Component and System Sensitivity Considerations for Design of a Lunar ISRU Oxygen Production Plant

Component and system sensitivities of some design parameters of ISRU system components are analyzed. The differences between terrestrial and lunar excavation are discussed, and a qualitative comparison of large and small excavators is started. The effect of excavator size on the size of the ISRU plant's regolith hoppers is presented. Optimum operating conditions of both hydrogen and carbothermal reduction reactors are explored using recently developed analytical models. Design parameters such as batch size, conversion fraction, and maximum particle size are considered for a hydrogen reduction reactor while batch size, conversion fraction, number of melt zones, and methane flow rate are considered for a carbothermal reduction reactor. For both reactor types the effect of reactor operation on system energy and regolith delivery requirements is presented

The thylakoid carbonic anhydrase associated with photosystem II is the component of inorganic carbon accumulating system in cells of halo- and alkaliphilic cyanobacterium Rhabdoderma lineare

AbstractThe organization of carbonic anhydrase (CA) system in halo- and alkaliphilic cyanobacterium Rhabdoderma lineare was studied by Western blot analysis and immunocytochemical electron microscopy. The presence of putative extracellular α-CA of 60 kDa in the glycocalyx, forming a tight sheath around the cell, and of two intracellular β-CA is reported. We show for the first time that the β-CA of 60 kDa is expressed constitutively and associated with polypeptides of photosystem II (β-CA-PS II). Another soluble β-CA of 25 kDa was induced in low-bicarbonate medium. Induction of synthesis of the latter β-CA was accompanied by an increase in the intracellular pool of inorganic carbon, which suggests an important role of this enzyme in the functioning of a CO2-concentrating mechanism

Waste Management Options for Long-Duration Space Missions: When to Reject, Reuse, or Recycle

The amount of waste generated on long-duration space missions away from Earth orbit creates the daunting challenge of how to manage the waste through reuse, rejection, or recycle. The option to merely dispose of the solid waste through an airlock to space was studied for both Earth-moon libration point missions and crewed Mars missions. Although the unique dynamic characteristics of an orbit around L2 might allow some discarded waste to intersect the lunar surface before re-impacting the spacecraft, the large amount of waste needed to be managed and potential hazards associated with volatiles recondensing on the spacecraft surfaces make this option problematic. A second option evaluated is to process the waste into useful gases to be either vented to space or used in various propulsion systems. These propellants could then be used to provide the yearly station-keeping needs at an L2 orbit, or if processed into oxygen and methane propellants, could be used to augment science exploration by enabling lunar mini landers to the far side of the moon

Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals.

BACKGROUND: One reason for the introduction of digital technologies into health care has been to try to improve safety and patient outcomes by providing real-time access to patient data and enhancing communication among health care professionals. However, the adoption of such technologies into clinical pathways has been less examined, and the impacts on users and the broader health system are poorly understood. We sought to address this by studying the impacts of introducing a digitally enabled care pathway for patients with acute kidney injury (AKI) at a tertiary referral hospital in the United Kingdom. A dedicated clinical response team-comprising existing nephrology and patient-at-risk and resuscitation teams-received AKI alerts in real time via Streams, a mobile app. Here, we present a qualitative evaluation of the experiences of users and other health care professionals whose work was affected by the implementation of the care pathway. OBJECTIVE: The aim of this study was to qualitatively evaluate the impact of mobile results viewing and automated alerting as part of a digitally enabled care pathway on the working practices of users and their interprofessional relationships. METHODS: A total of 19 semistructured interviews were conducted with members of the AKI response team and clinicians with whom they interacted across the hospital. Interviews were analyzed using inductive and deductive thematic analysis. RESULTS: The digitally enabled care pathway improved access to patient information and expedited early specialist care. Opportunities were identified for more constructive planning of end-of-life care due to the earlier detection and alerting of deterioration. However, the shift toward early detection also highlighted resource constraints and some clinical uncertainty about the value of intervening at this stage. The real-time availability of information altered communication flows within and between clinical teams and across professional groups. CONCLUSIONS: Digital technologies allow early detection of adverse events and of patients at risk of deterioration, with the potential to improve outcomes. They may also increase the efficiency of health care professionals' working practices. However, when planning and implementing digital information innovations in health care, the following factors should also be considered: the provision of clinical training to effectively manage early detection, resources to cope with additional workload, support to manage perceived information overload, and the optimization of algorithms to minimize unnecessary alerts

Spin Liquid Phases in 2D Frustrated XY Model

In this paper we consider the $J_1-J_2-J_3$ classical and quantum 2D XY model. Spin wave calculations show that a spin liquid phase still exists in the quantum case as for Heisenberg models. We formulate a semiclassical approach of these models based on spin wave action and use a variational method to study the role played by vortices. Liquid and crystal phases of vortex could emerge in this description. These phases seem to be directly correlated with the spin liquid one and to its crystalline interpretation.Comment: 16 pages, Latex, 4 figures. To be published in Phys. Rev.

Evaluation of a digitally-enabled care pathway for acute kidney injury management in hospital emergency admissions.

We developed a digitally enabled care pathway for acute kidney injury (AKI) management incorporating a mobile detection application, specialist clinical response team and care protocol. Clinical outcome data were collected from adults with AKI on emergency admission before (May 2016 to January 2017) and after (May to September 2017) deployment at the intervention site and another not receiving the intervention. Changes in primary outcome (serum creatinine recovery to ≤120% baseline at hospital discharge) and secondary outcomes (30-day survival, renal replacement therapy, renal or intensive care unit (ICU) admission, worsening AKI stage and length of stay) were measured using interrupted time-series regression. Processes of care data (time to AKI recognition, time to treatment) were extracted from casenotes, and compared over two 9-month periods before and after implementation (January to September 2016 and 2017, respectively) using pre-post analysis. There was no step change in renal recovery or any of the secondary outcomes. Trends for creatinine recovery rates (estimated odds ratio (OR) = 1.04, 95% confidence interval (95% CI): 1.00-1.08, p = 0.038) and renal or ICU admission (OR = 0.95, 95% CI: 0.90-1.00, p = 0.044) improved significantly at the intervention site. However, difference-in-difference analyses between sites for creatinine recovery (estimated OR = 0.95, 95% CI: 0.90-1.00, p = 0.053) and renal or ICU admission (OR = 1.06, 95% CI: 0.98-1.16, p = 0.140) were not significant. Among process measures, time to AKI recognition and treatment of nephrotoxicity improved significantly (p < 0.001 and 0.047 respectively)

Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care Costs.

BACKGROUND: The development of acute kidney injury (AKI) in hospitalized patients is associated with adverse outcomes and increased health care costs. Simple automated e-alerts indicating its presence do not appear to improve outcomes, perhaps because of a lack of explicitly defined integration with a clinical response. OBJECTIVE: We sought to test this hypothesis by evaluating the impact of a digitally enabled intervention on clinical outcomes and health care costs associated with AKI in hospitalized patients. METHODS: We developed a care pathway comprising automated AKI detection, mobile clinician notification, in-app triage, and a protocolized specialist clinical response. We evaluated its impact by comparing data from pre- and postimplementation phases (May 2016 to January 2017 and May to September 2017, respectively) at the intervention site and another site not receiving the intervention. Clinical outcomes were analyzed using segmented regression analysis. The primary outcome was recovery of renal function to ≤120% of baseline by hospital discharge. Secondary clinical outcomes were mortality within 30 days of alert, progression of AKI stage, transfer to renal/intensive care units, hospital re-admission within 30 days of discharge, dependence on renal replacement therapy 30 days after discharge, and hospital-wide cardiac arrest rate. Time taken for specialist review of AKI alerts was measured. Impact on health care costs as defined by Patient-Level Information and Costing System data was evaluated using difference-in-differences (DID) analysis. RESULTS: The median time to AKI alert review by a specialist was 14.0 min (interquartile range 1.0-60.0 min). There was no impact on the primary outcome (estimated odds ratio [OR] 1.00, 95% CI 0.58-1.71; P=.99). Although the hospital-wide cardiac arrest rate fell significantly at the intervention site (OR 0.55, 95% CI 0.38-0.76; P<.001), DID analysis with the comparator site was not significant (OR 1.13, 95% CI 0.63-1.99; P=.69). There was no impact on other secondary clinical outcomes. Mean health care costs per patient were reduced by £2123 (95% CI -£4024 to -£222; P=.03), not including costs of providing the technology. CONCLUSIONS: The digitally enabled clinical intervention to detect and treat AKI in hospitalized patients reduced health care costs and possibly reduced cardiac arrest rates. Its impact on other clinical outcomes and identification of the active components of the pathway requires clarification through evaluation across multiple sites

Energy efficiency of load balancing for data-parallel applications in heterogeneous systems

The use of heterogeneous systems in supercomputing is on the rise as they improve both performance and energy e ciency. However, the pro- gramming of these machines requires considerable e ort to get the best results in massively data-parallel applications. Maat is a library that enables OpenCL programmers to e ciently execute single data-parallel kernels using all the available devices on a heterogeneous system. It o ers a set of load balanc- ing methods, which perform the data partitioning and distribution among the devices, exploiting more of the performance of the system and consequently re- ducing execution time. Until now, however, a study of the implications of these on the energy consumption has not been made. Therefore, this paper analyses the energy e ciency of the di erent load balancing methods compared to a baseline system that uses just a single GPU. To evaluate the impact of the heterogeneity of the system, the GPUs were set to di erent frequencies. The obtained results show that in all the studied cases there is at least one load balancing method that improves energy e ciency

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

BACKGROUND: Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. OBJECTIVE: Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. METHODS: The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. RESULTS: We demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. CONCLUSIONS: Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks