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Background Fever improves pathogen control at a significant metabolic cost. No randomized clinical trials (RCT) have compared fever treatment thresholds in critically ill children. We performed a pilot RCT to determine whether a definitive trial of a permissive approach to fever in comparison to current restrictive practice is feasible in critically ill children with suspected infection. Methods An open, parallel-group pilot RCT with embedded mixed methods perspectives study in four UK paediatric intensive care units (PICUs) and associated retrieval services. Participants were emergency PICU admissions aged > 28 days to

An autonomous unmanned aerial system (UAS) needs, during the flight, accurate information about the current failure state of the aircraft and its capabilities in order to safely perform its mission and properly react to contingencies. The flight battery of an electric-propulsion aircraft is its most relevant resource. Model-based prognostics algorithms are used to obtain good estimates of its current state of charge and remaining capacity. However, these algorithms can have a large computational footprint. We present Prognostics-as-a-Service, a hybrid approach combining on-board computation with server-based prognostics on the ground.In this paper, we focus on the role, battery prognostics plays for the safe operation of a highly autonomous aircraft: prognostics for (1) continuous on-board safety monitoring, (2) for UAS operations, and (3) for contingency planning. We present the NASA Autonomous Operating System (AOS) and discuss how the autonomous components closely work together with on-board and server-based ground prognostics systems. We will illustrate the system with case studies on small NASA unmanned aircraft.

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

ObjectiveTo explore parent and staff views on the acceptability of a randomised controlled trial investigating temperature thresholds for antipyretic intervention in critically ill children with fever and infection (the FEVER trial) during a multi-phase pilot study.DesignMixed methods study with data collected at three time points: (1) before, (2) during and (3) after a pilot trial.SettingEnglish, Paediatric Intensive Care Units (PICUs).Participants(1) Pre-pilot trial focus groups with pilot site staff (n=56) and interviews with parents (n=25) whose child had been admitted to PICU in the last 3 years with a fever and suspected infection, (2) Questionnaires with parents of randomised children following pilot trial recruitment (n=48 from 47 families) and (3) post-pilot trial interviews with parents (n=19), focus groups (n=50) and a survey (n=48) with site staff. Analysis drew on Sekhon et al’s theoretical framework of acceptability.ResultsThere was initial support for the trial, yet some held concerns regarding the proposed temperature thresholds and not using paracetamol for pain or discomfort. Pre-trial findings informed protocol changes and training, which influenced views on trial acceptability. Staff trained by the FEVER team found the trial more acceptable than those trained by colleagues. Parents and staff found the trial acceptable. Some concerns about pain or discomfort during weaning from ventilation remained.ConclusionsPre-trial findings and pilot trial experience influenced acceptability, providing insight into how challenges may be overcome. We present an adapted theoretical framework of acceptability to inform future trial feasibility studies.Trial registration numbersISRCTN16022198 and NCT03028818.

Prognostics is a systems engineering discipline focused on predicting end-of-life of components and systems. As a relatively new and emerging technology, there are few fielded implementations of prognostics, due in part to practitioners perceiving a large hurdle in developing the models, algorithms, architecture, and integration pieces. Similarly, no open software frameworks for applying prognostics currently exist. This paper introduces the Generic Software Architecture for Prognostics (GSAP), an open-source, cross-platform, object-oriented software framework and support library for creating prognostics applications. GSAP was designed to make prognostics more accessible and enable faster adoption and implementation by industry, by reducing the effort and investment required to develop, test, and deploy prognostics. This paper describes the requirements, design, and testing of GSAP. Additionally, a detailed case study involving battery prognostics demonstrates its use.

Objective To identify parents’ prioritised outcomes by combining qualitative findings from two trial feasibility studies of interventions for paediatric suspected severe infection. Design Qualitative synthesis combining parent interview data from the Fluids in Shock (FiSh) and Fever feasibility studies. Parents had experience of their child being admitted to a UK emergency department or intensive care unit with a suspected infection. Participants n=: 85 parents. FiSh study: n=41 parents, 37 mothers, 4 fathers, 7 were bereaved. Fever study: n=44 parents, 33 mothers, 11 fathers, 7 were bereaved. Results In addition to survival, parents prioritised short-term outcomes including: organ and physiological functioning (eg, heart rate, breathing rate and temperature); their child looking and/or behaving more like their normal self; and length of time on treatments or mechanical support. Longer term prioritised outcomes included effects of illness on child health and development. We found that parents’ prioritisation of outcomes was influenced by their experience of their child’s illness, survival and the point at which they are asked about outcomes of importance in the course of their child’s illness. Conclusions Findings provide insight into parent prioritised outcomes to inform the design of future trials investigating treatments for paediatric suspected or proven severe infection as well as core outcome set development work. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made.

Comprehensive aircraft system health-state awareness is crit- ical for maintaining safe, efficient growth in global oper- ations, enabling higher levels of autonomy, and facilitat- ing new forms of aviation. Maintainers, vehicle operators, air traffic controllers, dispatchers, pilots, autonomous sys- tems, and other decision-makers must have reliable real-time knowledge of the vehicle health, the health of its critical com- posite systems, predictions of how health changes with time, and forecasts of how its capabilities change with health degra- dation to preserve safety and efficiency. Providing this infor- mation in a reliable manner in computationally constrained environments and across a wide range of vehicles and sys- tems continues to be a challenge. This challenge can be par- tially resolved through cloud computing, where the execu- tion of prognostic and diagnostic algorithms is performed on a network of remote servers hosted on the internet. NASA is developing a cloud computing service, Prognostics As-A- Service (PaaS), that explores the feasibility and challenges of cloud-enhanced prognostics. Though such a system has broad applicability, this research effort is focused on aviation applications.

Background Fever accelerates host immune system control of pathogens but at a high metabolic cost. The optimal approach to fever management and the optimal temperature thresholds used for treatment in critically ill children are unknown. Objectives To determine the feasibility of conducting a definitive randomised controlled trial (RCT) to evaluate the clinical effectiveness and cost-effectiveness of different temperature thresholds for antipyretic management. Design A mixed-methods feasibility study comprising three linked studies – (1) a qualitative study exploring parent and clinician views, (2) an observational study of the epidemiology of fever in children with infection in paediatric intensive care units (PICUs) and (3) a pilot RCT with an integrated-perspectives study. Setting Participants were recruited from (1) four hospitals in England via social media (for the FEVER qualitative study), (2) 22 PICUs in the UK (for the FEVER observational study) and (3) four PICUs in England (for the FEVER pilot RCT). Participants (1) Parents of children with relevant experience were recruited to the FEVER qualitative study, (2) patients who were unplanned admissions to PICUs were recruited to the FEVER observational study and (3) children admitted with infection requiring mechanical ventilation were recruited to the FEVER pilot RCT. Parents of children and clinicians involved in the pilot RCT. Interventions The FEVER qualitative study and the FEVER observational study had no interventions. In the FEVER pilot RCT, children were randomly allocated (1 : 1) using research without prior consent (RWPC) to permissive (39.5 °C) or restrictive (37.5 °C) temperature thresholds for antipyretics during their PICU stay while mechanically ventilated. Main outcome measures (1) The acceptability of FEVER, RWPC and potential outcomes (in the FEVER qualitative study), (2) the size of the potentially eligible population and the temperature thresholds used (in the FEVER observational study) and (3) recruitment and retention rates, protocol adherence and separation between groups and distribution of potential outcomes (in the FEVER pilot RCT). Results In the FEVER qualitative study, 25 parents were interviewed and 56 clinicians took part in focus groups. Both the parents and the clinicians found the study acceptable. Clinicians raised concerns regarding temperature thresholds and not using paracetamol for pain/discomfort. In the FEVER observational study, 1853 children with unplanned admissions and infection were admitted to 22 PICUs between March and August 2017. The recruitment rate was 10.9 per site per month. The majority of critically ill children with a maximum temperature of > 37.5 °C received antipyretics. In the FEVER pilot RCT, 100 eligible patients were randomised between September and December 2017 at a recruitment rate of 11.1 per site per month. Consent was provided for 49 out of 51 participants in the restrictive temperature group, but only for 38 out of 49 participants in the permissive temperature group. A separation of 0.5 °C (95% confidence interval 0.2 °C to 0.8 °C) between groups was achieved. A high completeness of outcome measures was achieved. Sixty parents of 57 children took part in interviews and/or completed questionnaires and 98 clinicians took part in focus groups or completed a survey. Parents and clinicians found the pilot RCT and RWPC acceptable. Concerns about children being in pain/discomfort were cited as reasons for withdrawal and non-consent by parents and non-adherence to the protocol by clinicians. Limitations Different recruitment periods for observational and pilot studies may not fully reflect the population that is eligible for a definitive RCT. Conclusions The results identified barriers to delivering the definitive FEVER RCT, including acceptability of the permissive temperature threshold. The findings also provided insight into how these barriers may be overcome, such as by limiting the patient inclusion criteria to invasive ventilation only and by improved site training. A definitive FEVER RCT using a modified protocol should be conducted, but further work is required to agree important outcome measures for clinical trials among critically ill children. Trial registration The FEVER observational study is registered as NCT03028818 and the FEVER pilot RCT is registered as Current Controlled Trials ISRCTN16022198. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 23, No. 5. See the NIHR Journals Library website for further project information.

In the domain of cultural heritage, a lot of archives and data collections already exist. Thus, one of the main challenges for curators and archivists in this domain consists of exchanging data with other archives and integrating similar collections. Typical usage scenarios in this area can only be realized by semantically integrating all available data sources. Therefore, the main task of semantic integration consists of bridging the different levels of heterogeneity. Semantic Web technologies might be a solution for these challenges. Ontology Matching, for example, is already successfully applied for bridging some of the heterogeneity types. However, it is not suitable for dissolving heterogeneity conflicts at all levels due to unsatisfactory matching qualities. Still, matching is crucial for the semantic integration success of the distributed data sources. As a result, a main part in semantic integration is still done manually by domain experts. It would be preferable to support their work by at least semi-automatic techniques.

To successfully negotiate and interact with one’s environment, optimal cognitive functioning is needed. Unfortunately, many neurological and psychiatric diseases impede certain cognitive abilities such as executive functioning or speed of processing; this can produce a poor fit between the patient and the cognitive demands of his or her environment. Such non-dementia diseases include bipolar disorder, schizophrenia, post-traumatic stress syndrome, depression, and anxiety disorders, just to name a few. Each of these diseases negatively affects particular areas of the brain, resulting in distinct cognitive profiles (e.g., deficits in executive functioning but normal speed of processing as seen in schizophrenia). In fact, it is from these cognitive deficits in which such behavioral and emotional symptoms may manifest (e.g., delusions, paranoia). This article highlights the distinct cognitive profiles of such common neurological and psychiatric diseases. An understanding of such disease-specific cognitive profiles can assist nurses in providing care to patients by knowing what cognitive deficits are associated with each disease and how these cognitive deficits impact everyday functioning and social interactions. Implications for nursing practice and research are posited within the framework of cognitive reserve and neuroplasticity.