GMLD: A TOOL TO INVESTIGATE AND DEMONSTRATE THE USE OF ML IN VARIOUS AREAS OF GNSS DOMAIN

This paper presents relevant results achieved during the NAVISP- EL1-035.02 project funded by the European Space Agency, which aimed to investigate the possible uses of Machine Learning (ML) based techniques for the processing of data in the field of Global Navigation Satellite Systems (GNSSs). For this purpose, we explored different kind of data present in the entire chain of the positioning process and different kind of ML approaches. In particular, this paper presents the system architecture and technologies adopted for developing the GNSS ML Demonstrator (GMLD), as well as the approaches and the results obtained for one of the most promising GNSS implemented applications, which is the prediction of daily maps of the ionosphere. Results show how, based on the historical data and the time correlation of the values, ML methods outperformed benchmark methods for the majority of the applications approached, improving the positioning performance at GNSS user level. Since the GMLD has been designed and implemented providing the general data management and ML capabilities as part of the framework, it can be easily reused to execute further investigation and implement new applications

Evaluation of an enhanced training package to support clinical trials training in low and middle income countries (LMICs): experiences from the Born Too Soon Optimising Nutrition study

Training is essential before working on a clinical trial, yet there is limited evidence on effective training methods. In low and middle income countries (LMICs), training of research staff was considered the second highest priority in a global health methodological research priority setting exercise. Methods We explored whether an enhanced training package in a neonatal feasibility study in Kenya and India, utilising elements of the train-the-trainer approach, altered clinicians and researchers’ clinical trials knowledge. A lead “trainer” was identified at each site who attended a UK-based introductory course on clinical trials. A two-day in-country training session was conducted at each hospital. Sessions included the study protocol, governance, data collection and ICH-Good Clinical Practice (GCP). To assess effectiveness of the training package, participants completed questionnaires at the start and end of the study period, including demographics, prior research experience, protocol-specific questions, informed consent and ICH-GCP. Results Thirty participants attended in-country training sessions and completed baseline questionnaires. Around three quarters had previously worked on a research study, yet only half had previously received training. Nineteen participants completed questionnaires at the end of the study period. Questionnaire scores were higher at the end of the study period, though not significantly so. Few participants ‘passed’ the informed consent and ICH-Good Clinical Practice (GCP) modules, using the Global Health Network Training Centre pass mark of ≥ 80%. Participants who reported having prior research experience scored higher in questionnaires before the start of the study period. Conclusions An enhanced training package can improve knowledge of research methods and governance though only small improvements in mean scores between questionnaires completed before and at the end of the study period were seen and were not statistically significant. This is the first report evaluating a clinical trial training package in a neonatal trial in LMICs. Due to the Covid-19 pandemic, research activity was paused and there was a significant time lapse between training and start of the study, which likely impacted upon the scores reported here. Given the burden of disease in LMICs, developing high-quality training materials which utilise a variety of approaches and build research capacity, is critical

A mixed-methods study to investigate feasibility and acceptability of an early warning score for preterm infants in neonatal units in Kenya: results of the NEWS-K study

Preterm birth (< 37 weeks gestation) complications are the leading cause of neonatal mortality. Early-warning scores (EWS) are charts where vital signs (e.g., temperature, heart rate, respiratory rate) are recorded, triggering action. To evaluate whether a neonatal EWS improves clinical outcomes in low-middle income countries, a randomised trial is needed. Determining whether the use of a neonatal EWS is feasible and acceptable in newborn units, is a prerequisite to conducting a trial. We implemented a neonatal EWS in three newborn units in Kenya. Staff were asked to record infants’ vital signs on the EWS during the study, triggering additional interventions as per existing local guidelines. No other aspects of care were altered. Feasibility criteria were pre-specified. We also interviewed health professionals (n = 28) and parents/family members (n = 42) to hear their opinions of the EWS. Data were collected on 465 preterm and/or low birthweight (< 2.5 kg) infants. In addition to qualitative study participants, 45 health professionals in participating hospitals also completed an online survey to share their views on the EWS. 94% of infants had the EWS completed at least once during their newborn unit admission. EWS completion was highest on the day of admission (93%). Completion rates were similar across shifts. 15% of vital signs triggered escalation to a more senior member of staff. Health professionals reported liking the EWS, though recognised the biggest barrier to implementation was poor staffing. Newborn unit infant to staff ratios varied between 10 and 53 staff per 1 infant, depending upon time of shift and staff type. A randomised trial of neonatal EWS in Kenya is possible and acceptable, though adaptations are required to the form before implementation

Emollients for preventing atopic eczema: Cost‐effectiveness analysis of the BEEP trial

BackgroundRecent discoveries have led to the suggestion that enhancing skin barrier from birth might prevent eczema and food allergy. ObjectiveTo determine the cost‐effectiveness of daily all‐over‐body application of emollient during the first year of life for preventing atopic eczema in high‐risk children at 2 years from a health service perspective. We also considered a 5‐year time horizon as a sensitivity analysis. MethodsA within‐trial economic evaluation using data on health resource use and quality of life captured as part of the BEEP trial alongside the trial data. Parents/carers of 1394 infants born to families at high risk of atopic disease were randomised 1:1 to the emollient group, which were advised to apply emollient (Doublebase Gel or Diprobase Cream) to their child at least once daily to the whole body during the first year of life or usual care. Both groups received advice on general skin care. The main economic outcomes were incremental cost‐effectiveness ratio (ICER), defined as incremental cost per percentage decrease in risk of eczema in the primary cost‐effectiveness analysis. Secondary analysis, undertaken as a cost‐utility analysis, reports incremental cost per Quality‐Adjusted Life Year (QALY) where child utility was elicited using the proxy CHU‐9D at 2 years. ResultsAt 2 years, the adjusted incremental cost was £87.45 (95% CI −54.31, 229.27) per participant, whilst the adjusted proportion without eczema was 0.0164 (95% CI −0.0329, 0.0656). The ICER was £5337 per percentage decrease in risk of eczema. Adjusted incremental QALYs were very slightly improved in the emollient group, 0.0010 (95% CI −0.0069, 0.0089). At 5 years, adjusted incremental costs were lower for the emollient group, −£106.89 (95% CI −354.66, 140.88) and the proportion without eczema was −0.0329 (95% CI −0.0659, 0.0002). The 5‐year ICER was £3201 per percentage decrease in risk of eczema. However, when inpatient costs due to wheezing were excluded, incremental costs were lower and incremental effects greater in the usual care group. ConclusionsIn line with effectiveness endpoints, advice given in the BEEP trial to apply daily emollient during infancy for eczema prevention in high‐risk children does not appear cost‐effective

Advanced RFI Detection, Alert and Analysis System Design and Monitoring Campaign Results

The Advanced Radio Frequency Interference (RFI) Detection Analysis and Alerting System (ARFIDAAS) projects focus on providing Global Navigation Satellite System (GNSS) users and GNSS-based service providers with low latency notifications of the detection of Radio Frequency Interference (RFI) impacting their receiving equipment while also building a centralized database of activity from multiple sites for subsequent analysis and reporting. With some stations operating continuously since 2019, more than twenty terabytes of captured spectrum and generated analyses of GNSS RFI events have been collected. Within this paper the design and deployment of the system are presented along with discussions of information gathered during full-year monitoring periods from five selected stations within Scandinavia and Europe.submittedVersio

On the Use of Machine Learning Algorithms to Improve GNSS Products

This paper presents the most relevant results on the investigation of possible uses of machine learning based techniques for the processing of data in the field of Global Navigation Satellite Systems. The work was performed under funding of the European Space Agency and addressed different kind of data present in the entire chain of the positioning process, as well as different kind of machine learning approaches. This paper presents the most promising results obtained for the prediction of ionospheric maps for the correction of the related error on the pseudorange measurement and for the forecast of fast corrections normally present in the EGNOS messages, when the latter might be missing. Results show how, based on the historical data and the time correlation of the values, machine learning methods outperformed simple regression algorithms, improving the positioning performance at GNSS user level. The work results also confirmed the validity of this approach for the automatic detection of outliers due to ionospheric scintillation phenomena

Lunar Navigation System ODTS Signal in Space Error Analysis

In recent years, the Moon has gained renewed interest in terms of human exploration for scientific purposes. In this context, Thales Alenia Space is leading a consortium to define the main concepts for a Lunar Radio Navigation System (LRNS) in terms of Orbit Determination and Time Synchronization (ODTS) as part of an ESA Technology Development Element (TDE) programme. This work focuses on the latest performance results achieved through a dedicated simulator in terms of Signal In Space Error (SISE)