the number of metrics considered for evaluating their performance. This dataset summarises the metrics and strategies to evaluate the performance of super-resolution machine-learning algorithms applied to MRI, from the articles published up to May 2021 in this field. The aims are two-fold: 1) to inform on the metrics used to evaluate results of super-resolution algorithms 2) to inform on publications that have applied the state-of-the-art deep-learning algorithms to increase the spatial resolution of magnetic resonance imagesCastorina, Leonardo V.; Li, Bryan M.; Storkey, Amos; Valdés Hernández, Maria. (2021). Metrics for quality control of results from super-resolution machine-learning algorithms – Data extracted from publications in the period 2017- May 2021, 2017-2021 [dataset]. University of Edinburgh. Centre for Clinical Brain Sciences and School of Informatics. https://doi.org/10.7488/ds/3062